Miracle-dz/UniFAR-FTUs huggingface dataset:
Comb Jamming Mitigation in Frequency Hopping Spread Spectrum Communications Via Aid Block Sparse Bayesian Learning

Comb jamming is an effective jamming mode for spectrum spread frequency hopping (FHSS) communication. In order to ensure the effectiveness of FHSS communication under strong interference conditions, it is necessary to suppress the comb jamming effectively. The existing FHSS communication comb jamming suppression method has the disadvantage that its application is limited by the high sampling rate. The compressive sensing (CS) is applied to the suppression of comb jamming in FHSS communication in this study. According to the different sparse characteristics of FHSS signal and comb jamming in frequency domain, a block sparse Bayesian learning (BSBL) based FHSS communication comb interference suppression model is constructed. To further improve the performance of the BSBL algorithm used in this model, a BSBL based algorithm, aid BSBL (ABSBL), is proposed exploiting the nature of comb jamming in the frequency domain, where the intra-correlation matrix is modeled as unit matrix. A comb jamming suppression algorithm for FHSS communications is designed based on ABSBL. The efficiency of comb jamming mitigation is improved while keeping the performance of comb jamming mitigation using the proposed algorithm. Besides, the performance of the algorithm does not rely on the block sparse structure information of the comb jamming. The simulation results show the performance of the proposed algorithm could suppress the comb jamming in FHSS communications effectively, and achieves better performance compared to other conventional algorithms.
The Effect of the Range of a Modulating Phase Mask on the Retrieval of a Complex Object from Intensity Measurements

Abstract In many fields of science, it is often impossible to preserve the information about the phase of the electromagnetic field, and only the information about the magnitude is available. This is known as the phase problem. Various algorithms have been proposed to recover the information about phase from intensity measurements. Nowadays, iterative algorithms of phase retrieval have become popular. Many of these algorithms are based on modulating the object under study with several masks and retrieving the missing information about the phase of an object by applying mathematical optimization methods. Several of these algorithms are able to retrieve not only the phase but also the magnitude of the object under study. In this study, we investigate the effect of the range of modulation of a mask on the accuracy of the retrieved magnitude and phase map. We conclude that there is a sharp boundary of the range of modulation separating the successfully retrieved magnitude and phase maps from those retrieved unsuccessfully. A decrease in the range of modulation affects the accuracy of the retrieved magnitude and phase map differently.
Biosynthesis and characterization of Saudi propolis-mediated silver nanoparticles and their biological properties

Abstract The aim of this investigation was to synthesize propolis-mediated silver nanoparticles (Pro-AgNPs) and to test their biological activities in comparison with the raw materials (Saudi propolis). The chemical–physical characterization of the product showed that Pro-AgNPs were well synthesized. For the study of biological properties, aqueous and methanolic extracts were prepared from both propolis and Pro-AgNPs. Total polyphenol contents (TPCs) and antiradical activities showed their highest values in methanolic extracts, in particular in propolis extract. But, a higher TPC was found in Pro-AgNP aqueous extract as compared to propolis aqueous extract. It seems that Pro-AgNP synthesis helped extract more phenolic compounds with water. These compounds did not enhance antiradical activity in Pro-AgNP aqueous extract but increased its antibacterial activity.
The Mathematical Analysis of Demands for Railway Passenger Transport in Kosovo

Abstract The purpose of this paper is to analyse the railway transport of passengers in Kosovo, to determine its intensity and structure, to analyse which factors have the greatest impact on the demand for regular rail transport. Furthermore, the paper will examine which trend, which model fits best in moving this mode of transport based on data from 2011 to 2019, and then predict future trends in transport demand. The existence and strength of the dependence among the selected factors, which are assumed to have an impact on passenger rail transport will be examined by the mathematical method of regression analysis.
Emissions from 3D Printers as Occupational Environmental Pollutants

Abstract While many people work remotely during the pandemic, three-dimensional (3D) printers are working to ensure the medical personnel and general public with the necessary specific materials. Ease of use, low cost, fast prototyping, and a wide range of materials are the advantages of 3D technology that can quickly adapt to specific needs in different application areas and result in increased popularity. The aim was to analyse the concentrations of particulate matter (PM) and volatile organic compounds (VOCs) emitted in 3D printing zone where printers are located throughout the room around the perimeter and where orthopaedics and designers develop models during their full shift. The average ultrafine particles (UFP) concentration level fluctuates from 4×103 to 26×103 particles/cm3 that exceed the background level (<3×103 particles/cm3) during 8 h-shift. Microclimate was evaluated as unsatisfied regarding permissible values for air quality at workplaces: air temperature exceeds permissible upper level +25 °C, relative humidity was 21.5 % and air velocity ≤0.05 m/s. The highest particles’ number concentrations in the nano-scale range (<0.1 μm) and significantly higher mass concentrations in the coarse particle range (>2.5 μm) were detected. The median diameters of particle number (0.019, 0.014, 0.015 μm) and mass concentrations (4.394, 4.433, and 4.677 μm) were similar in all observed premises. Total VOC concentration was increased and specific substances such as toluene and formaldehyde (0.56±0.1 and 0.23±0.034 mg/m3) were found at high concentration in comparison with indoor air quality recommendations.
Experimental And Numerical Studies on Mechanical Resonance of Piezoelectric Hemispherical Resonator Gyroscope

The Hemispherical Resonator Gyroscope (HRG) is a low-noise, high-performance angular rate sensor, or rotation sensor with a small footprint. A thin, solid-state hemispherical shell is used to fabricate the HRG, which is anchored by a thick stem. In the present study a mesoscale model of HRG was fabricated. The fabricated device is tested using a noncontact approach to measure its natural frequencies and drive and sense directions. Finite element analysis was carried out using commercial software COMSOL multiphysics to validate the experimentally obtained frequencies of the gyroscope. Numerically obtained drive and sense mode frequencies were found to be within 8% of the measured values.
Safety for HER: A systematic approach with coalescence of technology and citizens

The SOS (Save Our Soul) application software is designed primarily for women in situations of distress. A possible emergency happens without proper intimation hence detecting and reporting those emergencies on-time is a crucial problem in real-life situations. The proposed SOS application has unique characteristics that make it different from a predecessor of SOS frameworks and offers swift responsiveness that stands out. For anyone who can be in an uncertain situation, the proposed platform approach is to ensure safety, security, and emergency on nearly on-time. Initially, an application allows citizens to register as “SOS Responder” as these registered volunteers are the instant ones who would act immediately during emergencies. Apart from the SOS responder the application also has the feature of notifying enrolled Emergency contacts, family, friends, Patrol Police and share live GPS location, activated front and back camera video feeds, etc. SOS app has two inputs orange and red category, during a situation woman can press orange if she is unsure of the travel location, this case lives location, video feeds sent to the ICT (Information and communication technology) control centre to keep track of travel and ensuring safety. For emergencies, the red button can be pressed to activate a notification to “SOS Responder/s and enrolled emergency contacts. Since probability is very high for SOS responder/s to acknowledge the notification and track victim based on live map positioning. Based on the emergency notification the ICT command centre could track down the situation by coordinating with Patrol police, SOS responders, etc. The police command centre may also have the facility to GPS live imaging, Access to all the surveillance cameras within the proximity radius, identify the tower signals used by mobiles, analyse the crime database based on any critical pieces of evidence, etc. This framework overcomes the real-life challenges and can be quickly implemented into any security forces and e-government structure.
IoT network security using autoencoder deep neural network and channel access algorithm

Abstract Internet-of-Things (IoT) creates a significant impact in spectrum sensing, information retrieval, medical analysis, traffic management, etc. These applications require continuous information to perform a specific task. At the time, various intermediate attacks such as jamming, priority violation attacks, and spectrum poisoning attacks affect communication because of the open nature of wireless communication. These attacks create security and privacy issues while making data communication. Therefore, a new method autoencoder deep neural network (AENN) is developed by considering exploratory, evasion, causative, and priority violation attack. The created method classifies the transmission outcomes used to predict the transmission state, whether it is jam data transmission or sensing data. After that, the sensing data is applied for network training that predicts the intermediate attacks. In addition to this, the channel access algorithm is used to validate the channel for every access that minimizes unauthorized access. After validating the channel according to the neural network, data have been transmitted over the network. The defined process is implemented, and the system minimizes different attacks on various levels of energy consumption. The effectiveness of the system is implemented using TensorFlow, and the system ensures the 99.02% of detection rate when compared with other techniques.
Visible/Near-infrared Reflectance and Interactance Spectroscopy for Detection of Abnormal Poultry Carcasses

The results of the development of a nondestructive and noninvasive technique for the detection of abnormal poultry carcasses using a diode array spectrophotometer system were presented. Visible/near-infrared reflectance and interactance spectra (500 to 1113 nm) for both sides of the breast of normal, septicemic, and cadaver poultry carcasses were obtained using a fiber optic probe with the spectrophotometer. Optimal wavelengths for correlating the spectral reflectance and interactance and second difference with the condition of the poultry carcasses were obtained. In general, differentiating between the normal and cadaver carcasses was more accurate than differentiating between normal and septicemic and between septicemic and cadaver. It was concluded that this technique can be used to separate normal carcasses from the abnormal (septicemic and cadaver) ones with good accuracy. The model based on second differences of interactance spectra, which requires 24 wavelength readings, yielded a classification accuracy of 93.3% for normal carcasses and 96.2% for the abnormal carcasses. Alternative decision criteria could be chosen so that none of abnormal carcasses were misclassified as normal. However, for a sensor limited to four wavelength readings, the model based on the reflectances should be used. The reflectance model required four wavelength readings and provided a classification accuracy of 94.4% for normal carcasses and 93.1% for abnormal carcasses.
Information Reliability in Estimate Process: A Novel Blockchain Model

This study is based on the implementation of the Blockchain in the process of the “recuse for quote” process that feeds the system of estimates for the quotation of new products, or changes to existing ones, used by automotive companies These companies develop close working and business relationships with their suppliers, that’s why is extremely valuable to have accurate information, since it depends on it to make strategic decisions to improve the quality, reduce costs and minimize delivery times. The proposed data network model (blockchain) manages the exchange of information between the different departments involved in an estimated procedure, so that all parties can synchronize the reliable information, avoiding using modified information, which creates confidence in the decisions making. Decision-making with inaccurate information implies the loss of trust and competitiveness with suppliers and customers. The implementation the blockchain helps to detect any type of manipulation or alteration in the information in a timely manner and improve the integrity of the infrastructure in the information security.
Prediction Analysis of Pit Deformation Based on Spatial Data Mining

Pit excavation was easy to cause the deformation of the supporting structure and surrounding soil, and brought serious harm to the surrounding buildings and urban underground pipelines. How to carry on a comprehensive analysis of inter-linked pit monitoring points, and improve the overall prediction accuracy was the urgent problem needed to be solved in scientific predictions of pit deformation. In order to establish the multi-variable gray theory GM(1,N) first-order linear dynamic model, using pit mutual influential settlement deformation monitoring data, and the correlation degree analysis, it filtered out the parent sequence WY09 point as the object to be analyzed, and the remaining points were as the systematic analysis of influencing factors, and WY09 point settlement predictions was calculated. According to the comparison analysis of the prediction results and engineering measured results, GM(1,N) model overall prediction accuracy was higher than GM(2,1) model, and prediction results were almost consistent with the measured results, so good effects was produced.
Subset Regularized Zero-Forcing Precoders for Cell-Free C-RANs

This paper formulates regularized zero-forcing (RZF) precoders that are both network- and user-centric for the downlink of centralized radio-access networks operating in a cell-free fashion. The transmission to every user involves a distinct subset of access points (APs), and every AP participates in the transmission to a distinct subset of users, hence the moniker subset precoders. These subsets, defined on the basis of the large-scale channel gains between users and APs, capture the most relevant signal and interference contributions while disregarding those whose processing is cost-ineffective and whose associated channel estimations would incur unnecessary overheads. With that, subset precoding approaches the performance of network-wide RZF, for the power allocation strategies of interest, while being scalable.
Flow-relative control of an underwater robot

This paper describes flow-relative and flow-aided navigation of a biomimetic underwater vehicle using an artificial lateral line for flow sensing. Most of the aquatic animals have flow sensing organs, but there are no man-made analogues to those sensors currently in use on underwater vehicles. Here, we show that artificial lateral line sensing can be used for detecting hydrodynamic regimens and for controlling the robot’s motion with respect to the flow. We implement station holding of an underwater vehicle in a steady stream and in the wake of a bluff object. We show that lateral line sensing can provide a speed estimate of an underwater robot thus functioning as a short-term odometry for robot navigation. We also demonstrate navigation with respect to the flow in periodic turbulence and show that controlling the position of the robot in the reduced flow zone in the wake of an object reduces a vehicle’s energy consumption.
Performance Evaluation of Efficient Collection Method of Sensor Information in Wireless Sensor Networks for Event Detection

In order to detect the occurrence of an event in the area, wireless sensor networks are built in various places where there are many sensors for measuring the sensing data of the event in the area. When an event occurs, multiple sensors near the event measure the data over the certain threshold specifying the occurrence of event, but each sensor must immediately send each sensing data to the aggregation station. At this time, many sensors try to send data at the same time, resulting in frequent packet collisions. It takes much time to gather sensing data because of many repeated retransmissions of packets. In this research, we are developing a method to efficiently collect data from a large number of sensors that have measured the sensing data over the certain threshold. In the environment of the radio wave sensor system we have developed, the proposed method has reduced the time required to collect data by 60% compared to using conventional protocols.
User Experience and Usability of Conducting Examination via Google Classroom: A Case Study of NIDA Business School MBA Students

During the COVID-19 Pandemic, new normal practices have been applied to all usual activities including organizing examinations. During the first and second semesters of 2020 academic year at NIDA Business School, online examination via Google Classroom was used in 10 sessions of the Financial and Managerial Accounting course. To measure the NIDA MBA students' experience and usability of examination via Google Classroom, the Short User Experience Questionnaire (UEQ-S) and System Usability Scale (SUS) was employed to construct the online survey questionnaire. The result shows that 208 NIDA MBA students have had a good experience of examination via Google Classroom.
Common Spatial Pattern Based Data Augmentation Technique for Decoding Imagined Speech

Modern machine learning techniques require a huge amount of training data for satisfactory performance. This limits the application of these techniques in classifying EEG signals due to the non-availability of huge datasets. One way to mitigate this problem is by using data augmentation techniques to increase the number of training samples. In this paper, a novel common spatial pattern (CSP) based data augmentation technique is proposed. The efficiency of the proposed method is demonstrated by training a deep neural network (DNN) on the augmented dataset for decoding imagined speech from EEG. Using CSP, nine EEG channels that best represent the underlying cortical activity corresponding to the imagination of the words “in” and “cooperate” are identified, and discrete wavelet transform (DWT) features are extracted for each of these channels. Treating the selected EEG corresponding to each imagined word as an independent sample helps in providing enough samples to train the DNN. Maximum voting is applied to the results of individual feature vectors of each trial to obtain the predicted class label. We have obtained accuracies exceeding change level accuracies across subjects, which indicates that the network is able to generalize well.
Predicting Player Churn with Echo State Networks

We introduce the idea of utilizing a recurrent neural network based representation learning approach to extract and model the complex and sequentially dependent player behavior in games. Our approach is based on the dynamical systems of Echo State Networks, which are very simple to evaluate yet powerful temporal representation learners. We empirically evaluate our approach by illustrating a case study for predicting player churn.
A Robust RLS Implementation of the ANC Block in GSC Structures

Adaptive beamforming, and the minimum variance distortionless response (MVDR) beamformer in particular, is widely used in speech enhancement applications. We consider the enhancement of a single desired speaker in a vehicle (e.g. the driver), in road noise environment. For our problem of a fixed look direction and continuous noise tracking, the generalized sidelobe canceler (GSC) decomposition was shown to be computationally efficient for the implementation of the MVDR criterion. To address robustness issues that arise due to array imperfection, the projected least mean squares (LMS) algorithm, commonly adopted for the adaptive noise canceler (ANC) block realization, was shown to be effective. Here, due to the high dynamics of vehicle road and cabin noises, we propose utilizing a recursive least squares (RLS) flavor algorithm for the realization of the ANC block. To address the robustness issue, we introduce the Modified RLS algorithm. The Modified RLS converges to the required amount of diagonal loading which is associated with the array immunity to imperfection. The proposed diagonal loading algorithm can be easily employed in any diagonal loading problems utilizing RLS based adaptive filtering (AF). We present its operation considering audio signals recorded in a vehicle, demonstrating the way artifacts are mitigated when applied.
Ensemble learning as a prerogative method of predicting mortality of patients with cardiovascular diseases

Among diseases that account for higher death rates, Cardio Vascular Diseases (CVD) stand forefront. Many works have been carried out since long to predict effectiveness in mortality prediction using models like data mining, logistic regression, neural networks etc. considering only traditional cardiovascular risk factors. As time and technologies evolved with incorporation of newer features these models ended up with predicted mortality rate of an accuracy 60-70%. There are many more attributes to be explored that are significant in predicting mortality rate in CVD patients, opening the scope to develop prediction models with traditional and non-traditional risk factors, much wider. This paper is focused on predicting mortality rates using three models. Each model's performance metrics are calculated to check the accuracy of the model. This helps one to build models that could best predict the outcome. Use of Ensemble learning method enhanced the prediction accuracy to 91%. This helps to validate the decision more accurately about mortality predictions and thereby assessing the risk.
Analysis and Improvements of the Cepstrum Method for Fundamental Frequency Estimation in Music Signals

Fundamental frequency estimation has many applications in speech and music processing. Among other methods, peak tracking in the cepstrum domain has been established as a robust and successful method for speech signals. Its elegance stems from the fact that a harmonic pattern in the log-spectral domain is mapped onto a single bin in the cepstral domain. However, the cepstrum method had not been thoroughly analyzed in the context of music signals. In this work we introduce a novel method for detecting and compensating octave errors to enhance the fundamental frequency search in the cepstrum and combine it with a fine search based on a least squares approximation in the time domain. The performance of the method is evaluated with monophonic music signals across a wide range of fundamental frequencies. In addition, the achievable frequency resolution and estimation error and their dependence on general signal parameters are analyzed.