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Nuclear fusion research requires the production of high-temperature plasma. One of the plasma heating methods is the use of radio frequency (RF) waves. The RF wave heating system uses a high-power amplifier, which poses a concern about the leakage electromagnetic fields. Therefore, a new wide-area multipoint electromagnetic monitoring system using personal RF monitors has been developed. The developed monitoring system was applied to the megawatt-class amplifiers for the “ion cyclotron range of frequency” (ICRF) heating system in the frequency range of ion cyclotron resonance from 30 to 40 MHz. As a result, the leakage magnetic field was less than the measurement sensitivity during the plasma experiment, in which the leakage electric field was detected with sufficient sensitivity. The leakage electric field becomes larger as the RF forward power increases, and thus the electric field would be radiated through the stub and/or high voltage supply line connected to the anode of tetrode in the ICRF amplifier. The electric field around the ICRF amplifiers did not exceed the reference level of International Commission on Non-Ionizing Radiation Protection (ICNIRP). The developed wide-area multipoint monitoring system will be useful for monitoring the leakage RF electromagnetic fields around the ICRF heating system at the future fusion devices.

OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object Detection IEEE ROBOTICS AND AUTOMATION LETTERS, VOL. 8, NO. 3, MARCH 2023

In this paper, the design of a small wind turbine emulator based on dSPACE 1104 system is presented. This system is composed of a DC motor driven by a buck converter coupled to permanent magnet synchronous generator controlled by a hardware-in-the-loop configuration based on the dSPACE 1104 board. The DC motor emulates the rotary movement due to wind energy, where all the characteristics of a real wind turbine will be taken into account. By means of this control system the effect of different values of wind speed and torque is simulated in MATLAB® / Simulink® software, in order to constitute a tool that allows the analysis and development of power converters. The results obtained demonstrated the efficiency of the proposed emulator, given that the behavior of the experimental bench was the same as that of the theoretical calculation results.

In this study, we present an intuitive machine learningbased approach to evaluate and interpret surgical skills level of a participant working with robotic platforms. The proposed method is domain-adapted, i.e., jointly utilizes an end-to-end learning approach for smoothness detection and domain knowledge-based metrics such as fluidity and economy of motion for extracting skills-related features within a given trajectory. An advantage of our approach compared to similar stochastic or deep learning models is its intuitive and transparent manner for extraction and visualization of skills-related features within the data. We illustrate the performance of our proposed method on trials of the JIGSAWS data set as well as our own experimental data gathered from Phantom Premium 1.5A Haptic Device. This approach utilized t-SNE technique and provides visualized low-dimensional representation for different trials that highlights nuanced information within the executive task and returns unusual or faulty trials as outliers far away from their normal skill or participant clusters. This information regarding the input trajectory can be used for evaluation and education applications such as learning curve analysis in surgical assessment and training programs.

The conductor for helical coils of the Large Helical Device consists of a Rutherford-type NbTi/Cu cable, a pure aluminum stabilizer, and a copper sheath. The dimensions of the conductor and the stabilizer cross-sections are 18.0 mm × 12.5 mm and 12.4 mm × 5.2 mm, respectively. The measured cold-end recovery current in the magnetic field parallel to the shorter side ( B //12.5) is clearly lower than that in the field parallel to the longer side ( B //18.0) because of the difference in magnetoresistance by Hall currents. Since the minimum propagation current Imp is important to determine the upper limit of operation current, Imp has been measured for two types of one-turn coil samples, which were bent flatwise ( B //18.0) and edgewise ( B //12.5) with the inner radius of 0.14 m to extend the length in the uniform background field of the test facility. The measured Imp at B //12.5 is almost the same as that at B //18.0 in spite of the large difference in the steady-state resistance. Imp is considered to be determined by the heat balance before the current diffuses deeply into the stabilizer.

The motor-based brain–computer interface is widely used in the exoskeleton reconstruction of patients with muscle weakness and to enhance the operating experience of somatosensory game customers through the combination of actions and electroencephalography signals. However, the recognition algorithms in traditional motor-based brain–computer interfaces have problems such as “brain–computer interface blindness” (recognition accuracy is less than 70%) and “one person one model.” In this study, a regularized long short-term memory algorithm and a hardware platform for gesture recognition by using the motor-based brain–computer interface are proposed. Experimental results show that the gesture recognition accuracy rate based on the motor brain–computer interface is up to 95.69%, which is significantly better than that of other algorithms. The proposed model enhances the applicability and generalization ability of the brain–computer interface, for which the practicability and effectiveness are verified.

Cache is the bridge between CPU and memory for data exchange, which consumes up to 45% of the entire CPU energy. Low power cache and memory are urgently required for the computer system. The paper ...

The classification of Motor Imagery (MI) tasks constitutes one of the most challenging problems in Brain Computer Interfaces (BCI) mostly due to the varying conditions of its operation. These conditions may vary with respect to the number of electrodes, the time and effort that can be invested by the user for training/calibrating the system prior to its use, as well as the duration or even the type of the imaginary task that is most convenient for the user. Hence, it is desirable to design classification schemes that are not only accurate in terms of the classification output but also robust to changes in the operational conditions. Towards this goal, we propose a new sparse representation classification scheme that extends current sparse representation schemes by exploiting the group sparsity of relevant features. Based on this scheme each test signal is represented as a linear combination of train trials that are further constrained to belong in the same MI class. Our expectation is that this constrained linear combination exploiting the grouping structure of the training data will lead to representations that are more robust to varying operational conditions. Moreover, in order to avoid overfitting and provide a model with good generalization abilities we adopt the bayesian framework and, in particular, the Variational Bayesian Framework since we use a specific approximate posterior to exploit the grouping structure of the data. We have evaluated the proposed algorithm on two MI datasets using electroencephalograms (EEG) that allowed us to simulate different operational conditions like the number of available channels, the number of training trials, the type of MI tasks, as well as the duration of each trial. Results have shown that the proposed method presents state-of-the-art performance against well known classification methods in MI BCI literature.

Computational models of emotional learning observed in the mammalian brain have inspired diverse self-learning control approaches. These architectures are promising in terms of their fast learning ability and low computational cost. In this paper, the objective is to establish performance–guaranteed emotional learning–inspired control (ELIC) strategies for autonomous multi–agent systems (MAS), where each agent incorporates an ELIC structure to support the consensus controller. The objective of each ELIC structure is to identify and compensate model differences between the theoretical assumptions taken into account when tuning the consensus protocol, and the real conditions encountered in the real system to be stabilized. Stability of the closed-loop MAS is demonstrated using a Lyapunov analysis. Simulation results based on the consensus task of a group of inverted pendulums demonstrate the effectiveness of the proposed ELIC for stabilization of nonlinear MAS.

In this article, a multilayer inductor for millimeter-wave regime was fabricated based on 130 nm SiGe process with compact size and high quality factor (Q). The area of cross-section for the propos...

This work deals with the development of a real-time wind turbine emulator (WTE) for laboratory purpose using a robust sliding mode controller (SMC). The application of power electronic converters with various control strategies in machine control facilitate the imitation of a practical wind turbine characteristics using a separately excited DC motor. The SMC theory is used here to develop a control law that governs the system to track the reference current set by the mathematical model of the wind turbine to get the desired output. The static, dynamic characteristics and also the transient responses of the WTE are satisfactorily reproduced by the proposed SMC method. To validate the performance of WTE prototype, it is tested under various operating conditions. A performance comparison of the proposed SMC with the existing proportional and integral (PI) controller in terms of tracking speed, peak overshoot and settling time are discussed in detail. The experimental result analysis confirms that the proposed control scheme improves the imitation of the real-time wind turbine characteristics and performs efficiently under both steady-state and transient conditions.

Accuracy in remote sensing image change detection is an important area of study. A new approach for improving the change detection accuracy of SAR remote sensing images is investigated in this paper. The research in this paper is presented in three parts. First, we proposed a new image enhancement algorithm. We combined the image enhancement algorithm based on the combination of the wavelet domain and spatial domain and the power-law. The contrast of the original image was enhanced by histogram equalization; then, the high- and low-frequency coefficients of the image were processed by wavelet fusion, and the image was sharpened by a nonsharpening mask so that the image could be sharpened from the image. Compared with other correlation enhancement contrast algorithms, we assert that the algorithm retains the high-frequency details of the image while improving its sharpness. Second, we propose a new change detection algorithm. A power rate change detection algorithm is used to improve image brightness, a logarithmic difference map is used to obtain a difference map, and an FLCM clustering algorithm is used to improve the clustering effect by suppressing noise from domain information. To improve detection accuracy, a saliency map is extracted from the difference map, the former of which is then used. The detection template obtained from the graph greatly reduces the range of change detection and effectively reduces the noise of the SAR image. The proposed power rate change detection algorithm is superior to similar algorithms. Third, our proposed enhancement algorithm is applied to our proposed change detection algorithm, which further improves the accuracy of image change detection.

The photoplethysmogram (PPG) signal is an attractive candidate for unobtrusive blood pressure (BP) monitoring, as it is widely measured by wrist-worn devices. However, most studies of PPG-based BP estimation techniques have used finger PPG signals. This study compares PPG-based BP estimation when using finger and wrist optical sensors. Subject-specific linear regression models employing pulse transit time, PPG intensity ratio and heart rate as features for BP estimation were trained and tested using PPGs and reference continuous BP values obtained from 22 healthy participants performing 2 cold pressor tests. Mean±stan - dard deviation of differences, and the mean absolute difference between reference and estimated systolic BP values were: 0.47±10.44 mmHg and 7.78 mmHg for finger PPG signals; and 1.05±12.86 mmHg and 9.69 mmHg for wrist PPG signals. Increases and decreases in systolic BP of at least 10 mmHg were detected with F1 scores of: 0.81 and 0.76 for finger PPG; and 0.75 and 0.61 for wrist PPG. Models performed better with finger PPG signals. Different signal processing approaches were required for finger and wrist signals, indicating that finger-based BP estimation models should not be generalized directly to wrist PPGs.

Activity of the autonomic nervous system (ANS) is closely related to the cardiovascular system (CVS). Thus, any disturbance of the ANS may have a negative impact on CVS function. This study investigates the feasibility to assess autonomic cardiovascular integrity by using photo-plethysmogram (PPG) signals. PPG signals from 51 subjects were recorded. Two complementary tests (cold pressor and slow deep breathing) were used as stress and relaxation stimuli. Nine features of PPG pulse wave were extracted. The results showed that the most sensitive feature to the sequence of complementary stimuli for autonomic cardiovascular integrity testing is the pulse amplitude variability - values of effect size to two cold pressor and one slow deep breathing stimuli were 1.444, 1.129, and 1.030, respectively. The obtained results may have significance in developing testing methodologies of assessing ANS for physically weak persons.

Existing studies offer little insight on how atrial fibrillation (AF) detection performance is influenced by the properties of AF episode patterns. The aim of this study is to investigate the influence of AF burden and median AF episode length on detection performance. For this purpose, three types of AF detectors, using either information on rhythm, rhythm and morphology, or ECG segments, were investigated on 1-h simulated ECGs. Comparing AF burdens of 20% and 80% for a median episode length of 167 beats, the sensitivity of the rhythm- and morphology-based detector increases only slightly whereas the specificity drops from 99.5% to 93.3%. The corresponding figures of specificity are 99.0% and 90.6% for the rhythm-based detector; 88.1% and 70.7% for the segment-based detector. The influence of AF burden on specificity becomes even more pronounced for AF patterns with brief episodes (median episode length set to 30 beats). Therefore, patterns with briefepisodes and high AF burden imply higher demands on detection performance. Future research should focus on how well episode patterns are captured.

The segmentation of arteries in invasive coronary angiography is necessary to build quantitative models and eventually improve the diagnosis of cardiovascular diseases. Standard segmentation algorithms suffer due to the lack of fully annotated datasets and tend to return disconnected vessels. Thus, we explore a semi-supervised segmentation framework to address these issues. Specifically, we use a student model and a teacher model as the main framework with Nested U-Nets (UNet++) as their backbones. The student model learns by minimizing a segmentation loss between the output and the ground truth, and a consistency loss guided by the uncertainty information. Additionally, a special loss function based on elastic interaction is used to improve the connectivity of arterial branches. We demonstrate the effectiveness of our proposed techniques over 42 labeled and 60 unlabeled samples and find relative improvement of 5.59% for Dice score and 69.99% for Betti number compared to a U-Net.

We study population protocols, a model of distributed computing appropriate for modeling well-mixed chemical reaction networks and other physical systems where agents exchange information in pairwise interactions, but have no control over their schedule of interaction partners. The well-studied *majority* problem is that of determining in an initial population of $n$ agents, each with one of two opinions $A$ or $B$, whether there are more $A$, more $B$, or a tie. A *stable* protocol solves this problem with probability 1 by eventually entering a configuration in which all agents agree on a correct consensus decision of $\mathsf{A}$, $\mathsf{B}$, or $\mathsf{T}$, from which the consensus cannot change. We describe a protocol that solves this problem using $O(\log n)$ states ($\log \log n + O(1)$ bits of memory) and optimal expected time $O(\log n)$. The number of states $O(\log n)$ is known to be optimal for the class of polylogarithmic time stable protocols that are "output dominant" and "monotone". These are two natural constraints satisfied by our protocol, making it simultaneously time- and state-optimal for that class. We introduce a key technique called a "fixed resolution clock" to achieve partial synchronization. Our protocol is *nonuniform*: the transition function has the value $\left \lceil {\log n} \right \rceil$ encoded in it. We show that the protocol can be modified to be uniform, while increasing the state complexity to $\Theta(\log n \log \log n)$., Comment: Applied FOCS reviewers' comments

The 2022 IEEE AP-S awards are six awards given by the IEEE APS Society in 2022.

We propose a systematic framework for analysing forum datasets, which contain minimal structure, and are nontrivial to analyse at scale, aiming to support future analysis of underground forum communities. We use a multi-technique approach which draws on a combination of features, including post classifications extracted using natural language processing tools, and apply clustering and predictive techniques to this dataset, to predict potential key actors-individuals who have a central role in overtly criminal activities, or activities which could lead to later offending, and hence might benefit most from interventions. We predict 49 key actors on an underground gaming-specific cheating and hacking forum, validated by observing only overlaps of techniques, combined with topic analysis, to build a classifier for key actor status. In addition, we also use these techniques to provide further insight of key actor activity. We found one cluster and two posting trajectories to contain a high proportion of key actors, logistic regression found an actor's h-index to have higher odds for prediction than other features, and partial dependence plots found reputation to have a significant change in prediction between values of 100 to 1000., This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) [grant number EP/M020320/1

With the growing concern on energy consumption, optimization and control of Geothermal Heat Pump (GHP) systems have become a research hotspot, which can help solve the problem of building energy conservation and shortage. The superiority of current control schemes of GHP systems is often reflected on an individual building with a separate GHP system. However, with the development of urban construction and the increase of population density, study on the district/area case in built-up areas deserves more attention. This paper focuses on typical cases of one GHP system serving multiple buildings and the community-level coordination of GHP systems. In particular, we present a high-order thermal dynamic model of radiator pipes combined with a commonly used second-order resistance-capacitance model for radiator heating/cooling. We design controllers to improve the efficiency of heat pumps and the ability to track a given nominal point of electrical power consumption in a distributed way, without sacrificing too much user comfort. Simulation results show that the proposed real-time distributed temperature control schemes are effective.

This paper presents a control redesign approach to improve the performance of a certain class of dynamical systems. Motivated by recent research on re-engineering cyber-physical systems, we propose a three-step control retrofit procedure. First, we reverse-engineer a dynamical system to dig out an optimization problem it actually solves. Second, we apply an augmented Lagrangian or a hat-x method to solve this optimization problem. Finally, by comparing the original and new dynamics, we obtain the implementation of the redesigned part (i.e., the extra dynamics). As a result, the convergence rate/speed or transient behavior of the given system can be improved while the system structure remains. To show the effectiveness of the proposed approach and its potential applications, we present two practical examples including Internet congestion control and distributed proportional-integral (PI) control.

This paper presents results of the H2020 European E-LOBSTER project which is to propose an innovative Railway to Grid (R +G) Management system, combined with advanced power electronics and storage technologies (the smart Soft Open Point and the electric storage developed in the framework of the project). In particular, the R + G management system will be able to make the best use of the available energy on both grids by increasing their mutual synergies and increasing the energy efficiency of two networks through electric energy storages and at the same time by creating synergy with charging stations for Electric Vehicles (EV). This paper demonstrates an overview of the state of the art of the proposed smart R+G energy management with simulation results of the main case studies as well as an overview smart mobility through Electric Vehicles, charging stations for EVs, e-mobility in general and its synergies with railways.

Theoretical studies have shown that the use of simultaneous mono- and bistatic synthetic aperture radar (SAR) data could be beneficial to agriculture and soil moisture monitoring. This study makes use of extensive ground-truth measurements and synchronous high-resolution fully-polarimetric mono- and bistatic airborne SAR data in L-band to assess and compare the sensitivity of mono- and multistatic systems to maize crop variables, soil moisture, and surface roughness. Its results suggest that bistatic data, even with a very small bistatic angle, provide valuable additional information for maize crop biophysical parameter retrieval. However, this does not appear to be the case for soil moisture retrieval over bare soils.

Signal area estimation is a critical component of spectrum aware systems. It entails determining the subsets of elements of a time-frequency matrix where a signal is present. This study proposes and assesses the potential of a minesweeper algorithm in estimating accurately the signal area. The proposed method can be employed in two ways: as a standalone signal area estimation technique and also as a pre/post-processing technique in combination with other signal area estimation methods in order to correct signal detection errors before applying the other estimation method (pre-processing) and/or errors introduced by the other estimation method itself (post-processing). The performance of the proposed minesweeper algorithm in both application approaches is evaluated by means of software simulations. The obtained results show that, when used as a standalone method, it can provide similar or even better accuracy than other methods at a much lower computational cost. However, the best performance is obtained when used as a pre/post-processing technique in combination with other existing methods, without increasing significantly the total computation time.

Integrated circuits (ICs) may be exposed to counterfeiting due to the involvement of untrusted parties in the semiconductor supply chain; this threatens the security and reliability of electronic systems. This paper focusses on the most common type of counterfeiting namely, recycled and remarked ICs. The goal is to develop a technique to differentiate between new and recycled ICs that have been used for a short period of time. Detecting recycled ICs using aging sensors have been researched using sub-threshold leakage current and frequency degradation utilizing ring oscillators (ROs). The resolution of these sensors requires further development to accurately detect short usage time. This paper proposes a differential aging sensor to detect recycled ICs using ring oscillators with sub-threshold leakage current to detect aging effects using bias temperature instability (BTI) and hot carrier injection (HCI) on a 22-nm CMOS technology, provided by GlobalFoundries. Simulation results confirm that we are able to detect recycled ICs with high confidence using proposed technique. It is shown that the discharge time increases by 14.72% only after 15 days and by 60.49% after 3 years' usage, and outperforms techniques that use frequency degradation only, whilst considering process and temperature variation.

In this paper, we address a problem of atmospheric blocking pattern recognition in global climate model simulation data. Understanding blocking events is a crucial problem to society and natural infrastructure, as they often lead to weather extremes, such as heat waves, heavy precipitation, and the unusually poor air condition. Moreover, it is very challenging to detect these events as there is no physics-based model of blocking dynamic development that could account for their spatiotemporal characteristics. Here, we propose a new two-stage hierarchical pattern recognition method for detection and localisation of atmospheric blocking events in different regions over the globe. For both the detection stage and localisation stage, we train five different architectures of a convolutional neural network (CNN) based classifier and regressor. The results show the general pattern of the atmospheric blocking detection performance increasing significantly for the deep CNN architectures. In contrast, we see the estimation error of event location decreasing significantly in the localisation problem for the shallow CNN architectures. We demonstrate that CNN architectures tend to achieve the highest accuracy for blocking event detection and the lowest estimation error of event localisation in regions of the Northern Hemisphere than in regions of the Southern Hemisphere.

Radio frequency fingerprint identification (RFFI) is an emerging device authentication technique that relies on intrinsic hardware characteristics of wireless devices. We designed an RFFI scheme for Long Range (LoRa) systems based on spectrogram and convolutional neural network (CNN). Specifically, we used spectrogram to represent the fine-grained time-frequency characteristics of LoRa signals. In addition, we revealed that the instantaneous carrier frequency offset (CFO) is drifting, which will result in misclassification and significantly compromise the system stability; we demonstrated CFO compensation is an effective mitigation. Finally, we designed a hybrid classifier that can adjust CNN outputs with the estimated CFO. The mean value of CFO remains relatively stable, hence it can be used to rule out CNN predictions whose estimated CFO falls out of the range. We performed experiments in real wireless environments using 20 LoRa devices under test (DUTs) and a Universal Software Radio Peripheral (USRP) N210 receiver. By comparing with the IQ-based and FFT-based RFFI schemes, our spectrogram-based scheme can reach the best classification accuracy, i.e., 97.61% for 20 LoRa DUTs., Comment: Accepted for publication in IEEE INFOCOM 2021

The development of machine learning algorithms in the cyber security domain has been impeded by the complex, hierarchical, sequential and multimodal nature of the data involved. In this paper we introduce the notion of a streaming tree as a generic data structure encompassing a large portion of real-world cyber security data. Starting from host-based event logs we represent computer processes as streaming trees that evolve in continuous time. Leveraging the properties of the signature kernel, a machine learning tool that recently emerged as a leading technology for learning with complex sequences of data, we develop the SK-Tree algorithm. SK-Tree is a supervised learning method for systematic malware detection on streaming trees that is robust to irregular sampling and high dimensionality of the underlying streams. We demonstrate the effectiveness of SK-Tree to detect malicious events on a portion of the publicly available DARPA OpTC dataset, achieving an AUROC score of 98%., Comment: Published at IEEE-CSR (International Conference on Cybersecurity and Resilience) 2021

ion techniques provide formal guarantees for generic optimal control problems on nonlinear and hybrid systems. Computing an abstraction solving the problem over the whole state-space is computationally demanding in highdimensional spaces. We circumvent this curse of dimensionality by introducing a hierarchical abstraction approach for solving an optimal control problem for nonlinear systems with three nested partitions. These nested partitions allow the construction of auxiliary systems that characterize simulation relations, which are suitably exploited to provide upper and lower bounds for a branch and bound algorithm to yield an optimal solution for the control problem. An example illustrates the proposed method.

Drug safety and efficacy assessment remains as one of the biggest challenges in both preclinical and clinical drug development. Cardiac adverse outcomes may emerge even though they did not occur in early stages of drug development. Among them, the prediction of drug action on cardiac contraction and electrophysiology is especially complex. Human in-silico drug trials constitute a powerful methodology for their investigation and can integrate and augment biophysically detailed experimental information. In this study, we present an integrated modelling and simulation framework for the simultaneous assessment of electrophysiological and contractile effects of drug action in human cardiac function. We analyse both pure potassium and calcium channels blockers, given their prevailing use in clinical practice. Simulation results demonstrate the positive inotropic effect of potassium blockers, with the potential occurrence of contractile abnormalities triggered by repolarisation abnormalities, and the dose-dependent negative inotropic effect of calcium blockers. This study demonstrates the translational and preclinical potential of human-based in-silico drug trials to investigate drug-induced effects on human cardiac electromechanical function.

In this paper, we propose an unmanned aerial vehicle (UAV) base station (BS) assisted communication system for a special event (e.g., a football game) with heterogeneous traffic demands by all users and the uplink (UL)-downlink (DL) rate balancing requirement. With respect to UAV’s high mobility, we propose a hybrid mode multiple access (HMMA) strategy where both orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) techniques are utilized to meet heterogeneous traffic demands. Specifically, NOMA is utilized to achieve high average data rate, and OMA helps to meet the instantaneous rate demands of users. The proposed HMMA strategy has a high degree of freedom and provides superior minimum average rate across all users and a higher user fairness than the previous work with OMA only or NOMA only, where the instantaneous rate demands of users may not always be guaranteed during UAV’s flight time due to dynamic channel changes, the inter-user interference and successive interference cancellation (SIC) error propagation. Furthermore, we investigate joint UL-DL optimization for a UAV assisted wireless system. Based on the channel reciprocity of the air-ground channels, an alternative algorithm is proposed to conduct joint UL-DL optimization of bandwidth assignment and UAV trajectory to accommodate heterogeneous rate demands across users and achieve quasi-balanced average rates in UL and DL.

Recently, tactile sensing has attracted great interest in robotics, especially for facilitating exploration of unstructured environments and effective manipulation. A detailed understanding of the surface textures via tactile sensing is essential for many of these tasks. Previous works on texture recognition using camera based tactile sensors have been limited to treating all regions in one tactile image or all samples in one tactile sequence equally, which includes much irrelevant or redundant information. In this paper, we propose a novel Spatio-Temporal Attention Model (STAM) for tactile texture recognition, which is the very first of its kind to our best knowledge. The proposed STAM pays attention to both spatial focus of each single tactile texture and the temporal correlation of a tactile sequence. In the experiments to discriminate 100 different fabric textures, the spatially and temporally selective attention has resulted in a significant improvement of the recognition accuracy, by up to 18.8%, compared to the non-attention based models. Specifically, after introducing noisy data that is collected before the contact happens, our proposed STAM can learn the salient features efficiently and the accuracy can increase by 15.23% on average compared with the CNN based baseline approach. The improved tactile texture perception can be applied to facilitate robot tasks like grasping and manipulation., Comment: 7 pages, accepted by International Conference on Intelligent Robots and Systems 2020

Self-bearing machines offer a highly integrated solution to achieve the rotor drive and magnetic levitation within a single structure. Although the trend is toward a diminution in the number of actively stabilised degrees of freedom to reduce the requirement for sensors, power electronics and controllers, no self-bearing machine relying solely on passive phenomena has been successfully tested so far due to the restrictions highlighted by Earnshaw’s Theorem. Recent researches have demonstrated, through electromechanical models and experimental investigations, that electrodynamic thrust self-bearing machines (EDTSBMs) can gather, within one multifunction winding, both the rotor axial passive suspension and drive. Nonetheless, the test rigs implementing them still comprised external means to ensure the rotor radial and tilt support. In this context, this paper takes the study of EDTSBMs to the final step by combining them with two permanent magnet centring bearings, yielding the first fully passively levitated self-bearing machine. Thorough experimental analyses dealing with the axial forces that support the rotor and its resulting axial position are carried out, proving that stable fully passive levitation can be achieved. The motor and suspension currents as well as the corresponding losses are also investigated.

Speech signals of individuals contain informationrelated to their physical, mental as well as emotionalhealth. A first step in clinical diagnosis is to measure vitalsigns, which provide an indication of vital body functions.The four vital signs - heart rate, blood pressure, body temperatureand respiratory rate are useful in assessing the healthof an individual and early diagnosis of deteriorating healthconditions. The objective of this work is to measure heartrate of individuals based on their speech signal using signalprocessing and machine learning techniques.

Conventional request-grant based non-orthogonal multiple access (NOMA) incurs tremendous overhead and high latency. To enable grant-free access in NOMA systems, user activity detection (UAD) is essential. In this paper, we investigate compressive sensing (CS) aided UAD, by utilizing the property of quasi-time-invariant channel tap delays as the prior information. This does not require any prior knowledge of the number of active users like the previous approaches, and therefore is more practical. Two UAD algorithms are proposed, which are referred to as gradient based and time-invariant channel tap delays assisted CS (g-TIDCS) and mean value based and TIDCS (m-TIDCS), respectively. They achieve much higher UAD accuracy than the previous work at low signal-to-noise ratio (SNR). Based on the UAD results, we also propose a low-complexity CS based channel estimation scheme, which achieves higher accuracy than the previous channel estimation approaches.

In this paper, an overview of SOI technology for high-frequency telecommunication applications is presented. At the transistor level, thanks to SOI's low-parasitic architecture and good electrostatics, high oscillation and cut-off frequencies in the range of 400 GHz are achieved. At the RF-IC level, a review is made to reveal the position of SOI for key RF and millimeter wave circuits (switches, LNAs and PAs). Finally, substrate impact is described at RF frequencies. The existing flavors of SOI substrates are reviewed, highlighting substrate requirements at RF, as it seriously impacts parasitic coupling, RF losses, passive quality factor, and non-linear signal distortion. Overall, SOI is shown to be a prime candidate for high-performance millimeter wave and 5G applications, and also for low-power RF IoT.

The growing oil demand and serious environmental concerns have promoted the concept of the usage of electric vehicles (EVs) across the globe. EVs can be integrated into the grid for power transaction and to support the grid requirements, thereby drawing the attention of researchers, policy makers and industries. EVs are not only a transportation tool but also act as a distributed source or load. The EV battery plays a prominent role in grid integration and sustainable transportation. The monitoring and control aspect of the battery management system (BMS) plays a vital role in the successful deployment and usage of EVs. In this paper, an equivalent circuit model (ECM) of battery is proposed and analyzed that describes the battery behavior at various temperatures, considering the internal resistance of the battery. A stochastic model was developed for the battery ageing and replacement to ensure that systematic replacement of batteries based on the calendar ageing was performed. A reliability assessment of EV accessibility and availability was carried out by using Markov chain. A case study of a Diesel-renewable powered Electric Vehicle Charging Station (EVCS) in a micro-grid was carried out that suits the requirement of large-scale EV fleet integration to the grid for power transaction. The holistic approach of BMS was considered for the sustainable transportation and grid integration

Qualification tests of the ITER toroidal field (TF) conductor joints have been carried out by testing joint samples with test facilities in the National Institute for Fusion Science, NINS, Toki, Japan. The joint sample consists of two short TF conductors with the length of 1535 mm, which is restricted by the test facility with 9-T split coils and 100-kA current leads. The sample current is supplied from a dc 75-kA power supply. Each conductor has two joint boxes at both terminals. The lower joint is a testing part that is a full-size joint of the TF coil. The joint resistance of the lower joint is estimated from the increase of the average voltage drop among the six taps on the conductor against the currents. Five joint samples were tested until 2016, and all the samples satisfied the requirement of the joint resistance at less than 3 nΩ. The method of the measurement and the results are summarized, and the voltage distribution among the voltage taps is discussed.

With the transition towards a smart grid, the power system has become strongly intertwined with the information and communication technology (ICT) infrastructure. The interdependency of both domains requires a combined analysis of physical and ICT processes, but simulating these together is a major challenge due to the fundamentally different modeling and simulation concepts. After outlining these challenges, such as time synchronization and event handling, this manuscript presents an overview of state-of-the-art solutions to interface power system and ICT simulators. Due to their prominence in recent research, a special focus is set on co-simulation approaches and their challenges and potentials. Further, two case studies analyzing the impact of ICT on applications in power system operation illustrate the necessity of a holistic approach and show the capabilities of state-of-the-art co-simulation platforms.

The feasibility of using prior gravity anomaly measurements that are 1 nautical mile apart for underwater simultaneous localization and mapping is studied. This paper reports on modelling and simulation that investigates relationships between the characteristic anomaly parameters and the requirements for particle filter SLAM solutions. Map anomaly variability parameters suggest the gross likelihood of SLAM success in a map. However, the anomaly parameters that relate to the local anomaly measurement-to-measurement variability (i.e. localization) are better indicators for a SLAM mission’s success. The prior gravity anomaly measurements, coupled with the tools developed here, provide guidance to select optimal areas and missions the AUV could transit through towards minimal localization error at the goal location. Follow-on work will exploit the anomaly localization parameter to assist with SLAM path-planning through a vector field histogram approach and then, implement it on an AUV for validation.