Miracle-dz/UniFAR-FTUs huggingface dataset:
Covert Communication With Mismatched Decoders

This paper considers the problem of covert communication over binary-input discrete memoryless channels with mismatched decoding, in which a sender wishes to reliably communicate with a receiver whose decoder is fixed and possibly sub-optimal, and simultaneously to ensure that the communication is covert with respect to a warden. We present a single-letter lower bound and two single-letter upper bounds on the information-theoretically optimal throughput as a function of the given decoding metric, channel laws, and the desired level of covertness. These bounds match for a variety of scenarios of interest, such as (i) when the channel between the sender and receiver is a binary-input binary-output channel, and (ii) when the decoding metric is particularized to the so-called erasures-only metric. The lower bound is obtained based on a modified random coding union bound with pulse position modulation (PPM) codebooks, coupled with a non-standard expurgation argument. The upper bounds are based on the idea of translating the mismatched-decoding error of the original channel to the decoding error of an auxiliary channel.
Electric Vehicle Charging Station Aggregator Application

India is developing its Electric Vehicle (EV) infrastructure at a record breaking rate, with an estimate of 40% of its vehicles to be electric by 2030. The current gas crisis and emission problems are thought to be effectively solved by EVs. The shift to a cleaner source of energy has been welcomed by many, with governments even offering subsidiaries as a form of encouragement to the general public. This sudden increase has opened up prospects to develop an application which serves the purpose of a charging station aggregator. One of the primary drawbacks of the current charging station architecture is that it is provider-dependent, and therefore EV drivers are not presented with all the possible charging station options. While Google allows users to search for nearest stations, details such as charger type and availability of the station are often incomplete. ‘‘Find Green’’, is a web application which serves as a single spot for EV users to go to for verified listings, which provides them with all the necessary details as well the ability to reserve slots in advance. This in turn helps reduce the range anxiety of countless drivers.
Lifting of the 9/7 Wavelet Filter Bank for ECG Signal Analysis Implementation Based on FPGA Target

The electrocardiogram (ECG) signal is often used in healthcare as one of the most significant methods for determining the cardiac condition of a patient. It represents the variations of the electrical activity of the heart as a function of time. This paper presents the hardware Implementation OF THE Lifting 9/7 Wavelet Filter Bank ECG Signal Analysis on cyclone-II EP2C35F72C6 FPGA in Altera DE2 board using VHDL Hardware Description Language (HDL). Quartus-II version 8.1 is a synthesis software tool and ModelSim Altera WEB edition 6.4a is used as a simulator Multiply/divide operations using left/right shifters are used in contrast to standard operations with the aim of reducing time execution, the number of elementary logic, and power consumption.
The Selection of Time Series Models Perhaps with Regressors

We develop a Cp statistic with a known distribution for the selection of models for stationary and non-stationary time series, including ARIMA and structural models, that may include regressors. To provide a unified fra mework we use the state-space approach which readily incorporates explanatory variables. We exemplify use of the statistic through generalized ARMA modelling of a series of UK one-day ahead electricity prices that includes four explanatory variables. A plot of our time series Cp statistic is highly informative about the choice of model.
Design of an Active Power Filter in Cascaded Power Converters for Harmonic Mitigation

The most important and growing problem in the Microgrid is the power quality issues. As an excellent solution to the power quality, energy crisis, and environmental pollution, solar energy has drawn more attention. Power converters are used to integrate these renewable energy sources into the grid. The DC input energy is extensively transformed to AC output power by a two-stage conversion system consisting of a DC-DC converter at the front end followed by a DC-AC inverter. When the DC-DC converter and the DC-AC inverter are connected in a cascaded configuration, this cascaded configuration will generate harmonics in the system due to the inverter's pulsating output power. These harmonics flow in the system, affect the source (Solar PV), and significantly impact the voltage variation. This paper proposes to design power converters that will convert the DC power of solar PV into AC output power while producing minimum harmonics at the input. This research paper will employ an active power filter to reduce or eliminate harmonics in the cascaded power converters to increase solar PV lifetime.
E-Health monitoring using camera: Measurement of vital parameters in a noisy environment

We investigated contact-less solution to study micro circulation and its spatial heterogeneity by means of RGB imaging and to estimate blood oxygenation and heart rate. Oxygen saturation SpO2 and heart rate HR are indicators directly related to the cardiovascular diseases. Detecting low level of oxygen and abnormal heart rate can be an indicator of fatal heart problem and require urgent medical attention. Traditionally, measurement of HR and SpO2 is made through the probes or pulse oximeter attached to the skin surface and hence could be a potential way of spreading contagious infections. For a non-contact assessment of pulse rate, imaging photoplethysmogram iPPG has been proposed, where data is acquired through the video frames and different image analysis-based methods are used to extract required information. We detects face from the sequence of images using Viola Jones algorithm, followed by the extraction of region of interest, filtering performed in spatial domain removed artifacts, and intensity signal gives iPPG estimation by using CHROM method. The estimation of HR and SpO2 from these signals are evaluated on publicly available data set containing facial videos and reference contact photoplethysmogram. Furthermore, we also investigate the performance of these methods on data acquired under diverse illumination conditions. Relative correlation and % error gives satisfactory results to extend the method for more diverse scenario.
Bangla Speech Denoising and Identification using Deep Neural Network

Humans have always made a concerted effort to communicate with things using natural language. Communication has always been a vital aspect of human life and a potent instrument for passing on data from one generation to the next. One of the most difficult challenges in speech processing is creating text from recorded audio signals using automatic speech recognition methods. Speech processing can be seen as a specific instance of digital signal processing since the signals are often treated in a digital representation. In this work we aim to investigate Speech De-Noising and Speech Recognition of the Bangla Speech Corpus. As speech recognition is a particularly difficult application in digital signal processing and it varies from nation to nation. Additionally, combinations of vowel and consonant sounds in Bengali create the task more complicated. The research work is primarily separated into two sections. First is the Bangla speech denoising signal, followed by the Deep Neural Network medium and speech recognition exertion approach. For recognition purposes in this worked on, Mel-frequency cepstral coefficients (MFCCs) and hidden Markov model (HMM) classifiers are employed. We have attempted to analyze the performance of Deep Neural Network-based speech denoising. For more accurate data, We recorded voices in various situations, compiled our data set, and analyzed the performance with and without noise.The Objective of the work is to build a Bangla Speech Denoiser Recognizer Model as a contribution one step towards establishing the Bangla Language in this Artificial Intelligence world.
Novel high-pressure nitrogen phase formed by compression at low temperature

Raman spectroscopy and powder x-ray diffraction methods have been used to characterize a novel phase of nitrogen which forms on compression from ambient pressure at low temperatures. The new, $\ensuremath{\lambda}$, phase exhibits an exceptionally wide range of pressure stability from below 1 to 140 GPa, overlapping nine other known phases. On heating, its transformations are different to those observed in other phases, implying that the phase nitrogen adopts depends not only on $P\text{\ensuremath{-}}T$ path, but also on the initial structural configuration, which greatly complicates its phase diagram.
Design and Modeling of an Exoskeleton Robotic System for the Rehabilitation of Lower Limbs

This paper is mainly concerned with the mechanical design of a simple exoskeleton robotic system for the rehabilitation of the human lower limb. The designed exoskeleton allows to preserve the main movements of the different joints of the human leg. Thus, we describe the morphology of the exoskeleton and the selection of the different necessary joints. Moreover, a calculation of the torque required at each joint is realized allowing hence to the right choice of actuators. Furthermore, we present the different parts for the mechanical design of the exoskeleton structure, and also for the electrical design. Finally, we provide the nonlinear dynamic model of the designed lower- limb exoskeleton robot.
A Multiband Tri-branch Monopole Antenna Base on Step Impedance Technique for WLAN, WiMAX, 5G Technology, and IoT Application

A multiband tri-branch monopole antenna is presented. The antenna structure is constructed of radiating patches with three branches. It is fed by a microstrip transmission line. Essentially, the antenna is a simple structure created by a step impedance technique. In addition, the antenna can be separately regulated for each operational frequency band of 2.44 GHz, 3.36 GHz, and 5.52 GHz by the height of a central patch, a right patch with a thin strip line, and a small patch on the left, respectively. The antenna is manufactured on a size of 50x35 mm2 FR4 substrate with a thickness of 0.8 mm and a permittivity of 4.3. The suggested antenna is appropriate for network board internet of things (NB-IoT) technology working at 2.4 GHz/5.2 GHz/5.8 GHz (WLAN), 3.5 GHz (WiMAX), and 2.3 GHz/2.6 GHz/3.5 GHz (5G technology). The antenna’s radiation pattern is omnidirectional, and its average gain is approximately 2dBi.
Quantitative Investigation of Imaging Quality vs. Radar Position Errors in Millimeter-wave SAR

Millimeter wave (mmWave) imaging has promising potential for many applications because of its fine resolution, object-penetration capabilities, and all-weather, all-day working conditions. Combined with low-cost mmWave radars, the synthetic aperture radar (SAR) technique enables flexible, cost-effective, mmWave imaging solutions. This paper experimentally investigates how SAR imaging quality depends on in-aperture 2D Gaussian position errors. We emulate a wide range of position errors with root-mean-square values ranging from 1 mm up to 256 mm, covering between a fraction and a few tens of the wavelengths at 77 GHz. The presented results provide useful guidance for designing portable mmWave SAR systems that can benefit from flexible and low-cost motion tracking solutions. The data and code are publically shared at https://github.com/radar-lab/SAR.git.
Displacement back analysis for underground engineering based on immunized continuous ant colony optimization

The objective function of displacement back analysis for rock parameters in underground engineering is a very complicated nonlinear multiple hump function. The global optimization method can solve this problem very well. However, many numerical simulations must be performed during the optimization process, which is very time consuming. Therefore, it is important to improve the computational efficiency of optimization back analysis. To improve optimization back analysis, a new global optimization, immunized continuous ant colony optimization, is proposed. This is an improved continuous ant colony optimization using the basic principles of an artificial immune system and evolutionary algorithm. Based on this new global optimization, a new displacement optimization back analysis for rock parameters is proposed. The computational performance of the new back analysis is verified through a numerical example and a real engineering example. The results show that this new method can be used to obtain suitable parameters of rock mass with higher accuracy and less effort than previous methods. Moreover, the new back analysis is very robust.
A Novel Signal Detector based on Approximated Fractional Integrator in Frequency Domain

This paper investigates weak signal detection using approximated fractional integrator (AFI). An optimal spectrum sensing scheme based on the criterion of maximization of the deflection ratio coefficient (DR) has been proposed in the frequency domain. Due to the easy availability and less time complexity of fast Fourier transform (FFT) chips in the industry, signal detection is primarily done in the frequency domain. Approximated fractional integrator transforms the data to give the maximum DR because of its non-linear low-pass characteristic. Example and simulation results have been illustrated to observe the performance and properties of the proposed detector. A precise comparison has been discussed with state-of-the-art techniques.
Information Retrieval With Chessboard-Shaped Topology for Hyperspectral Target Detection

Given a priori knowledge, hyperspectral target detection aims to locate objects of interest within specific scenes by utilizing differences in spectral characteristics among various land covers. However, for those traditional model-driven detectors with monotonic analytical mode, they perform mediocrely in the disassembly of hyperspectral image (HSI) data, failing to cope with real scenes with complexity. The discrepancy between fixed model assumptions and HSI data severely reduces detection effects, leading to the inability of such methods to mine deep-level features and adapt to the variability of imaging scenes. To overcome the limitations of traditional methods, we propose a chessboard-shaped topological framework for high-dimensional data structures to disassemble an HSI from both spatial and spectral dimensions adaptively. With hyperspectral target detection refined into an information retrieval task in a topological space, a chessboard-shaped topology for hyperspectral target detection (CTTD) is proposed. In the topological space, latent and hidden data features of original images are presented in an intuitive way. Therefore, the differences in both spatial and spectral dimensions between the two classes of objects, namely, target and background, are specifically amplified and exploited to perform the information retrieval task with superior performance. Extensive experimental results on benchmark HSI datasets demonstrate that CTTD can efficiently adapt to the variability of real scenes while extracting abundant and detailed information for accurate target localization. Moreover, both detection effects and computational efficiency exhibited by the proposed method provide strong support for its popularization in practical applications.
SGWeS: A Framework to Safeguard Web Servers from PDF Malware Attacks

Web Servers are a critical asset in the Information Technology sector that are exposed to the Internet on a generic basis. The major players in the industry dealing in sectors like healthcare, education institutes, telecom, ecommerce, etc. create massive business through their Web presence. The intrusion through the organization’s web server can harm the industry’s day-to-day activities. Many organizations are required to have PDF files uploaded from the user of the website, that are being sent to Web Servers. Cyber Attackers or hackers widely target web servers using PDF malware attacks. A PDF file can contain malicious code, links, or attachments that, when accessed or downloaded on the web server, can infect the server or network. The existing methodologies work on the principle of checking malicious files on the web server. In this research paper, a framework is proposed to check the authenticity of PDF malware attacks at the client machine only using machine learning-trained models. The machine learning-trained embedded script is trained using the Evasive-PDFMal2022 dataset. This dataset contains the all-relevant features of benign and malicious PDF files that can be utilized to train Artificial intelligence-based techniques. The proposed methodology is validated using machine learning models like the decision tree classifier and the performance of the machine learning trained model is enhanced with XGBoost methodology. XGBoost outperforms and results in improved metrics used for evaluation.
Performance Analysis of Machine Learning Algorithms with Hyperparameter Tuning for Diabetes Prediction

All over the world, many people are affected by Diabetes. At present in hospitals various blood and urine tests were carried out to find whether a person is having Diabetes or not. Nowadays Machine learning plays a major role in the biomedical areas in prediction and identification of various diseases. In this paper, a detailed performance analysis about the prediction of Diabetes using various machine learning algorithms is presented. Performance of Linear Regression, Decision Tree, XGBoost, Support Vector Machine and K Nearest Neighbour algorithms are compared. Grid search and Randomized search hyperparameter tuning are also used to tune the hyperparameters of all these machine learning models. The PIMA Indians Diabetes Dataset is used for this work. The prediction results of these models are compared using Accuracy, Precision, Recall and F1 score. The results of this work show KNN algorithm with randomized search hyperparameter tuning gives better results than all other machine learning models with grid search hyperparameter tuning.
Extended Use of Paired Comparison Models, with Application to Chess Rankings

Linear paired comparison models are studied when ties or draws are allowed, where the probability of a win plus half the probability of a draw is modelled as a symmetric cumulative function evaluated at the difference of strength parameters. These models are extended to make use of covariate information and used for ranking 64 top chess players since 1800 with information on career periods. Some new methodology including an analysis of an appropriate goodness-of-fit test is given for this extended use of the linear model
Strategizing In-store retail experience with IoT for Gen Z: Retail 4.0

The accelerated development of Internet and communication has led to the emergence of a new era of the IoT in various industries. Amongst all the industries and sectors in India, retail is the most rapidly growing sector. It contributes about 10 percent of India’s GDP. In today’s business world, one of the major challenges that marketers are witnessing is addressing the needs of new generation consumers. This challenge is also faced by the retailers, since the majority of Gen Z individuals are so comfortable with technology that they only prefer online modes of shopping and are also referred to as E-loyal consumers. In order to attract this generation of consumers to offline retail stores, use of IoT in brick and mortar stores becomes significant. Thus, the current research evaluates the use of IoT in In-store retail and also searches for ways of improving the In-store retail experience and making it suitable to attract the Generation Z consumers. Major findings reveal that a more percentage of Generation Z consumers prefer online mode of shopping. Convenience, Ease of use & Tracking systems are the primary reasons for using online platforms. Thus, in this paper various ways of using IoT to enhance these three areas in In-store retail are explored.
A Novel Fast Power-Efficient Cascode Level-Shifter

The Voltage scaling technique is widely used in recent digital systems to resolve power issues. In these systems, Level Shifters (LS) are essential to make a solid interface between different voltage domains. This article presents a fast power and area-efficient ultra-low voltage level shifter (LS) that facilitates a wide range of conversion from the deep sub-threshold region up to the super-threshold region. The proposed LS achieves better performance by combining cross-coupled and current mirror-based structures' advantages. In addition, the split-input inverter and current limiter techniques help to reduce static power efficiently. Assuming a conversion from 400 mV to 1.2 V at 1MHz frequency, simulation results show that compared to the prior work, the proposed LS consumes 66% less power while performing the conversion about 13% faster. Implemented in a 65 nm standard CMOS process, the proposed SA consists of 9 transistors and occupies approximately 6.1 um x 18.2 um of silicon area.
The Impact of Information Technology and Social Network in Knowledge Sharing for Organization Performance (A Case Study Approach)

The Ministry of State-Owned Enterprises' (SOE) Making Indonesia 4.0 program will assess Indonesia's SOE readiness to lead and compete in the industrial 4.0 era. LKBN Antara is one SOE that has implemented knowledge management in its operation. As one of the SOEs that has been implementing knowledge management, researcher trying to configure whether there's an impact in Information Technology (IT) and Social Network in Knowledge Sharing to LKBN Antara's performance. In this research, researcher using structural equation modelling (SEM-PLS) technique and expert evaluation with structured questionnaires. The result was generated from the LKBN Antara's employees. the results showed that social networks have a positive impact on knowledge sharing, and knowledge sharing has a positive impact on organizational performance. However, the impact of information technology on knowledge sharing showed weak relevance. The results showed that social networks have a positive impact on knowledge sharing, and knowledge sharing has a positive impact on organizational performance. However, the impact of information technology on knowledge sharing showed weak relevance.
Digital Storyboards: Making CS Elementary

The Utah Computer Science Education Master Plan (UCSMP) called for significant increases in the CS offerings in K-12 schools. The Digital Storytelling project is a design-based research project which addresses this need by training both current and future teachers in CS education. Undergraduate teacher education students partnered with elementary teachers to facilitate a unit focused on designing, building, and coding “digital storyboards. ” These students- designed storyboards incorporate literacy, electronics, and coding. Students are given 10 weeks of guided instruction in storytelling, wiring, coding, and building. Student data from pre/post surveys related to their interest in, and aptitude for, computer science were collected, and both students and teachers were interviewed. We administered the ESCAS, a validated measure for gauging students' attitudes toward coding in a pre/post design. Initial results with 8 classrooms across 5 districts show statistically significant and high effect sizes in students' change in attitudes toward coding. We also interviewed teachers and students to gain a deeper understanding of their experience. We discuss how integrated projects such as this can help promote CS training and learning for students and teachers.