scholarly journals Hardware Neural Networks Modeling for Computing Different Performance Parameters of Rectangular, Circular, and Triangular Microstrip Antennas

2014 ◽  
Vol 2014 ◽  
pp. 1-11
Author(s):  
Taimoor Khan ◽  
Asok De
Keyword(s):  
Neural Networks ◽  
Low Cost ◽  
Microstrip Antennas ◽  
Performance Parameters ◽  
Neural Models ◽  
Hardware Platform ◽  
Generalized Neural Networks ◽  
Field Programmable ◽  
The Neural Networks ◽  
Good Agreement

In the last one decade, neural networks-based modeling has been used for computing different performance parameters of microstrip antennas because of learning and generalization features. Most of the created neural models are based on software simulation. As the neural networks show massive parallelism inherently, a parallel hardware needs to be created for creating faster computing machine by taking the advantages of the parallelism of the neural networks. This paper demonstrates a generalized neural networks model created on field programmable gate array- (FPGA-) based reconfigurable hardware platform for computing different performance parameters of microstrip antennas. Thus, the proposed approach provides a platform for developing low-cost neural network-based FPGA simulators for microwave applications. Also, the results obtained by this approach are in very good agreement with the measured results available in the literature.

scholarly journals Prediction of Slot Shape and Slot Size for Improving the Performance of Microstrip Antennas Using Knowledge-Based Neural Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Taimoor Khan ◽  
Asok De
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prior Knowledge ◽  
Neural Model ◽  
Microstrip Antennas ◽  
Performance Parameters ◽  
Dual Frequency ◽  
Knowledge Based ◽  
Slot Size ◽  
Good Agreement

In the last decade, artificial neural networks have become very popular techniques for computing different performance parameters of microstrip antennas. The proposed work illustrates a knowledge-based neural networks model for predicting the appropriate shape and accurate size of the slot introduced on the radiating patch for achieving desired level of resonance, gain, directivity, antenna efficiency, and radiation efficiency for dual-frequency operation. By incorporating prior knowledge in neural model, the number of required training patterns is drastically reduced. Further, the neural model incorporated with prior knowledge can be used for predicting response in extrapolation region beyond the training patterns region. For validation, a prototype is also fabricated and its performance parameters are measured. A very good agreement is attained between measured, simulated, and predicted results.

Performance parameters prediction of slotted microstrip antennas with modified ground plane using support vector machine

2016 ◽  
Vol 9 (5) ◽  
pp. 1169-1177 ◽  
Author(s):  
Chandan Roy ◽  
Taimoor Khan ◽  
Binod Kumar Kanaujia
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Resonant Frequency ◽  
Ground Plane ◽  
Microstrip Antennas ◽  
Support Vector ◽  
Performance Parameters ◽  
Local Minima ◽  
Vector Machines ◽  
Good Agreement

Artificial neural networks (ANNs) have acquired enormous importance in computing of the performance parameters of microstrip antennas due to their generalized and adaptive features. However, recently the concept of support vector machines (SVMs) has become very much popular in performance parameters computation due to several attractive features over ANNs. Specifically, SVMs outreach ANNs noticeably in terms of execution time. Likewise, ANNs are having multiple local minima problem, whereas a global and unique solution is provided by SVMs. In this paper, several performance parameters like: resonant frequency, gain, directivity, and radiation efficiency of slotted microstrip antennas with modified ground plane are computed with the help of SVM formulation. Comparisons of different parameters of simulated and computed values are illustrated. The achieved radiation patterns at particular resonant frequency in different planes are included as well. A prototype of the optimized antenna is also fabricated and characterized. A good agreement is attained among the computed, simulated, and measured results.

scholarly journals Estimation of Different Performance Parameters of Slotted Microstrip Antennas with Air-Gap Using Neural Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Taimoor Khan ◽  
Asok De
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Neural Model ◽  
Microstrip Antennas ◽  
Air Gap ◽  
Performance Parameters ◽  
Dual Frequency ◽  
The Past ◽  
Resonance Frequencies ◽  
Good Agreement

Over the past decade, artificial neural networks have emerged as fast computational medium for predicting different performance parameters of microstrip antennas due to their learning and generalization features. This paper illustrates a neural network model for instantly predicting the resonance frequencies, gains, directivities, antenna efficiencies, and radiation efficiencies for dual-frequency operation of slotted microstrip antennas with air-gap. The proposed neural model is valid for any arbitrary slot-dimensions and inserted air-gap within their specified ranges. A prototype is fabricated using Roger’s substrate and its performance is measured for validation. A very good agreement is achieved in simulated, predicted, and measured results.

scholarly journals An Image Compression Based Technique to Watermark a Neural Network

2020 ◽  
Vol 9 (4) ◽  
pp. 599-603
Keyword(s):  
Neural Networks ◽  
Digital Watermarking ◽  
Network Performance ◽  
Image Watermarking ◽  
Image Data ◽  
Discrete Wavelet ◽  
Digital Image Watermarking ◽  
Neural Models ◽  
The Neural Networks ◽  
Training Error

While neural networks have made considerable progress in the area of digital representation, training of neural models requires an enormous data and time. It is well known that the use of trained models as initial weights often leads in less training error than un-pre-trained neural networks. We propose in this paper a digital watermarking system for neural networks. We formulate a new challenge: the integration of watermarks into neural networks through discrete cosine transform (DCT) based approach. For discrete wavelet transform (DWT)-based digital image watermarking algorithms, additional performance enhancements could be obtained by combining DWT with DCT. Throughout the neural networks, we also describe specifications, embedded conditions, and attack forms of watermarking. The technique presented here does not affect the network performance in which a watermark is positioned as the watermark is embedded while the host network is being trained. Finally, we perform detailed image data experiments to demonstrate the potential of neural networks watermarking as the basis for this research attempt.

scholarly journals Chimera

2021 ◽  
Vol 2 (2) ◽  
pp. 1-25
Author(s):  
Emekcan Aras ◽  
Stéphane Delbruel ◽  
Fan Yang ◽  
Wouter Joosen ◽  
Danny Hughes
Keyword(s):  
Software Evolution ◽  
Low Cost ◽  
Hardware Platform ◽  
Battery Lifetime ◽  
Software Upgrades ◽  
Field Programmable ◽  
Industrial Monitoring ◽  
Computationally Intensive ◽  
Reconfigurable Software ◽  
The Internet Of Things

The Internet of Things (IoT) is being deployed in an ever-growing range of applications, from industrial monitoring to smart buildings to wearable devices. Each of these applications has specific computational requirements arising from their networking, system security, and edge analytics functionality. This diversity in requirements motivates the need for adaptable end-devices, which can be re-configured and re-used throughout their lifetime to handle computation-intensive tasks without sacrificing battery lifetime. To tackle this problem, this article presents Chimera, a low-power platform for research and experimentation with reconfigurable hardware for the IoT end-devices. Chimera achieves flexibility and re-usability through an architecture based on a Flash Field Programmable Gate Array (FPGA) with a reconfigurable software stack that enables over-the-air hardware and software evolution at runtime. This adaptability enables low-cost hardware/software upgrades on the end-devices and an increased ability to handle computationally-intensive tasks. This article describes the design of the Chimera hardware platform and software stack, evaluates it through three application scenarios, and reviews the factors that have thus far prevented FPGAs from being utilized in IoT end-devices.

scholarly journals LOW-COST FIELD PROGRAMMABLE GATE ARRAY ACCELERATES DEEP Q-LEARNING

2021 ◽  
Author(s):  
JINGHUI WANG ◽  
YUANCHAO ZHAO
Keyword(s):  
Neural Networks ◽  
Reinforcement Learning ◽  
Field Programmable Gate Array ◽  
Low Cost ◽  
Digital Technologies ◽  
Learning Problems ◽  
Memory Bandwidth ◽  
Q Learning ◽  
High Efficient ◽  
Field Programmable

Abstract. Due to recent advances in digital technologies, deep reinforcement learning has emerged, and has demonstrated its ability and effectiveness in solving complex learning problems not possible before. In particular, convolution neural networks (CNNs) have been demonstrated their effectiveness in reinforcement learning. However, they require intensive CPU operations and memory bandwidth that make general CPUs fail to achieve desired performance levels. In this paper, we used some low-cost field programming gates array (FPGA) designed a parallel Deep Qlearning accelerator to solve this problem. And the system has high efficient and flexibility.

scholarly journals Energy-Efficient Architecture for CNNs Inference on Heterogeneous FPGA

2019 ◽  
Vol 10 (1) ◽  
pp. 1 ◽  
Author(s):  
Fanny Spagnolo ◽  
Stefania Perri ◽  
Fabio Frustaci ◽  
Pasquale Corsonello
Keyword(s):  
Neural Networks ◽  
Embedded System ◽  
Convolutional Neural Networks ◽  
Energy Efficient ◽  
Power Efficiency ◽  
High Performance ◽  
Large Scale ◽  
Low Cost ◽  
Frame Rate ◽  
Field Programmable

Due to the huge requirements in terms of both computational and memory capabilities, implementing energy-efficient and high-performance Convolutional Neural Networks (CNNs) by exploiting embedded systems still represents a major challenge for hardware designers. This paper presents the complete design of a heterogeneous embedded system realized by using a Field-Programmable Gate Array Systems-on-Chip (SoC) and suitable to accelerate the inference of Convolutional Neural Networks in power-constrained environments, such as those related to IoT applications. The proposed architecture is validated through its exploitation in large-scale CNNs on low-cost devices. The prototype realized on a Zynq XC7Z045 device achieves a power efficiency up to 135 Gops/W. When the VGG-16 model is inferred, a frame rate up to 11.8 fps is reached.

scholarly journals A Generalized ANN Model for Analyzing and Synthesizing Rectangular, Circular, and Triangular Microstrip Antennas

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Taimoor Khan ◽  
Asok De
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Microstrip Antennas ◽  
Computational Technique ◽  
Performance Parameters ◽  
Ann Model ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Good Agreement

Since last one decade, artificial neural network (ANN) models have been used as fast computational technique for different performance parameters of microstrip antennas. Recently, the concept of creating a generalized neural approach for different performance parameters has been motivated in microstrip antennas. This paper illustrates a generalized neural approach for analyzing and synthesizing the rectangular, circular, and triangular MSAs, simultaneously. Such approach is very much required for the antenna designers for getting instant answer for the required parameters. Here, total seven performance parameters of three different MSAs are computed using generalized neural approach as such a method is rarely available in the open literature even for computing more than three performance parameters, simultaneously. The results thus obtained are in very good agreement with the measured results available in the referenced literature for all seven cases.

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