Miracle-dz/UniFAR-FTUs huggingface dataset:
Gravitational Search Algorithm for Estimating the Kinetic Parameters of the Propane Pre-reforming Reaction

The article describes a gravitational search algorithm and its application to solving the inverse problem of chemical kinetics. The relevance of the study of metaheuristic algorithms, including the gravitational search algorithm, is given. It is shown that recently, these algorithms are becoming increasingly popular. The optimization problem is formulated on the example of solving the inverse kinetic problem. The process under study is propane pre-reforming into methane- rich gas over Ni catalyst, which is an industrially important chemical process. The description of the algorithm and its pseudo-code are presented, after which the performance of the gravitational search algorithm is compared with other metaheuristic methods. The algorithm demonstrated its competitiveness, as a result of which it was applied to solve a specific industrial problem. Using this algorithm, the inverse problem of chemical kinetics was solved, and the optimal values of the kinetic parameters of the reaction were found. It was proved that the model correctly describes the available experimental data.
Running Gaussian Average as Method of Adaptive Background Subtraction in Radar Applications

Background subtraction is a usual step in impulse radar applications. It is widely used to remove clutter – reflections of all the stationary, mostly irrelevant objects, as well as crosstalk. Various techniques are used for clutter/crosstalk removal. These are briefly over-viewed. The focus of this paper, however, is on the proposed method of adaptive background subtraction, which is a statistical real-time method based on developments of entirely different field (Computer Vision).
Automatic Modulation Classification Using CNN-LSTM Based Dual-Stream Structure

Deep learning (DL) has recently aroused substantial concern due to its successful implementations in many fields. Currently, there are few studies on the applications of DL in the automatic modulation classification (AMC), which plays a critical role in non-cooperation communications. Besides, most previous work ignores the feature interaction, and only considers spatial or temporal attributes of signals. Combining the advantages of the convolutional neural network (CNN), and the long short-term memory (LSTM), this paper addresses the AMC using CNN-LSTM based dual-stream structure, which efficiently explores the feature interaction, and the spatial-temporal properties of raw complex temporal signals. Specifically, a preprocessing step is first implemented to convert signals into the temporal in-phase/quadrature ($I/Q$) format, and the amplitude/phase ($A/P$) representation, which facilitates the acquirement of more effective features for classification. To extract features from each signal pattern, each stream is composed of CNN, and LSTM (denoted as CNN-LSTM). Most importantly, the features learned from two streams interact in pairs, which increases the diversity of features and thereby further improves the performance. Finally, some comparisons with previous work are performed. The experimental results not only demonstrate the superior performance of the proposed method compared with the existing state-of-the-art methods, but also reveal the potential of DL-based approaches for AMC.
Influence of Dielectric Overlay Permittivity on Size and Performance of Miniaturized ESPAR Antenna

In this paper, influence of dielectric overlay permittivity on miniaturized ESPAR antenna parameters is presented. ESPAR antenna is a low-cost and energy-efficient way to implement beam steering capability to a node and improve network performance. The antenna size reduction is obtained by embedding its active and passive elements in ABS based materials of relative permittivity equal to 4, 5.5 and 7.5 in order to achieve network node compact size. Simulation results of three optimized for particular dielectric constant designs are presented and tradeoff between dimensions reduction and performance is discussed. Selected materials and antenna design are dedicated to be fabricated in 3D print technology, so can be easily prototyped.
Single Snapshot DoA Estimation from a Rotman Lens using Machine Learning Techniques

In this paper an efficient wideband Direction of Arrival (DoA) and frequency estimation technique is presented which uses only a single snapshot from a multi-beam antenna array. The simultaneous spatial beams (beamspace) are created by a Rotman lens which is composed of a free-space true time delay network attached to a Uniform Linear Array (ULA) of broadband Vivaldi antenna elements. The proposed technique uses machine learning techniques to establish an optimal Neural Network (NN) configuration obtained with a training set. To improve the spatial and frequency space resolution a further estimation stage follows to the NN topology. This results in a lower computational load during the training phase and finally a very fast estimation of direction and frequency of the impinging signal. The performance is evaluated by measurements obtained in an anechoic chamber.
Experience in Using Real Time Simulators in Variable-Frequency Drives

This paper reports the experiences obtained in using real time simulators in electric drives systems. Hardware-in-the-Loop (HiL) and Power-Hardware-in-the Loop (PHiL) are powerful techniques that are successively used for verification, validation and experimentation of electrical drive systems. A HiL simulator based on Nexys 4 DDR board with kernel FPGA Artix 7 real time simulator has been used to test scalar control method controlling induction motor. In addition, this work presents detailed modelling of sucker rod pump, and discusses implementation approach and challenges to test the sucker rod pump in PHiL platform. Experimental results on induction motor validate the effectiveness of the HiL approach and PHiL simulation results indicate the applicability of PHiL approach for testing diagnostic methods for sucker rod pumps.
Rumor Detection on Twitter Using Features Extraction Method

Rumors can widely and rapidly diffuse in social networks than in offline platforms. This work was an attempt to address this challenge by introducing an approach for rumors detection, which learns from the Twitter dataset. In other words, this paper is going to suggest new features were extracted from Twitter datasets based on user behavior, the propagation features and the temporal features that representing the social context to reduce variation in features. Also, the proposed work involves adopting an ensemble classifier as a more appropriate approach and for achieving a better performance compared to existing individual-based classifiers. Moreover, the proposed method processes unbalanced data sets to reduce their impact on detection algorithms and improve the classification accuracy rate. Experimental results on the PHEME dataset showed that the new features made the classification process more effective and accurate compared to many related works of rumor classification that use the same data. Experimental results achieved 78.54% accuracy as an average of the events used.
Novel method for the wireless synchronization of the anchors in the UWB localization system utilizing two reference nodes

Nowadays indoor localization is getting more and more attention as there is a considerable growth in the interest for the indoor location-based services (LBS). There are several different techniques that allow for the calculation of the position of objects and people inside the buildings. Methods utilizing measurements of the received signal strength (e.g. WiFi or BLE based) are most common but provide accuracy of roughly 2-3 meters. Recently, ultrawideband (UWB) technique is more often being considered for the indoor localization. It relies on the measurements of the time of arrival of received signals and allows for the calculation of the position with errors in the range of only dozens of centimeters. However, in order to work correctly, UWB-based systems require strict synchronization between the anchors that form the infrastructure. In the paper a novel method of wireless synchronization utilizing two reference nodes is presented. Synchronization method is described along with the explanation of the transmission scheme and discussion on the results of the performed experimental verification.
Miniaturized 3D-printed Antenna Realizations for Cylindrical Surface Mounting

In this paper two types of miniaturized 3D-printed, waveguide antennas been designed, simulated and measured. Several pieces of antennas have been manufactured to investigate the impact of dimensions inaccuracy of 3D-printing technology on return loss parameter. In order to reduce the impact of dimensions spread and differences in a printed material permittivity a tuning process in a dielectric-filled antenna is proposed.
Text Classification Based on A New Joint Network

In order to solve the problem that single convoluti- onal neural network (CNN) lacks the ability to make use of text context information and that simple recurrent neural network (RNN) cannot solve the problem of long-time dependence, this paper proposes the joint network (BL-CN) combine bidirectional long short term memory network (BiLSTM) with capsule network (CapsNet) for text classification. CapsNet is an improved version of CNN, which can retain more text features, increase the diversity of features, and capture the local pluralistic features of sentences. Combined with BiLSTM which has ability to extract context-related features. It can get not only the local phrase features with multiple information, but also the sequence structure features of text and the bidirectional semantic dependence of sentence, which can more comprehensively express the text features. Experiments were carried out on the data sets of IMDB and THUCNews to compare the accuracy and loss of each single network and the combined network respectively. The results show that the model proposed in this paper can improve the accuracy of text classification.
An Intelligent Malware Detection and Classification System Using Apps-to-Images Transformations and Convolutional Neural Networks

With the proliferation of Mobile Internet, handheld devices are facing continuous threats from apps that contain malicious intents. These malicious apps, or malware, have the capability of dynamically changing their intended code as they spread. Moreover, the diversity and volume of their variants severely undermine the effectiveness of traditional defenses, which typically use signature-based techniques, and make them unable to detect the previously unknown malware. However, the variants of malware families share typical behavioral patterns reflecting their origin and purpose. The behavioral patterns, obtained either statically or dynamically, can be exploited to detect and classify unknown malware into their known families using machine learning techniques. In this paper, we propose a new approach for detecting and analyzing a malware. Mainly focused on android apps, our approach adopts the two following steps: (1) performs a transformation of an APK file into a lightweight RGB image using a predefined dictionary and intelligent mapping, and (2) trains a convolutional neural network on the obtained images for the purpose of signature detection and malware family classification. The results obtained using the Androzoo dataset show that our system classifies both legacy and new malware apps with high accuracy, low false-negative rate (FNR), and low false-positive rate (FPR).
Physical and MAC Layer Design for Active Signaling Schemes in Vehicular Networks

Nowadays, many telecommunication systems (wifi, cable systems and 4G, 5G cellular networks) use Orthogonal Frequency Division Multiplexing (OFDM) as the physical layer standard. The design of efficient OFDM signal detection algorithms is very important to provide reliable systems, and this is particularly true for Vehicular Adhoc Networks (VANETs) involving autonomous vehicles, where missing a signal or detecting a fake one may cause a dangerous situation. The performance of these algorithms is generally evaluated in terms of their robustness against noise. In this paper, we evaluate the probability of error in signal detection in order to establish the minimum length of preamble needed for the active signaling process. This mechanism is used in AS-DTMAC (active signaling fully distributed TDMA-based MAC protocol) to reduce access collisions. Thus, by reducing the length of the preamble, greater time is given for the payload part of the packet, resulting in increased throughput.
SEIZE: Runtime Inspection for Parallel Dataflow Systems

Many Data-Intensive Scalable Computing (DISC) Systems provide easy-to-use functional APIs, and efficient scheduling and execution strategies allowing users to build concise data-parallel programs. In these systems, data transformations are concealed by exposed APIs, and intermediate execution states are masked under dataflow transitions. Consequently, many crucial features and optimizations (e.g., debugging, data provenance, runtime skew detection), which require runtime datafow states, are not well-supported. Inspired by our experience in implementing features and optimizations over DISC systems, we present <inline-formula><tex-math notation="LaTeX">$\mathsf {SEIZE}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">SEIZE</mml:mi></mml:math><inline-graphic xlink:href="li-ieq1-3035170.gif"/></alternatives></inline-formula>, a unified framework that enables dataflow inspection—wiretapping the data-path with listening logic—in MapReduce-style programming model. We generalize our lessons learned by providing a set of primitives defining dataflow inspection, orchestration options for different inspection granularities, and operator decomposition and dataflow punctuation strategy for dataflow intervention. We demonstrate the generality and flexibility of the approach by deploying <inline-formula><tex-math notation="LaTeX">$\mathsf {SEIZE}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">SEIZE</mml:mi></mml:math><inline-graphic xlink:href="li-ieq2-3035170.gif"/></alternatives></inline-formula> in both Apache Spark and Apache Flink, and by implementing a prototype runtime query optimizer for Spark. Our experiments show that, the overhead introduced by the inspection logic is most of the time negligible (less than 5 percent in Spark and 10 percent in Flink).
Literature Review on False Data Injection Attacks Against Power System

False data injection attacks against the power system make full use of the traditional state estimation flaw detection of bad data. Cyber attackers can successfully inject false data into the vector equipment value to modify value and state variables of power grids, control the operation state of power grids, obtain economic benefits and other illegal purposes. This paper expounds on the issues about the physical security of power grids, the principle of false data injection attacks, its influence on power grids, how to prevent this kind of cyber attack, analyzes research status of false data injection attacks and unresolved problems.
Metamaterial engineered C+L band 90° hybrid with 150 nm feature size

90° hybrids are important components for coherent optical communications. Conventional implementations only cover the C band, while broadband, metamaterial-based devices require sub-100nm feature sizes. We propose a design based on dual-etch silicon subwavelength structures that achieves a 170 nm bandwidth with deep-UV compatible feature sizes.
Target Tracking in Blind Range of Radars With Deep Learning

Surveillance radars form the first line of defense in border areas. But due to highly uneven terrains, there are pockets of vulnerability for the enemy to move undetected till they are in the blind range of the radar. This class of targets are termed the ‘pop up’ targets. They pose a serious threat as they can inflict severe damage to life and property. Blind ranges occur by way of design in pulsed radars. To minimize the blind range problem, multistatic radar configuration or dual pulse transmission methods were proposed. Multistatic radar configuration is highly hardware intensive and dual pulse transmission could only reduce the blind range, not eliminate it. In this work we propose, elimination of blind range using deep learning based video tracking for mono static surveillance radars. Since radars operate in deploy and forget mode, visual system must also operate in a similar way for added advantage. Deep Learning paved way for automatic target detection and classification. However, a deep learning architecture is inherently not capable of tracking because of frame to frame independence in processing. To overcome this limitation, we use prior information from past detections to establish frame to frame correlation and predict future positions of target using a method inspired from CFAR in a parallel channel for target tracking.
Model Predictive Control of a Direct Current Motor Utilizing Lookup Tables

The problem of unpredictable acoustic behaviour of the finite control set model predictive control (MPC) can be solved in continuous control set MPC. However, continuous control set MPC usually requires huge computation efforts or analytical solution of the non-linear equations of the motor, which is not always possible. This paper considers the option of precalculation of all control commands for each combination of the input variables. This approach is applicable for the drives with limited number of state variables, such as direct current motor or switched reluctance machine. An example of the control system of direct current motor with torque stabilization and minimization of the ohmic losses was implemented using simulation model. The 4-dimensional lookup table was used for armature current control and 2-dimensional lookup table was used for field winding control. The simulation results show rapid torque stabilization together with losses minimization.
1.55 μm Quantum Dash CW Lasers on Planar (001) Si

We present the growth of electrically pumped continuous-wave (CW) quantum dash (QDash) lasers on planar (001) Si emitting at 1.5 μm, with different active region structures. After growth optimization, the lowest threshold current, 2 kA/cm2 was obtained on a device grown on planar silicon.
Passive Acoustic Radar System for Flying Vehicle Localization

The proposed radar system is characterized by cheap equipment (omnidirectional microphones, a computer for signal processing, and peripherals) and by the special algorithm of signal processing, which has been developed and tested during full-scale experiments with real targets: aircrafts and UAVs. A key feature of the developed algorithm is a fast method of estimation of wideband ambiguity function. In addition, the algorithm provides visualization of the target location process using the projection of the ambiguity function onto a coordinate plane. The developed radar system is proposed to use for aircraft noise management in the vicinity of an airport and localization of small-sized flying vehicles. The results of the experiments are presented in the paper.
A Method of Phase Unwrapping Algorithms Efficiency Analysis for InSAR Data Processing

The experimental study of algorithms for radar data interferometric processing effectiveness is complicated by the fact that a quantitative result can be obtained only after the completion of the entire processing procedure, so it is difficult to assess the effectiveness of its first stages. A method of experimental evaluation of the accuracy of the results of interferometric processing by various algorithms of phase unwrapping with such combination of image and reference data, which does not require the use of the ambiguity height parameter, is proposed. The results of the method implementation for ALOS PALSAR data in relation to areas with hilly and low-mountain terrain are presented. Estimates of the vertical accuracy of ALOS PALSAR interferometric data for Region Growing and Minimal Cost Flow phase unwrapping algorithms implemented in the SARscape software package are obtained.
IoT based Energy Management System for Smart Grid

In the modern days the word “Smart Grid” becomes a common word which can also be used as a junction which monitor demand side energy as well as the incoming energy and make it balance when the demand is on the peak or in low position it contributes the saved electric energy with the help of Internet of Things (loT). The IoT make power grid (smart grid) more reliable and economical. By IoT there is no lack of information in demand and supply and also it helps to save the energy. It also make pre-pattern of electricity consumption as the load demand and send signal to generation side for the required energy for the specific time period. The smart grid with IoT integrates the high speed response and storage system also with energy consumption. This type of networking by using IoT makes the operation of Smart Grid more reliable and burden free from the high demand periods (because the stored energy is used for the required demand). It also makes a better environment to collect the power from distributed generation and fasten the use of operation in the smart grid environment using loT. In this paper the use of IoT is discussed to work with Smart Grid for the better, economical, loss free power and also it helps in energy saving by reduce wastage. IoT is also used for the transfer of real time information to the Smart Grid and when the increase or decrease of demand takes places it helps in the saving of energy. User and supplier communication has been established by IoT for the proposed system.
Double Insurance: Incentivized Federated Learning with Differential Privacy in Mobile Crowdsensing

Exploiting the computing capability of mobile devices with specialized engines (e.g., Neural Engine in iPhone), an attractive paradigm of federated learning that combines the mobile crowdsensing (MCS) has been deeply investigated recently (e.g., Google AI and Nvidia), where the training task is offloaded to the mobile crowd. However, this new paradigm still has numerous problems. Since executing the training task is costly for individual workers, the first problem is how to attract more participants. Following the incentive requirement, the second is how to preserve the workers’ bid privacy since the reported costs are usually sensitive. Finally, the third problem is to guarantee the privacy protection on locally training models in the federated learning which involve the private information of local data. In this paper, we propose an incentivized federated learning with differential privacy in MCS system, namely, SHIELD, to solve the three significant problems. In fact, SHIELD satisfies the truthfulness and individual rationality while preserving the differential privacy of workers’ bids and locally training models. Furthermore, for accuracy, the excess empirical risk of SHIELD is proved to be upper bounded by $\mathcal{O}\left(\frac{\left(\ln\left(Kn_{\min}\right)\right)^{\frac{1}{2}}}{Kn_{\min}}+\right. \left. \frac{\ln\left(Kn_{\min}\right)}{K^{2}n_{\min}^{2}}\right)$, where a special case for totally distributed scenario leads to a much sharper bound $\mathcal{O}\left(\frac{\log\left(n\right)}{n^{2}}\right)$ than the latest result $\mathcal{O}\left(\frac{\ln\left(mn_{\min}\right)}{m^{2}n^{2}}\right)$. Finally, comparing with the state-of-art approaches, SHIELl illustrates superior performance by numerous experiments in classification and regression tasks.