Performance analysis of triple-band miniaturized hexagonal ultra-wideband antenna for wireless body worn applications

The creation of a network of tiny sensors installed in, on or around the human body has been facilitated by advancements in wireless communications and wearable devices. Because of its potential to transform healthcare delivery, Wireless Body Area Network (WBAN) has been increasingly important in modern medical systems over the last decade. Individual nodes (sensors and actuators) embedded in a person\u27s clothing, body, or skin form a WBAN. Both academia and industry have increased their efforts in WBAN research and development. The wearable antenna, whether on or off the human body, is a critical component of contact with particular design in WBAN networks. Ultra-wideband (UWB) technology can provide high-capacity, short-range communications with minimal energy consumption, which is appropriate for wireless body area networks. The human body\u27s involvement in such a device creates significant challenges for both the wearable antenna\u27s construction and the broadcast paradigm. To achieve many functionalities, multi-band and broadband antennas are better solutions. The proposed multi-band antenna is constructed from a FR4 substrate with dimensions of (24 × 25 × 1.6) mm3. The proposed design was successfully tested with different configurations and enhanced with a broad impedance bandwidth of over 100 percent, where the UWB frequency spectrum encompassed the range from 3 to 9 GHz with a reflective coefficient of −15 dB and gain of 2.5 dBi, as well as fair radiation patterns in the Federal Communications Commission range. The SAR value of the devised antenna with and without SRR being 2 W/kg, 3.5 W/kg, respectively. This solution may be a worthy contender for meeting the UWB demands as a result, could be an excellent fit for wireless body technologies

A Unique Approach to 3D Localization in Wireless Sensor Network by Using Adaptive Stochastic Control Algorithm

In the modern era, wireless sensor network has turned into a critical innovation for various sorts of the clever algorithms, where nodes localization was complicated in research territory. A significant number of the advantages for wireless sensor networks are not profitable without a priory is known. Including Global Positioning System to every node is an exclusive plan and unsuitable for the indoor condition. Localization is a critical piece for wireless sensor networks innovation while current localization approaches, for the most part 2D plane has been concentrated, the rising 3D localization conveys WSNs nearer to improved exactness, and the 3D area innovation is more fitting for genuine applications. In any case, existing 3D localization has weaknesses, for example, high time complexity, low positioning exactness, and awesome energy utilization. Going for the current issues in present 3D localization methods, enhanced 3D localization technique based on adaptive stochastic control is proposed. Simulation results show that the average area precision of adaptive stochastic localization algorithm is vastly improved than established 3D DV-hop algorithm and centroid algorithm. Besides, the stability of the proposed method is superior to others. Anchor node system and propagation sample determination is used to simulate the spread of accuracy by 78.9% compared with 3D DV-HOP and 92.7% compared with the 3D centroid

Computer-Aided Diagnosis of Muscle Mass through Antenna as a Sensor

Wireless body area network (WBAN) incorporates a wireless sensor network and wearable devices in miniature size. In this paper, a dual-band microstrip patch (DBMSP) antenna as a sensor with a modified split ring resonator (SRR) and defective ground structure (DGS) is proposed for muscle mass measurement and prediction. Modified SRR on the ground plane forms a defected ground structure (DGS) for back radiation reduction and suits muscle mass measurement. The proposed dual-band microstrip patch antenna resonates at 5.2 GHz and 8.4 GHz, with impedance bandwidth of about 0.9 GHz and 1.89 GHz, input reflection coefficient is about -21.12 dB and -14.5 dB, respectively. This DBMSP antenna has an efficiency of 99.9%, with a negligible amount of specific absorption rate (SAR). From the proposed DBMSP antenna sensor, muscle mass is predicted from human muscle. The proposed antenna is fixed on the ventral surface of the forearm and biceps. DBMSP antenna sensor detects electromagnetic energy from muscle tissues under radiating near-field conditions. The muscle tissue signal is acquired through the proposed DBMSP antenna. The acquired antenna process with nondecimated wavelet transform (NDWT) and discrete wavelet transform (DWT) algorithms for noise reduction. Further, early prediction of muscle mass prevents humans from lack of protein and oxygen levels in the blood and avoids major issues in human health. The proposed DBMSP antenna-based muscle mass measurement achieves 89% accuracy when compared with laboratory measurement

Performance analysis of triple-band miniaturized hexagonal ultra-wideband antenna for wireless body worn applications

The creation of a network of tiny sensors installed in, on or around the human body has been facilitated by advancements in wireless communications and wearable devices. Because of its potential to transform healthcare delivery, Wireless Body Area Network (WBAN) has been increasingly important in modern medical systems over the last decade. Individual nodes (sensors and actuators) embedded in a person's clothing, body, or skin form a WBAN. Both academia and industry have increased their efforts in WBAN research and development. The wearable antenna, whether on or off the human body, is a critical component of contact with particular design in WBAN networks. Ultra-wideband (UWB) technology can provide high-capacity, short-range communications with minimal energy consumption, which is appropriate for wireless body area networks. The human body's involvement in such a device creates significant challenges for both the wearable antenna's construction and the broadcast paradigm. To achieve many functionalities, multi-band and broadband antennas are better solutions. The proposed multi-band antenna is constructed from a FR4 substrate with dimensions of (24 × 25 × 1.6) mm3. The proposed design was successfully tested with different configurations and enhanced with a broad impedance bandwidth of over 100 percent, where the UWB frequency spectrum encompassed the range from 3 to 9 GHz with a reflective coefficient of −15 dB and gain of 2.5 dBi, as well as fair radiation patterns in the Federal Communications Commission range. The SAR value of the devised antenna with and without SRR being 2 W/kg, 3.5 W/kg, respectively. This solution may be a worthy contender for meeting the UWB demands as a result, could be an excellent fit for wireless body technologies

Highly-parallelized simulation of a pixelated LArTPC on a GPU

The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on $10^3$ pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype

Highly-parallelized simulation of a pixelated LArTPC on a GPU

The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on $10^3$ pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype

Highly-parallelized simulation of a pixelated LArTPC on a GPU

The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on $10^3$ pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype

DUNE Offline Computing Conceptual Design Report

This document describes Offline Software and Computing for the Deep Underground Neutrino Experiment (DUNE) experiment, in particular, the conceptual design of the offline computing needed to accomplish its physics goals. Our emphasis in this document is the development of the computing infrastructure needed to acquire, catalog, reconstruct, simulate and analyze the data from the DUNE experiment and its prototypes. In this effort, we concentrate on developing the tools and systems thatfacilitate the development and deployment of advanced algorithms. Rather than prescribing particular algorithms, our goal is to provide resources that are flexible and accessible enough to support creative software solutions as HEP computing evolves and to provide computing that achieves the physics goals of the DUNE experiment