Subarray-based hybrid beamforming (SBHB) systems represent an efficient architectural solution to realize massive multiple-input multiple-output (MIMO) systems. Thereby, the beamforming is no longer realized exclusively in the digital domain, but in coordination with an analog beamforming network by connecting each digital channel to a dedicated number of antenna elements via phase shifters. Most channel estimation and beamforming algorithms for SBHB systems are based on beam steering approaches, demanding a precise calibration of the analog beamforming network. In this letter, the SBHB system is calibrated by over-the-air (OTA) scattering parameter measurements and two criteria to optimize the beam pattern are formulated exploiting the ambiguities of the phase shifters. Both optimization criteria are evaluated using a designed SBHB receiver system operating at 27.8 GHz.

Modern mobile devices come equipped with heterogeneous multi-processor system-on-chip (MPSoC) consisting of different types of cores such as CPU and GPU to cater for performance requirement of different types of applications. These MPSoCs also supports dynamic voltage and frequency scaling (DVFS) to reduce dynamic power consumption, which retrospectively affects the peak temperature of the multi-processors on the chip as well. In this poster presentation, we introduce three novel approaches to DVFS in mobile MPSoCs such that the executing applications can meet the performance requirement while consuming less power, reducing peak temperature and improving thermal side-channel security and reliability in terms of lifespan of the device.

For a ground moving target indication (GMTI) radar, the presence ofDoppler blind zone (DBZ) results in many short tracks with frequent label switching, which seriously deteriorates the tracking performance. When the DBZ masking is coupled with targets maneuvering, tracking multiple maneuvering targets hidden in the DBZ becomes very challenging, which is reflected in the fact that there is no public research on this issue. To overcome this complicated problem, we propose a practical and fully functional GMTI multi-maneuvering-target tracker based on the multiple model probability hypothesis density (MM-PHD) filter. Unlike the standard MM-PHD filter, the proposed tracker utilizes the Doppler information and incorporates the minimum detectable velocity (MDV) to suppress the DBZ masking. Furthermore, to cope with the problems of the fixed initiation and no label output of the standard MM-PHD filter, the resulting MM-PHD filter with the Doppler and MDV information is augmented with measurement-driven adaptive track initiation and track label propagation, which are necessary for a practical tracker and also required for evaluating the overall GMTI tracking performance. Finally, numerical examples show that the proposed tracker outperforms significantly the existing ones, thus verifying its effectiveness.

The sudden development of the COVID-19 pandemic exposed the limitations in modern healthcare systems to handle public health emergencies. It is evident that adopting innovative technologies such as blockchain can help in effective planning operations and resource deployments. Blockchain technology can play an important role in the healthcare sector such as improved clinical trial data management by reducing delays in regulatory approvals, streamline the communication between diverse stakeholders of the supply chain etc. Moreover, the spread of misinformation has intensely increased during the outbreak and existing platforms lack the ability to validate the authenticity of data, causing people to panic and act irrationally. Thus, developing a blockchain-based tracking system is important to ensure that the information received by the public and government agencies are reliable and trustworthy. In this paper, we focus on blockchain abilities to track the COVID-19 data collected from various sources including news, healthcare professionals, researchers etc, verify and append them in a secure and trusted distributed ledger. Thus, we propose a generic framework using Ethereum smart contracts and oracles to track real-time data related to the number of new cases, deaths and recovered cases obtained from trusted sources. We present detailed algorithms that capture the interactions between stakeholders in the network. The smart contract code was developed and tested in Remix environment. We present the cost and security analysis incurred by the stakeholders and highlight the challenges and future directions of our work. Our work demonstrates that the proposed solution is economically feasible and ensures data integrity, security, transparency, data traceability among stakeholders.

Hyper spectral imaging (HSI) is a technique that is used to obtain the spectrum for each pixel in the image. It helps in finding objects and identifying materials etc. Such an identification is very difficult using other imaging techniques. It allows the researchers to investigate the documents without any physical contact. Nowadays detection of unequal Ink mismatch based on HSI has shown vast improvement in distinguishing the inks. Detection of unequal Ink mismatch is an unbalanced clustering problem. This paper used K-means Clustering for ink mismatch detection. K-means Clustering find same subgroups in the data based on Euclidean distance. This paper demonstrates performance in unequal Ink mismatch based on HSI.

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.

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.

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 paper presents a comprehensive three-bus equivalent circuit model of three-phase step voltage regulators. The proposed model can be efficiently integrated in the Z-bus power flow method and can accurately simulate any configuration of step voltage regulators. In contrast to the conventional step voltage regulator models that include the tap variables inside the YBUS matrix of the network, the proposed model simulates them in the form of current sources, outside the YBUS matrix. As a result, the re-factorization of the YBUS matrix is avoided after every tap change reducing significantly the computational burden of the power flow. Furthermore, possible convergence issues caused by the low impedance of step voltage regulators are addressed by introducing fictitious impedances, without, however, affecting the accuracy of the model. The results of the proposed step voltage regulator model are compared against well-known commercial softwares such as Simulink and OpenDSS using the IEEE 4-Bus and an 8-Bus network. According to the simulations, the proposed model outputs almost identical results with Simulink and OpenDSS confirming its high accuracy. Furthermore, the proposed 3-bus equivalent model is compared against a recently published conventional step voltage regulator model in the IEEE 8500-Node test feeder. Simulation results indicate that the proposed step voltage regulator model produces as accurate results as the conventional one, while its computation time is significantly lower. More specifically, in the large IEEE 8500-node network consisting of four SVRs, the proposed model can reduce the computation time of power flow around one minute for every tap variation. Therefore, the proposed step voltage regulator model can constitute an efficient simulation tool in applications where subsequent tap variations are required.

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.

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)

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.

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.

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.

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