U.S. patent application number 17/543788 was filed with the patent office on 2022-03-24 for apparatus and method for object classification based on imagery.
This patent application is currently assigned to AT&T Intellectual Property I, L.P.. The applicant listed for this patent is AT&T Intellectual Property I, L.P.. Invention is credited to Arun Jotshi, Velin Kounev, Shang Li, Kathleen Meier-Hellstern, Gaurav Thakur.
Application Number | 20220094604 17/543788 |
Document ID | / |
Family ID | |
Filed Date | 2022-03-24 |
United States Patent
Application |
20220094604 |
Kind Code |
A1 |
Jotshi; Arun ; et
al. |
March 24, 2022 |
APPARATUS AND METHOD FOR OBJECT CLASSIFICATION BASED ON IMAGERY
Abstract
Aspects of the subject disclosure may include, for example,
identifying a first object included in at least one image in
accordance with an execution of an image processing algorithm,
analyzing a plurality of parameters in accordance with at least one
model responsive to the identifying of the first object included in
the at least one image, wherein each parameter of the plurality of
parameters is associated with the first object or a second object,
selecting one of the first object or the second object for
receiving at least one communication network resource responsive to
the analyzing of the plurality of parameters, wherein the selecting
results in a selected object, and presenting the selected object on
a presentation device. Other embodiments are disclosed.
Inventors: |
Jotshi; Arun; (Parsippany,
NJ) ; Meier-Hellstern; Kathleen; (Cranbury, NJ)
; Thakur; Gaurav; (Matawan, NJ) ; Li; Shang;
(Aberdeen, NJ) ; Kounev; Velin; (Weehawken,
NJ) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
AT&T Intellectual Property I, L.P. |
Atlanta |
GA |
US |
|
|
Assignee: |
AT&T Intellectual Property I,
L.P.
Atlanta
GA
|
Appl. No.: |
17/543788 |
Filed: |
December 7, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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16437057 |
Jun 11, 2019 |
11228501 |
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17543788 |
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International
Class: |
H04L 12/24 20060101
H04L012/24; G06K 9/00 20060101 G06K009/00; G06T 7/70 20060101
G06T007/70; G06K 9/40 20060101 G06K009/40 |
Claims
1. A non-transitory machine-readable medium, comprising executable
instructions that, when executed by a processing system including a
processor, facilitate performance of operations, the operations
comprising: obtaining a plurality of images, wherein the plurality
of images is captured by a vehicle, a user equipment, or any
combination thereof; identifying a first object included in the
plurality of images via an application of the plurality of images
to at least one model that comprises a machine learning model;
identifying at least one attribute associated with the first object
responsive to the identifying of the first object; generating a
recommendation that identifies the first object or a second object
for receiving a network resource responsive to the identifying of
the at least one attribute; and presenting the recommendation on a
presentation device.
2. The non-transitory machine-readable medium of claim 1, wherein
the at least one attribute comprises a geographical location of the
first object.
3. The non-transitory machine-readable medium of claim 1, wherein
the identifying of the first object comprises identifying the first
object as one of a building, a pole, or a tower.
4. The non-transitory machine-readable medium of claim 1, wherein
the network resource comprises an antenna, a transmitter, a
receiver, or any combination thereof.
5. The non-transitory machine-readable medium of claim 1, wherein
the presentation device comprises a display device, a speaker, a
print-out, or any combination thereof.
6. The non-transitory machine-readable medium of claim 1, wherein
the network resource is associated with a communication system.
7. The non-transitory machine-readable medium of claim 6, wherein
the operations further comprise: obtaining data associated with at
least one signal quality parameter of the communication system,
wherein the at least one signal quality parameter refers to a
received signal strength, interference, noise, or any combination
thereof, wherein the generating of the recommendation that
identifies the first object or the second object for receiving the
network resource is further responsive to an analysis of the
data.
8. The non-transitory machine-readable medium of claim 1, wherein
the operations further comprise: modifying the at least one model
subsequent to a deployment of the network resource about the first
object or the second object to generate a modified model, wherein
the modified model is based on an operating parameter of the
network resource.
9. The non-transitory machine-readable medium of claim 8, wherein
the operations further comprise: obtaining a second plurality of
images subsequent to the modifying of the at least one model;
identifying a third object included in the second plurality of
images via an application of the second plurality of images to the
modified model; identifying an attribute associated with the third
object responsive to the identifying of the third object;
generating a second recommendation that identifies the first
object, the second object, or the third object for receiving a
second network resource responsive to the identifying of the
attribute associated with the third object; and presenting the
second recommendation on the presentation device.
10. A method, comprising: identifying, by a processing system
including a processor, a first object included in at least one
image in accordance with an execution of an image processing
algorithm; analyzing, by the processing system, a plurality of
parameters in accordance with at least one model responsive to the
identifying of the first object included in the at least one image,
wherein each parameter of the plurality of parameters is associated
with the first object or a second object; selecting, by the
processing system, one of the first object or the second object for
receiving at least one communication network resource responsive to
the analyzing of the plurality of parameters, wherein the selecting
results in a selected object; and presenting, by the processing
system, the selected object on a presentation device.
11. The method of claim 10, wherein the image processing algorithm
filters background noise included in the at least one image.
12. The method of claim 10, further comprising: identifying, by the
processing system, a third object in the at least one image; and
analyzing, by the processing system, data that identifies a
restriction with respect to a placement of the at least one
communication network resource about the third object, wherein the
selecting of the one of the first object or the second object for
receiving the at least one communication network resource is
further responsive to the analyzing of the data.
13. The method of claim 10, wherein the identifying of the first
object comprises identifying the first object as one of a building,
a pole, or a tower.
14. The method of claim 10, wherein the at least one communication
network resource comprises an antenna, a transmitter, and a
receiver.
15. The method of claim 10, further comprising: obtaining, by the
processing system, data associated with at least one signal quality
parameter, wherein the at least one signal quality parameter refers
to a received signal strength, interference, noise, or any
combination thereof, wherein the selecting is further responsive to
an analysis of the data.
16. The method of claim 10, further comprising: modifying, by the
processing system, the at least one model subsequent to a
deployment of the at least one communication network resource about
the selected object to generate a modified model.
17. The method of claim 16, wherein the modified model is based on
an operating parameter of the at least one communication network
resource.
18. A device comprising: a processing system including a processor;
and a memory that stores executable instructions that, when
executed by the processing system, facilitate performance of
operations, the operations comprising: obtaining an image;
identifying, based on the obtaining, a first object included in the
image via an application of the image to a model; identifying at
least one attribute associated with the first object responsive to
the identifying of the first object; generating a recommendation
that identifies the first object or a second object for receiving a
network resource responsive to the identifying of the at least one
attribute; and providing the recommendation to a presentation
device to cause the presentation device to present the
recommendation.
19. The device of claim 18, wherein the operations further
comprise: obtaining data associated with at least one signal
quality parameter, wherein the generating of the recommendation
that identifies the first object or the second object for receiving
the network resource is further responsive to an analysis of the
data.
20. The device of claim 19, wherein the at least one signal quality
parameter refers to a received signal strength, interference, and
noise.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application is a divisional of U.S. patent application
Ser. No. 16/437,057 filed on Jun. 11, 2019. All sections of the
aforementioned application are incorporated herein by reference in
their entirety.
FIELD OF THE DISCLOSURE
[0002] The subject disclosure relates to an apparatus and method
for object classification based on imagery.
BACKGROUND
[0003] As the world continues to become increasingly connected over
vast/various communication networks, network/service
operators/providers are continuously confronted with the challenge
of providing efficient, high-quality service to users/devices. For
example, as a network/service operator seeks to implement
additional resources to support an existing network, or is
providing resources in the first instance (such as during an
initial deployment of a given, new network), technicians/site
surveyors are dispatched to identify candidate locations/objects
(e.g., utility poles) that will best serve as a host site of the
resources. Reports/Data prepared/gathered by the technicians are
subsequently reviewed/analyzed by, e.g., engineers to ultimately
select a location/object from the candidate locations/objects.
Thus, the identification/selection of a location/object is time and
labor intensive and is susceptible to error (e.g., is susceptible
to misinterpretation or miscommunication between technicians and
engineers), potentially resulting in costly rework and increased
product/service development cycle times. Still further, the
reports/data may potentially miss/overlook/ignore information, such
that a selected candidate location might not be the optimum
location. As a result, the service that is obtained/provided by the
resources when deployed/implemented may be sub-optimal in some
instances.
BRIEF DESCRIPTION OF THE DRAWINGS
[0004] Reference will now be made to the accompanying drawings,
which are not necessarily drawn to scale, and wherein:
[0005] FIG. 1 is a block diagram illustrating an exemplary,
non-limiting embodiment of a communications network in accordance
with various aspects described herein.
[0006] FIG. 2A is a block diagram illustrating an example,
non-limiting embodiment of a system functioning within the
communication network of FIG. 1 in accordance with various aspects
described herein.
[0007] FIG. 2B depicts an illustrative embodiment of a processed
image that identifies objects in accordance with various aspects
described herein.
[0008] FIG. 2C depicts an illustrative embodiment of a method in
accordance with various aspects described herein.
[0009] FIG. 2D depicts a deployment of a resource about an object
in accordance with various aspects described herein.
[0010] FIG. 3 is a block diagram illustrating an example,
non-limiting embodiment of a virtualized communication network in
accordance with various aspects described herein.
[0011] FIG. 4 is a block diagram of an example, non-limiting
embodiment of a computing environment in accordance with various
aspects described herein.
[0012] FIG. 5 is a block diagram of an example, non-limiting
embodiment of a mobile network platform in accordance with various
aspects described herein.
[0013] FIG. 6 is a block diagram of an example, non-limiting
embodiment of a communication device in accordance with various
aspects described herein.
DETAILED DESCRIPTION
[0014] The subject disclosure describes, among other things,
illustrative embodiments for identifying/selecting one or more
objects for placement/deployment of one or more resources (e.g.,
communication network resources). Other embodiments are described
in the subject disclosure.
[0015] One or more aspects of the subject disclosure include
obtaining an image that is sourced from a vehicle, applying the
image to a model to identify a plurality of objects in the image,
identifying a plurality of attributes associated with each of the
plurality of objects, obtaining data, wherein the data identifies a
location of each object of the plurality of objects, and selecting
an object included in the plurality of objects for deployment of a
communication network resource in accordance with the plurality of
attributes and the data
[0016] One or more aspects of the subject disclosure include
obtaining a plurality of images, wherein the plurality of images is
captured by a vehicle, a user equipment, or any combination
thereof, identifying a first object included in the plurality of
images via an application of the plurality of images to at least
one model that comprises a machine learning model, identifying at
least one attribute associated with the first object responsive to
the identifying of the first object, generating a recommendation
that identifies the first object or a second object for receiving a
network resource responsive to the identifying of the at least one
attribute, and presenting the recommendation on a presentation
device.
[0017] One or more aspects of the subject disclosure include
identifying a first object included in at least one image in
accordance with an execution of an image processing algorithm,
analyzing a plurality of parameters in accordance with at least one
model responsive to the identifying of the first object included in
the at least one image, wherein each parameter of the plurality of
parameters is associated with the first object or a second object,
selecting one of the first object or the second object for
receiving at least one communication network resource responsive to
the analyzing of the plurality of parameters, wherein the selecting
results in a selected object, and presenting the selected object on
a presentation device.
[0018] Referring now to FIG. 1, a block diagram is shown
illustrating an example, non-limiting embodiment of a
communications network 100 in accordance with various aspects
described herein. For example, communications network 100 can
facilitate in whole or in part obtaining an image that is sourced
from a vehicle, applying the image to a model to identify a
plurality of objects in the image, identifying a plurality of
attributes associated with each of the plurality of objects,
obtaining data, wherein the data identifies a location of each
object of the plurality of objects, and selecting an object
included in the plurality of objects for deployment of a
communication network resource in accordance with the plurality of
attributes and the data. Communications network 100 can facilitate
in whole or in part obtaining a plurality of images, wherein the
plurality of images is captured by a vehicle, a user equipment, or
any combination thereof, identifying a first object included in the
plurality of images via an application of the plurality of images
to at least one model that comprises a machine learning model,
identifying at least one attribute associated with the first object
responsive to the identifying of the first object, generating a
recommendation that identifies the first object or a second object
for receiving a network resource responsive to the identifying of
the at least one attribute, and presenting the recommendation on a
presentation device. Communications network 100 can facilitate in
whole or in part identifying a first object included in at least
one image in accordance with an execution of an image processing
algorithm, analyzing a plurality of parameters in accordance with
at least one model responsive to the identifying of the first
object included in the at least one image, wherein each parameter
of the plurality of parameters is associated with the first object
or a second object, selecting one of the first object or the second
object for receiving at least one communication network resource
responsive to the analyzing of the plurality of parameters, wherein
the selecting results in a selected object, and presenting the
selected object on a presentation device.
[0019] In particular, in FIG. 1 a communications network 125 is
presented for providing broadband access 110 to a plurality of data
terminals 114 via access terminal 112, wireless access 120 to a
plurality of mobile devices 124 and vehicle 126 via base station or
access point 122, voice access 130 to a plurality of telephony
devices 134, via switching device 132 and/or media access 140 to a
plurality of audio/video display devices 144 via media terminal
142. In addition, communication network 125 is coupled to one or
more content sources 175 of audio, video, graphics, text and/or
other media. While broadband access 110, wireless access 120, voice
access 130 and media access 140 are shown separately, one or more
of these forms of access can be combined to provide multiple access
services to a single client device (e.g., mobile devices 124 can
receive media content via media terminal 142, data terminal 114 can
be provided voice access via switching device 132, and so on).
[0020] The communications network 125 includes a plurality of
network elements (NE) 150, 152, 154, 156, etc. for facilitating the
broadband access 110, wireless access 120, voice access 130, media
access 140 and/or the distribution of content from content sources
175. The communications network 125 can include a circuit switched
or packet switched network, a voice over Internet protocol (VoIP)
network, Internet protocol (IP) network, a cable network, a passive
or active optical network, a 4G, 5G, or higher generation wireless
access network, WIMAX network, UltraWideband network, personal area
network or other wireless access network, a broadcast satellite
network and/or other communications network.
[0021] In various embodiments, the access terminal 112 can include
a digital subscriber line access multiplexer (DSLAM), cable modem
termination system (CMTS), optical line terminal (OLT) and/or other
access terminal. The data terminals 114 can include personal
computers, laptop computers, netbook computers, tablets or other
computing devices along with digital subscriber line (DSL) modems,
data over coax service interface specification (DOCSIS) modems or
other cable modems, a wireless modem such as a 4G, 5G, or higher
generation modem, an optical modem and/or other access devices.
[0022] In various embodiments, the base station or access point 122
can include a 4G, 5G, or higher generation base station, an access
point that operates via an 802.11 standard such as 802.11n,
802.11ac or other wireless access terminal. The mobile devices 124
can include mobile phones, e-readers, tablets, phablets, wireless
modems, and/or other mobile computing devices.
[0023] In various embodiments, the switching device 132 can include
a private branch exchange or central office switch, a media
services gateway, VoIP gateway or other gateway device and/or other
switching device. The telephony devices 134 can include traditional
telephones (with or without a terminal adapter), VoIP telephones
and/or other telephony devices.
[0024] In various embodiments, the media terminal 142 can include a
cable head-end or other TV head-end, a satellite receiver, gateway
or other media terminal 142. The display devices 144 can include
televisions with or without a set top box, personal computers
and/or other display devices.
[0025] In various embodiments, the content sources 175 include
broadcast television and radio sources, video on demand platforms
and streaming video and audio services platforms, one or more
content data networks, data servers, web servers and other content
servers, and/or other sources of media.
[0026] In various embodiments, the communications network 125 can
include wired, optical and/or wireless links and the network
elements 150, 152, 154, 156, etc. can include service switching
points, signal transfer points, service control points, network
gateways, media distribution hubs, servers, firewalls, routers,
edge devices, switches and other network nodes for routing and
controlling communications traffic over wired, optical and wireless
links as part of the Internet and other public networks as well as
one or more private networks, for managing subscriber access, for
billing and network management and for supporting other network
functions.
[0027] FIG. 2A is a block diagram illustrating an example,
non-limiting embodiment of a system 200a functioning within, or
operatively overlaid upon, the communication network 100 of FIG. 1
in accordance with various aspects described herein. In some
embodiments, aspects of the system 200a may be at least partially
implemented in hardware, software, firmware, or any combination
thereof.
[0028] The system 200a may incorporate various types/kinds of image
capture equipment, illustratively depicted as a camera 204a in FIG.
2A. For example, the image capture equipment 204a may include a
vehicle, such as an aircraft (e.g., fixed-wing aircraft, rotary
aircraft, etc.), a spacecraft (e.g., satellites), a motor vehicle
(e.g., a car, a truck, a bus, an all-terrain vehicle etc.), a
train/railcar/locomotive, a marine craft (e.g., a boat, a ship, a
ferry, a yacht, etc.), a bicycle, etc. In some embodiments, the
image capture equipment 204a may include user equipment (UE)/client
devices, such as for example handheld cameras, mobile devices
(e.g., smartphones), etc.
[0029] The image capture equipment 204a may be used to
generate/create one or more images, illustratively depicted as an
image 208a in FIG. 2A. The images 208a generated by the image
capture equipment 204a may be generated from various vantage points
and at various perspectives. For example, such vantage
points/perspectives may include a bird's eye/top-down view, oblique
angles, a street-side or street-view perspective, etc. The images
may be obtained directly from the image capture equipment 204a
and/or may be obtained indirectly from the image capture equipment
204a via one or more third-party sites/service/devices. In the
context of the vehicular image capture equipment described above,
the images may be captured when the vehicle is at rest/on the
ground and/or when the vehicle is in operation/deployed (e.g., in
motion).
[0030] In some embodiments, the image capture equipment 204a may
process an image 208a to generate a processed image. For example,
the image capture equipment 204a may apply one or more filters to
image data of the image 208a to enhance a particular object and/or
de-emphasize another object in the processed image. Still further,
in some embodiments the image capture equipment 204a may combine
various images 208a as part of the processing to generate a
composite image. In this respect, raw images and/or processed
images may be represented via reference character 208a in FIG. 2A.
In some embodiments, the processing of the images 208a may be at
least partially performed by one or more other components/devices.
The processing of the images 208a may be performed in accordance
with one or more image processing algorithms.
[0031] The images 208a may be provided to (e.g., may serve as input
to) one or more models 212a. The models 212a may incorporate
aspects of machine learning (ML) and/or artificial intelligence
(AI). In some embodiments, the models 212a may incorporate aspects
of deep learning (DL). For example, in some embodiments the models
212a may use a cascade of layers for purposes of feature extraction
and/or transformation, whereby a second/successive layer may
utilize an output from a first/prior layer as input.
Learning/Development in/of the models 212a may occur in a
supervised or unsupervised manner. For example, aspects of a
supervised model may be based on one or more classifications. An
unsupervised model may leverage pattern analysis techniques.
[0032] To take an illustrative example, in some embodiments a first
layer of a model 212a may abstract raw image data (e.g., pixels) of
an image 208a and encode edges of the image 208a. A second layer of
the model 212a may compose and encode arrangements of the edges. A
third layer may encode one or more objects contained within the
image 208a. A fourth layer may identify the objects by appending
one or more tags, labels, etc., to the image 208a as, e.g.,
metadata.
[0033] More generally, aspects of ML, AI, and/or DL may serve to
identify which characteristics of an image 208a (or, analogously,
image data) pertain to which layer of the model 212a and
slot/allocate such characteristics within the model 212a (e.g., the
layer), accordingly. In some embodiments, the model 212a may be
adapted/modified/tuned in accordance with one or more user inputs.
For example, user inputs may influence the count/number of layers
that are included in a given model and/or parameters of the
layers.
[0034] In some embodiments, a model 212a that is generated/created
may be static in nature. A static model may facilitate consistency
and ease in terms of a comparison of outputs (e.g., predicted
values of outputs) of the model over time (or between different
instances of an execution of the model). In some embodiments, a
model 212a that is generated/created may be
adapted/modified/updated in response to a change in one or more
conditions/inputs, resulting in a modified model. For example,
aspects of the model may incorporate a feedback representative of
an error between the predicted values of outputs as generated by
the model relative to actual values for the outputs (which may be
obtained via one or more out-of-band communication links/channels);
this feedback/error may be used to modify one or more
parameters/characteristics of the model. In this respect, the
predicted values and the actual values of the outputs may tend to
converge (e.g., the error may tend to converge towards zero), such
that the model may tend to become more accurate/consistent over
time in terms of its prediction capabilities.
[0035] An execution/invocation of the model(s) 212a may result in a
classification of one or more objects (denoted via reference
character 216a in FIG. 2A). To demonstrate, execution of the
model(s) 212a upon/relative to the image 208a may result in an
identification/classification 216a of objects 208b-1 through 208b-4
as shown in FIG. 2B. For example, and referring to FIGS. 2A-2B, an
execution of the model(s) 212a may identify/classify a first object
208b-1 as a building, a second object 208b-2 as a pole (e.g., a
light-pole, a utility pole, etc.), a third object 208b-3 as a tower
(e.g., a communications tower), and a fourth object 208b-4 as
foliage (e.g., as part of a plant/tree).
[0036] Once the objects (e.g., the objects 208b-1 through 208b-4)
have been classified in accordance with the object classification
216a, one or more attributes of, e.g., the object may be
identified/recognized (as denoted via reference character 220a in
FIG. 2A).
[0037] Attributes 220a of the first object/building 208b-1 may
include an identification of one or more trusses of the building
208b-1, one or more pilings of the building 208b-1, a
size/dimension (e.g., a height or footprint) of the building
208b-1, a dimension/style of a roof of the building 208b-1, a
material of the roof, etc.
[0038] Attributes 220a of the second object/pole 208b-2 may
include, e.g., a dimension (e.g., a height, a circumference, a
diameter) of the pole 208b-2, an identification of one or more
attachment mechanisms/attachments presently on the pole 208b-2
(e.g., when the image was captured) or capable of being
incorporated on the pole 208b-2, an identification of transmission
media (e.g., power cables, telephone lines, etc.) and/or signaling
equipment (e.g., a stop-light) presently on the pole 208b-2 or
capable of being incorporated on the pole 208b-2, a material of the
pole 208b-2, etc. To the extent that the attributes 220a of the
pole 208b-2 identify attachment mechanisms, transmission media,
and/or signaling equipment, the attributes may also specify a
location of the same relative to a reference location.
[0039] Attributes 220a of the third object/tower 208b-3 may
include, e.g., a dimension of the tower 208b-3, an identification
of one or more attachment mechanisms/attachments presently on the
tower 208b-3 (e.g., when the image was captured) or capable of
being incorporated on the tower 208b-3, an identification of
communications equipment (e.g., transmitters, receivers, antennas,
etc.) presently on the tower 208b-3 or cable of being incorporated
on the tower 208b-3, a material of the tower 208b-3, etc. To the
extent that the attributes 220a of the tower 208b-3 identify
attachment mechanisms and/or communications equipment, the
attributes may also specify a location of the same relative to a
reference location.
[0040] Attributes 220a of the fourth object/foliage 208b-4 may
include, e.g., a dimension (e.g., a height) of the foliage 208b-4,
an identification/specification of a thickness/density of the
foliage 208b-4, an identification of a type of plant/tree (e.g.,
oak, maple, pine, etc.) associated with the foliage 208b-4, etc.
The attributes 220a of the foliage 208b-4 may provide an indication
of how frequently the foliage 208b-4 may need to be tended to
(e.g., how frequently the foliage 208b-4 may be need to be subject
to maintenance) and/or may provide an indication of an impact on
network service/performance in terms of a lack of action/inactivity
with respect to a maintenance of the foliage 208b-4.
[0041] Once the attributes 220a are obtained, data (e.g., geography
[geo] tagged data) may be obtained/extracted (as denoted via
reference character 224a in FIG. 2A). For example, the extraction
of the data 224a may supplement the object classification 216a and
attributes 220a associated with the objects to obtain an
understanding of a topology/landscape associated with a network (or
a potential network that is being deployed as part of the
implementation of the system 200a). To demonstrate, in respect of
objects 208b-1 through 208b-4 of FIG. 2B, the data 224a of FIG. 2A
may identify a proximity of a given object (e.g., object 208b-1) to
one or more cell sites, backbone network infrastructure (e.g., one
or more cable bundles, optical fiber trunks, repeaters, couplers,
etc.), etc.
[0042] In some embodiments, the data 224a may include a
specification of restrictions (or, analogously, rights-of-way,
easements, etc.), as potentially imposed/overseen by a given
jurisdiction, a governmental entity (e.g., a local or regional
board of officials), and/or a private party. For example, if the
pole 208b-2 is a historical/decorative light-post, a town/city may
impose an ordinance that communications equipment (or the like) may
be prohibited from appearing on the light-post in order to avoid
detracting from the aesthetics of the light-post. Conversely, if
the pole 208b-2 is used in a transmission of electrical power (as
potentially identified via the attributes 220a), an agreement
between an electrical power provider and a communications equipment
provider may allow for communications equipment of the
communications equipment provider to be placed on (e.g., mounted
to) the pole 208b-2 as long as a (minimum) clearance is maintained
between the communications equipment and electrical power
generation and/or distribution equipment (e.g., a transformer).
[0043] In some embodiments, the data 224a may identify materials
used in the manufacture/fabrication of an object. In some
embodiments, the data 224a may identify patterns in terms of a
given object (e.g., instances of the pole 208b-2 spaced/separated
`X` meters apart).
[0044] In some embodiments, the data 224a may include information
associated with a communication system. For example, the data 224a
may include information (e.g., statistics) regarding signal quality
parameters (e.g., received signal strength, interference or noise,
etc.) of the communication system.
[0045] The classified objects 216a, the attributes 220a, and the
data 224a may be provided as inputs to a planning component,
illustratively denoted as a radio access network (RAN) planning
component 228a. The planning component 228a may analyze the inputs
that it receives/obtains to identify a subset of the classified
objects 216a as candidates for a potential placement of network
resources (e.g., network equipment). Still further, the planning
component 228a may provide recommendations for selecting (and may
perform a selection of) one or more of the objects from the pool of
candidates. The planning component 228a may provide an indication
of how a placement of a given resource on a given object (or,
analogously, at a given location) may impact other resources (on a
qualitative and/or quantitative basis).
[0046] In some embodiments, the planning component 228a may
generate and provide one or more outputs on the basis of additional
inputs (e.g., inputs beyond the classified objects 216a, the
attributes 220a, and the data 224a). For example, if a
utility/power company is erecting new poles in a given geographical
area at a given rate (e.g., five poles per month), the planning
component 228a may take the rate of pole erection into account when
deciding when and where to allocate network resources. Stated
differently, aspects of the planning component 228a may take into
consideration future events or conditions that have a probabilistic
chance of occurring in generating one or more outputs. In this
regard, aspects of the planning component 228a may include elements
of forecasting.
[0047] In some embodiments, inputs to the planning component 228a
may include a specification of a number of users/devices subscribed
to one or more services (e.g., one or more data or communication
services), types of users (e.g., single user, family plan, etc.) or
devices (e.g., make, model, serial number) that are subscribed,
traffic/network loads (on a historical basis, on an actual/current
basis, and/or on a forecasted basis), types of communications
sessions that are supported, etc. In some embodiments, the inputs
to the planning component 228a may include trends in population
growth/decline (e.g., number of people moving to or leaving a given
geographical area or jurisdiction). Analysis of trends may enable a
network/service operator to anticipate demand for services and
respond proportionately/accordingly.
[0048] Aspects of the system 200a may be invoked/executed
repeatedly/iteratively to obtain an allocation of resources
relative to objects/locations. For example, between instances of an
execution of the system 200a one or more parameters may be modified
to obtain a range of values associated with one or more outputs.
Still further, a given parameter may be dithered to obtain insight
into the sensitivity of one or more of the outputs/output values
relative to the given parameter. In some embodiments, aspects of
the system 200a (e.g., the data 224a) may be updated/refreshed at a
given rate, or in response to one or more events or conditions, in
order to ensure that the outputs generated and provided by the
system 200a are accurate (e.g., in order to ensure that the data
224a does not become stale).
[0049] FIG. 2C depicts an illustrative embodiment of a method 200c
in accordance with various aspects described herein. The method
200c may be implemented/executed/practiced in
accordance/conjunction/association with one or more systems,
devices, and/or components, such as for example the systems,
devices, and components described herein.
[0050] In block 202c, one or more images (e.g., image 208a of FIG.
2A), or data associated with the images, may be obtained. For
example, as part of block 202c the one or more images may be
captured via image capture equipment (e.g., image capture equipment
204a of FIG. 2A). As part of block 202c, the images may be obtained
(e.g., transmitted and received) via one or more networks.
[0051] In block 206c, the image(s) obtained as part of block 202c
may be applied to (e.g., may serve as an input to) one or more
models (e.g., model 212a of FIG. 2A). Execution/Operation of the
model(s) upon the image(s) in block 206c may result in an
identification/classification of one or more objects (see FIG. 2A:
object classification 216a). As part of block 206c, one or more
image processing algorithms may be applied/executed/invoked
relative to the image(s) to distinguish a first object (see, e.g.,
object 208b-1 of FIG. 2B) from one or more other objects (see,
e.g., objects 208b-2 through 208b-4 of FIG. 2B). The image
processing algorithm(s) may include one or more filters, such as
for example a filter that removes background noise from the
image(s).
[0052] In block 210c, one or more attributes of the objects of
block 206c may be identified/recognized (see FIG. 2A: attribute
recognition 220a). As part of block 210c, one or more image
processing algorithms may be applied to an object (of block 206c)
to distinguish a first attribute of the object from one or more
other attributes of the object.
[0053] In block 214c, (first) data associated with a location of
the objects (of block 206c) and/or (second) data associated with
the attributes (of block 210c) may be obtained (see FIG. 2A: geo
tagged data extraction 224a). The data of block 214c may supplement
the identification/classification of the objects and the
identification/recognition of the attributes. For example, the data
of block 214c may serve to establish relationships between the
objects and the attributes in some instances.
[0054] In block 218c, the identification/classification of the
objects (of block 206c), the identification/recognition of the
attributes (of block 210c) and the data (of block 214c) may be
applied as (e.g., may serve as) inputs to a planning algorithm (see
FIG. 2A: RAN planning component 228a). Based on those inputs (as
well as potential other inputs as set forth above), the planning
algorithm may recommend and/or select an object for
receiving/locating one or more network resources. As part of block
218c, the planning algorithm may recommend and/or identify/select
one or more operating parameters (e.g., a transmission power level,
a frequency band, a modulation/demodulation scheme, an
encoding/decoding scheme, an encryption/decryption scheme) of the
network resource(s).
[0055] In block 222c, the network resources may be placed/deployed
on the object(s) selected as part of block 218c. For example, and
as shown in FIG. 2D, a network resource 200d is shown as being
placed on/about an object 208d (where the object 208d may
correspond to one of the objects 208b-1 through 208b-4 of FIG. 2B).
In an illustrative embodiment, the object 208d may correspond to a
utility pole coupled to a second utility pole 218d via a
transmission medium 228d. In the example of FIG. 2D, the resource
200d may include an antenna 200d-1, a transmitter (TX) 200d-2,
and/or a receiver 200d-3. Other types of resources may be deployed
as part of block 222c in some embodiments.
[0056] As part of block 222c, directions may be generated and
presented in conjunction with a presentation device (e.g., a
display device, a speaker, a print-out, etc.). The directions may
advise a technician/operator of a geographical location where the
object 208d is located relative to a current location of the
technician/operator (e.g., driving directions to a site of the
object 208d may be provided). The directions may identify the
resource 200d (e.g., by a part number) and may provide an
indication (e.g., a visual indication) of where the resource is to
be placed on/about the object 208d. In some embodiments, the
directions may include a video tutorial.
[0057] In block 226c, the model(s) (of block 206c) may be modified
to generate one or more modified models. For example, the model(s)
may be updated to account for the deployment of the network
resource(s) as part of block 222c. The model(s) may be modified to
incorporate one or more operating parameters associated with the
deployed network resource(s).
[0058] While for purposes of simplicity of explanation, the
respective processes are shown and described as a series of blocks
in FIG. 2C, it is to be understood and appreciated that the claimed
subject matter is not limited by the order of the blocks, as some
blocks may occur in different orders and/or concurrently with other
blocks from what is depicted and described herein. Moreover, not
all illustrated blocks may be required to implement the methods
described herein.
[0059] In some embodiments, aspects of the method 200c may be
executed iteratively/repeatedly. For example, in some embodiments
one or more blocks 200c may be executed as part of a loop. In this
respect, various instances of images, attributes, and/or data may
be obtained and/or identified to continue to assess network
operability and performance, as well as identify opportunities for
placement of additional resources.
[0060] Aspects of the disclosure may be used to facilitate a
planning, development, implementation, and maintenance of one or
more networks. For example, aspects of the disclosure may automate
the procedure of identifying candidate locations to support network
resources (e.g., network infrastructure) and selecting one or more
locations from the candidate locations. In some embodiments,
artificial intelligence (AI)/machine learning (ML) based models may
be incorporated to facilitate the identification and/or selection
of one or more locations. In some embodiments, such locations may
include one or more buildings, building characteristics/objects
(e.g., trusses, pilings, etc.), trees, roads, poles, signs (e.g.,
road signage), traffic indicators (e.g., traffic lights), etc.
[0061] In some embodiments, one or more locations/objects may be
classified in accordance with the models. The models may be based,
at least in part, on imagery. The imagery may be at least partially
captured by a vehicle (e.g., an aircraft, spacecraft, or the like).
In some embodiments, the imagery may be at least partially captured
by a user equipment (UE), such as for example a handheld camera, a
mobile device (e.g., a smartphone), etc. In some embodiments, the
models may be based on (e.g., may be refined in accordance with)
one or more user inputs.
[0062] In some embodiments, the models may be used to extract
details/features/characteristics/parameters regarding a given
location or object. For example, in relation to a building, the
models may identify window/door placement, building materials, roof
characteristics (e.g., slope/style of roof), etc.
[0063] Aspects of the disclosure may facilitate an efficient
deployment and maintenance of network resources. For example,
aspects of the disclosure may reduce (e.g., minimize) the number of
site visits that may be required of technicians. Still further,
aspects of the disclosure may be used to identify opportunities
(e.g., locations/objects) for a deployment of resources that
otherwise may have been overlooked/missed.
[0064] Aspects of the disclosure may leverage pre-existing image
capture equipment (e.g., image capture equipment 204a of FIG. 2A)
and/or images (e.g., images 208a of FIG. 2A) (which may be stored
in, and may be accessible via, one or more databases) to identify
and/or select one or more locations/objects for receiving network
resources (e.g., network infrastructure). Stated slightly
differently, aspects of this disclosure may be facilitated via a
use of legacy/pre-existing equipment (which may initially have been
deployed for reasons unrelated to network resource
deployment/management), such that aspects of the disclosure may be
implemented with little-to-no additional cost/overhead.
[0065] Aspects of the disclosure may be used to enrich a database
of data regarding locations/objects for receiving network
resources. In some embodiments, locations/objects that have
demonstrated poor performance (e.g., performance that is less than
a metric/threshold) may be removed/banned from serving as a
candidate location/object in future deployments/implementations. In
this respect, a log/history of locations/objects may assist a
network/service operator/provider from incurring costly
mistakes/rework.
[0066] In some embodiments, the models may be executed/exercised to
identify/assess a prospective performance of network resources when
deployed/implemented at a given location. While aspects of such
model execution may provide insight into the performance of a
specific network resource at the given location, the execution of
the model may also identify the impact of one or more operations of
the resource on other resources (at the same location and/or at
other locations). For example, while a first resource may
operate/function as intended at a first location, the first
resource may negatively impact (e.g., may cause
signal/message/communication interference in relation to) a second
resource (at the first location or at a second location). In this
regard, an execution of one or more models may assist
engineers/technicians in identifying an impact of a deployment of a
first resource on one or more additional resources. In this
respect, aspects of the disclosure may facilitate a decision-making
procedure at both the device/component level and the system/network
level.
[0067] Aspects of this disclosure may facilitate an
identification/selection of objects or locations for
receiving/placing/mounting resources. Additionally, aspects of the
disclosure may facilitate a maintenance of such objects, locations,
and/or resources by proactively identifying when such maintenance
should be performed (e.g., relative to a probability of
inoperability of a resource exceeding a threshold), as well as
identifying equipment and/or personnel needed to perform such
maintenance. For example, in relation to the foliage 208b-4 of FIG.
2B, aspects of the disclosure may identify a particular crew of
arborists to trim trees using gas-powered saws in proximity to the
tower 208b-3 and schedule the tree trimming in advance of when a
growth of the foliage 208b-4 would obstruct a line-of-sight of
network communications equipment located on the tower 208b-3. In
relation to the poles 208b-2, a frequency band of communication
associated with a transmitter (e.g., TX 200d-2 of FIG. 2D) may be
adjusted to account for an aging/drift of an oscillator of the
transmitter over time.
[0068] Referring now to FIG. 3, a block diagram 300 is shown
illustrating an example, non-limiting embodiment of a virtualized
communication network in accordance with various aspects described
herein. In particular a virtualized communication network is
presented that can be used to implement some or all of the
subsystems and functions of communication network 100, the
subsystems and functions of system 200a, and method 200c presented
in FIGS. 1, 2A, and 2C. For example, virtualized communication
network 300 can facilitate in whole or in part obtaining an image
that is sourced from a vehicle, applying the image to a model to
identify a plurality of objects in the image, identifying a
plurality of attributes associated with each of the plurality of
objects, obtaining data, wherein the data identifies a location of
each object of the plurality of objects, and selecting an object
included in the plurality of objects for deployment of a
communication network resource in accordance with the plurality of
attributes and the data. Virtualized communication network 300 can
facilitate in whole or in part obtaining a plurality of images,
wherein the plurality of images is captured by a vehicle, a user
equipment, or any combination thereof, identifying a first object
included in the plurality of images via an application of the
plurality of images to at least one model that comprises a machine
learning model, identifying at least one attribute associated with
the first object responsive to the identifying of the first object,
generating a recommendation that identifies the first object or a
second object for receiving a network resource responsive to the
identifying of the at least one attribute, and presenting the
recommendation on a presentation device. Virtualized communication
network 300 can facilitate in whole or in part identifying a first
object included in at least one image in accordance with an
execution of an image processing algorithm, analyzing a plurality
of parameters in accordance with at least one model responsive to
the identifying of the first object included in the at least one
image, wherein each parameter of the plurality of parameters is
associated with the first object or a second object, selecting one
of the first object or the second object for receiving at least one
communication network resource responsive to the analyzing of the
plurality of parameters, wherein the selecting results in a
selected object, and presenting the selected object on a
presentation device.
[0069] In particular, a cloud networking architecture is shown that
leverages cloud technologies and supports rapid innovation and
scalability via a transport layer 350, a virtualized network
function cloud 325 and/or one or more cloud computing environments
375. In various embodiments, this cloud networking architecture is
an open architecture that leverages application programming
interfaces (APIs); reduces complexity from services and operations;
supports more nimble business models; and rapidly and seamlessly
scales to meet evolving customer requirements including traffic
growth, diversity of traffic types, and diversity of performance
and reliability expectations.
[0070] In contrast to traditional network elements--which are
typically integrated to perform a single function, the virtualized
communication network employs virtual network elements (VNEs) 330,
332, 334, etc. that perform some or all of the functions of network
elements 150, 152, 154, 156, etc. For example, the network
architecture can provide a substrate of networking capability,
often called Network Function Virtualization Infrastructure (NFVI)
or simply infrastructure that is capable of being directed with
software and Software Defined Networking (SDN) protocols to perform
a broad variety of network functions and services. This
infrastructure can include several types of substrates. The most
typical type of substrate being servers that support Network
Function Virtualization (NFV), followed by packet forwarding
capabilities based on generic computing resources, with specialized
network technologies brought to bear when general purpose
processors or general purpose integrated circuit devices offered by
merchants (referred to herein as merchant silicon) are not
appropriate. In this case, communication services can be
implemented as cloud-centric workloads.
[0071] As an example, a traditional network element 150 (shown in
FIG. 1), such as an edge router can be implemented via a VNE 330
composed of NFV software modules, merchant silicon, and associated
controllers. The software can be written so that increasing
workload consumes incremental resources from a common resource
pool, and moreover so that it's elastic: so the resources are only
consumed when needed. In a similar fashion, other network elements
such as other routers, switches, edge caches, and middle-boxes are
instantiated from the common resource pool. Such sharing of
infrastructure across a broad set of uses makes planning and
growing infrastructure easier to manage.
[0072] In an embodiment, the transport layer 350 includes fiber,
cable, wired and/or wireless transport elements, network elements
and interfaces to provide broadband access 110, wireless access
120, voice access 130, media access 140 and/or access to content
sources 175 for distribution of content to any or all of the access
technologies. In particular, in some cases a network element needs
to be positioned at a specific place, and this allows for less
sharing of common infrastructure. Other times, the network elements
have specific physical layer adapters that cannot be abstracted or
virtualized, and might require special DSP code and analog
front-ends (AFEs) that do not lend themselves to implementation as
VNEs 330, 332 or 334. These network elements can be included in
transport layer 350.
[0073] The virtualized network function cloud 325 interfaces with
the transport layer 350 to provide the VNEs 330, 332, 334, etc. to
provide specific NFVs. In particular, the virtualized network
function cloud 325 leverages cloud operations, applications, and
architectures to support networking workloads. The virtualized
network elements 330, 332 and 334 can employ network function
software that provides either a one-for-one mapping of traditional
network element function or alternately some combination of network
functions designed for cloud computing. For example, VNEs 330, 332
and 334 can include route reflectors, domain name system (DNS)
servers, and dynamic host configuration protocol (DHCP) servers,
system architecture evolution (SAE) and/or mobility management
entity (MME) gateways, broadband network gateways, IP edge routers
for IP-VPN, Ethernet and other services, load balancers,
distributers and other network elements. Because these elements
don't typically need to forward large amounts of traffic, their
workload can be distributed across a number of servers--each of
which adds a portion of the capability, and overall which creates
an elastic function with higher availability than its former
monolithic version. These virtual network elements 330, 332, 334,
etc. can be instantiated and managed using an orchestration
approach similar to those used in cloud compute services.
[0074] The cloud computing environments 375 can interface with the
virtualized network function cloud 325 via APIs that expose
functional capabilities of the VNEs 330, 332, 334, etc. to provide
the flexible and expanded capabilities to the virtualized network
function cloud 325. In particular, network workloads may have
applications distributed across the virtualized network function
cloud 325 and cloud computing environment 375 and in the commercial
cloud, or might simply orchestrate workloads supported entirely in
NFV infrastructure from these third party locations.
[0075] Turning now to FIG. 4, there is illustrated a block diagram
of a computing environment in accordance with various aspects
described herein. In order to provide additional context for
various embodiments of the embodiments described herein, FIG. 4 and
the following discussion are intended to provide a brief, general
description of a suitable computing environment 400 in which the
various embodiments of the subject disclosure can be implemented.
In particular, computing environment 400 can be used in the
implementation of network elements 150, 152, 154, 156, access
terminal 112, base station or access point 122, switching device
132, media terminal 142, and/or VNEs 330, 332, 334, etc. Each of
these devices can be implemented via computer-executable
instructions that can run on one or more computers, and/or in
combination with other program modules and/or as a combination of
hardware and software. For example, computing environment 400 can
facilitate in whole or in part obtaining an image that is sourced
from a vehicle, applying the image to a model to identify a
plurality of objects in the image, identifying a plurality of
attributes associated with each of the plurality of objects,
obtaining data, wherein the data identifies a location of each
object of the plurality of objects, and selecting an object
included in the plurality of objects for deployment of a
communication network resource in accordance with the plurality of
attributes and the data. Computing environment 400 can facilitate
in whole or in part obtaining a plurality of images, wherein the
plurality of images is captured by a vehicle, a user equipment, or
any combination thereof, identifying a first object included in the
plurality of images via an application of the plurality of images
to at least one model that comprises a machine learning model,
identifying at least one attribute associated with the first object
responsive to the identifying of the first object, generating a
recommendation that identifies the first object or a second object
for receiving a network resource responsive to the identifying of
the at least one attribute, and presenting the recommendation on a
presentation device. Computing environment 400 can facilitate in
whole or in part identifying a first object included in at least
one image in accordance with an execution of an image processing
algorithm, analyzing a plurality of parameters in accordance with
at least one model responsive to the identifying of the first
object included in the at least one image, wherein each parameter
of the plurality of parameters is associated with the first object
or a second object, selecting one of the first object or the second
object for receiving at least one communication network resource
responsive to the analyzing of the plurality of parameters, wherein
the selecting results in a selected object, and presenting the
selected object on a presentation device.
[0076] Generally, program modules comprise routines, programs,
components, data structures, etc., that perform particular tasks or
implement particular abstract data types. Moreover, those skilled
in the art will appreciate that the methods can be practiced with
other computer system configurations, comprising single-processor
or multiprocessor computer systems, minicomputers, mainframe
computers, as well as personal computers, hand-held computing
devices, microprocessor-based or programmable consumer electronics,
and the like, each of which can be operatively coupled to one or
more associated devices.
[0077] As used herein, a processing circuit includes one or more
processors as well as other application specific circuits such as
an application specific integrated circuit, digital logic circuit,
state machine, programmable gate array or other circuit that
processes input signals or data and that produces output signals or
data in response thereto. It should be noted that while any
functions and features described herein in association with the
operation of a processor could likewise be performed by a
processing circuit.
[0078] The illustrated embodiments of the embodiments herein can be
also practiced in distributed computing environments where certain
tasks are performed by remote processing devices that are linked
through a communications network. In a distributed computing
environment, program modules can be located in both local and
remote memory storage devices.
[0079] Computing devices typically comprise a variety of media,
which can comprise computer-readable storage media and/or
communications media, which two terms are used herein differently
from one another as follows. Computer-readable storage media can be
any available storage media that can be accessed by the computer
and comprises both volatile and nonvolatile media, removable and
non-removable media. By way of example, and not limitation,
computer-readable storage media can be implemented in connection
with any method or technology for storage of information such as
computer-readable instructions, program modules, structured data or
unstructured data.
[0080] Computer-readable storage media can comprise, but are not
limited to, random access memory (RAM), read only memory (ROM),
electrically erasable programmable read only memory (EEPROM), flash
memory or other memory technology, compact disk read only memory
(CD-ROM), digital versatile disk (DVD) or other optical disk
storage, magnetic cassettes, magnetic tape, magnetic disk storage
or other magnetic storage devices or other tangible and/or
non-transitory media which can be used to store desired
information. In this regard, the terms "tangible" or
"non-transitory" herein as applied to storage, memory or
computer-readable media, are to be understood to exclude only
propagating transitory signals per se as modifiers and do not
relinquish rights to all standard storage, memory or
computer-readable media that are not only propagating transitory
signals per se.
[0081] Computer-readable storage media can be accessed by one or
more local or remote computing devices, e.g., via access requests,
queries or other data retrieval protocols, for a variety of
operations with respect to the information stored by the
medium.
[0082] Communications media typically embody computer-readable
instructions, data structures, program modules or other structured
or unstructured data in a data signal such as a modulated data
signal, e.g., a carrier wave or other transport mechanism, and
comprises any information delivery or transport media. The term
"modulated data signal" or signals refers to a signal that has one
or more of its characteristics set or changed in such a manner as
to encode information in one or more signals. By way of example,
and not limitation, communication media comprise wired media, such
as a wired network or direct-wired connection, and wireless media
such as acoustic, RF, infrared and other wireless media.
[0083] With reference again to FIG. 4, the example environment can
comprise a computer 402, the computer 402 comprising a processing
unit 404, a system memory 406 and a system bus 408. The system bus
408 couples system components including, but not limited to, the
system memory 406 to the processing unit 404. The processing unit
404 can be any of various commercially available processors. Dual
microprocessors and other multiprocessor architectures can also be
employed as the processing unit 404.
[0084] The system bus 408 can be any of several types of bus
structure that can further interconnect to a memory bus (with or
without a memory controller), a peripheral bus, and a local bus
using any of a variety of commercially available bus architectures.
The system memory 406 comprises ROM 410 and RAM 412. A basic
input/output system (BIOS) can be stored in a non-volatile memory
such as ROM, erasable programmable read only memory (EPROM),
EEPROM, which BIOS contains the basic routines that help to
transfer information between elements within the computer 402, such
as during startup. The RAM 412 can also comprise a high-speed RAM
such as static RAM for caching data.
[0085] The computer 402 further comprises an internal hard disk
drive (HDD) 414 (e.g., EIDE, SATA), which internal HDD 414 can also
be configured for external use in a suitable chassis (not shown), a
magnetic floppy disk drive (FDD) 416, (e.g., to read from or write
to a removable diskette 418) and an optical disk drive 420, (e.g.,
reading a CD-ROM disk 422 or, to read from or write to other high
capacity optical media such as the DVD). The HDD 414, magnetic FDD
416 and optical disk drive 420 can be connected to the system bus
408 by a hard disk drive interface 424, a magnetic disk drive
interface 426 and an optical drive interface 428, respectively. The
hard disk drive interface 424 for external drive implementations
comprises at least one or both of Universal Serial Bus (USB) and
Institute of Electrical and Electronics Engineers (IEEE) 1394
interface technologies. Other external drive connection
technologies are within contemplation of the embodiments described
herein.
[0086] The drives and their associated computer-readable storage
media provide nonvolatile storage of data, data structures,
computer-executable instructions, and so forth. For the computer
402, the drives and storage media accommodate the storage of any
data in a suitable digital format. Although the description of
computer-readable storage media above refers to a hard disk drive
(HDD), a removable magnetic diskette, and a removable optical media
such as a CD or DVD, it should be appreciated by those skilled in
the art that other types of storage media which are readable by a
computer, such as zip drives, magnetic cassettes, flash memory
cards, cartridges, and the like, can also be used in the example
operating environment, and further, that any such storage media can
contain computer-executable instructions for performing the methods
described herein.
[0087] A number of program modules can be stored in the drives and
RAM 412, comprising an operating system 430, one or more
application programs 432, other program modules 434 and program
data 436. All or portions of the operating system, applications,
modules, and/or data can also be cached in the RAM 412. The systems
and methods described herein can be implemented utilizing various
commercially available operating systems or combinations of
operating systems.
[0088] A user can enter commands and information into the computer
402 through one or more wired/wireless input devices, e.g., a
keyboard 438 and a pointing device, such as a mouse 440. Other
input devices (not shown) can comprise a microphone, an infrared
(IR) remote control, a joystick, a game pad, a stylus pen, touch
screen or the like. These and other input devices are often
connected to the processing unit 404 through an input device
interface 442 that can be coupled to the system bus 408, but can be
connected by other interfaces, such as a parallel port, an IEEE
1394 serial port, a game port, a universal serial bus (USB) port,
an IR interface, etc.
[0089] A monitor 444 or other type of display device can be also
connected to the system bus 408 via an interface, such as a video
adapter 446. It will also be appreciated that in alternative
embodiments, a monitor 444 can also be any display device (e.g.,
another computer having a display, a smart phone, a tablet
computer, etc.) for receiving display information associated with
computer 402 via any communication means, including via the
Internet and cloud-based networks. In addition to the monitor 444,
a computer typically comprises other peripheral output devices (not
shown), such as speakers, printers, etc.
[0090] The computer 402 can operate in a networked environment
using logical connections via wired and/or wireless communications
to one or more remote computers, such as a remote computer(s) 448.
The remote computer(s) 448 can be a workstation, a server computer,
a router, a personal computer, portable computer,
microprocessor-based entertainment appliance, a peer device or
other common network node, and typically comprises many or all of
the elements described relative to the computer 402, although, for
purposes of brevity, only a remote memory/storage device 450 is
illustrated. The logical connections depicted comprise
wired/wireless connectivity to a local area network (LAN) 452
and/or larger networks, e.g., a wide area network (WAN) 454. Such
LAN and WAN networking environments are commonplace in offices and
companies, and facilitate enterprise-wide computer networks, such
as intranets, all of which can connect to a global communications
network, e.g., the Internet.
[0091] When used in a LAN networking environment, the computer 402
can be connected to the LAN 452 through a wired and/or wireless
communication network interface or adapter 456. The adapter 456 can
facilitate wired or wireless communication to the LAN 452, which
can also comprise a wireless AP disposed thereon for communicating
with the adapter 456.
[0092] When used in a WAN networking environment, the computer 402
can comprise a modem 458 or can be connected to a communications
server on the WAN 454 or has other means for establishing
communications over the WAN 454, such as by way of the Internet.
The modem 458, which can be internal or external and a wired or
wireless device, can be connected to the system bus 408 via the
input device interface 442. In a networked environment, program
modules depicted relative to the computer 402 or portions thereof,
can be stored in the remote memory/storage device 450. It will be
appreciated that the network connections shown are example and
other means of establishing a communications link between the
computers can be used.
[0093] The computer 402 can be operable to communicate with any
wireless devices or entities operatively disposed in wireless
communication, e.g., a printer, scanner, desktop and/or portable
computer, portable data assistant, communications satellite, any
piece of equipment or location associated with a wirelessly
detectable tag (e.g., a kiosk, news stand, restroom), and
telephone. This can comprise Wireless Fidelity (Wi-Fi) and
BLUETOOTH.RTM. wireless technologies. Thus, the communication can
be a predefined structure as with a conventional network or simply
an ad hoc communication between at least two devices.
[0094] Wi-Fi can allow connection to the Internet from a couch at
home, a bed in a hotel room or a conference room at work, without
wires. Wi-Fi is a wireless technology similar to that used in a
cell phone that enables such devices, e.g., computers, to send and
receive data indoors and out; anywhere within the range of a base
station. Wi-Fi networks use radio technologies called IEEE 802.11
(a, b, g, n, ac, ag, etc.) to provide secure, reliable, fast
wireless connectivity. A Wi-Fi network can be used to connect
computers to each other, to the Internet, and to wired networks
(which can use IEEE 802.3 or Ethernet). Wi-Fi networks operate in
the unlicensed 2.4 and 5 GHz radio bands for example or with
products that contain both bands (dual band), so the networks can
provide real-world performance similar to the basic 10BaseT wired
Ethernet networks used in many offices.
[0095] Turning now to FIG. 5, an embodiment 500 of a mobile network
platform 510 is shown that is an example of network elements 150,
152, 154, 156, and/or VNEs 330, 332, 334, etc. For example,
platform 510 can facilitate in whole or in part obtaining an image
that is sourced from a vehicle, applying the image to a model to
identify a plurality of objects in the image, identifying a
plurality of attributes associated with each of the plurality of
objects, obtaining data, wherein the data identifies a location of
each object of the plurality of objects, and selecting an object
included in the plurality of objects for deployment of a
communication network resource in accordance with the plurality of
attributes and the data. Platform 510 can facilitate in whole or in
part obtaining a plurality of images, wherein the plurality of
images is captured by a vehicle, a user equipment, or any
combination thereof, identifying a first object included in the
plurality of images via an application of the plurality of images
to at least one model that comprises a machine learning model,
identifying at least one attribute associated with the first object
responsive to the identifying of the first object, generating a
recommendation that identifies the first object or a second object
for receiving a network resource responsive to the identifying of
the at least one attribute, and presenting the recommendation on a
presentation device. Platform 510 can facilitate in whole or in
part identifying a first object included in at least one image in
accordance with an execution of an image processing algorithm,
analyzing a plurality of parameters in accordance with at least one
model responsive to the identifying of the first object included in
the at least one image, wherein each parameter of the plurality of
parameters is associated with the first object or a second object,
selecting one of the first object or the second object for
receiving at least one communication network resource responsive to
the analyzing of the plurality of parameters, wherein the selecting
results in a selected object, and presenting the selected object on
a presentation device.
[0096] In one or more embodiments, the mobile network platform 510
can generate and receive signals transmitted and received by base
stations or access points such as base station or access point 122.
Generally, mobile network platform 510 can comprise components,
e.g., nodes, gateways, interfaces, servers, or disparate platforms,
that facilitate both packet-switched (PS) (e.g., internet protocol
(IP), frame relay, asynchronous transfer mode (ATM)) and
circuit-switched (CS) traffic (e.g., voice and data), as well as
control generation for networked wireless telecommunication. As a
non-limiting example, mobile network platform 510 can be included
in telecommunications carrier networks, and can be considered
carrier-side components as discussed elsewhere herein. Mobile
network platform 510 comprises CS gateway node(s) 512 which can
interface CS traffic received from legacy networks like telephony
network(s) 540 (e.g., public switched telephone network (PSTN), or
public land mobile network (PLMN)) or a signaling system #7 (SS7)
network 560. CS gateway node(s) 512 can authorize and authenticate
traffic (e.g., voice) arising from such networks. Additionally, CS
gateway node(s) 512 can access mobility, or roaming, data generated
through SS7 network 560; for instance, mobility data stored in a
visited location register (VLR), which can reside in memory 530.
Moreover, CS gateway node(s) 512 interfaces CS-based traffic and
signaling and PS gateway node(s) 518. As an example, in a 3GPP UMTS
network, CS gateway node(s) 512 can be realized at least in part in
gateway GPRS support node(s) (GGSN). It should be appreciated that
functionality and specific operation of CS gateway node(s) 512, PS
gateway node(s) 518, and serving node(s) 516, is provided and
dictated by radio technology(ies) utilized by mobile network
platform 510 for telecommunication over a radio access network 520
with other devices, such as a radiotelephone 575.
[0097] In addition to receiving and processing CS-switched traffic
and signaling, PS gateway node(s) 518 can authorize and
authenticate PS-based data sessions with served mobile devices.
Data sessions can comprise traffic, or content(s), exchanged with
networks external to the mobile network platform 510, like wide
area network(s) (WANs) 550, enterprise network(s) 570, and service
network(s) 580, which can be embodied in local area network(s)
(LANs), can also be interfaced with mobile network platform 510
through PS gateway node(s) 518. It is to be noted that WANs 550 and
enterprise network(s) 570 can embody, at least in part, a service
network(s) like IP multimedia subsystem (IMS). Based on radio
technology layer(s) available in technology resource(s) or radio
access network 520, PS gateway node(s) 518 can generate packet data
protocol contexts when a data session is established; other data
structures that facilitate routing of packetized data also can be
generated. To that end, in an aspect, PS gateway node(s) 518 can
comprise a tunnel interface (e.g., tunnel termination gateway (TTG)
in 3GPP UMTS network(s) (not shown)) which can facilitate
packetized communication with disparate wireless network(s), such
as Wi-Fi networks.
[0098] In embodiment 500, mobile network platform 510 also
comprises serving node(s) 516 that, based upon available radio
technology layer(s) within technology resource(s) in the radio
access network 520, convey the various packetized flows of data
streams received through PS gateway node(s) 518. It is to be noted
that for technology resource(s) that rely primarily on CS
communication, server node(s) can deliver traffic without reliance
on PS gateway node(s) 518; for example, server node(s) can embody
at least in part a mobile switching center. As an example, in a
3GPP UMTS network, serving node(s) 516 can be embodied in serving
GPRS support node(s) (SGSN).
[0099] For radio technologies that exploit packetized
communication, server(s) 514 in mobile network platform 510 can
execute numerous applications that can generate multiple disparate
packetized data streams or flows, and manage (e.g., schedule,
queue, format . . . ) such flows. Such application(s) can comprise
add-on features to standard services (for example, provisioning,
billing, customer support . . . ) provided by mobile network
platform 510. Data streams (e.g., content(s) that are part of a
voice call or data session) can be conveyed to PS gateway node(s)
518 for authorization/authentication and initiation of a data
session, and to serving node(s) 516 for communication thereafter.
In addition to application server, server(s) 514 can comprise
utility server(s), a utility server can comprise a provisioning
server, an operations and maintenance server, a security server
that can implement at least in part a certificate authority and
firewalls as well as other security mechanisms, and the like. In an
aspect, security server(s) secure communication served through
mobile network platform 510 to ensure network's operation and data
integrity in addition to authorization and authentication
procedures that CS gateway node(s) 512 and PS gateway node(s) 518
can enact. Moreover, provisioning server(s) can provision services
from external network(s) like networks operated by a disparate
service provider; for instance, WAN 550 or Global Positioning
System (GPS) network(s) (not shown). Provisioning server(s) can
also provision coverage through networks associated to mobile
network platform 510 (e.g., deployed and operated by the same
service provider), such as the distributed antennas networks shown
in FIG. 1(s) that enhance wireless service coverage by providing
more network coverage.
[0100] It is to be noted that server(s) 514 can comprise one or
more processors configured to confer at least in part the
functionality of mobile network platform 510. To that end, the one
or more processor can execute code instructions stored in memory
530, for example. It is should be appreciated that server(s) 514
can comprise a content manager, which operates in substantially the
same manner as described hereinbefore.
[0101] In example embodiment 500, memory 530 can store information
related to operation of mobile network platform 510. Other
operational information can comprise provisioning information of
mobile devices served through mobile network platform 510,
subscriber databases; application intelligence, pricing schemes,
e.g., promotional rates, flat-rate programs, couponing campaigns;
technical specification(s) consistent with telecommunication
protocols for operation of disparate radio, or wireless, technology
layers; and so forth. Memory 530 can also store information from at
least one of telephony network(s) 540, WAN 550, SS7 network 560, or
enterprise network(s) 570. In an aspect, memory 530 can be, for
example, accessed as part of a data store component or as a
remotely connected memory store.
[0102] In order to provide a context for the various aspects of the
disclosed subject matter, FIG. 5, and the following discussion, are
intended to provide a brief, general description of a suitable
environment in which the various aspects of the disclosed subject
matter can be implemented. While the subject matter has been
described above in the general context of computer-executable
instructions of a computer program that runs on a computer and/or
computers, those skilled in the art will recognize that the
disclosed subject matter also can be implemented in combination
with other program modules. Generally, program modules comprise
routines, programs, components, data structures, etc. that perform
particular tasks and/or implement particular abstract data
types.
[0103] Turning now to FIG. 6, an illustrative embodiment of a
communication device 600 is shown. The communication device 600 can
serve as an illustrative embodiment of devices such as data
terminals 114, mobile devices 124, vehicle 126, display devices 144
or other client devices for communication via either communications
network 125. For example, computing device 600 can facilitate in
whole or in part obtaining an image that is sourced from a vehicle,
applying the image to a model to identify a plurality of objects in
the image, identifying a plurality of attributes associated with
each of the plurality of objects, obtaining data, wherein the data
identifies a location of each object of the plurality of objects,
and selecting an object included in the plurality of objects for
deployment of a communication network resource in accordance with
the plurality of attributes and the data. Computing device 600 can
facilitate in whole or in part obtaining a plurality of images,
wherein the plurality of images is captured by a vehicle, a user
equipment, or any combination thereof, identifying a first object
included in the plurality of images via an application of the
plurality of images to at least one model that comprises a machine
learning model, identifying at least one attribute associated with
the first object responsive to the identifying of the first object,
generating a recommendation that identifies the first object or a
second object for receiving a network resource responsive to the
identifying of the at least one attribute, and presenting the
recommendation on a presentation device. Computing device 600 can
facilitate in whole or in part identifying a first object included
in at least one image in accordance with an execution of an image
processing algorithm, analyzing a plurality of parameters in
accordance with at least one model responsive to the identifying of
the first object included in the at least one image, wherein each
parameter of the plurality of parameters is associated with the
first object or a second object, selecting one of the first object
or the second object for receiving at least one communication
network resource responsive to the analyzing of the plurality of
parameters, wherein the selecting results in a selected object, and
presenting the selected object on a presentation device.
[0104] The communication device 600 can comprise a wireline and/or
wireless transceiver 602 (herein transceiver 602), a user interface
(UI) 604, a power supply 614, a location receiver 616, a motion
sensor 618, an orientation sensor 620, and a controller 606 for
managing operations thereof. The transceiver 602 can support
short-range or long-range wireless access technologies such as
Bluetooth.RTM., ZigBee.RTM., WiFi, DECT, or cellular communication
technologies, just to mention a few (Bluetooth.RTM. and ZigBee.RTM.
are trademarks registered by the Bluetooth.RTM. Special Interest
Group and the ZigBee.RTM. Alliance, respectively). Cellular
technologies can include, for example, CDMA-1.times., UMTS/HSDPA,
GSM/GPRS, TDMA/EDGE, EV/DO, WiMAX, SDR, LTE, as well as other next
generation wireless communication technologies as they arise. The
transceiver 602 can also be adapted to support circuit-switched
wireline access technologies (such as PSTN), packet-switched
wireline access technologies (such as TCP/IP, VoIP, etc.), and
combinations thereof.
[0105] The UI 604 can include a depressible or touch-sensitive
keypad 608 with a navigation mechanism such as a roller ball, a
joystick, a mouse, or a navigation disk for manipulating operations
of the communication device 600. The keypad 608 can be an integral
part of a housing assembly of the communication device 600 or an
independent device operably coupled thereto by a tethered wireline
interface (such as a USB cable) or a wireless interface supporting
for example Bluetooth.RTM.. The keypad 608 can represent a numeric
keypad commonly used by phones, and/or a QWERTY keypad with
alphanumeric keys. The UI 604 can further include a display 610
such as monochrome or color LCD (Liquid Crystal Display), OLED
(Organic Light Emitting Diode) or other suitable display technology
for conveying images to an end user of the communication device
600. In an embodiment where the display 610 is touch-sensitive, a
portion or all of the keypad 608 can be presented by way of the
display 610 with navigation features.
[0106] The display 610 can use touch screen technology to also
serve as a user interface for detecting user input. As a touch
screen display, the communication device 600 can be adapted to
present a user interface having graphical user interface (GUI)
elements that can be selected by a user with a touch of a finger.
The display 610 can be equipped with capacitive, resistive or other
forms of sensing technology to detect how much surface area of a
user's finger has been placed on a portion of the touch screen
display. This sensing information can be used to control the
manipulation of the GUI elements or other functions of the user
interface. The display 610 can be an integral part of the housing
assembly of the communication device 600 or an independent device
communicatively coupled thereto by a tethered wireline interface
(such as a cable) or a wireless interface.
[0107] The UI 604 can also include an audio system 612 that
utilizes audio technology for conveying low volume audio (such as
audio heard in proximity of a human ear) and high volume audio
(such as speakerphone for hands free operation). The audio system
612 can further include a microphone for receiving audible signals
of an end user. The audio system 612 can also be used for voice
recognition applications. The UI 604 can further include an image
sensor 613 such as a charged coupled device (CCD) camera for
capturing still or moving images.
[0108] The power supply 614 can utilize common power management
technologies such as replaceable and rechargeable batteries, supply
regulation technologies, and/or charging system technologies for
supplying energy to the components of the communication device 600
to facilitate long-range or short-range portable communications.
Alternatively, or in combination, the charging system can utilize
external power sources such as DC power supplied over a physical
interface such as a USB port or other suitable tethering
technologies.
[0109] The location receiver 616 can utilize location technology
such as a global positioning system (GPS) receiver capable of
assisted GPS for identifying a location of the communication device
600 based on signals generated by a constellation of GPS
satellites, which can be used for facilitating location services
such as navigation. The motion sensor 618 can utilize motion
sensing technology such as an accelerometer, a gyroscope, or other
suitable motion sensing technology to detect motion of the
communication device 600 in three-dimensional space. The
orientation sensor 620 can utilize orientation sensing technology
such as a magnetometer to detect the orientation of the
communication device 600 (north, south, west, and east, as well as
combined orientations in degrees, minutes, or other suitable
orientation metrics).
[0110] The communication device 600 can use the transceiver 602 to
also determine a proximity to a cellular, WiFi, Bluetooth.RTM., or
other wireless access points by sensing techniques such as
utilizing a received signal strength indicator (RSSI) and/or signal
time of arrival (TOA) or time of flight (TOF) measurements. The
controller 606 can utilize computing technologies such as a
microprocessor, a digital signal processor (DSP), programmable gate
arrays, application specific integrated circuits, and/or a video
processor with associated storage memory such as Flash, ROM, RAM,
SRAM, DRAM or other storage technologies for executing computer
instructions, controlling, and processing data supplied by the
aforementioned components of the communication device 600.
[0111] Other components not shown in FIG. 6 can be used in one or
more embodiments of the subject disclosure. For instance, the
communication device 600 can include a slot for adding or removing
an identity module such as a Subscriber Identity Module (SIM) card
or Universal Integrated Circuit Card (UICC). SIM or UICC cards can
be used for identifying subscriber services, executing programs,
storing subscriber data, and so on.
[0112] The terms "first," "second," "third," and so forth, as used
in the claims, unless otherwise clear by context, is for clarity
only and doesn't otherwise indicate or imply any order in time. For
instance, "a first determination," "a second determination," and "a
third determination," does not indicate or imply that the first
determination is to be made before the second determination, or
vice versa, etc.
[0113] In the subject specification, terms such as "store,"
"storage," "data store," data storage," "database," and
substantially any other information storage component relevant to
operation and functionality of a component, refer to "memory
components," or entities embodied in a "memory" or components
comprising the memory. It will be appreciated that the memory
components described herein can be either volatile memory or
nonvolatile memory, or can comprise both volatile and nonvolatile
memory, by way of illustration, and not limitation, volatile
memory, non-volatile memory, disk storage, and memory storage.
Further, nonvolatile memory can be included in read only memory
(ROM), programmable ROM (PROM), electrically programmable ROM
(EPROM), electrically erasable ROM (EEPROM), or flash memory.
Volatile memory can comprise random access memory (RAM), which acts
as external cache memory. By way of illustration and not
limitation, RAM is available in many forms such as synchronous RAM
(SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data
rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM
(SLDRAM), and direct Rambus RAM (DRRAM). Additionally, the
disclosed memory components of systems or methods herein are
intended to comprise, without being limited to comprising, these
and any other suitable types of memory.
[0114] Moreover, it will be noted that the disclosed subject matter
can be practiced with other computer system configurations,
comprising single-processor or multiprocessor computer systems,
mini-computing devices, mainframe computers, as well as personal
computers, hand-held computing devices (e.g., PDA, phone,
smartphone, watch, tablet computers, netbook computers, etc.),
microprocessor-based or programmable consumer or industrial
electronics, and the like. The illustrated aspects can also be
practiced in distributed computing environments where tasks are
performed by remote processing devices that are linked through a
communications network; however, some if not all aspects of the
subject disclosure can be practiced on stand-alone computers. In a
distributed computing environment, program modules can be located
in both local and remote memory storage devices.
[0115] In one or more embodiments, information regarding use of
services can be generated including services being accessed, media
consumption history, user preferences, and so forth. This
information can be obtained by various methods including user
input, detecting types of communications (e.g., video content vs.
audio content), analysis of content streams, sampling, and so
forth. The generating, obtaining and/or monitoring of this
information can be responsive to an authorization provided by the
user. In one or more embodiments, an analysis of data can be
subject to authorization from user(s) associated with the data,
such as an opt-in, an opt-out, acknowledgement requirements,
notifications, selective authorization based on types of data, and
so forth.
[0116] Some of the embodiments described herein can also employ
artificial intelligence (AI) to facilitate automating one or more
features described herein. The embodiments (e.g., in connection
with automatically identifying acquired cell sites that provide a
maximum value/benefit after addition to an existing communication
network) can employ various AI-based schemes for carrying out
various embodiments thereof. Moreover, the classifier can be
employed to determine a ranking or priority of each cell site of
the acquired network. A classifier is a function that maps an input
attribute vector, x=(x1, x2, x3, x4, . . . , xn), to a confidence
that the input belongs to a class, that is, f(x)=confidence
(class). Such classification can employ a probabilistic and/or
statistical-based analysis (e.g., factoring into the analysis
utilities and costs) to determine or infer an action that a user
desires to be automatically performed. A support vector machine
(SVM) is an example of a classifier that can be employed. The SVM
operates by finding a hypersurface in the space of possible inputs,
which the hypersurface attempts to split the triggering criteria
from the non-triggering events. Intuitively, this makes the
classification correct for testing data that is near, but not
identical to training data. Other directed and undirected model
classification approaches comprise, e.g., naive Bayes, Bayesian
networks, decision trees, neural networks, fuzzy logic models, and
probabilistic classification models providing different patterns of
independence can be employed. Classification as used herein also is
inclusive of statistical regression that is utilized to develop
models of priority.
[0117] As will be readily appreciated, one or more of the
embodiments can employ classifiers that are explicitly trained
(e.g., via a generic training data) as well as implicitly trained
(e.g., via observing UE behavior, operator preferences, historical
information, receiving extrinsic information). For example, SVMs
can be configured via a learning or training phase within a
classifier constructor and feature selection module. Thus, the
classifier(s) can be used to automatically learn and perform a
number of functions, including but not limited to determining
according to predetermined criteria which of the acquired cell
sites will benefit a maximum number of subscribers and/or which of
the acquired cell sites will add minimum value to the existing
communication network coverage, etc.
[0118] As used in some contexts in this application, in some
embodiments, the terms "component," "system" and the like are
intended to refer to, or comprise, a computer-related entity or an
entity related to an operational apparatus with one or more
specific functionalities, wherein the entity can be either
hardware, a combination of hardware and software, software, or
software in execution. As an example, a component may be, but is
not limited to being, a process running on a processor, a
processor, an object, an executable, a thread of execution,
computer-executable instructions, a program, and/or a computer. By
way of illustration and not limitation, both an application running
on a server and the server can be a component. One or more
components may reside within a process and/or thread of execution
and a component may be localized on one computer and/or distributed
between two or more computers. In addition, these components can
execute from various computer readable media having various data
structures stored thereon. The components may communicate via local
and/or remote processes such as in accordance with a signal having
one or more data packets (e.g., data from one component interacting
with another component in a local system, distributed system,
and/or across a network such as the Internet with other systems via
the signal). As another example, a component can be an apparatus
with specific functionality provided by mechanical parts operated
by electric or electronic circuitry, which is operated by a
software or firmware application executed by a processor, wherein
the processor can be internal or external to the apparatus and
executes at least a part of the software or firmware application.
As yet another example, a component can be an apparatus that
provides specific functionality through electronic components
without mechanical parts, the electronic components can comprise a
processor therein to execute software or firmware that confers at
least in part the functionality of the electronic components. While
various components have been illustrated as separate components, it
will be appreciated that multiple components can be implemented as
a single component, or a single component can be implemented as
multiple components, without departing from example
embodiments.
[0119] Further, the various embodiments can be implemented as a
method, apparatus or article of manufacture using standard
programming and/or engineering techniques to produce software,
firmware, hardware or any combination thereof to control a computer
to implement the disclosed subject matter. The term "article of
manufacture" as used herein is intended to encompass a computer
program accessible from any computer-readable device or
computer-readable storage/communications media. For example,
computer readable storage media can include, but are not limited
to, magnetic storage devices (e.g., hard disk, floppy disk,
magnetic strips), optical disks (e.g., compact disk (CD), digital
versatile disk (DVD)), smart cards, and flash memory devices (e.g.,
card, stick, key drive). Of course, those skilled in the art will
recognize many modifications can be made to this configuration
without departing from the scope or spirit of the various
embodiments.
[0120] In addition, the words "example" and "exemplary" are used
herein to mean serving as an instance or illustration. Any
embodiment or design described herein as "example" or "exemplary"
is not necessarily to be construed as preferred or advantageous
over other embodiments or designs. Rather, use of the word example
or exemplary is intended to present concepts in a concrete fashion.
As used in this application, the term "or" is intended to mean an
inclusive "or" rather than an exclusive "or". That is, unless
specified otherwise or clear from context, "X employs A or B" is
intended to mean any of the natural inclusive permutations. That
is, if X employs A; X employs B; or X employs both A and B, then "X
employs A or B" is satisfied under any of the foregoing instances.
In addition, the articles "a" and "an" as used in this application
and the appended claims should generally be construed to mean "one
or more" unless specified otherwise or clear from context to be
directed to a singular form.
[0121] Moreover, terms such as "user equipment," "mobile station,"
"mobile," subscriber station," "access terminal," "terminal,"
"handset," "mobile device" (and/or terms representing similar
terminology) can refer to a wireless device utilized by a
subscriber or user of a wireless communication service to receive
or convey data, control, voice, video, sound, gaming or
substantially any data-stream or signaling-stream. The foregoing
terms are utilized interchangeably herein and with reference to the
related drawings.
[0122] Furthermore, the terms "user," "subscriber," "customer,"
"consumer" and the like are employed interchangeably throughout,
unless context warrants particular distinctions among the terms. It
should be appreciated that such terms can refer to human entities
or automated components supported through artificial intelligence
(e.g., a capacity to make inference based, at least, on complex
mathematical formalisms), which can provide simulated vision, sound
recognition and so forth.
[0123] As employed herein, the term "processor" can refer to
substantially any computing processing unit or device comprising,
but not limited to comprising, single-core processors;
single-processors with software multithread execution capability;
multi-core processors; multi-core processors with software
multithread execution capability; multi-core processors with
hardware multithread technology; parallel platforms; and parallel
platforms with distributed shared memory. Additionally, a processor
can refer to an integrated circuit, an application specific
integrated circuit (ASIC), a digital signal processor (DSP), a
field programmable gate array (FPGA), a programmable logic
controller (PLC), a complex programmable logic device (CPLD), a
discrete gate or transistor logic, discrete hardware components or
any combination thereof designed to perform the functions described
herein. Processors can exploit nano-scale architectures such as,
but not limited to, molecular and quantum-dot based transistors,
switches and gates, in order to optimize space usage or enhance
performance of user equipment. A processor can also be implemented
as a combination of computing processing units.
[0124] As used herein, terms such as "data storage," data storage,"
"database," and substantially any other information storage
component relevant to operation and functionality of a component,
refer to "memory components," or entities embodied in a "memory" or
components comprising the memory. It will be appreciated that the
memory components or computer-readable storage media, described
herein can be either volatile memory or nonvolatile memory or can
include both volatile and nonvolatile memory.
[0125] What has been described above includes mere examples of
various embodiments. It is, of course, not possible to describe
every conceivable combination of components or methodologies for
purposes of describing these examples, but one of ordinary skill in
the art can recognize that many further combinations and
permutations of the present embodiments are possible. Accordingly,
the embodiments disclosed and/or claimed herein are intended to
embrace all such alterations, modifications and variations that
fall within the spirit and scope of the appended claims.
Furthermore, to the extent that the term "includes" is used in
either the detailed description or the claims, such term is
intended to be inclusive in a manner similar to the term
"comprising" as "comprising" is interpreted when employed as a
transitional word in a claim.
[0126] In addition, a flow diagram may include a "start" and/or
"continue" indication. The "start" and "continue" indications
reflect that the steps presented can optionally be incorporated in
or otherwise used in conjunction with other routines. In this
context, "start" indicates the beginning of the first step
presented and may be preceded by other activities not specifically
shown. Further, the "continue" indication reflects that the steps
presented may be performed multiple times and/or may be succeeded
by other activities not specifically shown. Further, while a flow
diagram indicates a particular ordering of steps, other orderings
are likewise possible provided that the principles of causality are
maintained.
[0127] As may also be used herein, the term(s) "operably coupled
to", "coupled to", and/or "coupling" includes direct coupling
between items and/or indirect coupling between items via one or
more intervening items. Such items and intervening items include,
but are not limited to, junctions, communication paths, components,
circuit elements, circuits, functional blocks, and/or devices. As
an example of indirect coupling, a signal conveyed from a first
item to a second item may be modified by one or more intervening
items by modifying the form, nature or format of information in a
signal, while one or more elements of the information in the signal
are nevertheless conveyed in a manner than can be recognized by the
second item. In a further example of indirect coupling, an action
in a first item can cause a reaction on the second item, as a
result of actions and/or reactions in one or more intervening
items.
[0128] Although specific embodiments have been illustrated and
described herein, it should be appreciated that any arrangement
which achieves the same or similar purpose may be substituted for
the embodiments described or shown by the subject disclosure. The
subject disclosure is intended to cover any and all adaptations or
variations of various embodiments. Combinations of the above
embodiments, and other embodiments not specifically described
herein, can be used in the subject disclosure. For instance, one or
more features from one or more embodiments can be combined with one
or more features of one or more other embodiments. In one or more
embodiments, features that are positively recited can also be
negatively recited and excluded from the embodiment with or without
replacement by another structural and/or functional feature. The
steps or functions described with respect to the embodiments of the
subject disclosure can be performed in any order. The steps or
functions described with respect to the embodiments of the subject
disclosure can be performed alone or in combination with other
steps or functions of the subject disclosure, as well as from other
embodiments or from other steps that have not been described in the
subject disclosure. Further, more than or less than all of the
features described with respect to an embodiment can also be
utilized.
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