Miracle-dz/UniFAR-FTUs huggingface dataset:
Combinatorial species and tree-like structures

The combinatorial theory of species, introduced by Joyal in 1980, provides a unified understanding of the use of generating functions for both labelled and unlabelled structures and as a tool for the specification and analysis of these structures. Of particular importance is their capacity to transform recursive definitions of tree-like structures into functional or differential equations, and vice versa. The goal of this book is to present the basic elements of the theory and to give a unified account of its developments and applications. It offers a modern introduction to the use of various generating functions, with applications to graphical enumeration, Polya theory and analysis of data structures in computer science, and to other areas such as special functions, functional equations, asymptotic analysis and differential equations. This book will be a valuable reference to graduate students and researchers in combinatorics, analysis, and theoretical computer science.
A conceptual analysis for the measurement of stress intensity by deep learning using EEG signals

Several Health-related issues are caused due to mental stress. In Initial stages the level of mental stress can be measured using tools which are developed by Physicians and Scientists. Many neuroimaging tools have been suggested to measure the stress developed in the workplace. Electroencephalogram (EEG) signals are a key component as they contain vast knowledge related to conditions and states of mind. Existing methods for EEG Signal analysis are discussed to measure level of stress in the paper. This review focuses the crucial variations in-between the research results and debates those fluctuations from the data analysis methods give rise to many contrary results. The fluctuations in results may be because of several factors like no proper protocol, the area of interest within the brain, stressor types, experiment period, EEG analysis, methodology used by which properties are extracted, and classifier type. Hence, the major factor relating to detection stress in mind, by choosing the major suitable properties. A convoluted come varied band of properties relating to EEG, with variation in time, working, and dynamic connections of brain, required inclusion of different methods to learn about their connections with mental stress. Subsequently, the evaluation recommends merging cortical activations with the integration to the network calculates with approaches of deep learning to enhance the correctness range of mental stress calculation.
Sperm segmentation and abnormalities detection during the ICSI procedure using machine learning algorithms

(1) About 15-20% of couples struggle with the problem of infertility. 30 to 40% of these cases are caused by abnormalities in the structure and motility of sperm. Sometimes the only possibility for such people is to use the procedure of artificial insemination. CASA systems are used to increase the efficiency of this procedure by selecting the appropriate sperm cell. (2) This paper presents an approach to the sperm classification on the basis of its entire structure analysis, including flagella - often poorly visible and therefore ignored in the CASA systems element. The training of the Mask R-CNN architecture was performed on 2 publicly available and one specially created for this purpose sperm database. A 14-element feature vector was also proposed for the classification of 4 classes of typical head defects (amorphous, normal, tapered and pyriform) by the Support Vector Machine. (3) The sperm head (mAP 94.28%) and the whole flagellum (mAP 90.29%) were successfully detected. However, the flagella segmentation results were significantly lower (50.88%) than that the head segmentation (88.32%). Classification with SVM scored 82% accuracy. (4) Research has shown that segmentation and the use of a simple SVM classifier allow for quite good results in the classification of sperm defects. However, it is important to develop a larger whole sperm database, to improve the segmentation results.
FIVR compensator auto tunning and its comparison with manual tunning

Compensator is employed in a fully integrated voltage regulator for the better closed-loop performance of the system. Type 3 compensator has 6 parameters (R1,R2,R3,C1,C2,C3). Manual tunning of each parameter is a tedious and time-consuming process. Also, manual tunning may skip the best possible match and there is a possibility of human error. This paper talks about machine learning-based auto tunning algorithm for type 3 compensator to overcome drawbacks of manual tunning. In this work, the study is carried out on Intel 12th gen processor to know the benefits of the auto tunning algorithm.
Customer Segmentation and Preference Modeling of Indonesian Mobile Telecommunication Industry: A Data Mining Approach

The number of mobile internet users in Indonesia is overgrowing over the past years because of the emerging mobile telecommunication. However, lately, the growth is declining year by year. It makes each company struggles to increase their revenue. The continually increasing number of mobile internet users does not necessarily mean positive growth for the five major mobile telecommunication providers in Indonesia. Understanding customer segmentation and preferences are vital to increasing customers’ repurchase intention (i.e., data consumption per user) and customer satisfaction. In response, we present novel insights on mobile internet users’ segmentation and preference. We use online questionnaire data from more than 500 respondents and apply k-modes clustering and multiclass logistic regression analysis to establish the customer segmentation and preference models based on numerous attributes: internet speed, internet package price, internet quota size, call duration bonus, SMS bonus, network coverage, signal strength, customer care quality, service center availability, and other exclusive offers. We propose two dominant customers segments and characterize the main customers’ preference profile for each of the five major mobile telecommunication providers.
A simple countermeasure to mitigate buffer overflow attack using minimalistic hardware-integrated software simulation for FPGA

Buffer overflow attack in FPGAs is a persisting problem in the industry and various solutions to mitigate these vulnerabilities are continuously being addressed by manufacturers. A simple and effective mitigation method is proposed and simulated in Vivado using Verilog codes. Checking of bounds of the buffer memory, accompanied by using minimalistic number of logic gates as an encryption method, was simulated and demonstrated to successfully function as a strong countermeasure to this vulnerability.
Robustly Forward Invariant Sets for Mixed-Monotone Systems

Safety for dynamical systems is often posed as an invariance constraint, requiring the system trajectory to remain in some safe subset of the state-space for all time. This note presents new tools for studying reachability and set invariance for nondeterministic systems subject to a disturbance input using the theory of mixed-monotone dynamical systems. The vector field of a mixed-monotone system is characterized as being decomposable into increasing and decreasing components, which allows the dynamics to be embedded in a higher dimensional embedding system. Even though the original system is nondeterministic due to the unknown disturbance input, the embedding system has no disturbance and a single simulation of the embedding system provides bounds for reachable sets of the original dynamics. In this article, we present an efficient method for identifying robustly forward invariant and attractive sets for mixed-monotone systems by studying equilibria and their stability properties of the corresponding embedding system. We show how this approach can be applied to either the backward-time dynamics or a set of linearly transformed dynamics to establish different robustly forward invariant sets for the original dynamics, and we show also how periodic solutions to the embedding system establish invariant regions for the original dynamics as well. The findings of this work are demonstrated through two numerical examples and two case studies, including a five-dimensional planar quadrotor system.
Malware Classification using Bigram BOW, Pixel Intensity Features, and Multiprocessing

The malware classification task launched by Microsoft has been quite popular for the last half a decade. After detecting malware, classification plays an important role, because based on the type of malware, the corresponding action needs to be taken. Feature extraction plays a vital role to proceed ahead with the modeling. The data is in form of two separate files for each malware in consideration, from which we generate the features, choose the top important ones, and train a classical ensemble learning model. There have been various solutions proposed for this task earlier, over which we have made some modifications to achieve better accuracy. We have used the features generated using the bigram Bag of Words (BOW) and included pixel intensity features to approach this task. Since the dataset is quite huge, in this paper we proposed an approach based on multithreading, where instead of processing the data serially, we processed it parallelly through all the cores available in the CPU and optimize the computation time as much as possible.
A Passive Grounded MOSCAP- Memrisor Emulator

In this paper, a grounded Passive MOSCAP-Memristor Emulator is presented consisting of only three MOSFETs and one MOSCAP without biasing. Earlier the researchers have presented memristor emulators consisting of existing blocks and passive elements. The proposed circuit's structure is very simple compared to the existing one. This circuit is simulated on Cadence Virtuoso GPDK 180nm technology with sinusoidal and pulse input signals. The operating frequency of this circuit is up to 30MHz. This circuit analyzed the hysteresis loop and variation of current according to the voltage direction. The expected figure of merits of the memristor is successfully obtained and presented the memristor application in filters.
Environmental and Social monitoring in existing building structures – results of a case study within several historical buildings

One year ago the European Union defined the Environmental, Social and Governance (ESG) directive to foster investment in sustainability. This directive asks for energy monitoring / optimization and actions to foster human well-being. To cover that demand especially historical buildings in Europe need solutions in these respects, to be more energy efficient and at the same time to safeguard the well-being of its users. These buildings have almost no building automation installed. So, the basic prerequisite to identify high energy consumers valid usage data is not available. For that, the following research questions have to be answered: Which data is necessary to reach both, the environmental and social goals? How can data be captured in a valid and efficient way? How can data be made available to optimize energy usage and well-being? To answer these questions, a literature research was executed to define the relevant parameters. Firstly, based on the results of that step and a previous mixed-methods-research project, the relevant tools and the IT architecture were defined. Secondly, based on the ESG-reporting demands, data structure and relevant building structures were defined. Thirdly, a case study was conducted, which is based on the previously defined IT architecture, using IoT measuring devices, two different databases and two analytic tools. As a result, this paper presents the final decision on the database and the analytics tool, based on the users’ interaction and feedback of the case study.
Metal Additive Fabrication and Characterization of Horn Antenna

This paper presents a 3D printed rectangular horn antenna for 18 GHz to 26.5 GHz applications using 17-4PH stainless steel metal powder to demonstrate a powder bed fusion based Micro Laser Sintering (MLS) 3D printing technology. This technology allows manufacturing of metallic horn antennas at a lower cost and faster processing time than traditional high-cost fabrication processes. Also, MLS uses a finer metal powder that reduces component surface roughness, a significant downside of 3D printing technology. Furthermore, it can print a single-piece metal horn antenna, ensuring complete alignment and electrical compatibility between subcomponents and avoiding the need for any high-end post-processing steps before deployment. The claims made can be validated by the study of the effects of surface roughness on the gain, reflection coefficient, and transmission properties of the antenna. The findings for the two additively manufactured horn antennas (one polished and another as built) were compared to a standard one procured from Pasternack®.
Measuring the Left Ventricular Ejection Fraction using Geometric Features

One of the crucial indicators of the heart's functioning, is the so-called left ventricular ejection fraction (LVEF), which measures the heart's ability to pump blood, and corresponds to the relative change in volume within the heart's left ventricle between it's most expanded (end-diastole) and most contracted state (end-systole) during a cardiac cycle. A reduced LVEF is a key indicator of heart failure, and as such, its accurate measurement plays a prominent role in cardiology. This work proposes a machine learning approach for estimating the LVEF from short echocardiogram videos. Our model, based on gradient-boosted trees, is significantly simpler than the state of the art, but is competitive in terms of accuracy and has a higher degree of explainability. The proposed model operates on a set of geometric features of the heart's left ventricle, tracking its evolution during the cardiac cycle; some of these features are novel and are proposed here for the first time. We discuss the performance of our model on a dataset of over 10,000 samples, including the relative importance of our proposed features, and show that the model's estimation error is well within the margin of variation that occurs when the same LVEF is measured by different experts.
Analysis of Non-linear Cds Effect on Continuous Class-F Mode Power Amplifiers

For the first time, a detailed mathematical model and analysis is presented to understand the effect of a transistor’s non-linear drain source capacitor, Cds on the operation of continuous class-F mode power amplifiers. Due to the reactive current generated by the non-linear Cds, the impedance trajectories of a continuous class-F PA deviate significantly from ideal operation. Moreover, the presented analysis shows that the power and efficiency do not stay constant in the entire design space as predicted by the ideal continuous class-F mode theory. In some continuous mode operation areas, the power and efficiency improve from the ideal constant values, whereas in other areas they tend to degrade.
Textural features for automatic detection and categorisation of pneumonia in chest X-ray images

Pneumonia is an acute lung infection caused by a variety of organisms, such as viruses, bacteria, or fungi, that poses a serious risk to vulnerable populations. The first step in the diagnosis and treatment of pneumonia is a prompt and accurate diagnosis, especially in the context of an epidemic outbreak such as COVID-19, where pneumonia is an important symptom. To provide tools for this purpose, this article evaluates the potential of three textural image characterisation methods, fractal dimension, radiomics, and superpixel-based histon, as biomarkers both to distinguish between healthy individuals and patients affected by pneumonia and to differentiate between potential pneumonia causes. The results show the ability of the textural characterisation methods tested to discriminate between nonpathological images and images with pneumonia, and how some of the generated models show the potential to characterise the general textural patterns that define viral and bacterial pneumonia, and the specific features associated with a COVID-19 infection.
Analytical Hierarchy and Neural Network based Landslide Risk Assessment at Sikkim India: A Comparative Study

In the Himalayan region, landslides are mainly triggered due to severe rainfall and earthquake events. Especially in the Sikkim Himalayas, landslides occur every year, resulting in casualties and huge property losses. Therefore, it is essential to study the landslide susceptibility and risk zonation of the Himalayan landscape. This study attempts to classify 2656 landslide events in the Sikkim in their hazard impact. The Sikkim landslide repository of Bhukosh-GIS is combined with the digital elevation model (DEM) to classify landslides using the analytical hierarchy process (AHP). The state of Sikkim is divided into four landslide risk zones, with North Sikkim being the highest risk zone (Zone-D) and South Sikkim at the least (Zone-A). Thereafter, a Levenberg Marquardt (LM)-based backpropagation neural network (BPNN) is proposed to predict the risk scores based on the landslide input parameters and AHP-based target scores. After performing several numerical experiments, the input-output dataset is divided into 60:15:25 as training, validation, and testing set. Finally, it is observed from the simulation runs of comparative study, the proposed neural network with 92.5 % testing accuracy could be a promising alternative for the landslide risk assessment in Sikkim, India.
Distributed Negotiation for Reaching Agreement Among Reluctant Players in Cooperative Multiagent Systems

In this article, we propose a distributed negotiation framework that allows a set of cooperative agents to find a common ground with their neighbors while attempting to modify their initial opinion by the least possible amount. Based on such a framework, we develop a distributed agreement approach where the effort spent in the local agreement reflects the relevance of the agents in a weighted consensus process. In particular, we assume that players whose ideas happen to satisfactory mediate the standpoint of their interlocutors will end-up being more influential in the overall decision-making process. We conclude the article by applying the proposed methodology in the context of distributed data aggregation scenarios, as a way to mitigate the effect of outliers (e.g., faulty sensors).
Cooperative Control of a Pediatric Exoskeleton System for Active-Assist Gait Rehabilitation

During active-assist rehabilitation mode, the patient started to interact with lower-limb exoskeleton by applying the recovered muscle strength which demands a cooperative control strategy based on the outer and inner loop control scheme. This work proposes a such cooperative framework based on admittance and position control scheme to regulate the compliance of subject-exoskeleton system for pediatric gait rehabilitation. At first, the mechanical design and dynamic modeling of the exoskeleton robot are briefly presented. Thereafter, admittance controller is designed in outer loop which modifies the desired human trajectory and generates the reference trajectory. Thereafter, as an inner loop control scheme, a computed torque position controller is exploited to track the reference trajectory. The effectiveness of the proposed cooperative control is investigated with low and high admittance parameters. From the results, it is observed that the proposed control with low admittance parameters allows the subject to participate in the training process more actively. However, the inner loop position control promisingly tracks the reference trajectory in case of high admittance parameters.
MRI-guided Automated Delineation of Gross Tumor Volume for Nasopharyngeal Carcinoma using Deep Learning

In this paper, we propose a novel deep learning-based automatic delineation method of nasopharynx gross tumor volume (GTVnx) by combing computed tomography (CT) and magnetic resonance imaging (MRI) modalities. The purpose of this study is to explore whether MRI can provide additional information to improve the accuracy of delineation on CT. The proposed model can adaptively leverage the high contrast information of MRI into the automated delineation of GTVnx on CT in nasopharyngeal carcinoma (NPC) radiotherapy. In this study, the dataset collected from 192 patients with NPC was used to verify the performance of the proposed method. The average Dice Similarity Coefficient, 95% Hausdorff Distance and Average Symmetric Surface Distance of the segmentation results predicted by the proposed model are 0.7181, 9.6637mm, and 2.8014mm, respectively, which outperformed that of the single-modal and the concatenation-based multi-modal segmentation models.
Development of Software Application for Chronic Wound Assesement

Lower extremities ulcers or chronic wounds are known long-term treatment from month to some years. Their basic feature is the disturbance of the usual wound healing without consistent changing stages from inflammation through granulation to epithelization and scar remodeling. Necrotic tissue, fibrin and granulation can coexist at the same time. For decision making for kind of treatment is important to assess the stage of wound healing and wound area. We propose the method and software for non-invasive square determination during patient management using digital photo-making. Also, we describe the prototype application with graphical user interface developed by Federal Research Center for Information and Computational Technologies with Research Institute of Clinical and Experimental Lymphology-branch Institute Cytology and Genetics Siberian Branch of Russian Academy of Ssiences. This prototype allows to calculate the full area of wound and its different segments for observation of wound healing. Some factors have a significant impact on the accuracy of measuring area of the affected regions have been determined.
Wideband Beamforming using Digital Phase Only Compensation in Digital Array Radars

Phased array Radars use Wideband waveforms for high range resolution and imaging purpose. The classic active phased array radar with a transmit/receive (T/R) module with attenuator and phase shifter of its antenna elements works well for narrowband signals. For large phased arrays with wide band width, to seer the beams in off-broadside true time delay (TTD) devices should be used to avoid beam squinting or bandwidth dispersion effects. The TTD devices require temperature stabilization and provides only quantized delays. True Time delay digital beamforming using fractional delay filters eliminates drawbacks of analog TTD. But it requires high speed devices and interfaces at each element. In this paper a new approach is proposed for wideband digital beamforming (WDBF) for digital array radars with element level digitization capability. This novel approach uses digital phase compensation during transmit waveform generation. During reception stretch processing is used to limit the receive processing bandwidth. This method works with very less computational and does not require fractional delay digital filters. Numerical simulation results are provided to demonstrate the performance of the proposed method.