Miracle-dz/UniFAR-FTUs huggingface dataset:
RCS Reduction of Microstrip Antenna Based on Characteristic Mode Analysis

In this paper, microstrip antenna is analyzed using the theory of characteristic modes (TCMs). Modal weighting coefficient (MWC) under the excitation of plane wave are further calculated. Additional slots are introduced to lower the resonant frequency of scattering modes, which results in smaller modal weighting coefficient in the operation frequency band. Monostatic RCS of the microstrip antenna have a certain degree of reduction.
Symbolic Representation Model for Off-Line Signature Verification

Nowadays, in our daily life, the importance of handwritten signatures is increasing, because people are more comfortable with pen and papers for all the legal transactions. So it is necessary to verify the identity of a person based on his handwritten signature. Identifying the human identity is more difficult work. So to overcome this problem offline signature verification method is proposed based on symbolic representation model. Writer–dependent technique is used to generate symbolic representation model. The system verifies the signature of two class i.e. genuine signature and skilled forgery. In the proposed work, Local Binary Pattern (LBP) features are used for feature extraction and symbolic features are then extracted for each feature in every signature class. As a result, the number of symbolic features is obtained for each individual's handwritten signature class. The k-Nearest Neighbor algorithm is used to classify the genuine signature and skilled forgeries. For analyzing the proposed approach Bangla offline signatures dataset is used. Results are discussed based on the writer dependent and writer independent techniques.
Optimal Design and Experimental Test of a SPM Motor With Cost-Effective Magnet Utilization to Suppress Torque Pulsations

This paper presents an optimal design for a surface-mounted permanent magnet (SPM) motor to suppress torque pulsations and save the magnet cost, by using multi-grade permanent magnets. Based on a conventional SPM motor with single-grade bonded NdFeB magnets, the proposed SPM motor is designed with two-grade bonded NdFeB and one-grade ferrite magnets, and then optimized by combining the Kriging method with a genetic algorithm, for further minimizing torque pulsations and saving the magnet cost by maintaining the high average torque. As a result, the cogging torque, torque ripple, and magnet cost of the optimized SPM motor are highly reduced compared to those of the conventional SPM motor. Furthermore, the optimized SPM motor is verified to have superior endurance against the magnet’s irreversible demagnetization within three times of the rated current excitation. All motor characteristics, including airgap flux density, cogging torque, electromagnetic torque, and demagnetization ratio are first predicted with the aid of a finite-element method. Finally, the optimal design and analysis results are validated by the experimental results.
Remote Sensing Image Enhancement Based on Nonsubsampled Shearlet Transform and Local Laplacian Filter

To improve contrast for remote sensing image without suffering from insufficient details, this paper proposes an enhancement method for different remote sensing images based on nonsubsampled shearlet transform (NSST) and local laplacian filter. The input image is first decomposed into low-frequency and high-frequency components. Then, the chosen local laplacian filter is used to process the low-frequency coefficient, and the adaptive threshold is used to suppress the noise included in the high-frequency coefficient. Finally, the NSST is inversed to obtain the enhanced image. Visual and quantitative analysis indicate the significance of the proposed image enhancement scheme, as compared to state of the art techniques.
PI V.I.S.I.O.N: Portable Infinitesimal Video Input Signal Processed Intelligent Object Naming

The staggering numbers of visually impaired people especially in the third world countries motivated us to develop a system to help these people with the available technology at affordable prices. PI V.I. S.I. O.N1 1https://github:com/diganthp/pi-vision that stands for Portable Infinitesimal Video Input Signal Processed Intelligent Object Naming System is the system proposed. The object detection is implemented on Raspberry Pi. Furthermore, distance of the detected object is measured using a single camera with the help of concepts of similar triangles. Based on this, intensity of the haptic device is varied, which alerts the user. For the benefit of the user, Google assistant has been integrated. The final objective is to benefit the visually impaired people.
High-Performance Unsupervised Anomaly Detection for Cyber-Physical System Networks

While the ever-increasing connectivity of cyber-physical systems enlarges their attack surface, existing anomaly detection frameworks often do not incorporate the rising heterogeneity of involved systems. Existing frameworks focus on a single fieldbus protocol or require more detailed knowledge of the cyber-physical system itself. Thus, we introduce a uniform method and framework for applying anomaly detection to a variety of fieldbus protocols. We use stacked denoising autoencoders to derive a feature learning and packet classification method in one step. As the approach is based on the raw byte stream of the network traffic, neither specific protocols nor detailed knowledge of the application is needed. Additionally, we pay attention on creating an efficient framework which can also handle the increased amount of communication in cyber-physical systems. Our evaluation on a Secure Water Treatment dataset using EtherNet/IP and a Modbus dataset shows that we can acquire network packets up to 100 times faster than packet parsing based methods. However, we still achieve precision and recall metrics for longer lasting attacks of over 99%.
MAS-Based Approach for Scheduling Intensive Workflows in Cloud Computing

Cloud Computing is becoming popular model for delivering Information Technology (IT) that offers different services on demand over the Internet. This technology is dedicated to distribute computing resources and their consumption as software services. With the availability of data gathered from sophisticated scientific tools, workflows have proven their utility to implement relevant scientific realizations. Scheduling algorithms are primordial to efficiently automate these intensive workflows and many attemps have been made to develop new heuristics relying on a Cloud resource model. The majority of these heuristics address one or two-dimensional QoS issues. Instead, our work considers multi-dimensional QoS metrics for scheduling namely, execution time, cost, reliability, availability and ensures maximum balancing and exploitation of resources in order to minimize the energy consumption. In this paper, we propose a multi-agent approach to schedule intensive workflows in Cloud computing in such a way that all tasks will be executed with minimal possible time and smallest energy consumption. Our approach considers multi-dimensional QoS metrics for scheduling namely, execution time, cost, reliability, availability and ensures maximum balancing and exploitation of resources in order to minimize the energy consumption.
Efficient Self-Adaptive Image Deblurring Based on Model Parameter Optimization

Natural images suffer from degradations in imaging system, and image blur is a major source of them. Most existing approaches aim to estimate a blur kernel via an alternating optimization method in multiscale space. However, in our practical project application, we need to deal with motion blurs come from moving conveyor belts. In this case, the degradation model and its orientation are known to us. In this paper, we propose a self-adaptive image deblurring method to deal with it. The model parameters are optimized by a heuristic algorithm, and the latent images are deblurred by a deconvolution technique based on f 1 -norm constraint. Simulation results show that our method not only acts on motion blur model, but also can deal with atmosphere turbulence model and defocus model, and the comparison results indicate that it outperforms others’. Furthermore, it is able to deal with motion blur in real scenes with high efficiency.
Self-synchronization of multiple vehicles using ambient impulsive noise

Clock synchronization is important when considering a swarm of underwater vehicles. Unfortunately, accurate clocks are expensive, and acoustic communication is power intensive, preventing large numbers of vehicles from being deployed for long periods of time. We propose a technique where vehicles achieve relative synchronization by leveraging sounds already present in the underwater environment. This provides a synchronization method that does not rely on expensive clocks, communication, or additional infrastructure. We demonstrate that our technique accurately tracks clock offsets between vehicles during an at sea deployment.
Contingency Ranking in Power Systems Via Reliability Rates

This paper shows the scope of probabilistic contingency ranking algorithms when applied to transmission systems with high levels of renewable integration. Using our fast screening contingency ranking algorithm, a performance index is calculated through AC power flows. In order to capture the probabilistic behaviour of the system outages, we use the reliability data from the Reliability Test System, and combined with the performance index this yields a different assessment on the contingency ranking task. The contingency ranking is applied in this paper for an N-1 security criterion. The entire model was developed and implemented for steady-state power system simulations using the MATPOWER programme which runs in MATLAB environment.
Relay Assisted Device-to-Device Communication: Approaches and Issues

Enabling technologies for 5G and future wireless communication have attracted the interest of industry and research communities. One of such technologies is Device-to-Device (D2D) communication which exploits user proximity to offer spectral efficiency, energy efficiency and increased throughput. Data offloading, public safety communication, context aware communication and content sharing are some of the use cases for D2D communication. D2D communication can be direct or through a relay depending on the nature of the channel in between the D2D devices. Apart from the problem of interference, a key challenge of relay aided D2D communication is appropriately assigning relays to a D2D pair while maintaining the QoS requirement of the cellular users. In this article, relay assisted D2D communication is reviewed and research issues are highlighted. We also propose matching theory with incomplete information for relay allocation considering uncertainties which the mobility of the relay introduces to the set up.
A Multilevel Cell STT-MRAM-Based Computing In-Memory Accelerator for Binary Convolutional Neural Network

Due to additive operation’s dominated computation and simplified network in binary convolutional neural network (BCNN), it is promising for Internet of Things scenarios which demand ultralow power consumption. By means of fully exploiting the in-memory computing advantages and low current consumption design using multilevel cell (MLC) spin-toque transfer magnetic random access memory (STT-MRAM), this paper proposes an MLC-STT-computing in-memory-based computing in-memory architecture to achieve convolutional operation for BCNN to further reduce the power consumption. Simulation results show that compared with the resistive random access memory (RRAM)- and spin orbit torque-STT-MRAM-based counterparts, the architecture proposed in this paper reduces power consumption by ~ $35{\times}$ and 59% in Modified National Institute of Standards and Technology data set, respectively.
Research on User Gender Prediction of Chinese Microblog Based on Short Text Analysis

Gender information of Weibo users is taken as the research object. After pretreatment of user's text information, the linguistic features and topic features are extracted. For each user, building space vector model based on linguistic features, topic features and both. Then using SVM machine learning algorithm to construct classifier for gender prediction. Experiments show that the linguistic features and topic features can predict the sex of the users accurately, and the effect is superior to other features used in gender prediction.
Spatial Multiple Access (SMA): Enhancing performances of MIMO-NOMA systems

The error performance of the Non-Orthogonal Multiple Access (NOMA) technique suffers from inter-user interference (IUI) although it is a promising technique for future wireless systems in terms of the achievable sum rate. Hence, a multiple access technique design with limited IUI and competitive to NOMA in terms of spectral efficiency is essential. In this paper, we consider so-called spatial multiple access (SMA) which is based on applying the principle of spatial modulation (SM) through the different users’ data streams, as a strong alternative to multiple-input and multiple-output (MIMO)-NOMA systems. The analytical expressions of bit error probability (BEP), ergodic sum rate and outage probability are derived for the SMA. The derivations are validated via computer simulations. In addition, the comparison of the SMA system with NOMA is presented. The results reveal that SMA outperforms NOMA in terms of the all performance metrics (i.e., bit error rate (BER), outage probability and ergodic sum rate) besides it provides low implementation complexity.
Fog Computing as a Defensive Approach Against Distributed Denial of Service (DDoS): A Proposed Architecture

Cloud computing is a long-established technique which deals with storage and processing of information. In cloud computing, any user is liable to pay and demand according to its particular needs. Due to various limitations of cloud computing like higher latency and more bandwidth consumptions for transmitted information, the origination of Fog computing was essential. Fog computing inherits all the advantages of cloud computing, and also brings computing at the network's edge. In addition, security is a very serious concern for cloud computing. In this paper, fog computing is used as a defensive approach from the day-to-day increasing security threats particularly DDoS attacks in cloud computing. Here an architecture has been proposed to obstruct the malicious traffic generated by the DDoS attack from user to the cloud by utilizing the benefits of fog computing. Fog functions as a filtering layer for the traffic generated and is placed between user and cloud. This paper primarily works to improve the overall performance of the network and enhances reduction in the traffic forwarded to the cloud.
Target Classification and Tracking Using Symbolic Dynamic Filtering

Wireless sensor networks have emerged as a propitious solution to monitor human activity, such as pedestrian and vehicular movement, as well as detection of unauthorized persons. This can be especially advantageous in military applications such as tracking, surveillance, and security. For such systems, it is essential to implement efficient algorithms for onboard computation that save time and memory. In view of this, the work presented in this paper aims to implement classification and tracking of targets with reduced computation time and lower dimension feature vectors using Symbolic Dynamic Filtering - a wavelet based process. The extracted features follow particular trends that were distinct enough to be classified with the help of simple algorithms. This eliminates the need of an external classifier which saves quite a lot of computational time. Targets, here, vehicles were classified into two classes, namely tracked and wheeled using their seismic signatures. The results of tracking can also be used to monitor the heath of the sensors.
ADMM Based Distributed Volterra Modeling of Nonlinear Dynamical Systems Using Ad-Hoc WSNs

To model the behavior of nonlinear dynamical systems using wireless sensor networks (WSNs), the development of computation and energy efficient distributed modeling techniques is of crucial importance. In this work, Volterra model is considered for the modeling of nonlinear systems because of its significant modeling capability and generalized nonlinear structure. For real-time estimation of the Volterra model parameters, a simple distributed algorithm is designed for a particular framework of WSNs having predefined bridge sensor nodes. The pertinent cost function is expressed as an unconstrained minimization problem using a decomposable augmented Lagrangian form. To facilitate the distributed convex optimization, the augmented Lagrangian form is minimized using alternating direction method of multipliers. The communication and computational complexities involved in the proposed methodology are provided to show its effectiveness in the real-time applications over centralized and non-cooperative solutions. The convergence analysis is provided to guarantee the mean stability of the proposed algorithm. Simulation results obtained under the noisy environment are plotted to demonstrate the effective performance of the proposed algorithm.
A Standalone Open Hardware System for ECG Detection and Classification

Timely detection of cardiac arrhythmia is critical for diagnosis and treatment of heart related disorders. ECG signal provide valuble insight into heart functioning and enable diagnosis of arrhythmia conditions. An open hardware based system for the automated classification of heart conditions was developed. The Raspberry Pi 3b (RPi3b) single board computer was used to analyse the ECG signal and identify normal or abnormal heart conditions. The developed system can independently acquire ECG signals and classify arrythmia using ANN algorithm running on the RPi3b. The system was tested using MIT-BIH arrhythmia database. Realtime ECG signal acquisition and classification was also carried out.
A High-Sensitivity Tactile Sensor Array Based on Fiber Bragg Grating Sensing for Tissue Palpation in Minimally Invasive Surgery

This paper presents a novel and high-sensitivity optical tactile sensor array based on fiber Bragg grating (FBG) to explore and localize tissue abnormalities during tissue palpation in a minimally invasive surgery. This sensor consists of five separate tactile cells with the same configuration, associated fixations, and holder parts. Each tactile cell is mainly composed of a spiral elastomer, a suspended optical fiber inscribed with an FBG element, and a contact head connecting the elastomer with a threaded connection. The two ends of the fiber were glued on the two sides of the spiral elastomer to form a tight suspension status. This configuration can effectively avoid the FBG chirping failure and achieve a higher sensitivity compared with the conventional direct FBG-pasting methods. The corresponding theoretical model has been derived, and the finite element method based simulation has been performed to facilitate the structure design and investigate both static and dynamic performances. Both static calibration experiments and dynamic loading experiments have been conducted to characterize the sensor's performance. The prototyped sensor can achieve a high resolution of up to 0.93 mN and is able to operate within a force measurement range of 0–5 N with a maximum relative error of less than 8.22%. The further surface estimation experiments and in-vitro palpation implementation on a silicone phantom embedded with simulated tumors have validated the effectiveness of the proposed design. The in-vitro experiments have demonstrated an excellent capacity to detect tumors with an embedded depth of up to 8 mm.
A D-band Foam-Cladded Dielectric Waveguide Communication Link with Automatic Tuning

This paper presents a 140 GHz fully packaged frequency shift keying transceiver with automatic tuning loop for dielectric waveguide communication. A novel receiver topology is proposed to enable mitigation against process, voltage and temperature variations using an automatic tunable phase shift in the LO distribution. Implemented in 28 nm CMOS, the design has a total power consumption of 230 mW and occupies an active area of 0.9 mm2. The measured link achieves a data rate of 10 Gbps / 7 Gbps over a 2 m / 4 m foam-cladded and thus touchable PTFE channel.
Sound Field Reproduction via the Alternating Direction Method of Multipliers Based Lasso Plus Regularized Least-Square

This paper proposes a 3-D sound field reproduction (SFR) approach through the combination of alternating direction method of multipliers (ADMM)-based least-absolute shrinkage and selection operator (Lasso) and regularized least square (LS). The proposed SFR method is split into two parts through the pressure matching optimization of loudspeaker positions and the computation of driving signals. At the first part, a plurality of candidate positions of loudspeakers in planar array is given and, then, the active speaker selection method is proposed based on ADMM complex Lasso algorithm for selecting the optimal loudspeaker positions. Afterwards, regularized LS is adopted to calculate the selected loudspeaker weights and control the total power. The numerical simulation experiments demonstrate that the proposed SFR scheme outperforms the existing sparse loudspeakers’ placement and weight optimization algorithms especially in under-sampled sound fields. Meanwhile, the evaluations also confirmed that the proposed method could significantly reduce the computational complexity of the active loudspeaker selection compared to the state-of-the-art Lasso-based SFR. Effectively, the proposed method uses a relatively small number of loudspeakers for a satisfying reproduction quality.
The molecular basis of protein toxin HicA–dependent binding of the protein antitoxin HicB to DNA

Toxin–antitoxin (TA) systems are present in many bacteria and play important roles in bacterial growth, physiology, and pathogenicity. Those that are best studied are the type II TA systems, in which both toxins and antitoxins are proteins. The HicAB system is one of the prototypic TA systems, found in many bacterial species. Complex interactions between the protein toxin (HicA), the protein antitoxin (HicB), and the DNA upstream of the encoding genes regulate the activity of this system, but few structural details are available about how HicA destabilizes the HicB–DNA complex. Here, we determined the X-ray structures of HicB and the HicAB complex to 1.8 and 2.5 Å resolution, respectively, and characterized their DNA interactions. This revealed that HicB forms a tetramer and HicA and HicB form a heterooctameric complex that involves structural reorganization of the C-terminal (DNA-binding) region of HicB. Our observations indicated that HicA has a profound impact on binding of HicB to DNA sequences upstream of hicAB in a stoichiometric-dependent way. At low ratios of HicA:HicB, there was no effect on DNA binding, but at higher ratios, the affinity for DNA declined cooperatively, driving dissociation of the HicA:HicB:DNA complex. These results reveal the structural mechanisms by which HicA de-represses the HicB–DNA complex.
Attention Prediction Using Partial Differential on Color Spaces

Human attention prediction has attracted significant interest in recent years due to its promising contributions to various computer vision applications. In this paper, we present a simple and effective framework for extracting meaningful features from the color space. The feature extraction is based on computing the second order partial differential for the input image. The features extracted from the RGB and LUV spaces are combined to form a saliency map of natural images. Based on the public dataset used in previous works, we compare our proposed algorithm with several competitive approaches presented in the literature and the results demonstrate that the proposed method is effective. We also apply the feature based on the LUV space to improve some of the previous approaches, and our experimental results show that the application is meaningful for enhancing the accuracy of human attention prediction.