Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel MILP Formulation for Fault Isolation and Network Reconfiguration in Active Distribution Systems

This paper proposes a novel mixed-integer linear programming (MILP) model for distribution systems to respond to contingencies by forming multiple microgrids (MGs) energized by dispatchable distributed generators (DGs). The MILP model can model the outage propagation during faults, and minimize the load shedding while satisfying the radial topology and other operation constraints by controlling automatic switches and dispatchable DG outputs, thus, minimizing the outage areas. Case studies on a modified IEEE 123-bus test feeder are implemented to illustrate the effectiveness of the proposed MILP model.
Web Deception Towards Moving Target Defense

As security threat are rising, and due to the serious risk, that affect the safety of the data are increasing, taking on consideration the complexity of detection, and securing the data, a proactive implementation of technique like deception and Moving Target Defense (MTD) have been emerged as important focus of the security community. The way those systems work by enhancing the flexibility of the system has increased the complexity for the attacker, which lead to a huge reduce on the number of successful attacks against it. In this paper we add more level of assurance by combining both technique on certain layers on the web application system.
Passive Monitoring of HTTPS Service Use

HTTPS is used today to secure the majority of web communications and so enhance user privacy. Therefore, traffic monitoring techniques must evolve to remain useful, especially to support security considerations, as for example detecting and filtering the forbidden uses of a web service. However, privacy should remain as intact as possible. This paper describes a new passive and transparent method to infer the use of a HTTPS service by extracting and interpreting only meaningful meta-data derived from the encrypted traffic without deeply profile individual users. We propose a model using the sizes of objects loaded in the HTTPS service as a signature, by leveraging kernel density estimation, supporting then a classification function. We assess this approach extensively on the Google Images Service although our approach remains valid for some other services. We succeed to achieve an accuracy of 99.18% when detecting particular keywords to be searched over a large dataset of 115,500 distinct keywords.
Device Investigation of Nanoplate Transistor With Spacer Materials

In this paper, a comparison of gate-all-around nanowire-FETs, nanoplate (NP)-FETs, and FinFETs was undertaken for the same areas of not only the gate metal but also the silicon channel, assuming the same conditions regarding the parasitic components. It is known that an NP-FET structure not only improves the delay performance but also enhances the immunity to short-channel effects. In addition, it is found that the use of a dual-k spacer with an NP-FET further improves the on-state as well as the off-state performances.
Vegetation-Related Outages on Transmission Lines in North America

Ineffective vegetation management was identified as a major cause of the August 14, 2003 blackout, and has also been a causal factor in other large-scale North American outages. In response, the North American Electric Reliability Corporation (NERC) developed a vegetation management reliability standard, to provide industry focus and commitment to vegetation management practices. This paper examines industry performance over time to determine whether performance has improved. Analysis of two different data sets shows that the standard has been effective at reducing transmission outages due to vegetation on lines subject to the standard (based on voltage class).
Statistical MOSFET Modeling Methodology for Cryogenic Conditions

Conventional transistor models are unable to capture the electrical behavior of transistors at cryogenic temperatures. In this paper, a methodology has been developed to calibrate temperature dependence parameters of Berkeley Short-Channel Insulated Gate Field Effect Transistor Model (BSIM3). Rather than modifying BSIM3 equations, the algorithm only changes the values of relevant parameters through noniterative, analytic operations; therefore, it can be implemented in typical circuit simulation tools. Proposed methodology allows to estimate <inline-formula> <tex-math notation="LaTeX">${I}_{D}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${V}_{\textsf {GS}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${I}_{D}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${V}_{\textsf {DS}}$ </tex-math></inline-formula> curves of the transistors having different channel lengths and widths even under various operating voltages, including the body bias. The parameter extraction algorithm runs with a reasonable computation cost and can compute parameter sets for transistors at user-defined cryogenic conditions.
Monolithic Waveguide-Based Linear Photodetector Array for Use as Ultracompact Spectrometer

We propose a monolithic spectral waveguide photodetector (SWGPD) concept based on a waveguide with a chemical gradient of the semiconductor alloy forming the absorbing medium that allows for spectrally resolved detection. The chemical gradient correlates the absorption of different wavelengths with the position on the waveguide. The generated charge carriers are collected by a linear array of photodetectors along the waveguide. As model system and proof of concept (Mg,Zn)O alloys in the wurtzite phase (Zn-rich) are considered that allow photon detection in the near ultraviolet spectral region. Our concept allows for high overall detection of (in principle) all photons coupled into the waveguide. The spectral range and resolution are determined by the chemical gradient, the spectral broadening of the absorption edges, and the optical confinement factor. At the same time, the photodetectors can provide high-time resolution, limited by the photodetector geometry.
Identifying Individual Camera Device From RAW Images

This paper investigates the problem of identifying the individual imaging device source of the same model from a natural image in RAW format. We propose an enhanced Poissonian–Gaussian model describing the distribution of pixels from a RAW image. The parameters of this statistical noise model are considered as unique fingerprints and, hence, used to identify the source camera device. The source camera identification problem is cast within the framework of the hypothesis testing theory. In an ideal context, where all model parameters are perfectly known, the Likelihood Ratio Test (LRT) is presented, and its performance is theoretically established. The statistical performance of this optimal LRT serves as an upper bound for the detection power. For a practical use, when the nuisance parameters are unknown, a generalized LRT based on estimation of those parameters is designed to deal with unknown expectation of pixels (roughly, the image content). More importantly, by combining multiple sub classifiers together with the voting strategy, a multi-classifier is proposed to identify multiple individual devices. Numerical results on simulated data and on various large dataset of real natural images highlight the relevance of our proposed approach.
Toward Developing a Systematic Approach to Generate Benchmark Android Malware Datasets and Classification

Malware detection is one of the most important factors in the security of smartphones. Academic researchers have extensively studied Android malware detection problems. Machine learning methods proposed in previous work typically reported high detection performance and fast prediction times on fixed and defective datasets. Therefore, based on these shortcomings most datasets are not suitable for real-world deployment. The main goal of this paper is to propose a systematic approach to generate Android malware datasets using real smartphones instead of emulators and develop a new dataset, namely CI-CAndMal2017, which covers all the shortcomings and limitations of previous datasets. Also, we offer 80 traffic features to select the best feature sets for detecting and classifying the malicious families just by traffic analysis. The proposed method showed an average precision of 85% and recall of 88% for three classifiers, namely Random Forest(RF), K-Nearest Neighbor (KNN), and Decision Tree (DT).
Design of a PC-based Open Industrial Robot Control System Integrated with Real-Time Machine Vision*

This paper presents a new industrial robot control system integrated with real-time image processing based on a Windows PC. This enables both machine vision and robot control tasks to be carried out on a single general-purpose computer rather than using two separate systems to perform different tasks. To accomplish this, a three-layer software structure is developed along with a series of standard peripheral devices in the hardware layout. Real-time performance of the system is tested and we demonstrate the system on a DELTA parallel manipulator. Experimental results show that the manipulator can pick up 120 objects on a moving conveyor per minute. This work demonstrates the feasibility of integrating real-time machine vision tasks with robot control tasks without dedicated hardware.
Impact of Constrained Observability on Flexibility in Distribution System Operation

Innovative concepts regarding the flexible deployment of generation and load in distribution system operation often require the consideration of network restrictions. A key element for the determination of network restrictions is the sensitivity analysis. Since the commonly used sensitivity analysis, based on load flow or state estimation techniques, needs the distribution system to be completely observable, it cannot be applied to most today’s real distribution networks without massive enhancement of measurement devices. This paper proposes three novel methods to determine the sensitivities without having full observability. The novel methods take into account graph theoretical approaches, topological information, and only limited measurement information. In a case study the three methods are tested for applicability and quality in a real medium voltage distribution network in Germany.
Menu Control and Functionalities of 3D Digital Oscilloscope

It’s very rational to have menu control and display function for oscilloscope to provide various functionality and make it user friendly. A responsive and well-structured menu design can make the system convenient to practice. The menu is composed of menu block, point, color and character. In this study a system has been developed using c language and data structure to control the menu functions with pop up options in a digital storage oscilloscope. Using font generating software, the relevant font is generated for menu and is downloaded to the given system. Besides, the additional features such as responsive menu control functionalities for real time brightness control, adjustment with runtime and stop condition make it highly functional as well as scalable for a 3D digital oscilloscope.
GMSA: Gathering Multiple Signatures Approach to Defend Against Code Injection Attacks

Code injection attacks (CIAs) exploit security vulnerabilities and computer bugs that are caused by processing invalid codes. CIA is a problem which hackers attempt to introduce to any new method, their objective being to bypass the protection system. In this paper, we present a tool called GMSA, developed to detect a variety of CIAs, for example, cross-site scripting (XSS) attack, SQL injection attack, shell injection attack (command injection attack), and file inclusion attack. The latter consists of local file inclusion and remote file inclusion. Our empirical analysis reveals that compared with existing research, gathering multiple signatures approach (GMSA) executes a precision performance (accuracy of the proposed algorithm is 99.45%). The false positive rate (FPR) of GMSA is 0.59%, which is low compared with what other research has reported. The low FPR is the most important factor. Ideally, the defense algorithm should balance between the FPR and true positive rate (TPR) because with existing methodologies, security experts can defend against a broad range of CIAs with uncomplicated security software. Typical protection methods yield a high FPR. Our method results in high TPR while minimizing the resources needed to address the false positive. GMSA can detect four types of CIA. This is more comprehensive than other research techniques that are restricted to only two major types of CIA, namely, SQL injection and XSS attacks.
Indoor Robot Visual Positioning System Based on Floor Features

In order to solve the positioning problem of indoor robots, we present visual positioning system for indoor robots based on ground features. The system firstly collects the ground image through the fisheye camera, then uses the Canny operator to perform edge detection on the captured images, then performs the distortion correction of the fisheye camera on the contour points obtained by the edge detection. It restores the contour points to the undistorted plane. Using RANSAC algorithm and least squares method to compute the relative position of the camera to the ground line segment feature contained in the contour, then using the IPM algorithm to transform the feature into the real world space. We finally obtain the distance and orientation angle from the camera to the ground feature segment. Since we measured and stored all the feature features into a map beforehand, this method can locate robot position when a feature segment is observed. Experimental results show that the positioning method has high positioning accuracy, good real-time performance even with embedded CPU and low cost, so it can solve the positioning problem of indoor robots well.
Risk Assessment of Transformer Loss-of-Life due to PEV Charging in a Parking Garage with PV Generation

This paper presents a probabilistic risk assessment methodology to evaluate distribution transformer loss-of-life in a commercial building parking garage, in the presence of plug-in electric vehicle (PEV) charging stations and local photovoltaic generation. The Monte Carlo method is applied to address the uncertainties resulting from PEV charging, photovoltaic generation and garage load. A risk matrix combining the severity measures and associated probability distribution function is used to quantify the risk of transformer loss-of-life. The results show a significant reduction in transformer loss-of-life risk when connecting photovoltaic generation, resulting in saving avoiding transformer aging and early replacement. Based on the findings, the proposed method can be a helpful tool in the decision making process that needs to balance the risk and investment costs.
Exploring Deep Learning in Semantic Question Matching

Question duplication is the major problem encountered by Q &A forums like Quora, Stack-overflow, Reddit, etc. Answers get fragmented across different versions of the same question due to the redundancy of questions in these forums. Eventually, this results in lack of a sensible search, answer fatigue, segregation of information and the paucity of response to the questioners. The duplicate questions can be detected using Machine Learning and Natural Language Processing. Dataset of more than 400,000 questions pairs provided by Quora are preprocessed through tokenization, lemmatization and removal of stop words. This pre-processed dataset is used for the feature extraction. Artificial Neural Network is then designed and the features hence extracted, are fit into the model. This neural network gives accuracy of 86.09%. In a nutshell, this research predicts the semantic coincidence between the question pairs extracting highly dominant features and hence, determine the probability of question being duplicate.
The Effect of Proton Irradiation in Suppressing Current Collapse in AlGaN/GaN High-Electron-Mobility Transistors

Almost complete suppression of dynamic ON-resistance in AlGaN/GaN high-electron-mobility transistors is obtained by proton irradiation. In this paper, both small and large power transistors are characterized before and after 3-MeV proton irradiation at different fluences. The irradiated devices show a high robustness and for specific fluences unaltered threshold voltage and static ON-resistance. However, for fluences higher than 10<sup>13</sup> cm<sup>−2</sup>, the dynamic ON-resistance is almost completely suppressed at 600 V and <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> = 150 °C. After irradiation, a measurable increase in OFF-state leakage current is observed, indicating an increase in the unintentionally doped (UID) GaN layer conductivity. We propose a technology computer-aided design supported model in which this conductivity increase leads to an increased deionization rate, ultimately reducing the dynamic ON-resistance.
Two-Port Networks to Model Galvanic Coupling for Intrabody Communications and Power Transfer to Implants

Galvanic coupling, or more precisely, volume conduction, can be used to communicate with and to transfer power to electronic implants. Since no bulky components for power, such as coils or batteries, are required within the implants, this strategy can yield very thin devices suitable for implantation by injection. To design the circuitry of both the implants and the external systems, it is desirable to possess a model that encompasses the behavior of these circuits and also the volume conduction phenomenon. Here we propose to model volume conduction with a two-port network so that the whole system can be studied in circuit simulators. The two-port network consists only of three impedances whose values can be obtained through simple measurements or through numerical methods. We report a validation of this modeling approach in a geometrically simple in vitro setup that allowed us to determine the impedances of the two-port network not only by performing measurements or through a finite element method study but also through an analytical solution.
A Dynamic Time Evolution Method for Concurrent Device-Circuit Aging Simulations

A dynamic time evolution method is developed in this paper to implement concurrent device-circuit aging simulations. The dynamic stress on each device during operations is processed by an equivalent stress duration concept, which transfers a continuous-time-based reliability model to its incremental version. With it, the device aging parameters such as threshold voltages are calculated by implementing a given reliability equation inside a compact model. The method is implemented into an open source circuit simulator for function verifications. Aging feedback, which slows down the further aging, is naturally included. Circuit aging simulations show that an overestimation of 32% can be avoided compared to those without considering the aging feedback.
Backstepping Sliding Mode Control of FC-UC Based Hybrid Electric Vehicle

Depleting fossil fuels, and subsequently, high environmental pollution has made hybrid electric vehicles (HEVs), a vital replacement to its conventional counterpart. Electric and HEVs offer a compact, lightweight, and effective means of transportation that is economical and has sufficient life cycle. In this paper, the HEV under consideration uses two energy sources to operate its traction motor: a fuel cell works as the main source, whereas, an ultra-capacitor is used as an auxiliary source. Each source is connected to the DC bus via a power conditioning circuitry, which comprises electronic power converters. The power conditioning unit harnesses energy from the sources according to the load demand of the vehicle. A nonlinear control scheme, called Backstepping Sliding mode control, is employed on the power converters in the power conditioning unit. The reason for applying this nonlinear control scheme is due to the systems nonlinear nature, where this control law provides a robust and precise output in presence of these nonlinearities. Formal stability analysis of the proposed HEV system is provided in this paper, and the simulations are performed in MATLAB/Simulink environment. The simulation results show perfect regulation of the dc bus voltage with very fast convergence, low overshoots, and almost zero steady state error, which is our primary objective.
Slip suppression in prosthetic hands using a reflective optical sensor and MPI controller

Prosthetic hands have greatly evolved in mechatronic, robotic and control aspects. However, occasional accidents might happen due to excessive grip force or the breaking of contact due to slip. Fast transient slip events can be properly handled by a low-level controller that can behave like a reflex to maintain grasp stability in a shared control manner between the user and the prosthetic hand itself. Here we propose the use of a reflective optic sensor to capture slip events and evaluate the performance of a monotonic PI (MPI) control law that acts as to suppress slip. We have characterized the response of the sensor to motion and noted that transparent surfaces generate smaller responses. The proof-of-concept experiment demonstrated the effectiveness of the MPI controller where slip events were properly suppressed by an increase in grip force.
A Performance Comparison of the PRESENT Lightweight Cryptography Algorithm on Different Hardware Platforms

For decades the security of information during its transportation from one location to another has been held with great importance. The technology of cryptography has been used for applications such as banking, transport of classified information and even to transfer detailed instructions to the front lines of the battlefield in times of war. As technology moves forward and semiconductor devices miniaturise in accordance with Moores law electronic devices are becoming faster, smaller and cheaper. These devices are constantly increasing in what they are capable of doing. Small scale devices such are RFID tag and contact-less credit cards require the information stored on the device be kept secure. The technology of lightweight cryptography has been utilised in these devices to provide a fast lightweight solution to keeping information secure. This paper looks at the PRESENT cipher, which is a lightweight block cipher designed to be easily implemented in hardware. This paper looks at the different implementations of the PRESENT algorithm in both hardware and software, and compare its performance on different platforms.
The Next Generation of Robust Linux Memory Acquisition Technique via Sequential Memory Dumps at Designated Time Intervals

The memory forensics techniques assist digital investigators to identify and detect remaining evidence of the attacks on the compromised system. The accuracy of performing the analysis is depend to the completeness, atomicity, and reliability of the memory acquisition output. Regarding to our research, the most current critical challenges in memory forensics are increasing the size of physical memory, the elapsed time of memory acquisition, malicious tampering and page smearing effects, and anti-forensics techniques. By addressing these challenges, we proposed an approach to determine approximately how much sequential memory acquisition at a designated time-intervals can mitigate them. This mitigation includes reducing I/O operations in memory acquisition to speed it up, diminishing malicious tampering and page smearing effects, and impact of anti-forensics techniques. The results of our experiments on different Linux operating system families show the best interval time for sequential memory acquisition is 3 minutes with the similarity ration between 9% to 23%. The proposed approach is applicable to software-based and hardware-based memory acquisition methods.