Networking MATLAB Projects

Contributions to Edge Computing

Efforts related to Internet of Things (IoT), Cyber-Physical Systems (CPS), Machine to Machine (M2M) technologies, Industrial Internet, and Smart Cities aim to improve society through the coordination of distributed devices and analysis of resulting data. By the year 2020 there will be an estimated 50 billion network connected devices globally and 43 trillion gigabytes of electronic data. Current practices of moving data directly from end-devices to remote and potentially distant cloud computing services will not be sufficient to manage future device and data growth.

Edge Computing is the migration of computational functionality to sources of data generation. The importance of edge computing increases with the size and complexity of devices and resulting data. In addition, the coordination of global edge-to- edge communications, shared resources, high-level application scheduling, monitoring, measurement, and Quality of Service (QoS) enforcement will be critical to address the rapid growth of connected devices and associated data.

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Design of a Wideband Antenna for Wireless Network-On-Chip in Multimedia Applications

To allow fast communication—at several Gb/s—of multimedia content among processors and memories in a multi-processor system-on-chip, a new approach is emerging in literature: Wireless Network-on-Chip (WiNoC). With reference to this scenario, this paper presents the design of the key element of the WiNoC: the antenna. Specifically, a bow-tie antenna is proposed, which operates at mm-waves and can be implemented on-chip using the top metal layer of a conventional silicon CMOS (Complementary Metal Oxide Semiconductor) technology.

The antenna performance is discussed in the paper and is compared to the state-of-the-art, including the zig-zag antenna topology that is typically used in literature as a reference for WiNoC. The proposed bow-tie antenna design for WiNoC stands out for its good trade-off among bandwidth, gain, size and beamwidth vs. the state-of-the-art.

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A Matlab-based Testbed for Integration, Evaluation and Comparison of Heterogeneous Stereo Vision Matching Algorithms

Stereo matching is a heavily researched area with a prolific published literature and a broad spectrum of heterogeneous algorithms available in diverse programming languages. This paper presents a Matlab-based testbed that aims to centralize and standardize this variety of both current and prospective stereo matching approaches. The proposed testbed aims to facilitate the application of stereo-based methods to real situations.

It allows for configuring and executing algorithms, as well as comparing results, in a fast, easy and friendly setting. Algorithms can be combined so that a series of processes can be chained and executed consecutively, using the output of a process as input for the next; some additional filtering and image processing techniques have been included within the testbed for this purpose.

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Design and Evaluation of a Discrete Wavelet Transform based Multi-Signal Receiver using MATLAB

General purpose receivers of today are designed with a broad bandwidth so that the receiver can accept a wide range of signal frequencies. These receivers usually accept one signal along with an y interference that is included. To increase the signal detection capabilities of the wide band receiver, a design for a receiver that can detect two signals is needed. One of the requirements for this receiver is that the second weak signal needs to be processed in a timely manner so that the receiver can recognize it. To remedy the problem, a module was developed using wavelet-based techniques to remove spurs from the incoming signals to allow easier detection. The main basis for this concentration on wavelets comes from the way wavelets break down signals into portions (called resolutions) that allow easier determination of detail importance.

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An Event Reporting and Early-Warning Safety System based on the Internet of Things for Underground Coal Mines using MATLAB: A Case Study

Fatal accidents associated with underground coal mines require the implementation of high-level gas monitoring and miner's localization approaches to promote underground safety and health. This study introduces a real-time monitoring, event-reporting and early-warning platform, based on cluster analysis for outlier detection, spatiotemporal statistical analysis, and an RSS range-based weighted centroid localization algorithm for improving safety management and preventing accidents in underground coal mines. The proposed platform seamlessly integrates monitoring, analyzing, and localization approaches using the Internet of Things (IoT), cloud computing, a real-time operational database, application gateways, and application program interfaces. The prototype has been validated and verified at the operating underground Hassan Kishore coal mine. Sensors for air quality parameters including temperature, humidity, CH4, CO2, and CO demonstrated an excellent performance, with regression constants always greater than 0.97 for each parameter when compared to their commercial equivalent. This framework enables real-time monitoring, identification of abnormal events (>90%), and verification of a miner's localization in the harsh environment of underground mines. The main contribution of this study is the development of an open source, customizable, and cost-effective platform for effectively promoting underground coal mine safety. This system is helpful for solving the problems of accessibility, serviceability, interoperability, and flexibility associated with safety in coal mines.

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