Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensors trends: Smaller, cheaper, smarter, faster and under wireless control

Arguably, current trends in sensor research, development and commercialization can be classified in five broad categories (not necessarily in an order of significance or as they appear in the title). In this presentation, these trends will be briefly discussed using examples from the literature and from the authors’ laboratory. Sensor-powering schemes (ranging from energy harvesting, to energy scavenging, to self-powering) and to power-management approaches will also be outlined. Applicability and potential socio-economic impact will be briefly discussed, with particular emphasis in the future of sensor-applications in IoT (Internet of Things), in Industry 4.0 (and in the emerging but not yet well defined Industry 5.0) and in Society 5.0. The motivation is to summarize current and emerging trends that may be of interest to a wide audience.
A Hybrid Evolutionary Algorithm with Heuristic Mutation for Multi-objective Bi-clustering

Bi-clustering is one of the main tasks in data mining with several application domains. It consists in partitioning a data set based on both rows and columns simultaneously. One of the main difficulties in bi-clustering is the issue of finding the number of bi-clusters, which is usually a user-specified parameter. Recently, in 2017, a new multi-objective evolutionary clustering algorithm, called MOCK-II, has shown its effectiveness in data clustering while automatically determining the number of clusters. Motivated by the promising results of MOCK-II, we propose in this paper a hybrid extension of this algorithm for the case of bi-clustering. Our new algorithm, called MOBICK, uses an efficient solution encoding, an effective crossover operator, and a heuristic mutation strategy. Similarly to MOCK-II, MOBICK is able to find automatically the number of bi-clusters. The outperformance of our algorithm is shown on a set of real gene expression data sets against several existing state-of-the-art works. Moreover, to be able to compare MOBICK to MOCK-I and MOCK-II, we have designed two basic extensions of MOCK-I and MOCK-II for the case of bi-clustering that we named B-MOCK-I and B-MOCK-II. Again, the experimental results confirm the merits of our proposal.
Research on Integrated SVDD Rotating Machinery Vibration Fault Detection Method Based on Deep Autoencoder

There are some problems in vibration equipment of rotating machinery, such as complicated mechanism, confused field data and fewer fault samples. In order to improve the accuracy of fault detection, this paper proposes a rotating mechanical vibration fault detection method based on deep autoencoder and integrated SVDD(DAE-ISVDD). First, use the original normal sample data to train multiple different deep autoencoders, after that the trained autoencoders are used to extract features from the original data, then use the extracted features to train multiple basic SVDD weak classifiers, and finally, a strong classifier is obtained by means of weighted integration. Through the example verification, the resulting strong classifier can effectively improve the accuracy of the vibration fault detection of rotating machinery. Meanwhile, compared with other traditional methods, this method has good anti-noise performance and generalization ability, and can be well applied in practical engineering.
Adaptive Sliding Mode Control for Omnidirectional Mobile Robot Based on a New Friction Modeling

Due to the special structure of omni-directional wheels, friction forces among hubs, wheels and ground have a great influence on the motion accuracy of four-wheeled omnidirectional mobile robot. In order to reduce the effect of friction on motion control system, this paper proposes an adaptive sliding mode controller (ASMC) based on the dynamic model considering the friction on omni-directional wheels. The research on the omni-directional wheels friction force is described in detail, which lays a foundation for the accurate control of omnidirectional mobile robot. Firstly, the paper analyzes the Coulomb's friction on the driving wheel and the driven wheel respectively which is introduced to the friction model of the omni-directional wheel. Secondly, a new adaptive sliding mode control method is proposed to address the friction problem. Finally, the validity and performance of the control method are verified by simulations.
A Current-Mode CMOS Hall Sensor Microsystem based on Four-Phase Current Spinning Technique

A novel Hall sensor microsystem working in the current mode is presented. The current-mode four-phase current spinning technique is applied to modulate the polarity of the Hall current and the offset current. Further, the use of a current integrating amplifier and a correlated double sampling demodulator performs twice offset-cancellation operations and then demodulates the Hall signal to the low frequency. Designed by 0.18-μm CMOS technology, the simulation results show that the Hall sensor obtains a low residual offset less than 88 μT and high linearity up to 99.9% in the magnetic fields ranging from 5 to 155 mT.
Performance-Guaranteed Tracking Control of an Autonomous Surface Vessel With Parametric Uncertainties and Time-Varying Disturbances

In this paper, the model-based adaptive control combined with a continuous robust integral of the sign of the error (RISE) feedback strategy is presented for the trajectory tracking control of an autonomous surface vessel (ASV) subject to parametric uncertainties and time-varying disturbances. The control objective is to achieve asymptotic tracking and guarantee the transient tracking performance simultaneously. An adaptive feedforward term is used to compensate for the parametric uncertainties, and the effects generated by external disturbances can be overcome by a gain of the error sign term (a part of the RISE feedback term). In the previous RISE-based feedback control, the upper bounds of disturbances and their time derivatives are often required to be known, while the requirement is relaxed, in this paper, by rendering the gain of the error sign term adaptive. It should be noted that the adaptive gain of the error sign term adjusts automatically until it is sufficient to compensate for the disturbances, which helps to reduce the control effort. Moreover, the prescribed performance control technique is introduced to provide the transient performance specification of the tracking error, which guarantees the tracking performance and improves the robustness of the control system. Based on the backstepping procedure and the Lyapunov stability analysis, asymptotic trajectory tracking with transient tracking performance is theoretically guaranteed, and all the signals are shown to be bounded. The effectiveness of the proposed control scheme is demonstrated through the numerical simulation results.
Improved Detection in Successive Interference Cancellation NOMA OFDM Receiver

A successive interference cancellation receiver is one of the important blocks in non-orthogonal multiple access (NOMA) transmission. The quality of detection of the strongest user signals often decides about the quality of the whole system and minimizes the error propagation effect. In this paper, we propose an improved detection algorithm, which allows for using the NOMA transmission in a much smaller range of power differences between the terminals sharing common radio resources in the uplink, as compared with standard successive cancellation. The idea lies in the application of tentative decisions about weaker signals in the detection of stronger ones and then, after improved detection of stronger user signals, achieving more reliable decisions about the weaker ones. The simulation results reported in the paper confirm our idea, showing a much higher detection quality of the proposed receiver when compared with the standard solution.
Understanding Crowd Work in Online Crowdsourcing Platforms for Urban Planning: Systematic Review

Recently, crowdsourcing platforms have been used to solve problems in the field of urban planning by involving crowds of citizens in performing tasks. However, the success of this approach is directly related to how work is managed. The goal of the present study is to make a broad characterization of work management in crowdsourcing approaches applied to urban planning through a systematic literature review. More specifically, we aim to investigate aspects related to the quality of work.
End-Effector Approach Flexibilization in a Surface Approximation Task Using a Bioinspired Tactile Sensing Module

The flexibilization of the end-effector orientation constraint is critical in various robotic tasks, in particular when approaching surfaces of unknown objects and when unexpected contacts occur. This paper presents the use of a bioinspired multimodal tactile sensing module for tasks requiring knowledge about the inclination of a surface. The advantage of this module is that it can detect the pitch of the surface even though the pitch of the end-effector that carries the module during exploration is kept constant. Such flexibilization is achieved due to the compliant nature of the sensing module, its inner organization and the placement of embedded sensors.
Estimating Power Module Lifetime with a Driving Schedule to Determine Effectiveness of Module Packaging Technologies

A simple method to estimate the effect of power module or power device selection on the lifetime of an electric vehicle inverter is proposed and sample results are shown in this paper. It is relatively difficult to compare performance or cost and how they pertain directly to the lifetime but this method is able to give insight into their effects on the expected lifetime by comparing the losses in discrete events and the lifetime curves of the module to make a rain flow model. The result is a rough estimate of the expected lifetime to observe differences of potential order-of-magnitude differences in lifetime.
Learning to Solve Capacitated Arc Routing Problems by Policy Gradient

Most heuristic algorithms for NP-hard combinatorial optimization problems require expertise in both the problem domains and heuristic methods. Recent research has begun to apply Deep Neural Network to learning heuristics for combinatorial optimization problems automatically. These works mainly focus problems with simple formulations, such as Travelling Salesman Problem and Vehicle Routing Problem defined on Euclidean graphs. This paper presents a novel deep reinforcement learning based algorithm for the Capacitated Arc Routing Problem which is defined on more complex non-Euclidean information graphs. The proposed approach is a combination of a Graph Convolutional Network and two encoder-decoder models. By regrading the negative objective values of CARP instances as the rewards, the proposed method optimizes the parameters with REINFORCE algorithm. In empirical experiments, the proposed method is able to generate solutions approximate optimal solutions well with much less time than heuristic algorithms.
Improved Power Budget of 112 Gb/s/$\lambda$ Intra-Datacentre Links Using a Split-Carrier Transmitter Architecture

A split-carrier transmitter is proposed to improve loss budget for short distance 100 G-per-wavelength intra-datacentre links. By applying a low-complexity hardware modification to the transmitter, the proposed architecture reduces transmitter loss without sacrificing modulation quality. These benefits are demonstrated by implementing intensity modulated, direct-detection signals using a single-ended photodiode receiver. The architecture is evaluated using two formats: Nyquist-shaped 56 GBd PAM-4 and Nyquist-shaped double sideband 28 GBd 16-QAM. The results are compared against a conventional transmitter modulated with Nyquist-shaped 56 GBd PAM-4. Through simulation, the split-carrier transmitter is shown to achieve hard decision forward error correction ready performance after 2 km of transmission using a laser output power of just 0 dBm; a 5.2 dB improvement over the conventional transmitter.
Performance Evaluation of a Combined Anomaly Detection Platform

Hybrid Anomaly Detection Model (HADM) is a platform that filters network traffic and identifies malicious activities on the network. The platform applies data mining techniques to tackle effectively the security issues in high load communication networks. The platform uses a combination of linear and learning algorithms combined with protocol analyzer. The linear algorithms filter and extract distinctive attributes and features of the cyber-attacks while the learning algorithms use these attributes and features to identify new types of cyber-attacks. The protocol analyzer in this platform classifies and filters vulnerable protocols to avoid unnecessary computation load. The use of linear algorithms in conjunction with learning algorithms and protocol analyzer allows the HADM to achieve improved efficiency in terms of accuracy and computation time to detect cyber-attacks over existing solutions. While authors’ previous paper evaluated HADM efficiency (accuracy and computation time) against related studies, this paper, concentrates on HADM robustness and scalability. For this purpose, five datasets, including ISCX-2012, UNSW-NB15 Jan, UNSW-NB15 Feb, ISCX-2017, and MAWILab-2018, with various size and diverse attacks have been used. Different feature selection methods are applied to find the best features. The feature selection methods are selected based on the algorithms’ computation time and detection rate. The best algorithms are then selected through a benchmark on applied datasets and based on the metrics such as cross-entropy loss, precision, recall, and computation time. The result of HADM platform shows robustness and scalability against datasets with different size and diverse attacks.
Current Sensorless Model Free Control Applied on PMSM Drive System

In control of electric machines, removing the current sensors from the electric powertrains has a great importance owing to saving on manufacturing and maintenance costs. This paper addresses the Model-Free-Controller (MFC) as a current sensor-less controller that do the control task for a Permanent-Magnet-Synchronism-Machine (PMSM) without using neither sensor nor estimator for stator current. As this controller is model free, it doesn't require any information about parameters. MFC tries to control the drive system just with measuring the rotor speed and dc bus voltage. Simulation study has been done to show the MFC's robustness under ideal and parametric error condition.
EEG/EMG based Architecture for the Early Detection of Slip-induced Lack of Balance

In this paper, we propose the preliminary version of a novel pre-impact fall detection (PIFD) strategy, optimized for the early recognition of balance loss during the steady walking.The technique has been implemented in a multi-sensor architecture aiming to jointly analyzes the muscular and cortical activity. The physiological signals were acquired from 10 electromyography (EMC) electrodes on the lower limbs and 13 electroencephalography (EEG) sites all along the scalp.Data from the EMGs are statistically treated and used both to identify abnormal muscular activities and to trigger the cortical activity assessment. The EEG computation branch evaluate the rate of variation of the EEG power spectrum density, named m, to describe the cortical responsiveness in live bands of interest. Then, a logical conditions network allows the system to recognize the loss of balance induced by the slippage, by considering both the evaluated muscular parameters and the cortical ones.Experimental validation on six adults (supported by the motion capture system) showed that the system reacts in a time compliant with the fall dynamic request (403.16 ms), ensuring a competitive detection accuracy (Sensitivity =93.33%, Specificity=99.82 %).
Digital Built-in Self-Test for Phased Locked Loops to Enable Fault Detection

With the increasing pressure to obtain near-zero defect rates for the automotive industry, there is a need to explore built-in self-test and other non-traditional test techniques for embedded mixed-signal components, such as PLLs, DC-DC converters, and data converters. This paper presents an extremely low-cost built-in self-test technique for PLLs specifically designed for fault detection. The methodology relies on exciting the PLL loop in one location via a pseudo-random signal with noise characteristics and observing the response from another location in the loop via all digital circuitry, thereby inducing low area and performance overhead. The BIST circuit along with a PLL under test is designed in 65nm technology. Fault simulations performed at the transistor and system level show that majority of non-catastrophic faults that result in parametric failures can be detected with the proposed approach.
Infrastructure and Control Strategy of Microgrid Energy Switches for Regional Energy Internet

Firstly, the positioning and function of energy switches in regional energy internet are described, and a typical infrastructure and control strategy of eight-port intelligent microgrid energy switches are proposed. Then paper verifies the correctness of the control strategy by PSCAD simulation. The characteristics and advantages of energy switches in practical application are pointed out, which provides effective support for the construction of regional energy internet in the future.
Mutually Coupled Switched Reluctance Motor: Fundamentals, Control, Modeling, State of the Art Review and Future Trends

Switched reluctance motor (SRM) is gaining more interest in the last decades due to its simple and robust structure. SRMs are classified into conventional SRMs (CSRMs) and mutually coupled SRMs (MCSRMs). CSRMs are based on single-phase excitation and torque is generated by the variation of self-inductance with rotor position. MCSRMs are based on multi-phase excitation and torque is produced by the rate of change of both self- and mutual inductances. MCSRM has the advantages of using the standard voltage source inverter at balanced current operation, when the sum of the phase currents is zero, while CSRM requires an asymmetrical converter. This paper presents the state-of-the-art review of MCSRMs, including operating concept, winding, and pole configurations, control methods by using different current waveforms, performance comparison of MCSRM configurations, modeling methods, and future work for improving MCSRM performance.
Examining Patterns of Text Reuse in Digitized Text Collections

Repeating text presents challenges to access, retrieval, and mining of large-scale digital libraries. We discuss the various forms of duplicate works in scanned text collections and show the patterns of how those variations manifest within the structure of a book. Understanding these structural similarities is an important step to aligning and controlling variant and duplicate works in digital libraries.
A Probability Model-based Memetic Algorithm for Distributed Heterogeneous Flow-Shop Scheduling

With the trend of global manufacturing, distributed shop scheduling has been a hot research topic recently. This paper addresses the distributed heterogeneous permutation flow-shop scheduling problem with multiple non-identical factories to minimize makespan. To well solve the problem, a probability model-based memetic algorithm (PMMA) is presented in this paper. Since the non-identical factories have different process capabilities, it is crucial to determine reasonable factory assignment. In PMMA, a probability model is constructed to reflect the probability distribution of factory assignment. At each generation, the probability model is updated by elite individuals to search for good factory assignment scheme. The information from the probability model is extracted and integrated in the designed search operators to help adjusting factory assignment and processing order. Meanwhile, the search operators collaborate to achieve both exploration and exploitation ability. Besides, a local intensification operator is designed to further improve the solution quality. Extensive computational experiments are carried out to test the performance of PMMA. The experiment results demonstrate the effectiveness of the designed PMMA.
Application of Evolutionary Multiobjective Optimization in L1-Regularization of CNN

A convolutional neural network (CNN) is a type of multilayered neural network, which has a structure superior in handling images and natural languages. L1-regularization may be used as a means to prevent overfitting of CNN and improve generalization performance. However, it is difficult to set an appropriate coefficient of the regularization term. Therefore, in this research, we use MOEA/D to determine the value of hyper parameter. Experimental results on MNIST show the effectiveness of the proposed approach.
On Effectiveness of Detecting FDI Attacks on Power Grid using Moving Target Defense

Recent studies have considered thwarting false data injection (FDI) attacks against state estimation in a power grid by proactively perturbing branch susceptances. The approach is known as moving target defense (MTD). However, the effectiveness of MTD has not been thoroughly analyzed in the existing literature. In this paper, we prove that, despite of the deployment of MTD, it is possible for the attacker to launch stealthy FDI attacks if the number of branches $l$ of the power system is less than twice the number of system states $n$ (i.e., $l < 2n$, where $n+1$ is the number of buses) based on the DC power flow model. Moreover, MTD has the complete capability to detect any FDI attacks with the form $\mathbf{a}= \mathbf{Hc}$ only if the susceptances of more than $n$ branches, which cover all buses, are perturbed. In addition, we analyze the impact of the susceptance perturbations on the attack space. We illustrate our findings on an IEEE test power system using load traces from the state of New York.
Differential Evolutionary Multi-task Optimization

Evolutionary multi-task optimization (EMTO) studies on how to simultaneously solve multiple optimization problems, so-called component problems, via evolutionary algorithms, which has drawn much attention in the field of evolutionary computation. Knowledge transfer across multiple optimization problems (being solved) is the key to make EMTO to outperform traditional optimization paradigms. In this work, we propose a simple and effective knowledge transfer strategy which utilizes the best solution found so far for one problem to assist in solving the other problems during the optimization process. This strategy is based on random replacement. It does not introduce extra computational cost in terms of objective function evaluations for solving each component problem. However, it helps to improve optimization effectiveness and efficiency, compared to solving each component problem in a standalone way. This light-weight knowledge transfer strategy is implemented via differential evolution within a multi-population based EMTO paradigm, leading to a differential evolutionary multi-task optimization (DEMTO) algorithm. Experiments are conducted on the CEC’2017 competition test bed to compare the proposed DEMTO algorithm with five state-of-the-art EMTO algorithms, which demonstrate the superiority of DEMTO.