The 2.5D interposer becomes a crucial solution to realize grand bandwidth of HBM for the increasing data requirement of high performance computing (HPC) and Artificial Intelligence (AI) applications. To overcome high speed switching bottleneck caused by the large resistive and capacitive characteristics of interposer, design methods to achieve an optimized performance in a limited routing area are proposed. Unlike the conventional single through silicon via (TSV), considering the reliability, multiple TSV are used as the robust 3D interconnects for each signal path. An equivalent model to accurately describe the electrical characteristics of the multiple TSVs, and a configuration pattern strategy of TSV to mitigate crosstalk are also proposed.

This paper presents a compact, cost-effective, and contactless fractal modified EBG-based microwave sensing platform for dielectric characterization of liquids by analyzing the variation in the reflection coefficient of an antenna. The reported design is composed of a triangular-shaped antenna (0.323λ­o x 0.323λ­o) placed over a 3 x 3 array of Cesaro fractal based EBG plane (0.7λ­o x 0.7λ­o) operating at 2.45 GHz. A significant enhancement of the E-field in the sensing region has been achieved with the incorporation of Cesaro fractals in the EBG plane which results in increased sensitivity and compactness. To validate its performance, absolute solutions of butan-1-ol, methanol, and water are loaded, and a maximum measured sensitivity of 0.875% and a maximum quality factor of 90.05 is achieved. Moreover, a maximum RMS error in retrieved values of dielectric constant and loss tangent of liquid under test is found to be 1.092% and 0.813%, respectively. Our demonstrated EBG-based sensor has a compact footprint with good precision, affordability, and ease of operation in detecting liquids for microwave sensing applications.

This paper presents a state of the art on brushless Permanent Magnet Alternative Current (PMAC) and Permanent Magnet Direct Current (PMDC) motor drives. The brushless PMAC and PMDC motor drives are the latest choices of researchers due to their high efficiency, silent operation, compact size, high reliability, and low maintenance requirements. These motors are preferred for numerous applications. An in-depth analysis of PMAC and PMDC motor drives have been discussed. The Permanent Magnets (PMs) motor drives can be classified into two categories, first is PMAC motor drives, i.e. PMSM and the second one is PMDC motor drives, i.e. BLDC motors. There has been a long misunderstanding between the two ways of driving and controlling a motor with no windings on the rotor but only PMs. In this paper given a comprehensive analysis of both motor drives. So, beginner of the research scholars are easily able to understand about brushless PMAC and PMDC motor drives.

We introduce a novel method that is able to localize fluorescent labelled objects in multi-scale 2D microscopy, and is robust to highly variable imaging conditions. Localized objects are then classified in a novel way using belief theory, requiring only the image level label. Each object is assigned a ‘belief’ that describes how likely it is to appear in an image with a given set of labels. We apply our method successfully to identify amyloid-beta deposits, associated with Alzheimer’s disease, and to discover caveolae and their modular components in superresolution microscpy. We illustrate how our approach allows the fusion or combination of models learned across markedly different datasets. We show how we can compute the ‘conflict’, or disagreement between the models, an insight that can allow the domain expert to interpret the composite model.

Objectives: Telehealth and telemedicine systems aim to deliver remote healthcare services to mitigate the spread of COVID‐19. Also, they can help to manage scarce healthcare resources to control the massive burden of COVID-19 patients in hospitals. However, a large portion of today’s telehealth and telemedicine systems are centralized and fall short of providing necessary information security and privacy, operational transparency, health records immutability, and traceability to detect frauds related to patients’ insurance claims and physician credentials. Methods: The current study has explored the potential opportunities and adaptability challenges for blockchain technology in telehealth and telemedicine sector. It has explored the key role that blockchain technology can play to provide necessary information security and privacy, operational transparency, health records immutability, and traceability to detect frauds related to patients’ insurance claims and physician credentials. Results: Blockchain technology can improve telehealth and telemedicine services by offering remote healthcare services in a manner that is decentralized, tamper-proof, transparent, traceable, reliable, trustful, and secure. It enables health professionals to accurately identify frauds related to physician educational credentials and medical testing kits commonly used for home-based diagnosis. Conclusions: Wide deployment of blockchain in telehealth and telemedicine technology is still in its infancy. Several challenges and research problems need to be resolved to enable the widespread adoption of blockchain technology in telehealth and telemedicine systems.

In this modern day and age, wireless networks are everywhere. It is not a far stretch to say that it is downright crucial due to its ability to keep people connected and updated everywhere from universities and workplaces down to our personal homes. Though the benefits of wireless networks are undeniable, it also brings a new host of security concerns due to the amount of sensitive data being transmitted through these networks. A simple attack such as packet sniffing is enough to “sniff” out just enough data to cripple even giant corporate bodies if left unchecked. The aim of this research paper is to dissect and discuss the security and privacy issues of wireless networks that surround us. An interesting finding would be that wireless networks are heavily susceptible to both malicious attacks such as denial of service attacks and passive attacks like eavesdropping. It was also noted that information is more easily obtainable through wireless networks as compared to wired networks due to the ability to gleam information from long distances without having to be physically present on site. A literature review and a survey were conducted to learn about the opinions of casual users of wireless networks. The information gathered suggests that while there are many who care about privacy, they still lack the necessary knowledge to protect said privacy. These users carelessly connect to public wireless networks wherever it is available without proper precaution and care. Appropriate relevant solutions are presented based on literature study to overcome insecure wireless network usage practices in order to avoid active and passive attacks.

COVID-19, caused by SARS-CoV-2, is now a global pandemic disease. This outbreak has affected every aspect of life from work, leisure, and interaction with technology. Governments around the world have issued orders for travel bans, social distancing, and lockdown to control the spread of the virus and prevent strain on hospitals. This paper explores potential applications for radar-based non-contact remote respiration sensing technology that may help to combat the COVID-19 pandemic and outlines potential advantages that may also help to reduce the spread of the virus. Based on the state of the art engineering technologies, several emerging applications related to COVID-19 monitoring/or protection will be discussed with their technical implications. These applications include remote breathing rate monitoring, continuous identity authentication, occupancy detection, and hand gesture recognition. The paper also highlights future research directions that must be explored further to bring this innovative non-contact sensor technology into real-world implementation.

We present a method for the mitigation of quantization lobes in single-bit reconfigurable reflective surfaces (RRSs). Typically, RRSs are planar beamforming structures consisting of hundreds or thousands of antennas with integrated tunable switches. Under plane-wave illumination, single-bit RRSs suffer from undesired side lobes or quantization lobes, which are caused by the periodicity of the errors due to the limited number of bits used in phase quantization. In this work, we present a topology that suppresses the quantization lobes using single-layer, 1-bit RRSs, by implementing a fixed but random phase delay in every unit-cell. The introduction of phase randomization breaks the periodicity of the quantization errors, thus reducing the quantization lobe level (QLL). We carry out a theoretical analysis to demonstrate the effect of phase randomization in RRSs, and for the first time, provide the condition for choosing the range of randomization required to achieve the lowest sidelobe level (SLL). Leveraging this condition, we design a single-layer, 1-bit 30×30 randomized RRS at 222.5 GHz. The reflective surface is fabricated on a thin, low-loss alumina ribbon ceramic wafer from Corning Inc. using a simplified fabrication technique suitable for large-scale production of mmWave/THz RRSs. Finally, we present the radar cross-section (RCS) characterization results obtained from a quasi-optical measurement setup validating the mitigation of quantization lobes using the proposed randomization technique.

Human-robot joint action is a key requirement in many advanced robotic applications where robots are not only expected to work alongside humans but also collaborate with them in the performance of physical tasks. Robots are already programmed to model and predict human actions in order to ensure smooth collaboration and overall task efficiency. However, little is known on how humans represent and account for robot's actions as part of their own plans. This paper presents a first joint psychological and HRI user study designed to answer this question in the context of human-robot handover scenarios. Our analysis showed that the participants had a positive userexperience of the interaction and adopted gaze patterns similar to a large extent to the ones in human-to-human handover tasks. The EEG analysis suggests that, compared to solo action, the human participants were at a state of higher motor readiness when they prepared to hand over the object to the robot either because they represented the robot's action in advance or alternatively that they anticipated that passing the object to the robot would be a more effortful action, thus highlighting the increased demands in planning a human-to-robot interaction. Our findings highlight the value of gaze as a positive method of non-verbal communication in HRI and provides new insights in the neural mechanisms that allows a person to plan an effective interaction with a robot.

The year 2020 has witnessed the emergence of coronavirus (COVID-19) that has rapidly spread and adversely affected the global economy, health, and human lives. The COVID-19 pandemic has exposed the limitations of existing healthcare systems regarding their inadequacy to timely and efficiently handle public health emergencies. A large portion of today’s healthcare systems are centralized and fall short in providing necessary information security and privacy, data immutability, transparency, and traceability features to detect fraud related to COVID-19 vaccination certification, and anti-body testing. Blockchain technology can assist in combating the COVID-19 pandemic by ensuring safe and reliable medical supplies, accurate identification of virus hot spots, and establishing data provenance to verify the genuineness of personal protective equipment. This paper discusses the potential blockchain applications for the COVID-19 pandemic. It presents the high-level design of three blockchain-based systems to enable governments and medical professionals to efficiently handle health emergencies caused by COVID-19. It discusses the important ongoing blockchain-based research projects, use cases, and case studies to demonstrate the adoption of blockchain technology for COVID-19. Finally, it identifies and discusses future research challenges, along with their key causes and guidelines.

Microbial corrosion is considered the main reason for multi-billion dollar sewer asset degradation. Sewer pipe surface temperature is a vital parameter for predicting the micro-biologically induced concrete corrosion. Due to this important measure, a surface temperature sensor suite was recently developed and tested in an aggressive sewer environment. The sensors can fail and they may also put offline during the period of scheduled maintenance. In such situations, time series forecasting of sensor data can be an alternative measure for the operators managing the sewer network. In this regard, this paper focuses on the short-term forecasting of sensor measurements. The evaluation was carried out by forecasting the sensor measurements for different time periods and evaluated with different forecasting models. The ETS model leads to high short-term forecasting accuracy and the ARIMA model leads to high long-term forecasting accuracy. The models were evaluated on real data captured in a Sydney sewer

In this era of technology, people rely on online posted reviews before buying any product. These reviews are very important for both the consumers and people. Consumers and people use this information for decision making while buying products or investing money in any product. This has inclined the spammers to generate spam or fake reviews so that they can recommend their products and beat the competitors. Spammers have developed many systems to generate the bulk of spam reviews within hours. Many techniques, strategies have been designed and recommended to resolve the issue of spam reviews. In this paper, a complete review of existing techniques and strategies for detecting spam review is discussed. Apart from reviewing the state-of-the-art research studies on spam review detection, a taxonomy on techniques of machine learning for spam review detection has been proposed. Moreover, its focus on research gaps and future recommendations for spam review identification.

The potential for control design to dramatically improve the economic viability of wave energy has generated a great deal of interest and excitement. However, for a number of reasons, the promised benefits from better control designs have yet to be widely realized by wave energy devices and wave energy remains a relatively nascent technology. This brief paper summarizes a simple, yet powerful approach to wave energy dynamics modeling, and subsequent control design based on impedance matching. Our approach leverages the same concepts that are exploited by a simple FM radio to achieve a feedback controller for wave energy devices that approaches optimal power absorption. If fully utilized, this approach can deliver immediate and consequential reductions to the cost of wave energy. Additionally, this approach provides the necessary framework for control co-design of a WEC, in which an understanding of the control logic allows for synchronous design of the device control system and hardware.

Targeted advertising has transformed the marketing trend for any business by creating new opportunities for advertisers to reach prospective customers by delivering them personalised ads using an infrastructure of a variety of intermediary entities and technologies. The advertising and analytics companies collect, aggregate, process and trade a rich amount of user’s personal data, which has prompted serious privacy concerns among individuals and organisations. This article presents a detailed survey of privacy risks including the information flow between advertising platform and ad/analytics networks, the profiling process, the advertising sources and criteria, the measurement analysis of targeted advertising based on user’s interests and profiling context and ads delivery process in both in-app and in-browser targeted ads. We provide detailed discussion of challenges in preserving user privacy that includes privacy threats posed by the advertising and analytics companies, how private information is extracted and exchanged among various advertising entities, privacy threats from third-party tracking, re-identification of private information and associated privacy risks, in addition to, overview data and tracking sharing technologies. Following, we present various techniques for preserving user privacy and a comprehensive analysis of various proposals founded on those techniques and compare them based on the underlying architectures, the privacy mechanisms and the deployment scenarios. Finally we discuss some potential research challenges and open research issues.

The superposition theorem, a particular case of the superposition principle, states that in a linear circuit with several voltage and current sources, the current and voltage for any element of the circuit is the algebraic sum of the currents and voltages produced by each source acting independently. The superposition theorem is not applicable to power, because it is a non-linear quantity. Therefore, the total power dissipated in a resistor must be calculated using the total current through (or the total voltage across) it. The theorem proposed and proved in this paper states that in a linear DC network consisting of resistors and independent voltage and current sources, the total power dissipated in the resistors of the network is the sum of the power supplied simultaneously by the voltage sources with the current sources replaced by open circuit, and the power supplied simultaneously by the current sources when the voltage sources are replaced by short-circuit. This means that the power is superimposed. The theorem can be used to simplify the power analysis of DC networks. The analysis results are validated via numerical examples.

To avoid potential privacy threats and associated cyber-security issues in microgrids, this letter presents a distributed active power sharing and frequency regulation method with preserved privacy of local information. In the proposed approach, the transmitted data including the active power outputs and capacities are protected by adding noises to the original ones. Theoretical analysis and verification studies are performed to illustrate the advantages of the proposed method.

Datasets containing class noise present significant challenges to accurate classification, thus requiring classifiers that can refuse to classify noisy instances. We demonstrate the inability of the popular confidence-thresholding rejection method to learn from relationships between input features and not-at-random class noise. To take advantage of these relationships, we propose a novel null-labelling scheme based on iterative re-training with relabelled datasets that uses a classifier to learn to reject instances that are likely to be misclassified. We demonstrate the ability of null-labelling to achieve a significantly better tradeoff between classification error and coverage than the confidence-thresholding method. Models generated by the null-labelling scheme have the added advantage of interpretability, in that they are able to identify features correlated with class noise. We also unify prior theories for combining and evaluating sets of rejecting classifiers.

Hardware distortions (HWD) render drastic effects on the performance of communication systems. They are recently proven to bear asymmetric signatures; and hence can be efficiently mitigated using improper Gaussian signaling (IGS), thanks to its additional design degrees of freedom. Discrete asymmetric signaling (AS) can practically realize the IGS by shaping the signals' geometry or probability. In this paper, we adopt the probabilistic shaping (PS) instead of uniform symbols to mitigate the impact of HWD and derive the optimal maximum a posterior detector. Then, we design the symbols' probabilities to minimize the error rate performance while accommodating the improper nature of HWD. Although the design problem is a non-convex optimization problem, we simplified it using successive convex programming and propose an iterative algorithm. We further present a hybrid shaping (HS) design to gain the combined benefits of both PS and geometric shaping (GS). Finally, extensive numerical results and Monte-Carlo simulations highlight the superiority of the proposed PS over conventional uniform constellation and GS. Both PS and HS achieve substantial improvements over the traditional uniform constellation and GS with up to one order magnitude in error probability and throughput.