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Event horizon imaging requires a third-generation space Very Long Baseline Interferometer on a 500 GHz band. The interferometer challenges available frequency standards. Namely, the practical coherent integration time is about 45 times shorter than needed. A self-calibration could aid this though a 3rd satellite is needed hence, an increased mission cost. This paper is devoted to experimental verification of a novel concept for two-way frequency transfer.We built a dedicated breadboard, test set-up, and run tests with and without temperature differences between branches of the breadboard. A phase difference between derived oscillators is the white noise-like function of the time with 1-σ of 0.5° at 103.2 GHz over more than 3 hours. Varying the temperature difference between branches ±3 C during 4.5 hours, being the orbit period, confirmed that the phase difference is the function of this temperature difference too. The Allan Deviation demonstrated between two derived 103.2 GHz oscillators is of 1.1×10−14/τ over 10 ms < τ < 1000 s, where τ is the averaging time, and the optical communication link path length is 5 km (17 ms). Degradation of this Allan Deviation is of 2 times at the longest-possible delay of 0.2 s in the interferometer system; the 0.2 s is the biggest sum of geometric and communication delays. This worst-case satisfies the requirement with a margin of 11 times. An obtained coherence is in the range of 0.997−0.9998 which is beneficial for the interferometer, at 557 GHz. This is one of the key technology enablers for black hole imaging from space.

Energy harvesting technology is a key enabler for the application of sensing systems in remote locations. For condition monitoring of large-scale electrical engines (LSEE) magnetic energy harvesting (MEH) has been suggested as a suitable technique. MEH can provide comparatively high output powers. A critical parameter for the design of a MEH system is the ratio between the maximum and the minimum operating current. In the case of a large ration the design of a MEH system becomes challenging. Protective measures are required to prevent a destruction at high primary currents. In this paper we investigate a miniaturized magnetic energy harvesting design, where the transformer consists of a single sheet of steel. We investigate the output power performance of the design with respect to low power applications and show, that the miniaturized design is intrinsically safe with respect to high primary currents.

In order to predict heart failure disease in advance and keep heart failure patients away from pain, this paper combines genetic algorithms (GA) to optimize Extreme Learning Machine (ELM) and builds a network model that predicts heart failure disease based on some of the characteristic factors that may induce heart failure. The ELM model has the characteristics of fast training speed and strong generalization ability, but its randomly generated input weights and hidden layer bias will cause instability in the prediction results. In this paper, we use GA’s principle of survival of the fittest to find the most suitable parameter configuration of the ELM algorithm, and analyzes the simulation results, and compares them with the prediction results of the original ELM model. The results show that the GA-optimized ELM model can better predict the occurrence of heart failure disease and has better predictive performance. The ELM algorithm, which has undergone parameter optimization has made up for its original defects, which may affect clinical practice and become a new assistant for doctors to predict patients with heart failure.

Accurately modeling power distribution grids is crucial for designing effective monitoring and decision making algorithms. This paper addresses the partial observability issue of data-driven distribution modeling in order to improve the accuracy of line parameter estimation. Inspired by the sparse changes in residential loads, we advocate to regularize the group sparsity of the unobservable injections in a bi-linear estimation problem. The alternating minimization scheme of guaranteed convergence is proposed to take advantage of convex subproblems with efficient solutions. Numerical results using real-world load data on the single-phase equivalent of the IEEE 123-bus test case have demonstrated the accuracy improvements of the proposed solution over existing work for both parameter estimation and voltage modeling.

Internet of Things (IoT) is a scientific advancement that is likely to transfigure our traditional way of life by automating everything connected to the internet. Smart physical devices/objects have become indispensable part of our lives thereby making our being simplified and way more organized. Internet of Things encompass and empower several heterogeneous physical devices/objects for sensing and gathering information from surroundings, consequently exchanging it in a seamless mode to realize smart application goals without human intervention, for instance, home automation, smart cities, smart agriculture, and smart industries etc. The growth in the number of such wired/wireless physical devices/objects and the speed of that growth raises concern over security. To be able to implement a smart world with such billions of physical devices/objects, appropriate mechanisms are required to provide secure end-to-end communications amongst them. In addition to it, a variety of attacks may occur in various layers of Internet of Things. Without a well-founded and congruous Internet of Things environment, budding Internet of Things applications cannot be exploited to their full potential. Hence, reliability, security and recovery from attacks are amongst the most unsparing sides of the Internet of Things. In this paper, we have presented a comprehensive review of Internet of Things security issues followed by security measures in Internet of Things. The focus in this paper is also on research challenges and application domains of Internet of Things.

This paper aims to propose a nonlinear adaptive attitude control method for urban aerial mobility (UAM) to counteract its center of gravity (CG) variations, which is considered one of the challenging issues for UAM development. To this end, we firstly analyze the effect of the dynamic change caused by the CG variations rigorously. The analysis result uncovers that the model uncertainties (it can be regarded as the internal disturbances) due to the CG variations are not rapidly changing during the operation of UAM. Based on this observation, a nonlinear adaptive controller is suggested to attenuate the disturbances by leveraging on a two-stage design procedure with the concept of the disturbance observer-based control (DOBC) and the three-loop control topology in this paper. To be more specific, the baseline controller, based on the feedback linearization control (FBLC) in conjunction with the three-loop control structure, is designed by including the disturbances as exogenous inputs to the system. And then, the nonlinear disturbance observer is separately designed to estimate these disturbances. Finally, the performance of the proposed method is examined through numerical simulations.

With widespread deployment of virtualization technologies in datacenter networks, traditional tools used for network monitoring, such as hardware taps, become unfit. This is due to the inability of hardware solutions for dynamic deployment and virtual network monitoring. This paper presents the design and evaluation of SoftTap, a scalable alternative to hardware taps which is capable of operating over both physical and virtual switches. SoftTap is based on port and flow mirroring capabilities of commodity OpenFlow switches and is not limited to a specific network architecture or topology. A key design challenge in SoftTap is the fast computation of switch mirroring configurations in large-scale deployments. Our design is based on novel polynomial time approximation algorithms that are shown to achieve bounded approximation ratios compared to optimal solutions. We evaluate SoftTap using model-driven simulations as well as realistic Mininet experiments. Specifically, our simulations consider large networks to show the scalability of SoftTap. Mininet experiments, on the other hand, consider its real-world utility by implementing an intrusion detection system (IDS) and a VoIP metering application on top of SoftTap. In our experiments, under SoftTap, IDS achieves up to 25% higher detection recall, while VoIP metering achieves up to 23% less packet loss compared to existing mirroring-based traffic monitoring approaches.

This paper establishes a predictive control strategy for inventory management in a supply chain system with uncertain demands and time delays. A discrete-time dynamic model with some realistic constraints is proposed by characterizing a general supply chain system with a graph representation. Due to the mismatching between the goods quantity obtained from suppliers and the ordered one and it may lead to stockouts and lower degree of customer satisfaction. A predictive control strategy is well presented to obtain recent demands so that suppliers can make decisions according to their own stock levels on time. The effectiveness of the proposed methodology is verified by a a numerical example.

In this paper, a circularly polarized highly efficient broadside antenna based on spoof surface plasmon polaritons (SSPPs) using polarization dependent artificial ground plane (AGP) is proposed. Printed monopole is used to excite even mode of SSPPs in 45° rotated corrugated strip relative to ground plane. The field from the resonating even mode and reflected field from the polarization dependent artificial ground plane contribute for circular polarization in broadside direction. The proposed antenna offers 10-dB input impedance bandwidth and 3-dB axial ratio bandwidth (ARBW) of 15.3% and 11.7% (at 3.42 GHz center frequency) in broadside direction with right hand circular polarization (RHCP), respectively. The minimum axial ratio achieved is 0.39 dB. The total efficiency remains above 92.4% over 3-dB ARBW, while maximum total efficiency achieved is 98.4%. The proposed antenna provides very low cross polar component with maximum gain value of 7.17 dBic in broadside direction.

In 5G mobile communication systems, mm wave communication has become one of the most attractive techniques reason being a huge increase in wireless data traffic. Also, the mobile terminals have become thinner and in functionality, more versatile and continue to become more. A mobile terminal, nowadays consists 2G-4G antennas, NFC antennas, Wi-Fi antennas, and now 5G antennas as well. Thus, compact antenna arrays are required and are a very hot topic today for future mobiles. optimization technique has been used in this paper since it includes the searching and obtaining of the perfect solution possible for a given problem. In this project we’ve first proposed a rectangular patch antenna of size 3.237 mm $\times 2.8$ mm $\times 0.5$ mm with which a $1\times 4$ antenna array has been designed of the dimensions 9.787 mm $\times 34.76$ mm $\times 0.5$ mm. This $1\times 4$ antenna array was optimized using Particle Swarm optimization (PSO) and the parameters have been analysed and discussed. Dimensions are 13.237 mm $\times$ 47.76 mm $\times 0.5$ mm.

Multi-level AC-AC converter has the advantages of reducing switch voltage stress, adapting to high voltage input and reducing waveform distortion. In this paper, a symmetrical switched capacitor multi-level (SSCML) AC-AC converter is studied. The switched capacitor (SC) technology is introduced into the AC-AC converter, which has the advantages of small size, high efficiency and high power density. Firstly, the topology of SSCML AC-AC converter and its control strategy are introduced. Then, the working principle of the topology is analyzed, and the relationship between input voltage and output voltage is derived in detail, as well as the expression of switch voltage stress. Finally, a 300W prototype is built to verify the proportional relationship between input voltage and output voltage. The prototype and experimental results prove that the multi-level AC-AC converter using SC technology has the advantages of small size and low voltage stress. The experimental results also prove that the voltage stress is proportional to the modulation ratio, which is the key to reducing the voltage stress.

A Cyber-Physical System (CPS) refers to the interconnection of control (actuation), computational nodes and sensors, in order to manage physical processes. In recent years, the CPS design methodology has been adopted in several large-scale infrastructures such as smart power grids. Given the application criticality, sustaining the security of these systems is of utmost importance. One of the major security goals is to protect CPS against impersonation, where an adversary intends to manipulate the system state by sending erroneous data that appears to be reported by one of the system nodes, e.g. PMUs of a power grid. This paper proposes a novel hardware-assisted authentication scheme to counter such a threat, by exploiting imperfections that occur in the manufacturing process of integrated circuits. In essence, the proposed scheme associates a fingerprint for each system node so that the authenticity of the data source could be verified. In addition, the paper tackles the threat of message replay where the adversary re-transmits a legitimate message so that the system factors in outdated rather than fresh sensor measurements. This paper thwarts such a replay attack by leveraging the synchronized clocks across the CPS nodes, e.g., based on GPS; the idea is to employ a combination of time-stamp signatures and hardware fingerprints. Our proposed schemes can also detect and prevent data forgery, and Sybil attacks. The viability and performance of the proposed schemes are validated through analysis and prototype implementation.

In this paper, an adaptive control is discussed for the synchronous generator which is connected to a single machine infinite bus system. By using Lyapunov stability theory and backstepping technique, the excitation controller is proposed to stabilize the resulting systems. Fuzzy logic systems are employed to identify the unknown nonlinearities. Finally, simulation results illustrate the effectiveness of our results.

Current data-driven intelligent transportation systems are mainly reliant on IEEE 802.11p to collect and exchange information. Despite promising performance of IEEE 802.11p in providing low-latency communications, it is still vulnerable to jamming attacks due to the lack of a PHY-layer countermeasure technique in practice. In this paper, we propose JammingBird, a novel receiver design that tolerates strong constant jamming attacks. The enablers of JammingBird are two MIMO-based techniques: Jamming-resistant synchronizer and jamming suppressor. Collectively, these two new modules are able to detect, synchronize, and recover desired signals under jamming attacks, regardless of the PHY-layer technology employed by the jammers. We have implemented JammingBird on a vehicular testbed and conducted extensive experiments to evaluate its performance in three common vehicular scenarios: Parking lots (0~15 mph), local traffic areas (25~45 mph), and highways (60~70 mph). In our experiments, while the jamming attacks degrade the throughput of conventional 802.11p-based receivers by 86.7%, JammingBird maintains 83.0% of the throughput on average. Experimental results also show that JammingBird tolerates the jamming signals with 25 dB stronger power than the desired signals.

With the slowdown of the progressive pace of miniaturization, the development of microelectronic systems shows diverse features. This leads to the emergence of new types of packages and challenges, demanding systematic approaches to sort out characteristic issues, increase insightful visions and map out progressive schemes. Focusing on mechanical characterization, this paper identifies mechanical challenges emerged in the current and future progresses of microelectronic packages. Mechanical challenges are firstly identified from the three categories of new issues, thermal interfaces and new structural patterns to highlight the key points. This is then followed by a discussion of mechanical characterization on the aspects of the domination of surface effects, the coupling of material-structural integration and the advent of multiphysical systems to describe the main schemes. Finally, mechanical issues expected in the ongoing future are analyzed through three aspects of stacked 3D structures, in-situ packages and nanomechanics to give a perspective view. Miniaturization is highlighted as the fundamental reason bringing significant changes and issues to mechanics. The interactive couplings on interfaces and between nanomaterial phases are emphasized as the phenomenal issues for mechanical investigations. By highlighting the characteristics and challenges with mechanics, this paper roughly profiles a framework of mechanical issues of microsystems.

Low power VLSI circuit design faces the challenging issues of static power dissipation as transistor count doubles for every couple of years. static power dissipation also known as leakage power dissipation which increases in scaled down threshold voltage circuits. Downsizing of CMOS innovation improved the speed and simultaneously leakage currents are left over as struggle. This unconstrained leakage current ought to be decreased for untroubled working of the circuit. This paper proposed novel static power reduction strategy with the literature survey. The research study mainly concentrated on circuit performance parameters like speed, power and power delay product of the specific circuit. This work presents logic gates designed and evaluated by mentor graphics tools with 22 nm CMOS Technology.

This paper gives an insight on the simulative and theoretical analysis of the Optical Single Sideband(OSSB) with 90° and 120° phase shift using Mach Zehnder Modulator(MZM) and Optical Vestigial Sideband(OVSB) modulation techniques. It considers the issue of Chromatic Dispersion(CD) in Single Mode Fibers(SMF) and Non-Linearity of the transmitter system due to the presence of second and higher-order sidebands. The ubiquity of second and higher-order harmonics degrades the nature of signal in long-haul transmissions. The RF signal at the intake of MZM is divided equally with the help of an electrical hybrid coupler providing the phase shift of 90° and 120° to generate the OSSB. Whereas, the RF signal at the input of MZM with 90° phase shift passed through Fiber Bragg Grating(FBG) filter is used to generate OVSB. The 2nd and higher-order sidebands generated are due to the non-linearity of the MZM relying on the modulation index. It was observed that suppression of higher-order sidebands with an increase in Extinction Ratio. This work is evaluated on the basis of the extinction ratio of MZM, modulation index, and received RF power. The OSSB with 120° phase shift suppresses the second and higher-order sidebands which diminish the effect of chromatic dispersion resulting in the good long-haul transmission system.

There are multiple papers published about different AI models for the COVID-19 diagnosis with promising results. Unfortunately according to the reviews many of the papers do not reach the level of sophistication needed for a clinically usable model. In this paper I go through multiple review papers, guidelines, and other relevant material in order to generate more comprehensive requirements for the future papers proposing a AI based diagnosis of the COVID-19 from chest X-ray data (CXR). Main findings are that a clinically usable AI needs to have an extremely good documentation, comprehensive statistical analysis of the possible biases and performance, and an explainability module., Accepted to AI&BDvsPandemics BIBM 2021 Workshop

Analysing the traversability from the point cloud input is a critical task for mobile robot navigation. However, there is no mature method for mobile robots to exploring complicated 3D environments, especially those with multi-layered roads. This paper proposes a new traversability map based on the point cloud input, and the map will help analyze where the mobile robot can move in the whole space. The proposed 3D traversability map consists of nodes and edges generated according to the mobile robot’s geometry and driving performance. Nodes represent where the robot can be placed, and edges indicate the cost of moving to another node. The traversability map is stored in digraph form and is convenient to be applied to path planning. Experiments show the successful results of traversability map generation and path planning with D algorithm in different situations.

For the main diesel engines of the fishing vessels, the pressure in the high-pressure pipelines (p hpp ) of the fuel injection system and the end-of-stroke compression pressure (p c ) directly affect the jet structure, leading to a change process forming combustion mixture and burning fuel. Decreasing the above parameters will cause the engine power to decline (N e ). In this study, the paper presents experimental results on the Yanmar 4CHE main diesel engines of the fishing vessels, when conducting the adjustment of p hpp , p c to evaluate their impact on N e , thereby diagnosing the decrease of p hpp and pc to a specific value, the diesel engine must be serviced repaired to avoid terrible damage. The research results show that when the p c decreased by 12% and the p hpp decreased by 16% compared to the first state then N e decreased by 19.85%. From experimental data, using Lagrange interpolation method, building a multivariate interpolation function expressing the influence of p hpp and p c to N e , as a basis for diagnosing the technical state of the main diesel engines of the fishing vessels.

This paper investigates the consensus problem of linear multi-agent systems with event-triggered (ET) communication on undirected graphs. More practically, it is assumed that each agent can only access the relative output about its neighbors. Hence, the existing event-triggered (ET) protocols based on relative state sensing or absolute output sensing are not applicable. Inspired by this, an ET output-feedback protocol based on relative output sensing is developed. By a new Lyapunov function with more augmented states, it is proved that the resulting closed-loop system can reach consensus and the protocol states converge to zero. It is also shown that Zeno behavior is excluded from the triggering process. Furthermore, a method for reconstructing the continuous-time relative output from sampled data is formulated, based on which, the protocol can be implemented without continuous monitoring of neighbors. Finally, the effectiveness of the theoretic developments is illustrated by a numerical example.

The camera is affected by illumination changes and vignetting effect during the shooting process, resulting in the lack of brightness information, which affects the accuracy of target detection. In order to reduce the impact of illumination changes and vignetting effect on image quality, obtain images closer to the real scene, and improve the accuracy of target detection, this paper conducts photometric calibration on the image from two aspects: the calibration of CRF (Camera Response Function) and the compensation function of vignetting effect. This paper first uses the N-degree polynomial model to construct the camera response function, proposes a functional model of vignetting compensation based on the position of the pixel point, and finally combines the first two experimental results and conducts experimental verification on the data set before and after the photometric calibration on different algorithms of target detection. The experimental results show that the application of photometric calibration in the field of machine vision can improve the adaptability of machine vision to illumination changes and vignetting effect to a certain extent. The self-made data set is trained and tested, when IoU is 0.7, the mAP calculated by the test set after photometric calibration is improved by 2.3% compared with the original data set.

Extreme events such as natural disasters can cause severe damage to the power system and lead to outages. After outages, various power sources, such as distributed generations (DGs) and microgrids, can be used to restore the critical loads in the urban distribution system (UDS). However, microgrids are usually operated by different owners and they have different preference on feeding their own loads or participating in the UDS restoration under the power outage scenario. In this paper, a critical load restoration method is proposed considering the preference of multiple participants. First, the concept of virtual flow decomposition for a microgrid participant is proposed to distinguish that the loads in it are restored by internal or external energy. Based on the virtual flow decomposition, a mixed-integer linear program (MILP) for critical load restoration is proposed to maximize the weighted load restoration time considering the preference of participants. Finally, the effectiveness of the proposed method is validated on a modified IEEE 13-node test system.

The performance increase up to the sensor resolution in repetitive control (RC) invalidates the standard assumption in RC that data is available at equidistant time instances, e.g., in systems with package loss or when exploiting timestamped data from optical encoders. The aim of this paper is to develop an intermittent sampling RC framework for non-equidistant measurements. Sufficient stability conditions are derived that can be verified using non-parametric frequency response function data. This results in a frequency domain design procedure to explicitly address uncertainty. The RC framework is validated on an industrial printbelt setup for which exact non-equidistant measurement data is available., Comment: Accepted for presentation at the 60th Conference on Decision and Control 2021 (CDC2021)

With the development of human-computer interaction technology and machine learning technology, facial expression recognition technology has gradually become a research hotspot. In recent years, with the rapid development of convolutional neural network and facial expression recognition, automatic facial expression recognition system has been widely used. In this paper, the method of facial expression recognition based on convolution neural network is studied. Because the differences between facial expressions are very subtle, in order to improve the ability of convolution neural network to extract facial expression features and its recognition accuracy, continuous convolution is introduced into convolution neural network model. The improved model uses small-scale convolution kernel to make the extracted facial expression features more precise, Two continuous convolution layers enhance the nonlinear expression ability of the model. With the development of human-computer interaction technology and machine learning technology, facial expression recognition technology has gradually become a research hotspot.

Satellite hyperspectral remote sensing is a useful technology to extract soil heavy metal content. This paper introduces an inversion method of soil copper (Cu) content using AHSI/ZY1-02D data and assesses the inversion accuracy with field investigation data. Different spectral pretreatments are compared and then an inversion model of soil Cu content based on random frog leaping and regression tree ensemble is established. Compared with the original spectrum, the utilized spectral transformations can significantly increase the number of sensitive bands for soil Cu inversion, and the correlation between Cu content and spectrum can be improved as well. We also find the reciprocal spectrum and its differential transformations have the highest correlation with the soil Cu content. Moreover, the R2 and RMSE of the inversion model are 0.826 and 596.79 mg/kg, respectively, indicating that AHSI/ZY1-02D hyperspectral data can be used to estimate the total Cu content in the soil at mining area.

In the modern machine tool manufacturing scene, as the milling tool of CNC machine tool, the health of the tool directly affects the processing efficiency and product quality. Effective prognostic and health management of tool is critical. Precise monitoring of tool wear helps to avoid product quality problems caused by tool fault and improve production efficiency. Therefore, this paper constructs a tool fault diagnosis method based on deep learning. In order to effectively fuse the vibration signals features from different directions of machine tool spindle, we apply a variety of Channel Attention (CA) mechanisms to Multiscale Network (MSNet) to construct Multiscale-Channel Attention Network (MS-CA Net), and explore the performance gains of these modules in tool wear classification tasks. Among these modules, the CA blocks include Channel Attention Block (CAB), Squeeze and Excitation Block (SEB), and Efficient Channel Attention Block (ECAB). We use the three proposed networks to classify the tool wear status by identifying the vibration signal of the machine tool spindle. At the same time, in order to verify the performance of the proposed tool fault diagnosis method in the actual milling scene, this paper designs a tool wear test platform to collect sample data that meets actual industrial production scenarios, which uses a three-axis accelerometer to collect tool life cycle vibration signals, and a digital universal tool microscope to measure tool wear values. The experimental results show that, compared with the MSNet method, the tool fault diagnosis accuracy rate of the improved method is increased by 4.47%-7.38%.

The world of computing has progressed from centralized (client-server rather than web-based) to distributed systems, and we are now returning to virtual centralization (Cloud Computing). In the world of computation, the location of data and processes makes all the difference. On the one hand, a person has complete control over the data and processes in their machine. The internet is used as a networking tool in cloud computing. When it comes to data protection in cloud computing, the provider must have some assurance in service level agreements (SLAs) to reassure the customer about security concerns. Organizations that use cloud computing as a service platform are keen to look at security and confidentiality concerns for their mission-critical, non-sensitive applications. This paper addresses the fundamentals of cloud computing and security concerns, risks, and solutions. The paper also goes through several open research questions about cloud protection. Keywords—cloud, cloud security, data protection, cloud infrastructure

The quality of target waveform recovery directly affects the result of target feature analysis, and seriously affects the performance of active and passive sonar target recognition. Considering the different application environment and performance of various waveform recovery methods, this paper makes a systematic analysis of several commonly used waveform recovery methods from the perspective of theory and simulation, including four algorithms: Conventional Beamforming (CBF), Dolph-Chebyshev weighting Beamforming (DCBF), Constant Beam-Width Beamforming (CBWBF) and STeered Minimum Variance adaptive beamforming (STMV). Combined with the simulation results, obtained the following conclusions: (a) The low sidelobe characteristics of DCBF can suppress the energy leakage of strong interference. (b) When there exists azimuth mismatch, CBWBF has the characteristics of equal beamwidth within full frequency band, which can effectively protect the high frequency energy of the signal. (c) When there exists strong interference in the main lobe of the target, STMV can suppress the leakage of interference components and recover the best waveform quality. (d) When there exists strong interference outside the main lobe of the target and there exists azimuth mismatch, the performance of STMV degrades seriously. At this time, CBWBF has better waveform protection ability than DCBF and is more robust to model disturbance. The results of this paper can provide a basis for the selection of waveform recovery methods in different working environments.

Taking into account the geographic location of some rural areas, it is not viable, due to technical or economical reasons, to connect certain villages to the electrical grid. Since access to energy is essential for human development, a DC-DC-AC micro-inverter topology for a photovoltaic kit able to address the previous scenario is proposed in this paper. This kit is capable of working in both isolated and parallel modes, with other kits. This solution is also adequate for a post-natural catastrophe scenario, given the power outages associated with these types of phenomena. Each kit consists of a photovoltaic module, an energy storage system and a DC-DC-AC micro-inverter. A control system for all the subsystems is also presented in this paper. Besides that, the management of the DC bus, taking into consideration the photovoltaic system and energy storage system is also proposed. All the theoretical analysis of this kit is detailed in the paper and confirmed through simulations.

Action Recognition is a new world-specific branch implemented in various applications like surveillance, health monitoring, and computer vision. Human Action Recognition is a challenging task in the research area field. Most of the researchers suggested efficient machine learning HAR algorithms like SVM and KNN. The dataset used in state of the art HAR schemes is readily available on a social platform. The features learning and classification approaches are used for motion detection. Activities like walking, running, jumping, sleeping, falling, and interaction are recognized by the machine learning approach. In this paper, we are presenting various machine learning and hybrid algorithm for the HAR task. Different datasets tested by the researchers with their developed work. The accuracy achieved by all reviewed studies also mentioned with their advantages. Based on these studies, we work on the combination of deep learning and machine approaches for the HAR. The HAR approaches depend on the wearable sensor as well as the recording devices.

This paper presents an energy management strategy for multi networked microgrids (MGs) comprising of dispatchable and non-dispatchable distributed generation, energy storage system (ESS), and plug-in hybrid electric vehicles (PHEV). The proposed approach allows the cooperation for the direct energy exchange among the microgrids and also with the utility grid. This study investigates the impacts of stochastic charging effects of PHEVs and intermittencies of the renewables and the load demands on the proposed strategy. Furthermore, the active participation of the microgrids in the demand response (DR) program by transferring flexible loads in response to high pricing signals aid in reducing the overall cost of microgrids. The energy management (EM) problem has been formulated as a multi-objective optimization problem of minimization of the microgrids’ overall cost and emission and maximization of the distribution network operator’s (DNO) profit. Simulation results are illustrated to validate the effectiveness of the proposed method.

Fault's diagnosis prior to its existence and the entire shut-down is critically important for the industry. Diagnosing the faults based on condition monitoring methods and artificial intelligence techniques is extremely potent. Detecting the bearing fault is a challenging task at the incipient level, which can lead to the malfunctioning of the machine. The usage of deep learning and machine learning algorithms produces a remarkable outcome in the domain of bearing fault diagnosis. In addition, the mentioned algorithms provide effective and reliable solutions for preventive maintenance. This paper summarizes the recently published works of artificial intelligence applications when it comes to bearing faults detection in rotary machines. Moreover, this work also presents several research gaps in this field.

Layered structures widely used today as substrates for surface acoustic wave (SAW) devices are characterized by asymmetry of wave propagation, which manifests itself as variation of SAW or leaky SAW characteristics and admittance function of SAW resonators built on these substrates with inversion of propagation direction or interchange of top/bottom surfaces in one of combined materials. In this paper, matrix formalism known as an effective tool for theoretical and numerical investigation of acoustic wave propagation in multilayered structures is used to explain the existence of asymmetry in the behavior of acoustic waves. This phenomenon is illustrated by two examples of layered structures using different types of acoustic waves and previously reported as promising substrates for SAW devices with improved performance.

Density-based spatial clustering of data with noise (DBSCAN) is a popular clustering algorithm that groups data points which are close together using two parameters eps - which is the radius of each cluster, and Minpts, which is the minimum number of points in each cluster. However, the performance of DBSCAN reduces for the datasets with varying density clusters. This paper proposes the implementation of a distributed and adaptive DBSCAN algorithm on the HPCC Systems platform. The proposed approach uses techniques such as grid search and Gaussian kernel to search optimized values for the threshold density of clusters, thus eliminating the requirement for users to specify the parameters. Further, the experimental investigation suggests that proposed ADBSAN performs better compared to existing ADBSCAN implementations using k-dist and Gaussian kernels.

This paper presents an 11-bit 500 MS/s two-step SAR ADC with non-attenuated passive residue transfer for high-speed and high-resolution operations. The proposed partial interleaved CDACs scheme in the second stage reduces the parasitic capacitance for the first stage. The comparator reusing scheme avoids the complex calibration of each offset for multi-comparators. An edge-detected calibrator is adopted to achieve low power and a constant calibrated voltage in the calibration of interstage gain error and offset. The prototype is implemented in 28 nm CMOS process. The simulation shows that the proposed ADC achieves an SNDR of 61.98 dB and an SFDR of 81.73 dB at Nyquist input at 0.9-V supply. The power consumption is 3.06 mW, showing the FoM of 5.58 fJ/con-step.

With the rapid development of the mobile Internet, how to efficiently extract information from the massive data in cyberspace and use it has become a key concern of all walks of life. Sentiment analysis aims to dig out the subjective emotional information contained in large-scale texts. It has become a hot research topic in the field of natural language processing and has important application value in many fields. This paper focuses on the aspect-level sentiment analysis problem, which is an important sub-problem in fine-grained sentiment analysis. The purpose of aspect-level sentiment classification is to dig out the sentiment polarity of users' opinions on a specific aspect in the text. This paper proposes a sentiment classification method combining BiLSTM and aspect attention module. BiLSTM has fewer parameters, faster model training, and can effectively extract deep-level information of text; combining the attention mechanism with aspect information can fully extract specific aspects of information. The experimental results show that the F1 score that solves the problem of aspect term extraction and aspect sentiment classification on two data sets at the same time, compared with the existing sentiment analysis method, obtains a better classification effect.

For the purpose of quality control in some production lines each product undergoes an end of line test. Early detection of products that will fail during or at the end of the test, can save time and material. Besides, it prevents hazards and damages to the test equipment and the product which can be caused by operating defective products. Utilizing data analysis for prediction of such anomalies can be challenging due to the non-stationary and multi-phased nature of the test/measurement process. This paper presents a model-driven machine learning method (using welding machine test data), where instead of fully data driven anomaly detection analysis, the analysis starts with approximate modeling of temperature evolution at the location of temperature sensors which is governed by physical process of heat transfer, originated from any unspecified heat source. This approach allows decision making about the test result just at the beginning of the test. In addition, since this method reduces the dimensionality of the input space, the machine learning model can be trained with much less number of samples, which is an enabler for many cases where access to training data is limited.

In this paper, we present the design considerations for a sub-25μW phase-locked loop (PLL) with a wide tuning range and multi-phase outputs, which makes it suitable for applications that involve clock-and-data-recovery with variable data rates, such as broadband body-area-networks. Several architectures for the voltage- controlled-oscillator (VCO) are analyzed for power and performance, and the considerations for keeping the VCO's low-dropout-regulator (LDO) within the loop and outside the loop are discussed. Power consumption is minimized by keeping the LDO outside the loop, which exempts the error-amplifier (EA) from the bandwidth constraints posed by the PLL. Conforming to the analysis, the PLL is designed and simulated in a standard 65nm CMOS process, and the results show that energy-efficiencies as low as 70fJ/cycle can be achieved with a tuning range of 1-450MHz along with multi-phase outputs with RMS timing jitter of 11.4ps (frequency offset < 100ppm) from a 31-stage split-tuned ring oscillator VCO.

This paper introduces the notion of an Input Constrained Control Barrier Function (ICCBF), as a method to synthesize safety-critical controllers for non-linear control affine systems with input constraints. The method identifies a subset of the safe set of states, and constructs a controller to render the subset forward invariant. The feedback controller is represented as the solution to a quadratic program, which can be solved efficiently for real-time implementation. Furthermore, we show that ICCBFs are a generalization of Higher Order Control Barrier Functions, and thus are applicable to systems of non-uniform relative degree. Simulation results are presented for the adaptive cruise control problem, and a spacecraft rendezvous problem., 8 pages, 3 figures, submitted to Conference on Decision and Control 2021

A tri-band symmetrically truncated modified square slot antenna with enhanced gain integrated with a triple-band rectifier for low-power RF energy harvesting applications is presented in this paper. The antenna triple bands of 1.9 GHz, 3.1 GHz, and 6.4 GHz are obtained by coupling the slot with an optimized microstrip feed line length of 37 mm. To enhance the realized gain, a full copper patch reflector size greater than the antenna size is positioned in the bottom of the antenna at a distance of $\lambda/4$ . where $^{\prime}\lambda^{\prime}$ is free space wavelength at the lowest frequency of 1.9 GHz. A single diode series-configuration-based triple-band rectifier along with an impedance matching network is designed at the aforementioned similar frequencies of the antenna. The designed prototypes are fabricated, measured the essential parameters, and compared with the simulation results. The measurement results show that the enhanced gains of the antenna are 4.8 dB, 6.7 dB, and 8.9 dB obtained at the corresponding frequencies, and the rectenna peak conversion efficiency reached 27% at an optimized values of 1 KΩ load resistance and −5 dBm input power. The proposed triple-band rectenna is found to be suitable for low-power RF energy harvesting and wireless power transfer applications.

The advent of smaller SAR satellites and cheaper access to space is bringing the notion of a multistatic SAR constellation into the realm of feasibility. Researchers at JPL are studying a Distributed Aperture Radar Tomographic Sensors (DARTS) mission concept intended to measure Earth's surface topography and vegetation using TomoSAR techniques. This paper describes progress on the airborne testbed for the DARTS study. The testbed is the union of a software-defined radio that implements a radar and synchronization link together with a small uninhabited aerial system (sUAS) that serves as a platform with precise control of the observation geometry. Initial experiments have demonstrated successful multi-sensor synchronization as well as acquisition and processing of monostatic SAR imagery.

The media are introduced into teaching based on knowledge of theories of learning and teaching. A stimulating learning environment is important to ensure an appropriate learning culture, in this context a stimulating multimedia environment. An innovative approach to the teaching process implies a selection of appropriate teaching media, approaches, methods and teaching strategies, with regard to the defined outcomes and characteristics of individual media within the stimulating environment. In this paper the attitudes of students of the Faculty of Teacher Education in Rijeka to the use of digital media in the teaching process was examined, with an emphasis on self-assessment of the use of digital technology. Based on the assessment of their own ability to recognize different media, the results indicate that students choose media in accordance with the teaching content and that they believe their digital skills are sufficient for quality implementation of media in teaching. Student attitudes are that the lessons in which the media are used are more interesting. According to students' answers, the conclusion is that they believe that greater importance should be given to the use of digital media in the teaching process because they enable individualization of work, contribute to collaborative learning, game - based learning and project learning.

Aiming at the characteristics of the various test data storage formats and various test data query methods in the process of spacecraft automated testing, this paper proposes a test data query and analysis platform implementation plan based on MVC, including the design of platform architecture, data query analysis modeling. The scheme can realize the decoupling of test data and query analysis business to the greatest extent, and realize the openness and flexibility of the platform. The design scheme has been implemented in actual projects, and the application effect is good.

Blockchain is a digital technology built on three pillars: decentralization, transparency and immutability. Bitcoin and Ethereum are two prevalent Blockchain platforms, where the participants are globally connected in a peer-to-peer manner and anonymously perform trade electronically. The vast number of decentralized transactions and the pseudo-anonymity of participants open the door for scams, cyber frauds, hacks, money laundering and fraudulent transactions. It is challenging to detect such fraudulent activities using traditional auditing techniques, since they need more processing power, time and memory for complex queries to join combinations of tables. This paper proposes several algorithms to extract the transaction- related features from the Bitcoin and Ethereum networks and to represent the features as graphs. Moreover, the paper discusses how visualisation of graphs can reflect the anomalies and patterns of fraudulent activities.

In multiple-input multiple-output (MIMO) systems, the high-resolution channel information (CSI) is required at the base station (BS) to ensure optimal performance, especially in the case of multi-user MIMO (MU-MIMO) systems. In the absence of channel reciprocity in frequency division duplex (FDD) systems, the user needs to send the CSI to the BS. Often the large overhead associated with this CSI feedback in FDD systems becomes the bottleneck in improving the system performance. In this paper, we propose an AI-based CSI feedback based on an auto-encoder architecture that encodes the CSI at UE into a low-dimensional latent space and decodes it back at the BS by effectively reducing the feedback overhead while minimizing the loss during recovery. Our simulation results show that the AI-based proposed architecture outperforms the state-of-the-art high-resolution linear combination codebook using the DFT basis adopted in the 5G New Radio (NR) system.

Pneumatic artificial muscles (PAMs), as a type of actuators in soft robots, have been attracted in the past few decades. Because of their higher power density ratio and flexibility, PAMs are now widely used as a flexible actuator. When PAMs are applied to different robot systems, their dynamic output characteristics are influenced by the amplitude and frequency of input signals and external loads. The experimental results show that with the increase of input signals frequency, the width of PAMs dynamic hysteresis loop increases and the height decreases. Meanwhile, in order to verify that the fatigue life of the Mckibben PAMs made in the laboratory can meet the use requirements, this paper has completed the fatigue tests of them.

This paper investigates the impact of battery ageing on the fuel economy and drivability capability of a power-split hybrid electric vehicle (HEV). The HEV is modelled first, an optimal energy management strategy based on dynamic programming is then implemented, and experimental characterization data for the battery cell is presented. The batteries are tested to a heavily aged state, with up to an 84% loss of capacity. Numerical simulations for the HEV performing the WLTP cycle and full power acceleration maneuvers are used to calculate the progressive worsening of fuel economy and rate of acceleration as the battery ages. The fuel economy and acceleration of the vehicle are found to be relatively unaffected until the battery loses more than 20% of original capacity. For the most aged case, with 84% loss of capacity, vehicle fuel economy increases by 25% and 0 to 100 km/h acceleration time reduces by 3.5 seconds compared to performance with a new battery.

First, different cultural backgrounds of China and western countries are introduced in this paper. Then, the cultural differences between Chinese and English in the use of polite language are illustrated through the examples, including the differences of salutation, greetings, appreciation language, praise language, taboo language and other polite language. Finally, we summarize the various differences between Chinese and Western culture in the use of polite language, so we can eliminate the cultural interference, avoid the cultural conflict, seek common ground while reserving differences and create a good communicative atmosphere to promote the cross-cultural communication.

The full-duplex (FD) integrated access and backhaul (IAB) technique is considered in the 3rd Generation Partnership Project (3GPP) release 16 to address the demand for high capacity, large coverage and low latency millimeter wave (mmWave) beyond 5G networks. However, the self-interference (SI) appears on the FD-IAB-node, becoming the major bottleneck in the development of these novel networks. With the assumption of successful antenna isolation, this paper proposes an SI cancellation (SIC) technique for the wideband mmWave-FD-IAB multiuser networks with the cost-efficient subarray hybrid precoding structure. The fiber Bragg grating-based analog canceler is established between the RF chain pairs of the FD-IAB-node, and the minimum mean square error baseband combiner is used at the FD-IAB-node receiver to cancel the residual SI digitally. Further, hardware impairment (HWI) is considered to capture the transceiver distortion. Simulation results show that the proposed FD-IAB network capacity is almost doubled compared to the half-duplex-IAB system when the effect of HWI is low.