Adaptive myoelectric pattern recognition for controlling upper limb prosthesis. HD-sEMG signals are obtained from CapgMyo dataset.

Hybrid Deep Neural Network Based Generation Rescheduling for Congestion Mitigation in Spot Power Market Agrawal, Anjali; Pandey, Seema N.; Srivastava, Laxmi; Walde, Pratima; Singh, Saumya; Khan, Baseem; Saket, R.K.

This paper investigates the synergy between 6G and AI. It argues that they can unlock future horizons, by discussing how they can address future challenges in healthcare, transportation, virtual reality, education, resource management, robotics, in addition to public safety and warfare. However, these great opportunities come also with greater risk. Therefore, the paper provides an overview of the security risks and challenges, along with possible mitigation techniques.

Internet of Things (IoT) vision has astoundingly transcended the environmental sensing with integrated computing systems and smart devices, providing seamless connectivity among humans, machines and their environment to cooperate for convenience and economical benefits. Apart from all the tremendous benefits of IoT, this paradigm still suffers from challenges of security and privacy vulnerabilities and demands a secure system for effective utilization of services in real world IoT scenarios relaying on which the IoT consumers expect secure and trustworthy communications. Trust Management (TM) being a crucial aspect of security, plays a vital role in ensuring the exchange of information in a secure manner and maintaining the reliability of a system by measuring the degree of trust on IoT devices, reducing the uncertainties and risks involved in the systems. Centralized TM systems for IoT have a potential risk of failure and attacks and decentralized TM systems are not completely decentralized, requiring the assistance of central servers for the completion of final calculation and maintenance of trust credits. Thus, in recent years, Blockchain technology has been utilized for developing security innovations in Trust management field for different classes of IoT applications. It can provide tamper proof data by enabling a more reliable trust information and integrity verification, ultimately enhancing its availability and privacy during storage and sharing. This paper provides a comprehensive survey that aims at analyzing and assessing Blockchain based Decentralized Trust Management Systems (BCDTMS) for IoT. In this paper, our contributions are twofold; first we provide a comprehensive comparative analysis of state-of-the-art BCDTMS devised for different IoT classes including IoMT, IoV, IIoT and SIoT. To make it an extensive study, we perform a detailed Assessment of existing BCDTMS in the literature (for last three years 2018-2020) on the basis of Blockchain and trust based aspects. Secondly, we present requirements and challenges in the context of using blockchain for TM in IoT.

In search of a real three-dimensional, normed, associative, division algebra, Hamilton discovered quaternions that form a non-commutative division algebra of quadruples. Later works showed that there are only four real division algebras with 1, 2, 4, or 8 dimensions. This work overcomes this limitation and introduces generalized hypercomplex numbers of all dimensions that are extensions of the traditional complex numbers. The space of these numbers forms non-distributive normed division algebra that is extendable to all finite dimensions. To obtain these extensions, we defined a unified multiplication, designated as scaling and rotative multiplication, fully compatible with the existing multiplication. Therefore, these numbers and the corresponding algebras reduce to distributive normed algebras for dimensions 1 and 2. Thus, this work presents a generalization of $\mathbb{C}$ in higher dimensions along with interesting insights into the geometry of the vectors in the corresponding spaces.

Objective: This study aims to develop a neural network (foot2hip) for long-term recording of gait kinematics with improved user comfort. Methods: Foot2hip predicts ankle, knee, and hip joint angle profiles in the sagittal plane using foot kinematics and kinetics during walking. Foot2hip consists of three convolution, two max-pooling, two LSTM and three dense layers. An indigenously developed insole and an outsole were used to measure the kinetics and kinematics of the foot, respectively. Seven healthy participants were recruited to follow an experimental protocol consisting of six walking conditions: slow, medium, fast walking speed, rearfoot, flatfoot, and forefoot landing pattern. Results: When tested for leave-one-out and nested cross-validation, foot2hip obtained 3.04° ±0.20 RMSE and 0.97±0.01 correlation coefficient for knee joint, 1.7°± 0.09 RMSE and 0.95±0.01 correlation coefficient for hip joint, and 1.32°±0.08 RMSE and 0.91±0.02 correlation coefficient for ankle joint (averaged across all folds). Conclusion: The prediction performance of foot2hip is encouraging and shows its applicability in accurately predicting lower limb kinematics with minimal wearables. Significance: The hardware used along with foot2hip is low cost ($268 + N× $35, N is the number of different foot sizes), comfortable, and easy to use. Therefore, it is suitable for most clinical and personal applications

A systematic, analytical method to obtain the charge density and surface potential in an N-polar GaN/AlGaN heterostructure is presented. This method also provides the wave functions, sub-band energies, and band diagram. The model accounts for the greater leakage of the wave functions into the spacer and barrier layers in the N-polar structure as compared to the Ga-polar structure. The framework is based on the solution of Schrodinger-Poisson equations for an approximately triangular well potential with finite confinement, achieved using first-order perturbation theory. No fitting parameters are used. Results obtained using our model are in good agreement with numerical results (obtained using the finite difference method) for a wide range of bias conditions.

Deep clustering incorporates embedding into clustering in order to find a lower-dimensional space suitable for clustering task. Conventional deep clustering methods aim to obtain a single global embedding subspace (aka latent space) for all the data clusters. In contrast, in this paper, we propose a deep multi-representation learning (DML) framework for data clustering whereby each difficult to cluster data group is associated with its own distinct optimized latent space, and all the easy to cluster data groups are associated to a general common latent space. Autoencoders are employed for generating the cluster-specific and general latent spaces. To specialize each autoencoder in its associated data cluster(s), we propose a novel and effective loss function which consists of weighted reconstruction and clustering losses of the data points, where higher weights are assigned to the samples more probable to belong to the corresponding cluster(s). Experimental results on benchmark datasets demonstrate that the proposed DML framework and loss function outperform state-of-the-art clustering approaches. In addition, the results show that the DML method significantly outperforms the SOTA on imbalanced datasets as a result of assigning an individual latent space to the difficult clusters.

The paper presents the results of experimental studies on the radiation flux generation in the sub-millimeter wavelength range due to a strong beam-plasma interaction. A relativistic electron beam (REB) with parameters 0.5 MeV / 12 kA / 6 us pumps plasma waves in a plasma column with a length of ~2 m at a plasma density of ~10^15cm-3 in a magnetic field of B~4 T. In the presence of density gradients in the plasma column, direct measurements of the energy content of the radiation flux 18 cm in diameter leaving the plasma column into the atmosphere showed that its value reaches 5-7 J. The pulse duration of the flux at half of its amplitude was about of 0.5 us, therefore, the pulse power was at the level of ~ 10 MW. In this series of experiments, the spectral composition of the radiation flux in the frequency range 0.1-0.5 THz as well as the energy distribution function of the beam electrons passed through the plasma have been measured.

A platform for student interaction and collaboration. The main aim of the project is to increase interaction among campus students and build a platform which will help us to achieve this goal. College Commune will provide students event alerts, notes, a Q & A platform and a discussion forum as well. As we still continue to battle the ongoing Coronavirus pandemic, it has become very difficult for college students to interact with each other. As a group of engineering students we have always felt that there is a huge communication gap between seniors and freshers.

The Internet of Things (IoT) is creating a network of cyber-physical devices – sensors, actuators, and other devices, which monitor and control physical systems, such as manufacturing facilities operating with the Industry 4.0 paradigm, while collecting and sharing massive data globally over the internet. As the IoT security landscape evolves, so does the “cyber mafia,” which targets cyber-physical systems from the corporate level all the way to Industry 4.0 floor-shop control systems, in which IoT is a key component. IoT devices are becoming ubiquitous and controllable from hand-held devices, raising the potential to disrupt or destroy large industrial complexes in a touch of a screen if not protected properly. This growing risk mandates the adoption of new approaches to incorporating rigorous security standards into IoT systems as early as possible in their system lifecycle. A methodology is presented for incorporating into IoT systems security as a bona fide, quantifiable attribute that is built into the components (objects) and operations (processes). To this end, the synergy of combining model-based systems engineering with embedded IoT system security is leveraged. At the heart of the methodology is a combined qualitative-quantitative IoT OPM model of the system with security scores, which provides for evaluating underlying system configurations, each with its level of security and possibly other optimization criteria. This approach enables balancing IoT systems’ metrics, such as performance and cost, with security, and even optimizing the system under specified constraints.

Underwater marine object detection, as one of the most fundamental techniques in the community of marine science and engineering, has been shown to exhibit tremendous potential for exploring the oceans in recent years. It has been widely applied in practical applications, such as monitoring of underwater ecosystems, exploration of natural resources, management of commercial fisheries, etc. However, due to complexity of the underwater environment, characteristics of marine objects, and limitations imposed by exploration equipment, detection performance in terms of speed, accuracy, and robustness could be degraded dramatically when conventional approaches are used. Deep learning has been found to have significant impact on a variety of applications, including marine engineering. In this context, we offer a review of deep-learning-based underwater marine object detection techniques. Underwater object detection can be performed by different sensors, such as acoustic sonar, or optical camera. In this paper, we prefer to focus on vision-based object detection due to some significant competitive advantages. To facilitate a thorough understanding of this subject, we organize research challenges of vision-based underwater object detection into Four categories, namely image quality degradation, small object detection, poor generalization, and real-time detection. We review recent advances in underwater marine object detection and highlight advantages and disadvantages of existing solutions for each challenge. In addition, we examine the most extensively used datasets in detail and critically. Comparative studies with previous reviews, notably approaches that leverage artificial intelligence, and future trends on this hot topic are also presented. * This work has been published on Sensors.

The phenomena of the rotating grating have been studied and have the varieties application. In this article, we show in detail how to break the diffraction pattern of a rotating grating into three portions corresponding to different diffraction orders “m”, and each portion is described by an equation. Grating CCW rotation CW rotation

We present a first-ever waveform-tracking receiver (RX) with type-II phase-tracking loop. In this preprint, a novel nonlinear filter in the feedback path is proposed to obtain better interference rejection performance. This digital nonlinear filter assists the receiver with tracking the waveform intelligently by predicting the incoming symbols; hence, we define this proposed RX as a waveform-tracking (WT)-RX. The simulation and preliminary measurement results are demonstrated in this paper. Moreover, we explore the issues of the current version WT-RX to provide guidance on possible future follow-up activities.

CubeSats, the nanosatellites with a wet mass up to 10 kg, accompanied by the cost decrease of accessing the space, amplified the rapid development of the Earth Observation industry. Acquired image data serve as an essential source of information in various disciplines like environmental protection, geosciences, or the military. As the quantity of remote sensing data grows, the bandwidth resources for the data transmission (downlink) are exhausted. Therefore, new techniques that reduce the downlink utilization of the satellites must be investigated and developed. For that reason, we are presenting CloudSatNet-1: an FPGA based hardware-accelerated quantized convolutional neural network (CNN) for satellite on-board cloud coverage classification. We aim to explore the effects of the quantization process on the proposed CNN architecture. Additionally, the performance of cloud coverage classification by biomes diversity is investigated, and the hardware architecture design space is explored to identify the optimal FPGA resource utilization. Results of this study showed that the weights and activations quantization adds a minor effect on the model performance. Nevertheless, the memory footprint reduction allows the model deployment on low-cost FPGA Xilinx Zynq-7020. Using the RGB bands only, up to 90% of accuracy was achieved, and when omitting the tiles with snow and ice, the performance increased up to 94.4% of accuracy with a low false-positive rate of 2.23% for the 4-bit width model. With the maximum parallelization settings, the hardware accelerator achieved 15 FPS with 2.5W of average power consumption (0.2W increase over the idle state).

The classical problem of phase retrieval has found a wide array of applications in optics, imaging and signal processing. In this paper, we consider the phase retrieval problem in a one-bit setting, where the signals are sampled using one-bit analog-to-digital converters (ADCs). A significant advantage of deploying one-bit ADCs in signal processing systems is their superior sampling rates as compared to their high-resolution counterparts. This leads to an enormous amount of one-bit samples gathered at the output of the ADC in a short period of time. We demonstrate that this advantage pays extraordinary dividends when it comes to convex phase retrieval formulations—namely that the often encountered matrix semi-definiteness constraints as well as rank constraints (that are computationally prohibitive to enforce), become redundant for phase retrieval in the face of a growing sample size. Several numerical results are presented to illustrate the effectiveness of the proposed methodologies

The purpose of the invention is to reduce excessive inrush currents at start-up of power distribution transformers. The addition of a small tertiary winding was installed on a shell type transformer primary winding along with a simple trigger circuit comprised of two commonly available passive components, such as a resistor and a relay. An oscilloscope set for one-shot captures to verify proper operation of the invention. Excessive inrush currents at start-up are repeatedly reduced by >50%.

We study analytically shocks and solitary waves in Josephson transmission line in the context of waves in strongly nonlinear discrete systems