Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey of Cyber-Physical Attacks and Detection Methods in Smart Water Distribution Systems

Modern technologies empower water distribution systems (WDS) for better services in the processes of water supply, storage, distribution, and recycling. They improve real-time monitoring, automating, and managing. However, the limitations of these technologies introduce cyber-physical attacks to the WDS. The main goals of cyber-physical attacks include disrupting normal operations and tampering the critical data, which have negative impacts on the WDS. Therefore, it is vital to develop and implement solutions to increase the security of the WDS by detecting and mitigating cyber-physical attacks. Since security for WDS is relatively new, there are no surveys on this topic despite its vital importance. Therefore, in this paper, we provide a comprehensive survey for the common cyber-physical attacks and common detection mechanisms for the WDS. We compare the attacks and detection methods with emphasis on ideas, methods, evaluation results, advantages, limitations, etc. We further provide a future research direction. We realize that there are still not many research attempts in this area and we hope that this work can trigger more research activities related to the WDS.
Analytical Deflection Profiles and Pull-In Voltage Calculations of Prestressed Electrostatic Actuated MEMS Structures

This paper presents a zeroth and first order perturbative analysis of prestressed electrostatic actuated Microelectromechanical systems (MEMS). Perturbation theory is used to calculate the deflection profile of various MEMS structures, from which the pull-in voltage is estimated using the weighed residual method, where both a Galerkin expression and a Dirac delta function have been used as weight functions. A prestressed circular Capacitative Micromachined Ultrasonic Transducer (CMUT) is used as the main example in this paper. This device is modeled as a circular clamped plate subjected to an electrostatic pressure. The calculated pull-in voltage has been compared with experimental data of highly prestressed CMUTs, where a relative error between −36% and −8% is observed for a model that does not include stress. The model that includes the residual stress lowers the range of the relative error to values between −5% and 21%. To improve the accuracy of the pull-in voltage estimate Richardson extrapolation has been calculated from the zeroth and first order estimates. The pull-in voltage models are compared with a Finite Element Model (FEM), where an overestimation, in the high stress regime, of 10% is observed for the zeroth order model, less than 5% for the Galerkin method and 3% for the Richardson extrapolation. [2020-0358]
Compact Slow-Wave SIW H-Plane Horn Antenna With Increased Gain for Vehicular Millimeter Wave Communication

A substrate integrated waveguide (SIW) H-plane horn antenna with miniaturized longitudinal dimension and increased gain is proposed by loading slow-wave (SW) structures in the form of metallized blind via-holes inside the flare region of the horn for vehicular millimeter wave communications. The slow-wave structure can reduce the phase velocity around the center line of the broad wall and compensate the large phase difference between the center and edge of the aperture caused by reduction of longitudinal length of the horn. Compared with optimum horn with the same aperture width, the gain of miniaturized SIW horn is successfully increased, while the longitudinal dimension is decreased. A SW-SIW horn antenna which covers the 28 GHz band (27.5–28.35 GHz) allocated by Federal Communications Commission for the 5G millimeter wave communications is designed to verify the feasibility of the miniaturized SIW horn antenna facilitated by the proposed slow-wave structure. The SW-SIW horn realizes 53.83% reduction of longitudinal length compared with the optimum horn. The gain of the SW-SIW horn is improved by 1.1 dB on average over 27.5–28.5 GHz compared with optimum horn. A prototype of the proposed SW-SIW H-plane horn antenna is manufactured and measured. The measured results agree well with the simulations.
A Robust Multi-Objective Feature Selection Model Based on Local Neighborhood Multi-Verse Optimization

Classification tasks often include, among the large number of features to be processed in the datasets, many irrelevant and redundant ones, which can even decrease the efficiency of classifiers. Feature Selection (FS) is the most common preprocessing technique utilized to overcome the drawbacks of the high dimensionality of datasets and often has two conflicting objectives: The first function aims to maximize the classification performance or reduce the error rate of the classifier. In contrast, the second function is designed to minimize the number of features. However, the majority of wrapper FS techniques are developed for single-objective scenarios. Multi-verse optimizer (MVO) is considered as one of the well-regarded optimization approaches in recent years. In this paper, the binary multi-objective variant of MVO (MOMVO) is proposed to deal with feature selection tasks. The standard MOMVO suffers from local optima stagnation, so we propose an improved binary MOMVO to deal with this issue using the memory concept and personal best of the universes. The experimental results and comparisons indicate that the proposed binary MOMVO approach can effectively eliminate irrelevant and/or redundant features and maintain a minimum classification error rate when dealing with different datasets compared with the most popular feature selection techniques. Furthermore, the 14 benchmark datasets showed that the proposed approach outperforms the stat-of-art multi-objective optimization algorithms for feature selection.
A Two-Step Optimization for Extrinsic Calibration of Multiple Camera System (MCS) Using Depth-Weighted Normalized Points

A multiple camera system (MCS), which allows only a limited common field-of-view between adjacent cameras, has been chiefly calibrated using a typical 2D-3D point correspondence. However, the correspondence contains a potential instability by which the calibration result can diverge, and the instability has not been studied nor overcome before. We propose a MCS extrinsic calibration method with high robustness and accuracy based on a two-step optimization strategy. Using depth-weighted normalized points, we develop two novel types of point correspondence as follows. The 1st correspondence, <inline-formula><tex-math notation="LaTeX">$F_{cb}$</tex-math></inline-formula>, aims to robustly estimate the MCS extrinsic parameters by overcoming the potential instability that exists in the typical 2D-3D point correspondence. The 2nd correspondence, <inline-formula><tex-math notation="LaTeX">$F_{cc}$</tex-math></inline-formula>, aims to refine the MCS extrinsic parameters by using the direct relation between adjacent cameras. In the simulation, we validated the robustness and high accuracy of the proposed method. We validated its high precision in the field test. In both the simulation and the field test, our method was compared with the state-of-the-art method.
Multi-Stage Series Model Predictive Control for PMSM Drives

This paper presents a multi-stage series model predictive control (MS-MPC) for PMSM drives in order to further improve the control performance of MPC. Firstly, the basic principle of conventional MPC is introduced. Then, the multi-stage series MPC is proposed in this paper, in which the cost functions of different prediction instant are adopted to form series control structure. In this method, the current prediction trajectory of one voltage vector is extrapolated to multiple control periods, and the control performances of every voltage vector during multiple control periods are evaluated by multi-stage series cost functions. Additionally, the implementation process of the proposed method is described. Finally, the comparison experiments between the conventional MPC and the proposed method show that the proposed method can effectively reduce the switching frequency under the same control frequency. In addition, the improvement of current control performance can be achieved by the proposed method under the condition of the same switching frequency.
Patchwork: Concentric Zone-Based Region-Wise Ground Segmentation With Ground Likelihood Estimation Using a 3D LiDAR Sensor

Ground segmentation is crucial for terrestrial mobile platforms to perform navigation or neighboring object recognition. Unfortunately, the ground is not flat, as it features steep slopes; bumpy roads; or objects, such as curbs, flower beds, and so forth. To tackle the problem, this letter presents a novel ground segmentation method called Patchwork, which is robust for addressing the under-segmentation problem and operates at more than 40 Hz. In this letter, a point cloud is encoded into a Concentric Zone Model–based representation to assign an appropriate density of cloud points among bins in a way that is not computationally complex. This is followed by Region-wise Ground Plane Fitting, which is performed to estimate the partial ground for each bin. Finally, Ground Likelihood Estimation is introduced to dramatically reduce false positives. As experimentally verified on SemanticKITTI and rough terrain datasets, our proposed method yields promising performance compared with the state-of-the-art methods, showing faster speed compared with existing plane fitting–based methods. Code is available: https://github.com/LimHyungTae/patchwork
Comparison of Two Filter Design Methods for Conducted EMI Suppression of PMSM Drive System for Electric Vehicle

In order to solve the problem that the conducted voltage of a motor drive system for electric vehicle (EV) exceeds the standard limits of CISPR25-2016, two different design methods of electromagnetic interference (EMI) filter for HV power supply were proposed. The first method is to design a wide-band EMI filter based on engineering method. It can achieve the insertion loss of 60 dB within 150 kHz--108 MHz. Another proposed method is to design the EMI filter based on the resonance peaks suppression. During the second filter design process, by analyzing the common mode (CM) and differential mode (DM) interference paths at two key frequency points 1 MHz and 30 MHz, the dominated parameters responsible for the over-standard points were determined, and the CM and DM filter circuit was designed accordingly. Compared with the filter designed by the first method, the filter designed based on resonance peaks suppression can better meet the design goals, without further rectification according to the tests, and it is smaller in size, only 1/4 of the filter size designed by the first method, lower in cost, and easy to be engineering.
Three-Phase Four-Wire AC–DC–AC Converter With Shared Legs

This article proposes a three-phase four-wire ac–dc–ac converter with legs shared between grid and load sides. This topology is suitable for operation as uninterruptible power supply and unified power quality conditioner. Also, two scenarios of the converter are considered, first, load and grid voltages with same amplitude with possibility to compensate grid-voltage swells, and second, grid voltage with twice the amplitude of load. Converter model, pulsewidth modulation, control strategies, and passive elements design are developed. Comparisons between conventional and proposed topologies in terms of harmonic distortion and semiconductor losses are carried out. The proposed converter is suitable for operation in low-voltage high-power applications, presenting lower conduction losses than the considered conventional topologies.
Business Process Management as a driver for Digital Transformation : A case study in a higher education institution

Digital transformation of the organization's processes accelerated in recent years and boosted with the Covid-19 pandemic. Digital transformation usually implies process changes and leads to increased automation of flows, which results in many benefits. In this scenario, the use of business process management systems presents clear advantages due to the speed of implementation and, above all, the ease and rapid adjustment of digital support to changes in business processes. To concretely evaluate the benefits of adopting business process management systems in higher education, it was carried out a case study based on the implementation of an internal process on a business process management system, and the subsequent completion of a user survey. The survey, based on the ISO/IEC-25010 quality standard, concluded that the process implemented under the case study is significantly better in terms of quality of use.
An Adaptive Clustering Algorithm Based on Circular Units

The traditional K-means algorithm has been widely studied and applied in data analysis due to its simplicity, efficiency and effectiveness. However, the traditional K-means algorithm strongly relies on the pre-defined number of clusters k, which leads to different training iterations and make the clustering results fall into local minimum. In addition, the traditional K-means algorithm is computationally complex and uses a proximity allocation strategy to handle outliers, which results in lower clustering accuracy. To address such issues, we present a novel improved Circular Units-based Adaptive Clustering (CUCA) algorithm in this paper. Our proposed algorithm describes each cluster in terms of circular data units and selects the initial centroids based on the density of each unit to solve the above-mentioned issues, i. e. , different training iterations and the possibility of converging to local minimum. The algorithm can exactly find the neighbouring circular data units of each prime centroid based on the tree neighbour cluster search, relationship judgment only between the prime centroids and its neighbour circular data units instead of all units, reducing the time overhead. The algorithm uses predictive affiliation mechanism to determine the relationship between outliers and each cluster, and obtains the result that the outliers belong to a cluster or become a separate cluster, improving clustering accuracy.
Facial Expression Recognition: A Review

Advancement in Computer Vision technology has opened many doors for emerging fields like Facial Expression Recognition (FER) to make human beings-computers interaction more efficient. It will look more natural if computers can read and respond to human emotions. In this (FER) process, steps like face detection, feature extraction, and feature classification are performed to recognize facial expressions. And with the increase in the interaction between humans and computers, the need for such kinds of the system has emerged, which can easily understand human emotions and can interact with them. And this all happened due to the rise of the internet. In this paper, a comparative study of some of the best papers published in recent years and future directions has been presented which covers various approaches with the same goal i.e., high recognition rate.
Color Constancy Model Optimization with Small Dataset via Pruning of CNN Filters

Color constancy is an essential part of the Image Signal Processor (ISP) pipeline, which removes the color bias of the captured image generated by scene illumination. Recently, several supervised algorithms, including Convolutional Neural Networks (CNN)-based methods, have been proved to work correctly on this problem. It is time-consuming and costly to collect many raw images of various scenes with different lighting conditions and measure corresponding illumination values. To reduce the dependence on a large scale labeled dataset and take advantage of standard CNNs architectures, we proposed an approach to create an efficient color constancy algorithm. Firstly, we utilized a structure channel pruning method to thin our baseline model. We iteratively pruned 75% channels of a specific Mobilenet version used as our model’s backbone, trained on a large-scale classification dataset. It means the backbone with the classification head is used to deal with our network pruning task. Then the resulted compact model was transferred and trained on a small dataset doing color constancy. During training on the color constancy task, we applied the DSD technique. The proposed method reaches comparative performance with other state-of-the-art models, produces fewer MACs, and can significantly decrease computational costs.
Exploring 1D and 2D Nanomaterials for Health Monitoring Wearable Devices

In this work we explore 1D and 2D nanomaterials (carbon nanotubes and MoS2, respectively) as the core sensing components of novel sensitive and highly flexible strain sensors to monitor patients' vital signs. We assess the materials performance following a multi-disciplinary and multi-level approach, starting from atomistic simulations, up to device fabrication and applications. We successfully developed a CNT-based sensor able to detect very low strain (~2%), and with GF up to ~60 at 120% strain. We tested such sensor for the detection of the respiratory rate in living beings, and we showed that these materials are excellent candidates to be used for the development of next generation health monitoring wearable devices.
Decentralized Max-Min Resource Allocation for Monotonic Utility Functions

We consider a decentralized solution to max-min resource allocation for a multi-agent system. Limited resources are allocated to the agents in a network, each of which has a utility function monotonically increasing in its allocated resource. We aim at finding the allocation that maximizes the minimum utility among all agents. Although the problem can be easily solved with a centralized algorithm, developing a decentralized algorithm in absence of a central coordinator is challenging. We show that the decentralized max-min resource allocation problem can be nontrivially transformed to a canonical decentralized optimization. By using the gradient tracking technique in the decentralized optimization, we develop a decentralized algorithm to solve the max-min resource allocation. The algorithm converges to a solution at a linear convergence rate (in a log-scale) for strongly monotonic and Lipschitz continuous utility functions. Moreover, the algorithm is privacy-preserving since the agents only transmit encoded utilities and allocated resource to their intermediate neighbors. Numerical simulations show the advantage of our problem reformulation and validate the theoretical convergence result.
Evaluation of the Improved Envelope Spectrum via Feature Optimization-gram (IESFOgram) for bearing diagnostics under low rotating speeds

one of the crucial parts in the rotating equipment are rolling element bearings in which the faults can lead to machine breakdown and product loss. Nowadays, many approaches have been proposed for the detection of bearing faults operating at medium to high rotating speeds. On the other hand, state of the art methods often fail to detect bearing faults under low speeds due to several obstacles. Therefore early, robust and accurate bearing diagnostics under low-speed conditions is still a challenge. In this paper the effectiveness of the IESFOgram for bearing diagnostics under slow-speed conditions is investigated and compared with the well-known Kurtogram. The approach is implemented on vibration data, taken from a test rig operating under low-speed during an Accelerated Life Test (ALT). Interestingly due to the character of the ALTs more than one types of faults occur. Various Features, targeted and blind, are tested in the IESFOgram. The method is evaluated at different stages of the occurring faults and it can be concluded that IESFOgram can accurately diagnose the worn-out bearing operating under low-speed conditions.
Experimental Comparison of Multi-Sharpening Methods Applied To Sentinel-2 MSI and Sentinel-3 OLCI Images

Multi-spectral images are crucial to detect and to understand phenomena in marine observation. However, in coastal areas, these phenomena are complex and their analyze requires multi-spectral images with both a high spatial and spectral resolution. Unfortunately, no satellite is able to provide both at the same time. As a consequence, multi-sharpening techniques—a.k. a. fusion or super- resolution of multi-spectral and/or hyper-spectral images—were proposed and consist of combining information from at least two multi-spectral images with different spatial and spectral resolutions. The fused image then combines their best characteristics. Various methods—based on different strategies and tools—have been proposed to solve this problem. This article presents a comparative review of fusion methods applied to Sentinel-2 MSI (13 spectral bands with a spatial resolution ranging from 10 to 60 m) and Sentinel-3 OLCI (21 spectral bands with a spatial resolution of 300 m) images. Indeed, both satellites are extensively used in marine observation and, to the best of the authors’ knowledge, the fusion of their data was partially investigated (and not in the way we aim to do in this paper). To that end, we provide both a quantitative analysis of the performance of some state-of-the-art methods on simulated images, and a qualitative analysis on real images.
A Current-fed Three-port DC/DC Converter for Integration of On-board Charger and Auxiliary Power Module in Electric Vehicles

On-board charger (OBC) and auxiliary power module (APM) are two major power electronic units in electric vehicles (EVs). OBC is the interface between the grid and HV propulsion battery, and the APM is the bridge connecting the HV system and LV system inside the EV. To save the cost and shirk the size, this paper proposed a three-port current-fed triple-active bridge (CFTAB) converter to integrate OBC with APM both electrically and magnetically. Compared with state-of-art integration approaches, the proposed converter features a simple structure, free of function-select switches, and fewer transformer turns. With the corresponding power decoupling method developed, the proposed topology also allows charging HV battery and LV battery simultaneously. Due to the current-fed nature, there is no need for large output capacitors, and much lower current stress is exhibited. In addition, an integrated prototype for 11kW/250V~450V OBC plus 3.5kW/10V~16V APM is developed to prove the superiorities of the proposed integrated charger.
AMO-Net: abdominal multi-organ segmentation in MRI with a extend Unet

Abdominal organ-related diseases have become one of the main diseases that affect people’s healthy life. MRI is a clinical diagnosis method for abdominal-related diseases. Through MRI, doctors can have a more intuitive observation of the tissue lesions in the human abdomen to make more detailed observations. Accurate diagnosis, therefore, accurate image segmentation of MRI images has very important clinical value. Traditional segmentation methods are relatively inefficient for organ segmentation with large abdominal deformation, small volume and blurry tissue edges. In this paper, we propose a AMO-Net to overcome these limitations. First, we extend the single encoder-decoder architecture to 2 layers to learn richer feature representations. Second, the feature pyramid structure is introduced into the proposed network, which can effectively handle multi-scale changes, is friendly to small target object recognition, and can be associated with remote feature information. Finally, a module called Hierarchical-Split Block is introduced to improve CNN performance. We evaluate our model on the CHAOS challenge dataset, and the final experiment proves that our model achieves better segmentation performance compared with other state-of-the-art segmentation networks.