Circuit solutions for a new subclass of fourth-order band-pass filters have been developed, which are implemented on operational amplifiers and have, among other things, paraphase outputs. Basic equations for their transfer function are obtained. In the Micro-Cap environment, computer simulation of the amplitude-frequency characteristics (AFC) was carried out, which shows the possibility of independent adjustment, adjustment of the bandwidth, uneven AFC and center frequency gain.

The paper studies face spoofing, a.k.a. presentation attack detection (PAD) in the demanding scenarios of unknown attacks. While earlier studies have revealed the benefits of ensemble methods, and in particular, a multiple kernel learning (MKL) approach to the problem, one limitation of such techniques is that they typically treat the entire observation space similarly and ignore any variability and \textit{local} structure inherent to the data. This work studies this aspect of face presentation attack detection with regards to one-class multiple kernel learning to benefit from the intrinsic local structure in bona fide samples to adaptively weight each representation in the composite kernel. More concretely, inspired by the success of the one-class Fisher null formalism, we formulate a convex \textit{localised} multiple kernel learning algorithm by imposing a joint matrix-norm constraint on the collection of local kernel weights and infer locally adaptive weights for zero-shot one-class unseen attack detection. We present a theoretical study of the proposed localised MKL algorithm using Rademacher complexities to characterise its generalisation capability and demonstrate the advantages of the proposed technique over some other options. An assessment of the proposed approach on general object image datasets illustrates its efficacy for abnormality and novelty detection while the results of the experiments on face PAD datasets verify its potential in detecting unknown/unseen face presentation attacks.

This is the final part of the three part series paper where the novel idea of representing flyback transformer losses as loss functions using hyperbolic secant spline interpolation,determination of the flyback transformer core saturation and instability in the control feedback loop from the loss function using Lyapunov method has been presented with scientific proof and validity. In this paper, the comparative analysis of 36 different flyback transformers from 30W to 85W is being presented.These two-winding transformers are operated in continuous/discontinuous mode,switched from MOSFET controlled by microcontroller from 60 kHz to 97 kHz. Eighteen wattage’s which are used internationally at different voltage and current ratings have been analyzed on fifteen different cores - EEL22, EE25, EEL25,EF25, EE28, EER28, EER28L, EE35, RM8/I, ETD29/16/10,ETD34/17/11, E25/13/7, PR30x19, ATQ27/18, RM12/I.

This is the second part of the three part series paper where the novel idea of representing flyback transformer losses as loss functions using hyperbolic secant spline interpolation, and its use in determining flyback transformer core saturation and instability in the control feedback loop from the loss function using Lyapunov method has been presented with scientific proof and validity. In this paper, the comparative analysis of 36 different flyback transformers from 2W to 27W is being presented. These two-winding transformers are operated in continuous/discontinuous mode, switched from MOSFET controlled by microcontroller from 41 kHz to 97 kHz. Eighteen wattage’s which are used internationally at different voltage and current ratings have been analyzed on fifteen different cores - EPC13, EF12.6, EFD15, EE13, EE16, EEL16,EE19, EEL19,EFD20,EE25, E20/10/6, PR18x11,EEL22,RM6S/I, RM8/I.

This is a three part series paper, where the novel idea of representing flyback transformer losses as loss functions has been presented with scientific proof.Thirteen calculations for each transformer have been tabulated and used to derive mathematical loss function using hyperbolic secant spline interpolation which is subjected to Lyapunov method to predict core saturation and instability in the control loop. For scientific validity, 72 working transformers at thirty-six wattage’s which are being internationally used, have been examined to check, whether such proposition is valid or not.The definiteness of matrix,singular value decomposition,Lyapunov exponents are also tabulated. Then each of these transformers are simulated with non-linearies of Li-ion battery namely state of charge (SoC), self-discharge resistance, temperature-dependent leakage resistance,five time-constant dynamics, deterioration of battery performance over repeated charge and discharge cycles. The coefficients in the Jiles-Atherton model have been altered with fractional perturbations to obtain hysteresis curves.Various interesting comparisons in Bm,Bp,AC flux densities, effect on varying the coefficients in JA, residual errors, Lyapunov exponents, switching frequency vs MOSFET and transformer losses, reflected voltages, are tabulated and plotted.

The whole world today, is facing the COVID-19 pandemic. This situation make people must adapt to the new normal, for example: work from home, online communication, keep clean to reduce transmission of COVID-19. Moreover, research has found that wearing a mask is one way to reduce the risk of viral transmission. It makes many public places have a measure about their customer must keep a distance between people and wearing the face mask correctly. But public service providers are not feasible to thoroughly check whether all a customer wear the mask or not. So, in this project, we propose face mask detection via webcam cameras using image recognition which is one of the high-accuracy and efficient face mask detector. This proposed system consists of three methods to detect face mask: 1) Image preprocessing 2) Face detection and crop 3) Classification of face mask. This system can detection a face mask, not wearing a face mask and incorrect face mask. This system will help public service providers ensure that all people in this place wear face mask correctly, promote people to use a face masks and it ensure that these public places have a safe environment.

The controlled fault current of converter-interfaced renewable energy sources (CIRESs) may bring about lower sensitivity level or misoperation of fault component based directional relays (FCBDRs). In order to cope with this issue, a new control scheme is proposed to adjust sequence superimposed impedance angles of CIRESs by computing suitable current references of the CIRES controller. Meanwhile, these current references are maximized by an iterative algorithm to make full use of the short-circuit capacity of CIRESs. The proposed control scheme is applicable to various faulty conditions such as different fault types, power factors, weak grids and larger fault resistances. Compared with new directional relays that need to update protection devices, the proposed control strategy can make CIRESs compatible with commercially existing directional relays whilst the necessary fault ride through (FRT) requirements can still be satisfied. Furthermore, all the controller parameters are not required to be revised according to the detected fault type, even with only local measured data collected. The associated PSCAD simulations, real-time digital simulator (RTDS) experiment and downscale hardware experiment verify the proposed method.

The dielectric-modulated top contact bi-layer electrodes organic thin-film transistor (DMTCBE-OTFT) based sensor is proposed to reduce the cost and complexity of nanoscale device fabrication for label-free biomolecule detection. In the proposed structure, a cavity is carved in gate dielectric and filled with biomolecules to realise the function of the sensor. The immobilisation of biomolecules in the cavity region alters the performance parameters like on-current (ION), on-off current ratio (ION/IOFF ), and subthreshold swing (SS) of the proposed device. We have also investigated the effects of gate work-function and cavity thickness on the sensitivity of a DMTCBE-OTFT based biosensor. A bi-layer electrodes is also used for a significant reduction in barrier height to enhance the performance of the proposed device. The change in the on-current (ION) and on-off current ratio (ION/IOFF ) has been utilised to evaluate the sensitivity of the DMTCBE-OTFT for biomolecule recognition. It has different dielectric constants and corresponding charge densities using 2-D simulation when biomolecules are immobilised in the cavity area. The ATLAS TCAD tool is used to investigate the sensitivity performance of the DMTCBE-OTFT. The proposed DMTCBEOTFT has almost four times higher drain current sensitivity in comparison to the recently reported OTFT-based biosensors for label-free detection of biomolecules. The DMTCBE-OTFT biosensor has enormous potential for future flexible biosensing applications due to its good sensitivity, flexibility, low cost, and biocompatibility.

Game Theoretic Approach Toward Detection of Input-Driven Evasive Malware in the IoT

In recent years, automatic mobile robots (AMR) have been widely concerned in various fields, such as material handling, unmanned aerial vehicle (UAV) cruise, automatic factories and so on. Compared with traditional robots, an AMR bring system poses several challenges such as adaptation to the environment, stable communication and robust control. For example, when in a complex environment, it can observe the surrounding environment in real time to avoid obstacles, and track the target in real time to change its trajectory until the task is completed, which improves the flexibility and mobility of the robot. More importantly, AMR systems require supports from various technologies including real-time positioning and navigation, trajectory planning, target tracking, multi-agent systems, robot monitoring and so on. In the near future, its geometric adaptability, design characteristics, mobility, flexibility, obstacle avoidance and fault-tolerant control will be further improved. This article gives a comprehensive elaboration on the four aspects of AMR systems, namely, AMR applications, perception technologies, robot communications, and control mechanisms. Future research directions are also pointed out.

Object detection is an essential component of many vision systems. For example, pedestrian detection is used in advanced driver assistance systems (ADAS) and advanced video surveillance systems (AVSS). Currently, most detectors use deep convolutional neural networks (e.g., the YOLO - You Only Look Once –family), which, however, due to their high computational complexity, are not able to process a very high-resolution video stream in real-time, especially within a limited energy budget. In this paper we present a hardware implementation of the well-known pedestrian detector with HOG (Histogram of Oriented Gradients) feature extraction and SVM (Support Vector Machine) classification. Our system running on AMD Xilinx Zynq UltraScale+ MPSoC (Multiprocessor System on Chip) device allows real-time processing of 4K resolution (UHD - Ultra High Definition, 3840 x 2160 pixels) video for 60 frames per second. The system is capable of detecting a pedestrian in a single scale. The results obtained confirm the high suitability of reprogrammable devices in the real-time implementation of embedded vision systems.

The rapid growth of large-scale trajectory data has prompted researchers to develop multiple large-scale trajectory data management systems. One of the fundamental requirements of all these systems, regardless of their architecture, is to partition data efficiently between machines. In the typical query operations of tracks, the query on ID is a frequent operation of track query, such as ID time range query, ID space range query, etc. A widely used ID indexing technique is to reuse an existing search tree, such as a Kd-tree, by building a temporary tree for the input samples and using its leaf nodes as partition boundaries. However, we show in this paper that this approach has significant limitations. To overcome these limitations, we propose a new index, BOD-tree, which inherits the main features of the Kd-tree and can also partition the dataset into multiple balanced splits. We test the method on real datasets, and extensive experiments show that our algorithm can improve resource usage efficiency. \end{abstract}

The experimental datasets are collected in an office building and mall scenes. A pedestrian handholding a smartphone walked casually in the experiment scenes and acquired images, Wi-Fi signals, and inertial measurements.

Driven by an unprecedented boom in artificial intelligence (AI) and Internet of Things (IoT), edge intelligence (EI) pushes the frontier of AI from cloud to network edge, serving as a remarkable solution that unlocks the full potential of AI services. It is yet facing critical challenges in its decentralized management and security, limiting its capabilities to support services with numerous requirements. In this context, blockchain (BC) has been seen as a promising solution to tackle the above issues, and further support EI. Based on the number of citations or the relevance of emerging methods, this paper presents the results of a literature survey on the integration of EI and BC. Accordingly, we summarize the recent research efforts reported in the existing works on EI and BC. We then paint a comprehensive picture of the limitations of EI and why BC could benefit from EI. From there, we explore how BC benefits EI in terms of computing power management, data administration, and model optimization. In order to narrow the gap between immature BC and EI-amicable BC, we also probe into how to tailor BC to EI from four perspectives, including flexible consensus protocol, effective incentive, intellectuality smart contract, and scalability. Finally, some research challenges and future directions are presented. Different from existing surveys, our work focuses on the integration of EI and BC, develops some general models to help the reader build relevant optimization models in the integrated system, as well as provides detailed tutorials on implementation. We anticipate that this survey will motivate further discussions on the synergy of EI and BC, and offer some guidance in EI, BC, future networks, and other areas.

This paper proposes a loss-separation strategy to improve efficiency of inductive wireless high-power transfer systems (WPTs) in weak-coupling conditions. Wireless power transfer efficiency of inductive systems significantly reduces under weak-coupling states due to the increased losses associated with low mutual inductance. To overcome the challenge, this paper proposes a strategy to separate primary loss components from each other and from the weak-coupling stage. In the proposed strategy, a gyrator in form of a double-resonance T-block is added just before the weak-coupling stage to improve the overall efficiency. Then the strategy is realized by various topologies such as compensating circuits, added coils, isolated transformers, and integrated-/split- inductors/coils. Comparative analyses of the topologies are presented by considering practical aspects including component designs, power flows, transfer efficiency, resonance frequency shifting, and optimal loads. The comparative experiments show an efficiency increment of about four percent for the case with a coupling factor of 0.1. The proposed strategy offers suggestions for industrial designs of high-power wireless battery charging systems using resonant inductive coils. Loss-Separation Strategy to Improve Efficiency of Wireless High-Power Transfer Systems in Weak-Coupling Conditions

Physical layer secret key generation schemes can provide lightweight encryption for resource-constrained Internet of Things (IoT) devices. However, many IoT devices are stationary and the environment is usually static, which leads to ultra-low/zero secret key rates. To circumvent this problem, a novel secret key generation scheme based on Reconfigurable Intelligent Surface (RIS)  is  proposed for Multiple-Input Single-Output  IoT communications. The RIS is introduced into the network, and the phase shifts at RIS are tuned randomly in each frame, which  makes time-varying reflection channels superimposed on the original channels. And then, the secret key capacity of all nodes within the cell coverage area can be remarkably improved. Compared with the existing methods, the scheme is loosely coupled with the existing communication system. The closed-form expressions of secret key capacity taking into account eavesdropping are first derived for the case studies of independent and correlated channels. We can conclude that sharing the same phase shifts randomness will not bring key information leakage. Compared with existing user-introduced randomness schemes, the proposed scheme can  maintain good performance as the number of eavesdropping nodes increases. Further,  the impact of parameters, such as the number of reflecting units, the transmit antennas, and the Rician   factor, on secret key performance are  analyzed respectively, which can offer guidelines for system design. Finally, simulation results verify the theoretical analysis.

Observability analysis (OA) is vital to obtaining the available input measurements of state estimation (SE) in an integrated electricity and heating system (IEHS). Considering the thermal quasi-dynamics in pipelines, the measurement equations in heating systems are dependent on the estimated results, leading to an interdependency between OA and SE. The conventional OA methods require that the measurement equations be known exactly before SE is performed, and they are not applicable to IEHSs. To bridge this gap, a scenario-based OA scheme for IEHSs is devised that yields reliable analysis results for a predefined set of time-delay scenarios to cope with this interdependency. As its core procedure, the observable state identification and observability restoration are formulated in terms of integer linear programming. Numerical tests are carried out to demonstrate the validity and superiority of the proposed formulation.

Satellite constellations have become a promising solution for future Terabits/s connectivity. However, conventional radio frequency (RF) communication links are unable to achieve such high throughput due to the limited spectrum available. Free-space optical (FSO) communication has recently acquired a growing interest in the satellite communication (Satcom) community as a potential solution. Nonetheless, FSO communication is sensitive to the adverse effects of beam scintillation, beam-wander-induced pointing errors, free-space loss, and weather conditions. To mitigate these effects, we present a new space-air-ground (SAG) FSO transmission system with a strategically deployed high-altitude platform relay. It can effectively remedy the effect of atmospheric impairments on FSO transmission, particularly at high zenith angles. Then, we integrate the proposed SAG-FSO link and traditional space-ground FSO/RF links to further improve system performance and reliability. We carry out a thorough performance analysis of the resulting hybrid SAG-FSO/RF communication systems. Tractable analytical expressions for the symbol error and outage probabilities are derived. The numerical results highlight the significant potential of the proposed, highly innovative Satcom systems over existing solutions. Furthermore, Monte-Carlo simulations are performed to validate the accuracy of the analytical results.