Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of Spectrum Misuse Behavior in Satellite-Terrestrial Spectrum Sensing Based on Multi-Hypothesis Tests

As the increasing of both users’ demand and the number of communication satellites, spectrum resource is becoming increasingly scarce. Cognitive radio (CR) can effectively alleviate the scarcity of spectrum resource. By using CR technology in the satellite and terrestrial integrated network, the satellite can share the spectrum resource with the terrestrial communication equipment to improve the spectrum utilization. However, due to the satellite communication’s inherent openness and the increasing intelligent level of devices, it makes the satellite communication vulnerable to spectrum misuse, which seriously threatens the reliability and efficiency of the satellite communication system. This paper studies the detection of spectrum misuse in the satellite-terrestrial spectrum sharing system. First, we model the problem as a ternary hypothesis test problem by using generalized multi-hypothesis Neyman-Pearson (GMNP). Then, we utilize a two-step method to solve the complex formula problem in the multiple authorized users scenario. Reasonable decision thresholds are derived by means of the generalized likelihood ratio test (GLRT) and maximum a posterior (MAP) criterion, respectively. Finally, the performance of detection is evaluated and analyzed comprehensively to verify the feasibility of the study.
Low-Cost Direction-of-Arrival Measurements using Multiplexed Antenna Switching

In this publication a method for direction-of-arrival measurements is introduced. It allows very low cost implementations. Instead of using multiple wide-band receivers with an antenna each, a single narrow-band receiver is used in combination with six antennas. The different antenna signals are time multiplexed to the receiver using an integrated 6-to-l RF switch. Signal processing allows to synchronize the corresponding signals to allow the calculation of the incident angle. As exact synchronization is not necessary, fast transitions between multiple frequencies are possible and therefore accuracy can be significantly increased.
Automatic Food Intake Monitoring Based on Chewing Activity: A Survey

Good nutrition is essential for optimal growth, development, and prevention of disease. Due to the importance of nutrition in human life, researchers have been interested in understanding the science of assessing food intake episodes for decades. With the advancement of technology, automated food monitoring tool develops with the help of sensors to address issues related to self-reporting methods. Food monitoring technology is evolving rapidly due to the advancement of sensors; however, automatic monitoring of food intake remains open problems to be solved. For food intake episode detection and monitoring, the sensors used to detect bites, chew, swallow, and hand gestures movement. This survey will be focusing on chewing activity detection during eating episodes. In this survey, a wide range of chewing activity detection explored to outline the sensing design, classification methods, performances, chewing parameters, chewing data analysis as well as the challenges and limitations associated with them.
Failure Diagnosis of Microstrip Antenna Array Based on Convolutional Neural Network

Antenna failure diagnosis is one of the essential problems in the area of radar and wireless communications. In this paper, a new method based on convolutional neural network (CNN) is proposed for antenna array failure diagnosis and compared with support vector machine (SVM). Numerical test results demonstrate that the method of antenna failure diagnosis by CNN is accurate and feasible. The accuracy of CNN under different amounts of training data are also discussed in this paper.
Double Coated VGG16 Architecture: An Enhanced Approach for Genre Classification of Spectrographic Representation of Musical Pieces

In the field of Music Information Retrieval(MIR), one of the most demanding task would be music genre classification. The search for automated categorization of music on the basis of genre yielded some diverse techniques, prominent ones of them rely on machine learning, and recently deep learning. In this paper we approached this very problem with Convolutional Neural Network(CNN), which has already been proven very useful in categorizing images in the past few years. In this work, some of the popular CNN architectures including ResNet, VGG etc., are experimented with, of which a version of the latter-the VGG16 has outperformed the rest by a handsome degree. On top of the vanilla VGG16 architecture, we coupled that with two layers of additional dense layers tuned for this specific task. We used audio clips of each genre, in this case ten genres, and got the spectrogram images of them and fed them into the network. In this approach we were able to achieve 84% accuracy, thus providing a promising stance in the genre classification problem.
On the Impact of System Nonlinearities in Continuous-Wave Radar Systems for Vital Parameter Sensing

This paper investigates the influence of nonlinearities on relative distance measurements by continuous-wave radar systems. For the case of a six-port receiver architecture, a mathematical relationship between detector nonlinearity and measurement error is derived. Moreover, a generalized sinusoidal error model, which fits both six-port and mixer-based radar systems, is used to study the effect of nonlinear mixing when one or more displacement movements with different amplitudes are to be detected.
Partitioning Analysis in Temporal Decomposition for Security-Constrained Economic Dispatch

Distributed optimization algorithms are proposed to, potentially, reduce the computational time of large-scale optimization problems, such as security-constrained economic dispatch (SCED). While various geographical decomposition strategies have been presented in the literature, we proposed a temporal decomposition strategy to divide the SCED problem over the considered scheduling horizon. The proposed algorithm breaks SCED over the scheduling time and takes advantage of parallel computing using multi-core machines. In this paper, we investigate how to partition the overall time horizon. We study the effect of the number of partitions (i.e., SCED subproblems) on the overall performance of the distributed coordination algorithm and the effect of partitioning time interval on the optimal solution. In addition, the impact of system loading condition and ramp limits of the generating units on the number of iterations and solution time are analyzed. The results show that by increasing the number of subproblems, the computational burden of each subproblem is reduced, but more shared variables and constraints need to be modeled between the subproblems. This can result in increasing the total number of iterations and consequently the solution time. Moreover, since the load behavior affects the active ramping between the subproblems, the breaking hour determines the difference between shared variables. Hence, the optimal number of subproblems is problem dependent. A 3-bus and the IEEE 118-bus system are selected to analyze the effect of the number of partitions.
Cascaded Kalman Filtering-Based Attitude and Gyro Bias Estimation With Efficient Compensation of External Accelerations

We consider the problem of attitude estimation of rigid bodies in motion using low cost inertial measurement unit (IMU). An efficient scheme is proposed using two different Kalman filters by deriving their measurement models for precise attitude (pitch and roll) estimation in the presence of high and prolonged dynamic conditions and gyro bias. Both filters work in a coupled fashion where one of the filters provides accurate estimates of rigid body attitude and external acceleration using the accelerometer in conjunction with the gyroscope while the second filter is responsible to estimate the gyro bias, allowing the proposed scheme to be used in any application with minimal calibration. A new threshold based external acceleration detection module is also introduced to change the confidence level on external acceleration prediction to assist the estimation process. The proposed scheme is tested and compared with other existing estimators in the literature under different dynamical conditions and real-world experimental data sets in order to validate its effectiveness.
Intelligent Machining System Based on CNC Controller Parameter Selection and Optimization

This paper introduces an intelligent machining system (IMS) using an adaptive-network-based fuzzy inference system (ANFIS) predictor and the particle swarm optimization (PSO) algorithm with a hybrid objective function. The proposed IMS provides suitable machining parameters for the users, to satisfy different machining requirements such as accuracy, surface smoothness, and speed. First, the key computer numerical control parameters are selected, and the actual trajectories under different machining parameters obtained by linear scales are collected. These data are analyzed to obtain the machining time, contouring error, and tracking error, corresponding to the speed, milling accuracy, and surface smoothness, respectively. Second, a data-driven approach using ANFIS is established to obtain the corresponding relationship model between the machining parameters and three aforementioned performance indices. Subsequently, to establish the IMS, we combine the trained ANFIS model and establish a hybrid objective function optimization problem solved by PSO algorithm according the specific requirement of the user. Finally, the performance and effectiveness of the proposed machining system is demonstrated by experimental practical machining.
Control Methods in Automated Glycemia Maintaining System

The work is aimed at an automated glycemia maintaining system synthesis and considering of control methods applicable in the system. In the system, insulin pump is considered as the effector and glucose monitor as feedback sensor. Insulin pump has feedback based on current sensor and the encoder. The system uses combined control principle providing both the feedback and the disturbances compensation. As the main control method, the model predictive control based on additive glucose regulation mathematical model is used. The parameters affecting the system efficiency are considered and corresponding efficiency increasing methods are suggested. Optimization problem is defined. Primarily, system efficiency depends on the insulin dose, the insulin infusion rate and the start time point. Optimality criterion is maximum of the function depending on an overshoot and regulation time in hyperglycemia and on minimal BG in hypoglycemia.
A review of control-comparison interventions on girls and health in low and middle-income countries.

The explosion of programs aimed at improving adolescent girls health in the developing world in recent years has almost certainly been beneficial but do we really know how girls are faring in the wake of these various interventions? Good intentions are needed but they do not always result in improved outcomes. The time is right to assess the impact of girl-focused programs. Through a structured in-depth literature review the authors of this paper on low and middle-income countries shed light on what we have learned and what we still need to learn. This report is one of a series of issue papers commissioned by the Department for International Development UK (DFID) and Girl Hub that synthesize key evidence on integrated approaches to economic assets health education social norms and preventing violence in improving the lives of adolescent girls. This paper is accompanied by a mapping study that details the programs assessed including demographics of the participants the logistics of the programs and the results.
The Ollie: A Case Study in Trajectory Optimization with Varied Contacts

Modelling the discrete dynamics of contacts with objects and the environment is a challenging aspect of trajectory optimization. In this paper, we use the ollie, a skateboarding manoeuvre, as case study to develop formulations for a variety of contact types: the elastic collision between the skateboard tail and the ground, the inelastic collision between the wheels and a ground surface with discrete changes in height, and the interaction between the skateboarder's feet and the skateboard - an inelastic, frictional contact that can be applied anywhere on the skateboard's surface. With the exception of the tail-ground interaction, all formulations are unscheduled. The model we developed was able to replicate the key phases of the ollie motion, but comparison of the ground reaction force profiles to those in the literature indicates that further development is necessary for it to be a sufficiently accurate model for biomechanical analysis. The contact models behaved as expected, however, and they could easily be applied to other problems such as robotic motion planning.
Anti-Eavesdropping Scheme Based on Random Mapping for GSM-MBM Systems

Media-based modulation (MBM) is a recently proposed modulation scheme which has the advantages of increased spectral efficiency and improved performance. In this paper, a physical layer security (PLS) scheme is proposed for the multiple-input multiple-output (MIMO) media-based modulation system using generalized spatial modulation (GSM) to resist the passive eavesdropping from a multiple-antenna eavesdropper. Secure mapping rules between the information bits and the transmit patterns are designed based on the abundant legitimate channel realizations of the MIMO MBM system. Due to the independent fading of the legitimate link and the eavesdropping link, the designed mapping rules are unavailable and appear random to the eavesdropper. Therefore, the correct detection of the information bits at the eavesdropper is prevented, which is validated through bit error rate (BER) analysis and simulations. The ergodic secrecy rate is analyzed and a tight closed-form approximation is derived. Simulation results show that the proposed secure GSM MBM scheme outperforms the existing secure MBM schemes without consuming jamming power or requiring eavesdropper’s channel state information.
Comparing Connection Topologies of PV Integrated Curved Roof Tile for Improved Performance

Partial shaded conditions diminish the performance of photovoltaic (PV) applications. Depending on the intensity and pattern of shading, the PV system will generate varying levels of current. Methods of maximum power point tracking have been developed to deal with the current mismatch generated by the inconsistent radiation across a system. Adjusting the connection topology to give various avenues for the mismatch current to travel can also increase the operating performance under shaded conditions. Shade dispersion techniques have also proved to increase performance. This work investigates the performance difference when applying this method of current mismatch mitigation to thin-film photovoltaics applied to a curved roof tile. Experimental testing of material on a curved surface was done and the results suggest that the interconnection method can improve the performance capabilities of curved applications.
Comparative Study of PI and Fuzzy logic controllers for three-phases parallel multi-cell converter

The multiphase interleaved synchronous buck converters with coupled phase inductors, are being preferred for voltage regulator modules, they are widely used in the industry, requiring low output voltage, high output current, and fast transient response, since they simultaneously offer better steady-state efficiency and faster dynamic response. The reduction of the output current ripple and the increase of its frequency are possible due to the use of interleaving techniques.In this paper, we present a comparative study between PI and fuzzy controllers in purpose to regulate output current and voltage. Therefore, the overshoot decreased and the ripples will reduced. Using Matlab/Simulink, simulation results, showed that the performance of the fuzzy controller, its robustness and simplicity which characterize this controller.
A 28-32GHz CMOS LNA with Broadband Approach for 5G Mm-Wave Communication cells

This paper first time reports a wideband low noise amplifier (LNA) with achievable minimum atmospheric absorption frequency band for 5G millimeter wave communication cells. A novel suspended substrate line based parallel-series network is optimized and analyzed that demonstrates a wideband response. The proposed LNA consists of two stage Cascode topology with incorporated parallel-series network and microwave components that provides broadband ranging from 28GHz to 32GHz. A full of two stage Cascode LNA overcoming the traditional mismatching constraints with consideration of suspended substrate lines (SSL) and Tee-junction in the proposed design. It is observed that suspended lines reduce parasitic and bulk effects of devices and enables LNA to provide broadband communication for 5G macro and micro cells. The proposed design is realized using RF 65nm Magna Hynix CMOS process with layout cell. The simulation results reveals that 28GHz-32GHz wide band with maximum forward gain of 25dB. The minimum noise figure of 2.5dB is achieved with optimization of passive components. The input impedance (real and imaginary) and smith chart realization for LNA provides satisfactory performance.
Personnel Subsystems of Automated Industrial Enterprise Management Systems as a Factor of the Effectiveness of Digital Transformation

Quality of digital transformations is determined by the effectiveness of human resource management of innovative enterprises. The article considers the influence of the industrial enterprises automated personnel subsystem on economic indicators, introduces the concept of the digital maturity of the personnel subsystem. Descriptions of the maturity levels both personnel system and the enterprise as a whole as well as their main characteristics are given. The system of personnel metrics of personnel management processes is considered as one of the significant tools for enterprise management and, at the same time, as an indicator of the digital maturity of the personnel subsystem.
Arrhythmia Recognition and Classification Using Combined Parametric and Visual Pattern Features of ECG Morphology

ECG is a non-invasive tool used to detect cardiac arrhythmias. Many arrhythmias classification solutions with various ECG features have been reported in literature. In this work, a new method combined with a novel morphological feature is proposed for accurate recognition and classification of arrhythmias. First, the events of the ECG signals are detected. Then, parametric features of ECG morphology, i.e., amplitude, interval and duration, are extracted from selected ECG regions. Next, a novel feature for analyzing QRS complex morphology changes as visual patterns as well as a new clustering-based feature extraction algorithm is proposed. Finally, the feature vectors are applied to three well-known classifiers (neural network, SVM, and KNN) for automatic diagnosis. The proposed method was assessed with all fifteen types of heartbeats as recommended by the Association for Advancement of Medical Instrumentation from the MIT-BIH arrhythmia database and achieved the best overall accuracy of 97.70% based on KNN, using the combined parametric and visual pattern features of ECG Morphology. The accuracies for the six main types – normal (N), left bundled branch blocks (L), right bundled branch blocks (R), premature ventricular contractions (V), atrial premature beats (A) and paced beats (P) are 97.79%, 99.50%, 99.59%, 97.69%, 89.70%, and 99.92%, respectively. Comparisons with peer works prove a marginal progress in automatic heart arrhythmia classification performance.
Non-Invasive Blood Glucose Measurement Using Near Infra-Red Spectroscopy

The primary focus in designing medical diagnosis and instrumentation devices apart from improving accuracy is to enhance their ease of usage and making them more accessible and inexpensive. Blood sugar level is a significant parameter to consider during any medical check-up. High or inconsistent levels of glucose in blood is an alarming sign and may imply the presence of diabetes. Hence, regular monitoring the concentration of sugar in blood thus becomes imperative. Conventionally this is done using devices which invade the skin barrier and require using needles and strips. The invasive process not only constitutes health hazards but also requires spending on replacements of the strips, the cost of which is not negligible. Using infra-red spectroscopy a quantitative correlation between the concentration of glucose and of near IR radiation can be established. The work presented here introduces a device capable of acquiring a measurement in a non-invasive manner. The device which is to be worn on a fingertip calculates the concentration of glucose present by first measuring the amount of radiation passing through the blood vessels in the subject's finger and then using a calibration curve to find a value in mmol/L. To compensate for variations in finger depth, the distance between the IR emitter and receiver was measured using an IR sensor and the IR absorbance readings were normalized to eliminate any aberrant effect this could potentially pose.
An In-phase Balanced-to-Single-Ended Power Divider with Bandpass Response

A balanced-to-single-ended power divider with inphase outputs is proposed in this paper. By utilizing two bandpass filters of different orders, phase difference between single-ended output ports could be theoretically zero. Good frequency selectivity is also achieved. Though the bandpass filters are of different orders, their physical lengths are almost identical to facilitate the connection with the following stage, such as an antenna array. A 2.45-GHz design has been theoretically and experimentally validated. To the best of authors' knowledge, this is the first balance-to-single-ended power divider with bandpass response and in-phase outputs.
Materials for Manufacturing of Experimental Phantoms of the Human Chest Based on 3D Printing Technology

In this paper, we consider the problem of manufacturing experimental phantoms of the human chest, taking into account the presence of internal organs and tissues. Such phantoms can be used primarily for the development and testing of various methods and devices for the diagnosis and treatment of diseases, as well as for the experimental study of devices designed to be worn on the body or in close proximity to it and determine their functional parameters in conditions close to real ones. As a basis for the manufacture of anatomically accurate phantoms, 3D printing technology is considered.
Analyzing the Effects of LED Lamp Arrangements on Performance of an Indoor Visible Light Communication System

With the emergence of Internet-of-Things (IoT) and its growing demands for increased capacity, radio-frequency based communication between such devices may soon encounter problems such as spectrum congestion and coexistence. Such issues may have adverse effects on communication which must be considered especially when transmitting sensitive information like heart rate in healthcare applications. As an alternate low-cost technology, Visible Light Communication (VLC) systems are becoming increasingly popular given its unregulated spectrum and inherent security feature with no potential threats to human safety. While numerous studies have been presented in the literature for channel modelling and characterization of indoor VLC links based on transmitter and receiver related physical parameters, in this paper an appropriate statistical attribute has been used to analyze the effects of LED lamps' layout on uniformity of illuminance and Root Mean Square (RMS) delay spreads using two main types of arrangements: single LED array and four LED arrays.