The widely spread CoronaVirus Disease (COVID)- 19 is one of the worst infectious disease outbreaks in history and has become an emergency of primary international concern. As the pandemic evolves, academic communities have been actively involved in various capacities, including accurate epidemic estimation, fast clinical diagnosis, policy effectiveness evaluation and development of contract tracing technologies. There are more than 23,000 academic papers on the COVID-19 outbreak, and this number is doubling every 20 days while the pandemic is still on-going [1]. The literature, however, at its early stage, lacks a comprehensive survey from a data analytics perspective. In this paper, we review the latest models for analyzing COVID19 related data, conduct post-publication model evaluations and cross-model comparisons, and collect data sources from different projects.

No-insulation high temperature superconductor (NI HTS) (RE)Ba2Cu3Ox coil technology is effective in enhancing the thermal stability of HTS coils. Applying the NI technique on the rotor windings of HTS machines can improve the stability and reliability of the machines. However, the NI HTS rotor windings experience ripple magnetic fields, which leads to induced eddy currents through the turn-to-turn contacts. The accompanying turn-to-turn losses will considerably affect the machine efficiency. In this study, we compared experimentally the losses of NI HTS coils subjected to external AC magnetic fields with those of insulated coils. Measurement system based on calibration-free method is developed for the AC loss measurement on HTS coils exposed to external AC magnetic fields. The results show that the AC loss of NI HTS coils can be 20 times higher than that of insulated HTS coils, and lowering turn-to-turn resistivity can significantly reduce this AC loss. Modeling analysis shows that most of induced current flows in the outer turns of the NI HTS coil because of skin effect, and lower turn-to-turn resistivity leads to higher induced current in superconducting layers and more significant accumulation of turn-to-turn loss. This will increase quench risk. Optimization of turn-to-turn resistivity is required when the NI HTS coil is applied in the machines environments.

Polarization-adjusted convolutional (PAC) codes are special concatenated codes in which we employ a one-to-one convolutional transform as a pre-coding step before the polar transform. In this scheme, the polar transform (as a mapper) and the successive cancellation process (as a demapper) present a synthetic vector channel to the convolutional transformation. The numerical results show that this concatenation improves the Hamming distance properties of polar codes. In this work, we implement the parallel list Viterbi algorithm (LVA) and show how the error correction performance moves from the poor performance of the Viterbi algorithm (VA) to the superior performance of list decoding by changing the constraint length, list size, and the sorting strategy (local sorting and global sorting) in the LVA. Also, we analyze the latency of the local sorting of the paths in LVA relative to the global sorting in the list decoding and the trade-off between the sorting latency and the error correction performance.

We might have to live with COVID-19 until 2025 according to a recent report published in Science! But if we act smartly, the adverse consequence of living with the virus could be minimized. Currently, many states in the United States are seeing spiking cases on a daily basis due to their inadequate policy responses to COVID-19. This paper promotes more research on improving SEIR modeling as it will play a critical role in facilitating the decision-making on promoting and implementing appropriate public health and social interventions. Hopefully, policymakers will listen to science and enact and implement adequate policy responses in combating the COVID-19 pandemic in each of the states across the United States so that we can win this “war” and be well prepared for the promising future.

This letter presents a comprehensive model of on-load tap-changer (OLTC) transformers that connect 3-wire medium voltage (MV) with 4-wire multigrounded low voltage (LV) networks. The proposed model enables the inclusion of the 3-wire MV network and the 4-wire multigrounded LV network into a single YBUS matrix without any assumption or simplification. Its distinct feature is that the tap changer of the transformer is simulated outside the YBUS matrix, thus a refactorization of the YBUS matrix is not required in every tap change. The proposed transformer model has been validated in a 4-Bus network, while its performance has been tested in the IEEE 8500-Node and IEEE 906-Bus test networks.

In the existing multi-period robust optimization method of active and reactive power coordination in distribution networks, the reactive power regulation capability of distributed generators (DGs), operation costs of regulating equipment, and the current of shunt capacitance of cables are not taken into account. In this paper, based on branch flow equations, a multi-period two-stage mixed integer second-order cone programming (SOCP) robust optimization model of active and reactive coordination in distribution system with cables is developed considering the reactive power regulation capability of DG, action costs of switched capacitor reactor (SCR), on load tap changer transformer (OLTC), energy storage system (ESS), and current of shunt capacitor of cables. Further, against to the deficiency of low computational rate of column and constraint generation method (CCG), a novel method iteratively solving the first and second stage model on cutting plane is proposed. In the first stage model, the number of variables and constraints keeps constant during the iteration. In the second stage model, it only needs to solve the model of each single time period. Then their results are accumulated. Thus, the computational speed using the proposed method is much higher than CCG. The effectiveness of the proposed method is separately validated on the 4-bus, IEEE 33-bus, and PG69-bus distribution systems.

This letter presents a comprehensive Step Voltage Regulator (SVR) model suitable for the three-phase ZBUS power flow. The model can be applied in all SVR configurations such as open delta, close delta, wye. Its advantage is that the tap variations are simulated outside the YBUS matrix, without compromising the convergence of the power flow. Therefore, a refactorization of the YBUS matrix is not required after every tap change reducing significantly the computation time of the power flow. The proposed SVR model is validated in a 4-Bus network, while its performance is tested in the IEEE 8500-Node test feeder.

A design-oriented numerical study of vertical Si-nanowires to be used as sensing elements for the detection of the intracellular electrical activity of neurons. An equivalent lumped-element circuit model is derived and validated by comparison with physics-based numerical simulations. Most of the component values can be identified individually by geometrical and physical considerations. The transfer function and the SNR of the sensor in presence of thermal noise are derived, and the impact of the device geometry is shown.

Microscopic blood cell analysis is an important methodology for medical diagnosis, and complete blood cell counts (CBCs) are one of the routine tests operated in hospitals. Results of the CBCs include amounts of red blood cells, white blood cells and platelets in a unit blood sample. It is possible to diagnose diseases such as anemia when the numbers or shapes of red blood cells become abnormal. The percentage of white blood cells is one of the important indicators of many severe illnesses such as infection and cancer. The amounts of platelets are decreased when the patient suffers hemophilia. Doctors often use these as criteria to monitor the general health conditions and recovery stages of the patients in the hospital. However, many hospitals are relying on expensive hematology analyzers to perform these tests, and these procedures are often time consuming. There is a huge demand for an automated, fast and easily used CBCs method in order to avoid redundant procedures and minimize patients’ burden on costs of healthcare. In this research, we investigate a new CBC detection method by using deep neural networks, and discuss state of the art machine learning methods in order to meet the medical usage requirements. The approach we applied in this work is based on YOLOv3 algorithm, and our experimental results show the applied deep learning algorithms have a great potential for CBCs tests, promising for deployment of deep learning methods into microfluidic point-of-care medical devices. As a case of study, we applied our blood cell detector to the blood samples of COVID-19 patients, where blood cell clots are a typical symptom of COVID-19.

The shift towards reconfigurable systems -hardware and software that adapt themselves to an external context- promises to unlock unprecedented performance, power consumption, and quality of service. However, reconfiguration imposes several challenges on the design of cyber-physical systems. Current design practices, including software frameworks and programming languages, are ill-prepared for supporting reconfiguration. In this paper, we explore Asynchronous Graph Programming, a programming paradigm and an associated model of computation designed for efficient and automated parallelization across processing elements, efficient reconfiguration (i.e., mapping of computational tasks across processing elements), and combining synchronous and asynchronous I/O handling within the same conceptual programming model. We also introduce an analytical model of parallelization, unlocked by Asynchronous Graph Programming, that can inform reconfiguration strategies. We analyze the implications of our model through an analysis of reconfiguration scenarios given a program’s characteristics; our analysis quantifies the benefits of reconfiguring software for higher levels of parallelism, given an amount of data left to process. We also introduce Horde, an open source Asynchronous Graph Programming interpreter, and use it to empirically validate the performance advantage of its automatic parallelism capabilities; in our experiments, automatic parallelization from one to four cores improves average case execution time by a factor of 2 and worst case execution time by a factor of 3. This manuscript has been accepted at the IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2020)

In this paper the research on optimisation of visual object tracking using a Siamese neural network for embedded vision systems is presented. It was assumed that the solution shall operate in real-time, preferably for a high resolution video stream, with the lowest possible energy consumption. To meet these requirements, techniques such as the reduction of computational precision and pruning were considered. Brevitas, a tool dedicated for optimisation and quantisation of neural networks for FPGA implementation, was used. A number of training scenarios were tested with varying levels of optimisations-from integer uniform quantisation with 16 bits to ternary and binary networks. Next, the influence of these optimisations on the tracking performance was evaluated. It was possible to reduce the size of the convolutional filters up to 10 times in relation to the original network. The obtained results indicate that using quantisation can significantly reduce the memory and computational complexity of the proposed network while still enabling precise tracking, thus allow to use it in embedded vision systems. Moreover , quantisation of weights positively affects the network training by decreasing overfitting.

We derive an analytical model for 1/f noise in MOSFETs, highlighting a term that is often neglected in literature but becomes important for ultra-thin oxides. Furthermore, we identify an interesting relationship between the thermal noise of the gate impedance and the gate noise due to trapping/detrapping between the free carriers in the channel and the oxide traps, as well as the 1/f noise cross-correlation between drain and gate, showing that a single voltage noise generator is not enough to describe completely the 1/f noise. TCAD simulations are used to verify the model predictive capabilities.

We propose a new iterative detection and decoding algorithm for multiple-input multiple-output (MIMO) based on expectation propagation (EP) with application to massive MIMO scenarios. Two main results are presented. We first introduce EP to iteratively improve the Gaussian approximations of both the estimation of the posterior by the MIMO detector and the soft output of the channel decoder. With this novel approach, denoted by double-EP (DEP), the convergence is very much improved with a computational complexity just two times the one of the linear minimum mean square error (LMMSE), as illustrated by the included experiments. Besides, as in the LMMSE MIMO detector, when the number of antennas increases, the computational cost of the matrix inversion operation required by the DEP becomes unaffordable. In this work we also develop approaches of DEP where the mean and the covariance matrix of the posterior are approximated by using the Gauss-Seidel and Neumann series methods, respectively. This low-complexity DEP detector has quadratic complexity in the number of antennas, i.e., the same as the low-complexity LMMSE techniques. Experimental results show that the new low-complexity DEP achieves the performance of the DEP as the ratio between the number of transmitting and receiving antennas decreases

This paper presents the first experimental demonstration of a ternary memristor-CMOS logic family. We systematically design, simulate and experimentally verify the primitive logic functions: the ternary AND, OR and NOT gates. These are then used to build combinational ternary NAND, NOR, XOR and XNOR gates, as well as data handling ternary MAX and MIN gates. Our simulations are performed using a 50-nm process which are verified with in-house fabricated indium-tin-oxide memristors, optimized for fast switching, high transconductance, and low current leakage. We obtain close to an order of magnitude improvement in data density over conventional CMOS logic, and a reduction of switching speed by a factor of 13 over prior state-of-the-art ternary memristor results. We anticipate extensions of this work can realize practical implementation where high data density is of critical importance.

In this paper we present our research on the optimisation of a deep neural network for 3D object detection in a point cloud. Techniques like quantisation and pruning available in the Brevitas and PyTorch tools were used. We performed the experiments for the PointPillars network, which offers a reasonable compromise between detection accuracy and calculation complexity. The aim of this work was to propose a variant of the network which we will ultimately implement in an FPGA device. This will allow for real-time LiDAR data processing with low energy consumption. The obtained results indicate that even a significant quantisation from 32-bit floating point to 2-bit integer in the main part of the algorithm, results in 5%-9% decrease of the detection accuracy, while allowing for almost a 16-fold reduction in size of the model.

We propose an image warping-based remote rendering technique for volumes that decouples the rendering and display phases. Our work builds on prior work that samples the volume on the client using ray casting and reconstructs a z-value based on some heuristic. The color and depth buffer are then sent to the client that reuses this depth image as a stand-in for subsequent frames by warping it according to the current camera position until new data was received from the server. We augment that method by implementing the client renderer using ray tracing. By representing the pixel contributions as spheres, this allows us to effectively vary their footprint based on the distance to the viewer, which we find to give better results than point-based rasterization when applied to volumetric data sets.

In this work, we analyze the effect of residual frame timing offset and carrier frequency offset along with the presence of fractional Doppler on the bit error rate performance of OFTS system. We show in this work that channel equalization with appropriate channel estimates can compensate for the combined effect of the time-varying multipath channel (TVMC) as well as residual synchronization errors. We also present a unique time-domain channel estimation method for OTFS, which can be used along with the above-mentioned channel equalization. It is seen from the extensive Monte Carlo simulation results of the LDPC coded OTFS system that the estimation and compensation methods presented here provide necessary resilience properties to OTFS against the effects of TVMC channel and residual synchronization errors. A unified signal processing flow for OTFS and OFDM is also described in this work to motivate studies on coexistence between the two as well as to encourage investigations on a seamless transition from OFDM based system to OTFS based systems for future air interface design. This Work is submiited to an IEEE Journal for possible future publication.

Modulational stability of envelope soliton is studied in a quantum dusty plasma in three dimension. The Krylov-Bogoliubov-Mitropolsky method is applied to the three dimension plasma governing equations. A generalised form of Nonlinear Schr¨odinger equation is obtained whose dispersive term has a tensorial character. Stability condition is deduced abintio and the stability zones are plotted as a function of plasma parameters