Electrical Engineering

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Shiv Nadar University

Cognitive activity prediction (CAP) from electroencephalogram (EEG) signals is progressively utilized in the field of brain-computer interface (BCI) and mental health management. Various machine and deep learning methods have been proposed recently for CAP. However, since Internet-of-Things-based real-time BCI systems demand low latency, power, and portability, these methods need to be deployable on resource-constrained edge devices. Towards this aspect, we propose a real-time implementation of a lightweight 1-D convolutional neural network on an Arduino Due microcontroller for CAP from EEG signals. The performance evaluation on two public datasets and one real-time recorded dataset indicates that the proposed work achieves subject-independent prediction accuracies of 99.30%, 82.50%, and 99.02% in these datasets. Furthermore, the prediction of real-time recorded EEG signals is accurate for majority of the subjects. The proposed work outperforms the existing techniques and achieves low power consumption of $\text{0.63}\,\text{W}$ in real-time on-device implementation with an average latency of $\text{455.12}\,\text{ms}$ in model deployment, test output prediction, and activity-based transmission. © 2017 IEEE.

World University root node

(SoE) School of Engineering

IEEE Sensors Letters

On-Device Implementation for Deep-Learning-Based Cognitive Activity Prediction

Cognitive/mental task classification using single/limited channel(s) electroencephalogram (EEG) signals in real-time play an important role in designing portable brain-computer interface (BCI) and neurofeedback (NFB) systems. However, real-time recorded EEG signals are often contaminated with ocular artifacts (OAs) and muscle artifacts (MAs), which deteriorate the hand-crafted features extracted from EEG signal, resulting inadequate classification of cognitive tasks. Therefore, we investigate the use of deep learning techniques which do not require manual feature extraction or artifact suppression. In this paper, we propose a shallow one-dimensional convolutional neural network (1D-CNN) architecture for cognitive task classification. The robustness of the proposed architecture is evaluated using artifact-free and artifact-contaminated EEG signals taken from two publicly available databases (i.e, Keirn and Aunon (K) database and EEGMAT (E) database) and in-house (R) database recorded using single-channel device in performing not only cognitive/non-cognitive binary task classification but also cognitive/cognitive multi-tasks classification. Evaluation results demonstrate that the proposed architecture achieves the highest subject-independent classification accuracy of 99.70% and 100.00% for multi-class classification and pair-wise classification respectively in database K. Further, subject-independent classification accuracies of 99.00% and 98.00% are achieved in databases E and R respectively. Comparative performance analysis demonstrates that the proposed architecture outperforms existing approaches not only in terms of classification accuracy but also in robustness against artifacts. © 2022 Elsevier Ltd

Fulltext

Biomedical Signal Processing and Control

One-dimensional convolutional neural network architecture for classification of mental tasks from electroencephalogram

In this paper, a low-cost gain switchable antenna system operating at 28 GHz is proposed for 5G applications. The antenna system consists of a 3×1 element series fed array antenna on one side and a single inset fed patch antenna on the opposite side on same substrate. The proposed antenna has a compact size with overall dimensions of 38.76mm x 6.83mm x 0.66mm. A low-cost flexible polycarbonate of 500μm thickness and relative permittivity, ϵr of 2.9 is used as substrate. The simulated and measured results are presented. The proposed antenna system can be integrated with modern 5G smartphones. © 2022 IEEE.

2022 IEEE Region 10 Symposium, TENSYMP 2022

Low-Cost Gain Switchable Antenna System for 5G Smartphones

Very large scale integration (VLSI)-based neuromorphic systems have been evolving quickly in recent years. These systems have been used in complex cognitive tasks such as image classification and pattern recognition. A neuromorphic encoder is an essential component in a neuromorphic system, which converts sensory data into spike trains. The advancement in CMOS technology nodes leads to various reliability issues. Bias temperature instability (BTI) and hot carrier injection (HCI) are major reliability issues in analog/mixed VLSI circuits. This paper implements a temporal neuromorphic encoder, and a corresponding mathematical model is derived for image processing applications. The relationship between an input image pixel value and output of the encoder, that is, interspike interval (ISI), is found to be exponential. The impact of BTI and HCI on the temporal neuromorphic encoder is analyzed. The degradation analysis revealed a loss of encoding functionality for which three mitigation techniques are discussed. Finally, a reliability-aware neuromorphic encoder is proposed to minimize the effect of degradation over its lifetime. The power consumption of conventional and proposed reliability-aware neuromorphic encoders is also presented. © 2021 John Wiley & Sons, Ltd.

International Journal of Circuit Theory and Applications

Reliability-aware design of temporal neuromorphic encoder for image recognition

NBTI degradation of FinFET devices is stochastic due to the inevitable variations in manufacturing processes. The stochastic behavior of NBTI degradation is modeled with different ΔVTH distributions. Various leading semiconductor industries report different ΔVTH distributions like Gamma, Normal, and Dispersive Skellam (DS) to model the NBTI induced variability. In this work, different NBTI induced variability models from the literature are used to investigate the impact on circuit performance. The combined effect of time-zero and various NBTI induced variability distributions on SRAM read and write metrics is discussed in detail. NBTI degradation in SRAM depends on the stored data pattern. Hence, we also report the impact of stored data patterns on the SRAM metric. To further analyze the impact of ΔVTH distributions on SRAM metrics, the correlation between SRAM metrics is studied. © 2021 Elsevier B.V.

Integration

Impact of various NBTI distributions on SRAM performance for FinFET technology

A single-layer, four-element multiple-input-multiple-output (MIMO) antenna for 5 GHz (5150–5850 MHz) band WLAN application is proposed. Each unit element consists of three sections and this behaves as a travelling wave antenna, which enables frequency scanning feature. The combination of orthogonal feeds and placement of elements introduce dual-polarisation, and pattern diversity respectively. Thus, the proposed MIMO antenna offers a higher degree of freedom (pattern diversity, frequency scanning and dual polarisation), which makes it exceptional. The proposed MIMO antenna has a maximum impedance bandwidth of 17% (4.93–5.87 GHz) corresponding to port 4 and isolation of 17.5 dB, which is achieved without additional decoupling/isolation enhancement circuits. The envelope correlation coefficient of 0.25, capacity loss less than 0.4 bits/sec/Hz, and the mean effective gain (MEG) ratio of 0 dB in the desired frequency band quantifies the diversity performance for WLAN applications. The proposed MIMO has gain from 2 to 5.5 dB and occupies a compact volume of 1.3 λ × 1.3 λ × 0.02 λ mm3, which may be one of the most miniaturised electrical configurations. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

International Journal of Electronics

Travelling Wave Antenna Based MIMO for 5GHz WLAN Band Application With Pattern Diversity

Smart transportation for warehouses focuses on moving of the products more secure and efficient. The paper focuses on different parts such as maintaining the safety of the products and the container, keeping track of the products being loaded, tracking the location of the container, alerting in the case of an accident, and also using efficient communication system in the case of an emergency. The first part of the paper deals with the safety of the products which involves keeping track of the surrounding parameters such as temperature, humidity, and fire (in case a fire broke out) by utilizing temperature, humidity sensor, and the fire sensor and also the accident detection using accelerometer and gyroscope. This part also deals with the information of the products using RFID and using conditional statements to verify whether the product is allotted to a specific container. The second part focuses on the communication system with which all the required alerts can be transmitted in the form of SMS and emails with the help of third-party APIs. Using the data from the emails, a software platform is used to automate the task of storing that data in Google sheets. The second part also deals with location tracking of the container using Google maps, which helps the person in-charge to keep track of the locations the container has been to. The final part deals with the implementation of the dashboard. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Lecture Notes in Electrical Engineering

Smart Transportation for Warehouses

The inner winding of transformers may get buckled under the short-circuit condition. To avoid the buckling of the inner winding, the critical stress at which buckling occurs should be more than the applied stress during a short-circuit event. The important parameters on which the critical buckling stress depends are the inner winding radial dimension, number and dimension of strands, distance between the two adjacent axial sticks, dimension of the axial sticks, proof stress of the conductor material, etc. In literature, for the given geometrical details of the inner winding, the variation of critical stress with the number of axial sticks and the value of proof stress of the conductor material are not reported. In this letter, the effects of varying the number of axial sticks and the proof stress of the conductor material on the value of critical stress of the inner winding have been investigated. © 1986-2012 IEEE.

IEEE Transactions on Power Delivery

Effects of Axial Sticks and Proof Stress of Conductor Material on the Buckling Strength of Transformer Inner Winding

Bias Temperature Instability (BTI) and Hot Carrier Injection (HCI) are the most severe reliability concerns in advanced CMOS devices. Investigating the extent of device degradation in analog and digital circuits has been an ongoing focus of several research groups. However, emerging fields like spiking neural networks (SNN) constituting neuromorphic circuit blocks, lack insight of device degradation impact over its operative lifetime. This work emphasizes the importance of device degradation in a crucial neuromorphic circuit block, the silicon neuron (SiN). A comprehensive degradation analysis on the stimulated SiN recently reported demonstrated significant fault/mismatches incurred when the SiN was on the verge of spiking. Here, we investigate the presence of fault/mismatches when the neuron spikes for different uniformly- and randomly- distributed input strengths. It was realized through key SiN performance parameters in 'Time0' (pre-degradation) and 'Aged' (post-degradation) simulations. Although the neuron circuit functions effectively for most of the applied uniformly-distributed inputs, its detrimental effects were still very apparent in the randomly-distributed inputs potentially impacting signal/information transmission in an SNN. © 2022 IEEE.

Device Reliability Affecting Coding Schemes in Neuromorphic Circuits

<p>This article reports an in-depth study of interdigitated photoconductive antenna (IPCA) structures for broadband, high electric field, efficient terahertz (THz) generation and detection. The photoconductive antenna (PCA) is the key component which can be used as both the emitter and detector in the THz-time domain spectroscopy for imaging application. The proposed IPCA can easily overcome the limitations of conventional dipole or bow-tie PCA, like, less bandwidth, low optical-to-THz conversion efficiency and less radiated THz power. In this work, the performance of IPCAs, which is made of silver interdigitated electrodes and thin layer of low-temperature-grown GaAs (LTG-GaAs) on top of semi-insulating GaAs (SI-GaAs) substrate, has been studied by varying the geometry of the interdigitated elements until it reaches to the plasmonic limit. Several time- and frequency-dependent parameters, such as carrier density, conductance, impedance, photocurrent, bandwidth, optical-to-THz efficiency, THz radiated power, have been investigated thoroughly depending on the input ultrafast laser parameters, temperature-dependent mobility of photo-carriers and the dimension of the IPCAs. The Scharfetter and Gummel’s drift-diffusion model has been used to calculate the photo-carrier density while focuses the laser pulses at the gap of IPCA structures. The optimum results have been found in terms of single-cycle THz pulses of 0.3 ps, bandwidth of ∼<math xmlns="http://www.w3.org/1998/Math/MathML"><mo>∼</mo></math>&nbsp;13.8 THz, optical-to-THz efficiency of 0.1265% and average radiated power of 3.9564 mW for the 20-nm interdigitated electrode (teeth) width, i.e. 44 elements IPCA. The results of different dimensions of IPCAs have been compared among them and with the published conventional PCAs. These IPCAs will be very useful for future generation THz-transceiver systems.</p>