Miracle-dz/UniFAR-FTUs huggingface dataset:
Thermal Evaporation of Different Copper Nanotip Structures under High Electric Field Based on Electrodynamics – Molecular Dynamics Simulations

As the miniaturization of microelectromechanical systems (MEMS), nanoelectromechanical systems (NEMS) and molecular devices, the problem of vacuum insulation in the devices becomes more and more prominent. The nano-scale thermal effect caused by electron emission or dielectric breakdown under high electromagnetic field and local temperature, will lead to gasification and migration of the material in the device. In this work, the electrodynamics in the combination with discretized classic molecular dynamics method is used to simulate the thermal evaporation of nano-tips under high electric field. Cu nano-tips with different initial geometries and different electric fields are modeled using finite element methods to further analyze the deformation and damage mechanism of nano-tips under high electric field. Simulation results show that the length-diameter ratio of nano-tips have a significant influence on the atomic evaporation of nano-tips. The study is helpful to further explore the fundamental properties of vacuum breakdown at nano-scale.
Low-Complexity Coherent Iterative Receiver for SCMA-Based LEO Satellite Communications

In this paper, we consider the sparse code multiple access (SCMA) for low earth orbit (LEO) satellite communications to achieve high user capacity and provide technical support for satellite-based Internet of Things (IoT) applications. Note that Doppler shift in the LEO satellite link can not be ignored and the message passing algorithm (MPA) extensively utilized by SCMA is quite time-consuming due to high complexity. Thus we propose a coherent iterative receiver with low complexity for SCMA-based LEO satellite communication systems. First, we design a data-aided carrier synchronization scheme using multiple disjoint pilot blocks to estimate the Doppler shift precisely. Then, a soft-out approximate message passing (SO-AMP) algorithm with linear complexity is derived from the first-order approximation of MPA based on a novel framework of SCMA mapper. Further, a damping method is introduced into the SO-AMP detector developing a damped SO-AMP (DSO- AMP) to improve the overall performance. Numerical results show that the designed receiver can achieve excellent performance close to ideal performance with no Doppler shift while maintaining low complexity.
Deep Latent Spectral Representation Learning-Based Hyperspectral Band Selection for Target Detection

Hyperspectral images (HSIs) can provide discriminative spectral signatures regarding the physical nature of different materials. It is this unique nature that makes HSIs to be of great interest in many fields. However, HSI application faces various challenges due to high dimensionality, redundant information, noisy bands, and insufficient samples. To address these problems, we propose an unsupervised band selection method based on deep latent spectral representation learning, called DLSRL, in this article. It imposes spectral consistency on deep latent space that resolves the issue of insufficient samples and spectral information lost in HSI interpretation. It pursues the low-dimensional optimal representation of the high-dimensional HSIs. In particular, an adaptive mapping relationship is constructed between the deep latent representation and the optimal subset to preserve physical significance optimally. Furthermore, a hierarchical optimization approach is introduced to achieve target detection with the selected subset. To verify the superiority of the proposed method, experiments have been conducted on four data sets captured by different sensors over different scenes. Comparative analyses validate that the proposed method presents superior performance in terms of high detection accuracy and low false alarm rate.
Comparison of segmentation methods for the detection of breast cancer using thermal images

Breast cancer is the most diagnosed cancer among woman and one of the deadliest. Early detection is key in bettering the chance of a recovery, it ranges from a survival change of 100% at stage 1 to 22% at stage 4. There are several methods designed for the early detection of breast cancer, with the gold standard being mammography. There is a need for the development of non-invasive and cost-effective methods. This has led to other methods being investigated. One such method is thermography, which analyses heat patterns captured using an infrared camera. It then looks for asymmetry between the left and right breasts as an indication of abnormality. Segmenting out the breasts correctly is a vital part of the detection process. There are many ways to extract the breasts. This study compares five segmentation methods, namely: distance-based cropping (Method 1), manual zoom cropping (Method 2), semi-automatic segmentation (Method 3), fully manual segmentation (Method 4), and fully automatic segmentation (Method 5). Once the breasts are extracted via each method statistical and textural features are calculated and reduced to only the significant ones. From this a simple feed forward neural network is trained using leave out one cross validation for robustness. Method 1 has 63.33% accuracy, Method 2 had 76.67%%, Method 3 had 88.33%, Method 4 had 90%, and Method 5 had 80%. Method 5 had 6 unsuccessful segmentations due to it being unable to detect the inframammary folds, due to this semi-automatic segmentation (Method 3) is the recommended approach due to its high accuracy and a degree of automation which saves time.
Generalized Differential OFDM Index Modulation

In this paper, a novel non-coherent communication technique called generalized differential index modulation (GDIM) is presented. The key idea of GDIM is to modulate information bits to a joint permutation of rows and columns for a unitary matrix. It is shown that the proposed GDIM achieves a higher spectral efficiency than that attained by the existing differential modulation techniques including differential subcarrier index modulation and differential Alamouti code. An average bit-error-rate for GDIM is analyzed and applied to optimize GDIM constellation sets. Simulation results demonstrate that the proposed GDIM outperforms the existing differential modulation techniques in terms of bit-error-rates.
Automatic and Manual Switch Mode Targeting Weapon System for Border Security

Indian borders are continuously guarded by soldiers even if it is a normal day or during a war day. They observe any kind of terrorist movement and unfortunately this is not safe for soldiers. The proposed system is an Automatic and Manual switch mode targeting weapon system for border security using ultrasonic sensor, servo motor, microcontroller, RF module and Bluetooth, laser/led. It works in two modes mainly in automatic mode it detects the target and shoots down when the target crosses a specified trespassing prohibited area using ultrasonic sensor and laser firing weapon and upon the failure of this system it switches to manual mode during which the weapon is monitored from a remote base camp via a live steaming and the weapon rotations and shoot controls are provided at the base station which will be controlled by a specific authorized person following intrusion.
Implementation of Backward-Forward Sweep Method on Load Model Variation of Distribution Systems

The power flow method in distribution system has its own challenges attributable to its unique characteristics. Several methods have been developed to achieve a simple, fast, and precise result. One of the method is Backward-Forward Sweep (BFS) which has simple characteristic to implement in radial distribution systems. The method was improved using a new data structure and implemented in an unbalanced load with a variety of model variations. The test referred to 37 bus of IEEE radial distribution system and compare to the network without load model variation or assume all PQ load. The result shows that both performances have equally good convergence but much faster in timing computation and larger of voltage magnitude with load model variation.
Mobile Millimeter Wave Channel Tracking: A Bayesian Beamforming Framework against DOA Uncertainty

A Bayesian approach for joint beamforming and tracking is presented, which is robust to uncertain direction-of-arrival (DOA) estimation in millimeter wave (mmWave) multiple input multiple output (MIMO) systems. The uncertain or completely unknown DOA is modeled as a discrete random variable with a priori distribution defined over a set of candidate DOAs, which describes the level of uncertainty. The estimation problem of DOA is formulated as a weighted sum of previously observed DOA values, where the weights are chosen according to a posteriori probability density function (pdf) of the DOA. In particular, we present a motion trajectory-based a priori probability approximation method, which implies a high probability to perform a directional estimate within a specific spatial region. We demonstrate that the proposed approach is robust to DOA uncertainty, and the beam tracking problem can be addressed by incorporating the Bayesian approach with an expectation-maximization (EM) algorithm. Simulation results validate the theoretical analysis and demonstrate the effectiveness of the proposed solution.
Open-Circuit Fault Detection of Asymmetrical Six-phase Induction Motor Fed from Z-Source Inverter

Recently, asymmetrical six-phase induction motors have proved their performance and easiness of design and control over many other types of multiphase machines. On the other hand, it is well-known that the key feature of any type of multiphase machines is their fault tolerant capability with no additional hardware. To ensure performing of the post-fault control strategy immediately after the fault occurrence, fast fault detection method is a necessity or more devices will be damaged. On the other hand, despite of the multiple features of ZSI in healthy state, the impedance network is expected to delay the fault detection time. The vector space decomposition fault detection (VSDFD) method has proved its high velocity and robustness in fault detection for six-phase machine fed by sinusoidal supply and for a five-phase machine fed by VSI. Therefore, this method is tested in this paper for an asymmetrical six-phase induction machine fed by ZSI. A simulation is performed to test the performance of the VSDFD method under three different types of fault and transient states also. Some of the suggested parameters for this method in the other literatures is adjusted to ensure it is adequate with the new machine and inverter type with a final recommendation for choosing the parameters. Finally, the simulation results show fast detection of all types of fault which ensures the reliabilty and robustness of this method.
Optimal Control Strategy Design with Minimum Energy Consumption for Connected Vehicle Systems

In this paper, an optimal control algorithm for connected vehicle systems is proposed in order to ensure the vehicular platoon stable as well as reduce the transmission power consumptions in the presence of the network-induced delays. First, the vehicle dynamic modeling and power consumption analysis are addressed based on a typical 3-vehicle platoon. With the objective of minimizing the deviations of vehicle's headway and velocity as well as reducing power consumption, an optimization problem is formulated using a quadratic cost function. Then, the design of the optimal control strategy with minimum power consumption for connected vehicle systems can be divided into two steps: first the minimal hop routings for human- driven vehicles are obtained based on the network topology, and then the optimal control strategy is derived based on the determined transmission routing.
Active Voltage Control in Distribution Networks including Distributed Generations using Hardware-In-The-Loop Technique

In weak distribution networks, the amount of distributed generation (DG) is often limited by the voltages limits of the network due to the voltage rise effect. As the penetration level of DG increases, active voltage control methods need to be implemented, which can lower the connection costs in many cases and increase the capacity of connectable DG substantially if used instead of the passive approach. In this paper, operation of a modified active voltage control algorithm is applied on a distribution network in a coordinated manner using hardware-in-the-loop technique. Real time simulations are done to test the data transfer and the reliability of execution of the control algorithm.
Interleaver Design for Small-Coupling-Length Spatially Coupled Protograph LDPC-Coded BICM Systems Over Wireless Fading Channels

As a class of convolutional error-correction codes (ECCs), spatially coupled protograph low-density parity-check (SC-PLDPC) codes with a sufficiently large coupling length can approach the channel capacity under belief propagation (BP) decoding. However, such codes suffer from poor performance in the case of small coupling lengths. To address the above weakness, we investigate the optimization and analysis of SC-PLDPC-coded bit-interleaved coded modulation (BICM) systems over Rayleigh fading channels. Specially, inspired by the unequal-error-protection (UEP) mechanism of <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula>-ary phase-shift keying/quadrature amplitude modulation (<inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula>-PSK/QAM), we put forward a novel interleaver design scheme, referred to as <italic>spatial position match mapping (SPMM) scheme</italic>. Based on the mutual information (MI) analysis, the proposed interleaver can be used to significantly boost the performance of the SC-PLDPC codes in BICM systems. Furthermore, we propose a block-scheduling protograph-based extrinsic information transfer (BS-PEXIT) algorithm to estimate the convergence performance (i.e, decoding threshold) of the SPMM-aided SC-PLDPC codes in BICM scenarios. Simulation results are well consistent with the theoretical analyses, and show that the proposed SPMM-aided SC-PLDPC-coded BICM systems are superior to the state-of-the-art counterparts.
A Registration Method for Real Bronchoscopy Image with Virtual Bronchoscope Images Using Similarity Measures

Bronchoscopy is the procedure for inspecting, and retrieving diagnostic samples for lung-related surgeries. Based on similarity measures, this paper proposes a novel registration method for matching real bronchoscopy image with virtual CT-based bronchoscope images to obtain the current 3D position of the bronchoscope in the airways. Using this method, the 3D locations of the bronchoscope can be discovered and co-displayed with the real bronchoscopy 2D images to help the surgeon navigate the bronchial airways during surgery. Experimental results shows that our method performs better than other methods and can provide the clinicians the extra spatial information required for guiding the bronchoscope down the correct air passage.
Detection and classification of vehicles from heterogeneous traffic video data collected using a probe vehicle

On typical roads in India where traffic are heterogeneous, road planning and management become crucial. Vehicle detection and classification is useful for traffic survey, planning, signal time optimization, and surveillance. A vehicle with a video camera placed at the top can be used for collecting video of the traffic for a two lane undivided road. The camera is placed at an angle of approximately 45 degrees. Both audio and video are used for detection and classification. The data collected is later processed for detecting and categorizing vehicles in the traffic into seven categories namely, one vehicle, more than one vehicle, no vehicle, two wheeler, three wheeler, cars and heavy motor vehicle. The presence of vehicle is identified by analyzing the short time energy of the audio peaks and the process of adaptive background subtraction. Selection of detected vehicles is carried out using Speeded up Robust Features (SURF) matching algorithm. Histogram Oriented Gradient (HOG) features of selected vehicles is extracted and classification is performed in two stages, using Support Vector Machine (SVM) classifier.
Joint Optimization of Energy Consumption and Time Delay in Energy-Constrained Fog Computing Networks

In this paper, we study a joint energy harvesting (EH) and task offloading (TO) design for an energy- constrained fog computing network, which consists of a mobile terminal and two fog nodes. The energy- constrained terminal employs time switching (TS) protocol to harvest energy from the signals sent by the circuit-powered fog node, and then exploits the harvested energy to perform local computing and offload computing. Our aim is to minimize the product of energy consumption and time delay with the constraints of TS ratio and EH requirements. To determine the optimal solution of the original non-convex problem, we decouple it into three subproblems based on the time delay assumption and then solve these subproblems through our proposed constraints activation algorithm. Furthermore, we also derive the closed form expressions for the optimal TO and TS ratios. Simulation results are presented to illustrate the effectiveness and superior performance of the proposed joint design against other conventional schemes in the literature.
A Joint Scheduling Scheme for Relay-Involved D2D Communications in Cellular Systems

To fully explore the benefits of developing device- to-device (D2D) communications in cellular systems, enabling relay-assisted (RA) D2D transmissions is a promising way. However, involving RA D2D mode will make the design of scheduling scheme at the base station (BS) side even more challenging. This work focuses on design of a scheduling scheme involving RA D2D mode for BS, which jointly considers power coordination, relay selection, mode selection, and resource allocation. Aiming to maximize the cell- wise throughput, we formulate such a scheduling issue into a mathematical optimization problem. We show how to decompose the formulated problem into two subproblems and solve them separately by using exiting algorithm and corresponding mathematical optimization theories. Particularly, the integer programming problem on mode and channel assignments is transformed into a linear programming problem to improve the solving efficiency. Simulation results validate the performance of the joint scheduling scheme in terms of cell-wise system capacity.
Distributed Graph Computation Meets Machine Learning

<sc>TuX<sup>2</sup></sc> is a new distributed graph engine that bridges graph computation and distributed machine learning. <sc>TuX<sup>2</sup></sc> inherits the benefits of elegant graph computation model, efficient graph layout, and balanced parallelism to scale to billion-edge graphs, while extended and optimized for distributed machine learning to support heterogeneity in data model, Stale Synchronous Parallel in scheduling, and a new Mini-batch, Exchange, GlobalSync, and Apply (<italic>MEGA</italic>) model for programming. <sc>TuX<sup>2</sup></sc> further introduces a hybrid vertex-cut graph optimization and supports various consistency models in fault tolerance for machine learning. We have developed a set of representative distributed machine learning algorithms in <sc>TuX<sup>2</sup></sc>, covering both supervised and unsupervised learning. Compared to the implementations on distributed machine learning platforms, writing those algorithms in <sc>TuX<sup>2</sup></sc> takes only about 25 percent of the code: our graph computation model hides the detailed management of data layout, partitioning, and parallelism from developers. The extensive evaluation of <sc>TuX<sup>2</sup></sc>, using large datasets with up to 64 billion of edges, shows that <sc>TuX<sup>2</sup></sc> outperforms PowerGraph/PowerLyra, the state-of-the-art distributed graph engines, by an order of magnitude, while beating two state-of-the-art distributed machine learning systems by at least 60 percent.
Remote Fingerprinting on Internet-Wide Printers Based on Neural Network

Nowadays, a large number of printers are connecting to the Internet. It is undoubtedly true that these online printers are facing severe cyber threats. However, the research/researchers so far cannot answer the current security status of Internet-wide printers. The principal difficulty is to identify the exact brand and model of online printers with high coverage, which is the primary element in vulnerability description. In this work, we design and implement a system called PrinterRadar. Based on Neural Network, PrinterRadar can automatically generate fingerprints from the application layer protocol banners of online printers, and the precision and recall rate of fingerprints can achieve 98% and 97%. By fingerprint matching with the banners which were collected from Censys and Shodan, PrinterRadar found that 508,719 printers were connected to the Internet, covering 92 printer brands and 4,188 printer models. As to the discovered printers, it is about twice the number of those detected by Censys and Shodan.
Intelligent Online Auto-Tuning Technique for IPMSM Servo Drive

A real-time moment of inertia identification technique using wavelet fuzzy neural network (WFNN) for an interior permanent magnet synchronous motor (IPMSM) servo drive is proposed in this study. The estimated moment of inertia will be used in the online design of an integral-proportional (IP) speed controller to achieve the gains auto-tuning of the IPMSM servo drive. In this study, first, the dynamic analysis of a field-oriented control (FOC) IPMSM servo drive system with an IP speed controller is studied. Then, a heuristic approach using the WFNN is proposed for the real-time identification of the moment of inertia of the IPMSM servo drive system. Moreover, the network structure and the convergence analysis of the WFNN are introduced. Furthermore, an IPMSM servo drive based on a high performance digital signal processor (DSP) is developed. Finally, from the experimental results, the gains of the IP speed controller can be effectively tuned online at different operating conditions.
Clustering Method of 3D Point Cloud of Muck-Pile Based on Connectivity of Adjacent Surface

This paper proposes a method to measure the fragmentation distribution of a pile of rocks (muck-pile) using image-based 3D reconstruction. One of the most important aspects of mine-blasting is appropriate rock fragmentation to optimize the cost of the blasting operation. The conventional method of measuring fragmentation distribution is based on 2D image processing including segmentation of muck-pile regions into rock clusters. However, in the 2D method, the measurement accuracy is limited. To accurately measure rock fragmentation distribution, we reconstructed a 3D model of a muck-pile from multi-view images and segment the 3D model based on rock-features such as color, normal vector, distance and adjacent angles of surface planes. As a result, the size of each rock was calculated by fitting a bounding box. Based on experimental evaluations, it was confirmed that the accuracy of the proposed method is higher than that of previous methods.