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.

Point-of-view article. Abstract: For more than a century, the grid has operated in a centralized top-down fashion. However, as the penetration of the distributed energy resources (DERs) grows, the grid edge is increasingly infused with intelligent computing and communication capabilities. Thus, the bottom-up approach to grid operations inclined toward decentralization of energy systems will likely gain momentum alongside the existing centralized paradigm. In this point of view article, we aim to highlight the need for and outline a credible path toward restructuring the current operational architecture of the electricity grid in view of the ongoing decentralization trends with an emphasis on peer-to-peer energy trading. We further suggest that blockchain is an effective technology for facilitating the synergistic operations of top-down and bottom-up approaches to grid management.

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.

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.

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

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.

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.

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 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.

In this paper, the patch concept is applied to the design of millimeter-wave band-pass filters in BiCMOS 55 nm technology. The operation frequencies are 120 GHz and 200 GHz, respectively. A via- and slot-based approach makes filters very compact, albeit patch-based, through a multi-mode approach. At 200 GHz, the side of the patch is only 194 μm long, or about a quarter wavelength, compared to half a wavelength for a conventional patch. The dependence of performance on the thickness of the Back-End-Of-Line is studied, and the design method is detailed. The performance is very good, with for example a filter operating at 200 GHz with a relative bandwidth of 23% and insertion loss of 3 dB. Finally, the prospect of using a thicker Back-End-Of-Line, offered as an option in 55-nm BiCMOS technology, makes it possible to show that even better performance can be achieved, with insertion loss below 3 dB.

A new window function is proposed to enhance the nonlinearity for memristor model.

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

Wearable Photoplethysmography (PPG) has gained prominence as a low cost, unobtrusive and continuous method for physiological monitoring. The quality of the collected PPG signals is affected by several sources of interference, predominantly due to physical motion. Many methods for estimating heart rate (HR) from PPG signals have been proposed with Deep Neural Networks (DNNs) gaining popularity in recent years. However, the ‘black-box’ and complex nature of DNNs has caused a lack of trust in the predicted values. This paper contributes DeepPulse, an uncertainty-aware DNN method for estimating HR from PPG and accelerometer signals, with aims of increasing the reliability, usability and interpretability of the predicted HR values. To the best of the authors’ knowledge no PPG HR estimation method has considered aleatoric and epistemic uncertainty metrics. The results show DeepPulse is the most accurate method for DNNs with less than 1 million network parameters. Finally, recommendations are given to reduce epistemic uncertainty, validate uncertainty estimates, improve the accuracy of DeepPulse as well as reduce the model size for resource-constrained edge devices.

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%.

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

Our work introduces a novel architecture for extracting information from neighboring slices in the image segmentation. The Proposed architecture consists of parallel UNets for each slice/image. These UNets are fused using Residual Network for late fusion. We passed features from central slice only in the residual network so that the features from the image to be segmented will not be lost. We performed segmentation using this architecture on the brain Magnetic Resonance Images (MRI). The processed data is obtained from the Open Access Series of Imaging Studies (OASIS) dataset. Data of each subject is in 3D. We extracted 2D slices/images from each subject and train/test for each view (axial, coronal, sagittal). As the 3D data has neighboring slices, that are similar to each other, we hypothesize and considered before and after slices as neighboring slice for segmentation of central slice.