A network-enabled audio device that provides a display device that allows the user to select playlists of music much like a jukebox is disclosed. The user can compose playlists from disk files, CD's, Internet streaming audio broadcasts, online music sites, and other audio sources. The user can also select a desired Web broadcast from a list of available Web broadcasts. In addition, the user can play standard audio CD's and MP3 encoded CD's and have access to local AM/FM stations. Further, the software, the user controls, and the display in the network-enabled audio device are operably configured and connected such that the user can listen to playlists that include CD's and other audio sources just as the user would choose a playlist in a jukebox. The user accesses a server site via a PC and the Internet. From the server site, the user obtains a list of the devices in his or her Internet Personal Audio Network (IPAN) and what songs are on those devices. The IPAN includes an IPAN server, an IPAN client, and IPAN software stored on the network-enabled audio device. Thus, the network-enabled audio device provides people who are or are not comfortable with computers a way of taking music from various sources and putting it into one place for listening pleasure. In one embodiment, the Personal Computer (PC) is used to compose the playlists, but the user is able to listen to playlists and other audio sources without using the PC.

Network-enabled audio device

The present invention discloses a home network system using a living network control protocol. The home network system includes: a network based on a predetermined protocol; at least electric device connected to the network; and a network manager connected to the network, for controlling and/or monitoring the electric device, wherein the protocol includes an application layer (60) for handling a message for controlling and monitoring the electric device, a network layer (70) for network-connecting the electric device to the network manager, a data link layer (80) for accessing a shared transmission medium, and a physical layer (90) for providing a physical interface between the electric device and the network manager, wherein the physical layer (90) further includes a special protocol for providing an interface with a dependent transmission medium, and the network layer (70) further includes a home code control sub-layer (71) for managing a home code for network security when accessing the dependent transmission medium.

Home network system

Systems and methods are presented for detecting users within a range of a media device. A detection region may be defined that is within the range of the media device and smaller than the range. The detection region may be stored. It may be determined whether a user is within the detection region. The media device may be activated and settings associated with the user may be applied when a user is within the detection region. In some embodiments, settings associated with a user may be compared to provided media content when the user is within the detection region. The content may change when the settings conflict with the media content. Reminders may be provided to or directed to a plurality of users within the range of the media device.

Systems and methods for automatically detecting users within detection regions of media devices

Methods, systems, and products notify of alarms in security systems. Sensor data is received from an alarm sensor, and an alarm controller determines an alarm condition. Video data associated with the alarm sensor is retrieved. An alarm message may be sent over a wireless network connection, while the video data may be sent over a wireline broadband connection.

Methods, systems, and products for security services

Apparatus and methods implement aggregation frames and allocation frames. The aggregation frames include a plurality of MSDUs or fragments thereof aggregated or otherwise combined together. An aggregation frame makes more efficient use of the wireless communication resources. The allocation frame defines a plurality of time intervals. The allocation frame specifies a pair of stations that are permitted to communicate with each other during each time interval as well as the antenna configuration to be used for the communication. This permits stations to know ahead of time when they are to communicate, with which other stations and the antenna configuration that should be used. A buffered traffic field can also be added to the frames to specify how much data remains to be transmitted following the current frame. This enables network traffic to be scheduled more effectively.

Mac extensions for smart antenna support

This paper describes the design and implementation of a home network system using UPnP middleware and an embedded interface device. The developed home network system comprises a home server program, appliance emulators (e.g., for TV, refrigerator, and air-conditioner), and an embedded interface device for networked home appliances.

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Design and implementation of home network systems using UPnP middleware for networked appliances

In this letter, we propose an improved convolutional neural network (CNN)-based automatic modulation classification network (IC-AMCNet), an algorithm to classify the modulation type of a wireless signal. Since adaptive coding and modulation is widely used in wireless communication, high accuracy and short computing time of classifier is needed. Compared with the existing CNN architectures, we adjusted the number of layers and added new type of layers to comply with the estimated latency standards in beyond fifth-generation (B5G) communications. According to the simulation results, the proposed scheme significantly outperforms the previous works in terms of both classification accuracy and computing time.

CNN-Based Automatic Modulation Classification for Beyond 5G Communications

A method and apparatus for providing a user input back channel (UIBC) in an audio/video (AV) source device and an AV sink device communicating according to a wireless fidelity (Wi-Fi) display (WFD) standard is provided. The method includes: setting up an AV control session and an AV data session between the AV source devices according to the WFD standards; enabling the UIBC from the AV sink device to the AV source device by using the AV control session; and transmitting a user input from the AV sink device to the AV source device through the UIBC.

Method and apparatus for providing user input back channel in audio/video system

A method and an apparatus are provided for enabling a legacy station to perform virtual carrier sensing when a plurality of stations with heterogeneous capabilities coexist in a wireless network. The method includes receiving first data compliant with a first protocol, and transmitting second data compliant with a second protocol, where the first protocol is downward compatible with the second protocol.

Method and apparatus for transmitting and receiving legacy format data in high throughput wireless network

This paper proposes a convolutional neural network (CNN), called SCGNet, for low-complexity and robust modulation recognition in intelligent communication receivers. Principally, the network combines two types of sparse convolutional layers–depthwise and regular grouped in an architecture to achieve high recognition accuracy while keeping the network more lightweight. The network architecture leverages sparsely connected convolutional layers in three principal modules: speed-accuracy tradeoff (SAT), deep feature extraction and processing (DFEP), and generic feature extraction (GFE) data pre-processing module. For a good tradeoff between complexity and accuracy, SAT deploys depthwise convolutional layers to enrich the relevant features outputted by the former GFE module. In addition to SAT, DFEP employs a cascade of regular grouped convolutional layers for mining more discriminative features from SAT via a multilayer transformation module. This cascade structure aims to prevent a loss of essential details of the signal as the network becomes deeper. Additionally, skip connections are deployed between sub-blocks within SAT and DFEP to allow inter-module feature sharing and to handle inter-block features loss. Experimental results on the RadioML2018.01A dataset indicate that SCGNet achieves an overall recognition accuracy of around 94.39% at a signal-to-noise ratio of +20 dB.

Jae-Min Lee

Papers

Design and implementation of home network systems using UPnP middleware for networked appliances

CNN-Based Automatic Modulation Classification for Beyond 5G Communications

Method and apparatus for providing user input back channel in audio/video system

Method and apparatus for transmitting and receiving legacy format data in high throughput wireless network

Sparsely Connected CNN for Efficient Automatic Modulation Recognition