Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards UAV-Based Ultra-Wideband Multi-Baseline SAR Interferometry

Unmanned Aerial Vehicle (UAV)-based synthetic aperture radar (SAR) systems enable very accurate and cost-effective monitoring of local areas with unprecedentedly short revisit intervals. This paper discusses specific aspects that arise in the context of UAV-based multi-baseline SAR interferometry (InSAR) for the generation of digital elevation models (DEMs). UAV-based SAR systems are often characterized by a large fractional bandwidth, thus the common narrowband spaceborne approximations yield inaccurate predictions for the geometric decorrelation and the critical baseline. A new formulation is proposed and validated by simulation. Based on the theoretical analyses, a multi-baseline InSAR experiment is planned. The DEM performance analysis shows that large baselines enable height accuracies in the sub-decimeter range, where geometric and volume decorrelation become the limiting factors. This paves the way to height measurements with unprecedented accuracy for multiple applications.
Design Study of a Frequency Divider Using Injection Locking Technique for RF Communication Transceiver

The increasing variety of radio standards utilized by modern mobile devices necessitates that these devices handle numerous frequency bands. As the frequency range increases, the support for multiple bands creates design issues for frequency synthesizers. The increased frequency range provides the benefit of broader bandwidth and smaller antennas that require less space. An existing frequency synthesizer's performance and range can be enhanced by including frequency dividers. Injection-locked frequency dividers (ILFD) receive the most attention among other frequency dividers due to their better phase noise performance and low-power at high frequencies. This paper presents a divide-by-2 Complementary Metal-Oxide-Semiconductor (CMOS) LC-ILFD with a wide locking range percentage and low power. The proposed CMOS LC-ILFD uses sinusoidal tail current-shaping and cross-coupled topology to reduce phase noise and power consumption. At an injection power of 0 dBm and an injection voltage of 0.6 V, the proposed ILFD has an input frequency locking range of 3.8 GHz to 6.5 GHz (52.43%). Aside from that, the proposed ILFD had a phase noise of -125.97 dBc/Hz at a noise frequency of 1 MHz. The proposed ILFD was realized in 180 nm CMOS process with ADS software and had a power dissipation of 1 mW at a 1V DC supply.
A Combined Substrate-integrated Cavity with Time-modulated Graphene Aperture for the Effective Frequency Generation at the THz Regime

The analysis of a substrate-integrated cavity with a time-modulated graphene layer located at its aperture is realized in the present work focusing on the frequency up-conversion characteristics. The cavity is designed with a rectangular profile, while metallic-coated via holes are drilled into the substrate for field confinement. The radiation at the resonance frequencies is facilitated using an aperture slot that is filled with graphene. Its surface conductivity varies in time through the effective adjustment of the electric bias field permitting the successful generation of higher frequencies.
Observation Of Phonon-Driven Enhanced THz Generation In Thin-Film Lithium Niobate

We investigated the THz generation from a 500 nm lithium niobate thin film by optical rectification of femtosecond pulses. A comparison between the numerical studies and polarization-resolved measurements of the THz signal reveals an enhancement of the nonlinear response in the thin film due to the phonon-driven ionic contributions. Our results pave the way to the development of new integrated THz emitters and detectors operating up to 8 THz based on nanophotonic structures, such as nanowires and metasurfaces, realized in the thin film lithium niobate platform.
Design and Analysis of an Interleaved Bi-Directional AC-DC Converter with Reduced Switch Count for Efficient Energy Harvesting

This study proposes a novel interleaved AC-DC converter as a potential solution to address the requirements of high-capacity energy storage systems. The number of semiconductor components in the converter was intentionally reduced during its design process. There is a growing demand for energy storage systems with higher power capacities, primarily driven by the rising adoption of alternative power plants and battery-powered vehicles. In order for the energy system to advance in the future, it is imperative to establish a robust interface which is an essential link between the power plant or utility grid and the battery. The efficacy of these systems is significantly contingent upon the functionality of this interface. It is frequently advised to employ a bi-directional system, as opposed to two separate unidirectional systems, in order to mitigate the expenses and dimensions of energy storage applications. The implementation of an interleaved design offers the potential to decrease the level of AC side current ripple, while simultaneously enhancing the overall current rating. The proposed arrangement also results in a reduction in the frequency of switching. Theoretically, the established switching pattern has the capacity to handle an infinite number of interleaved legs. The proposed extension has the potential to reduce overall system losses. The suggested architecture was subjected to simulations and analysis using MATLAB/Simulink. The working model of the system with a power output of 3.5 kilowatts (kW) has been constructed and subjected to testing within a laboratory setting. The findings obtained from the study provide evidence to support the assertion that the projected topology is effective. The operation of power factor correction exhibits a consistent continuity without any noticeable interruptions, while the output current ripple is significantly diminished.
Comparative study of object detection models for abnormality detection on spinal X-ray images

Detecting abnormalities on spinal X-ray images plays a crucial role in early diagnosis and treatment of spinal disorders. In this paper, we propose a comparative study of three models namely Faster R-CNN, RetinaNet, and FCOS for abnormality detection on spinal X-ray images. Experimental results were measured by the mean average precision at a threshold of 0.5 (mAP@0.5). We investigate the impact of varying input image sizes and the number of updated weight layers in ResNet FPN backbone for Faster R-CNN model. Among the models, the Faster R-CNN model with the ResNet152 FPN backbone achieves the highest accuracy. Additionally, these analysis and evaluation results can serve as reference material for researchers working in the field of abnormality detection on model selection and configuration aiming to enhance diagnostic outcomes.
Evaluating Security of MQTT Protocol in Internet of Things

The Internet of Things (IoT) has revolutionized the way people interact, communicate, and perform daily activities in various domains ranging from households to industries and cities. MQTT is one of the commonly adopted protocols for implementing IoT. However, IoT systems that are connected through MQTT are susceptible to security breaches as MQTT was not originally designed with security as a priority. The credentials and messages transmitted in plaintext by default, thereby compromising data confidentiality and integrity. This study presents a comprehensive analysis of the MQTT protocol, including experimentation on an MQTT system using various cryptographic implementations, such as AES-CBC, RSA, and ECC AES Hybrid Scheme, to assess the processing time and message size. The findings indicate that payload encryption increases processing time and message bytes. Among the cryptographic implementations, RSA incurs the highest processing time, followed by ECC AES Hybrid Scheme and AES- CBC. Furthermore, the study demonstrates the effectiveness of attack prevention between standard MQTT and secured MQTT implementations by simulating various IoT attacks, such as black-box penetration attack, identity spoofing, DoS attack, and MITM attack. The results and subsequent discussion provide insights that answer the research question, revealing the cryptographic algorithms that result in the most overhead on the standard MQTT implementation and their capacity to resist common attacks.
Analysis of Reluctance Synchronous Motor Under Hybrid Fault Condition

A small degree of static eccentricity is inevitable due to manufacturing tolerances and assembly imperfections. Therefore, when stator inter-turn fault happens, it is important to analyze it along with static eccentric condition. Unfortunately, there is not much literature that analyzes such a condition. This paper focuses on the analysis of a Reluctance Synchronous Machine (RSM) when subjected to stator inter-turn and static eccentricity faults simultaneously. In particular, the work focuses on determining the impact of relative position between the minimum airgap point and the stator inter-turn fault. The goal of the paper is achieved by simulating a 1.5 hp, 4-pole, RSM using Finite Element (FE) software. Line current data is captured under different fault conditions and motor current signature analysis is carried out. Furthermore, the lower sideband harmonic frequency is reconstructed in time domain using Inverse Fourier Fast Transform. Clarke’s transformation is applied on the reconstructed harmonic frequency currents to estimate the alpha, beta components. Afterwards, Principal Component Analysis (PCA) is implemented on the alpha, beta currents. The major benefits of the work include establishing the impact of hybrid faults on motor current signatures, developing a new measure to predict the relative position of the point of minimum airgap.
The Impact of Traffic Conditions on Air Quality: A Case Study of IoT Monitoring and Machine Learning Analysis

In many developing countries, air quality is not given the priority it deserves, despite its significant impact on public health. Motorized vehicles are a major contributor to poor air quality, particularly in areas with heavy traffic congestion. However, people often fail to recognize the correlation between air quality and traffic conditions in their environment. To address this issue, we used an IoT device equipped with sensors to monitor air quality indices such as O2, CO2, CH2O, PM2.5, temperature, humidity, and pressure. We also monitored traffic conditions in real-time to investigate the relationship between traffic and air quality. By conducting a correlation analysis, we discovered a noteworthy inverse relationship between traffic and various air quality indicators, including CO2, CH2O, and ozone. Regression analysis allowed us to develop a predictive model that estimates air quality indices based on traffic conditions. Machine learning algorithms, such as linear regression and decision trees, were used to build the forecast model. Our results show that traffic conditions have a substantial impact on air quality, and areas with heavy traffic congestion are particularly vulnerable. This study provides valuable insights into the factors that contribute to poor air quality and can inform strategies to mitigate its effects in these areas. Overall, our approach provides a powerful tool for policymakers, urban planners, and the public to better understand the impact of traffic on air quality and take steps to improve it.
Tale of Two Cs: Computation vs. Communication Scaling for Future Transformers on Future Hardware

Scaling neural network models has delivered dramatic quality gains across ML problems. However, this scaling also increased the reliance on efficient distributed training techniques. Accordingly, like other distributed computing scenarios, it is important to understand how compute and communication will scale relative to one another as models scale and hardware evolves? A careful study which answers this question can better guide the design of future systems which can efficiently train future large models.Accordingly, we comprehensively analyze compute vs. communication (Comp-vs.-Comm) scaling for future Transformer models on future hardware, across multiple axes (algorithmic, empirical, hardware evolution). First, our algorithmic analysis shows that compute generally enjoys an edge over communication as models scale. However, these trends are being stressed since device memory capacity scales much slower than model size. We quantify this edge by empirically studying how Comp-vs.-Comm scales for future models on future hardware. To avoid profiling numerous Transformer models across many setups, we extract execution regions and project costs using operator models. This allows a spectrum (hundreds) of future model/hardware scenarios to be accurately studied (< 15% error) and reduces profiling costs by 2100×. Our experiments show that communication will be a significant portion (40-75%) of runtime as models and hardware evolve. Moreover, communication that is often hidden by overlapped computation in today’s models cannot be hidden in future, larger models. Overall, this work highlights communication’s increasingly large role as models scale, discusses promising techniques to potentially tackle communication, and discusses how our analysis influences their potential improvements.
Enhancing ML-Based DoS Attack Detection Through Combinatorial Fusion Analysis

Mitigating Denial-of-Service (DoS) attacks is vital for online service security and availability. While machine learning (ML) models are used for DoS attack detection, new strategies are needed to enhance their performance. We suggest an innovative method, combinatorial fusion, which combines multiple ML models using advanced algorithms. This includes score and rank combinations, weighted techniques, and diversity strength of scoring systems. Through rigorous evaluations, we demonstrate the effectiveness of this fusion approach, considering metrics like precision, recall, and F1-score. We address the challenge of low-profiled attack classification by fusing models to create a comprehensive solution. Our findings emphasize the potential of this approach to improve DoS attack detection and contribute to stronger defense mechanisms.
Multifunctional MSAW sensor based on Love wave with RFID tags functionalities

This paper shows the feasibility and the performance of a multifunctional device based on a surface acoustic wave delay line. It combines a magnetic field detection with a temperature sensing and has also a radio frequency identification (RFID) capability. This promising device consists of a Co-Fe-B thin film as a magnetic field sensitive material over a ZnO/LiNbO3 Y-cut (X-direction) structure, used as a platform for guided Love waves. The experimental results show high sensitivities of -774 ppm/mT and -67.7 ppm/°C for magnetic field and temperature detection, respectively. The device thus allows magnetic field measurements with the possibility of temperature compensation which make it particularly attractive for industrial applications.
Deploying NLP Techniques for Earnings Call Transcripts for Financial Analysis: A Reverse Phenomenon Paradigm

This study analyses the influence of quarterly board room discussions conducted in the form of "Earnings Call Transcripts" and company’s stock price changes in the subsequent periods. In this study, sentiments were extracted from the "textual quarterly transcripts" of three major software companies for the last ten years. The extracted sentiments were statistically analyzed for patterns and types. The study led to the development of a new response variable called the ‘Inverse Effect’. The ‘Inverse Effect’ simply refers to the discordance between the sentiment in the boardroom discussions available in the document form and changes in the stock price movements. If the sentiment for the current quarter is positive and the changes in the stock price movements is also positive in the subsequent quarter, it is considered as "concordance" and if the changes in the stock price movements is opposite to the sentiments it will be called as "discordance" which is the inverse effect. The study basically looks at the areas where the Weak Market Hypothesis (WMH) is not valid.The findings emerged from the study suggest a possible causality between the sentiments in the transcripts and the stock price changes. It was also found that sentiment polarity, three-quarter average stock price and the previous quarter stock price are the key determinants of the ‘Inverse Effect’. Based on the findings from the study, appropriate machine learning models were developed and evaluated to predict the ‘Inverse Effect’ on the performance of individual stocks of a few select companies.
An Embedded System for Non-Invasive Glucose Monitoring

Diabetes is a persistent condition characterized by the body’s inability to properly utilize insulin. Therefore, noninvasive methods have been proposed to mitigate the pain and risk of infection due to non-required blood extraction compared to the methods for glucose measurement. The invasive methods are done by finger-pricking blood samples from the body. However, this method increases the risk of blood-related infections and pains. Hence, non-invasive Glucose in the blood is an essential method to measure glucose levels. This study successfully implemented a portable embedded system device based on a multi-spectral sensor and Raspberry Pi 4 microprocessor for noninvasive glucose measurement. This study used machine learning, including Multiple Linear Regression and Support Vector Machine, to predict the glucose level with over 90% accuracy. Moreover, the Clarke error grid analysis was used to analyze the accuracy results of this study. It also compared with invasive measurements and previous studies.
A Simulator for CaCS-based Radar Systems in Multi-User Automotive Scenarios

A simulator for evaluating the impact of interference on the communication link built between communication-assisted chirp sequence (CaCS) nodes in multi-user scenarios is presented. The proposed simulator mainly supports chirp modulation systems that adopt a narrow bandwidth for communication in radar-based systems and can be adapted for similar approaches that employ chirp modulation schemes. The simulator analyzes the interference by adopting a microscopic software tool (VISSIM) for building the scenarios anticipated in automotive applications. Furthermore, wave propagation has been implemented by adopting a ray-tracer tool to represent the communication link as one or several trajectories between the nodes. Finally, the output of the wave propagation tool has been evaluated and further processed in MATLAB in terms of the relative cumulative interference and bit error ratio based on the examined scenarios.
Multimodal Deep Learning Model for Subject-Independent EEG-based Emotion Recognition

Regulating emotion is crucial for maintaining well-being and social relationships. However, as we age, the volume of the frontal lobes is reduced, which can cause difficulties in regulating emotions. Electroencephalography (EEG)-based emotion recognition has the potential to understand the complexity of human emotions and the atrophy of the frontal lobes that leads to cognitive impairment. In this study, we investigated a multimodal deep learning approach for subject-independent emotion recognition using EEG and eye movement data. To that end, we proposed an attention mechanism layer to fuse features extracted from the EEG and eye movement data. We tested our approach in two benchmarking emotion recognition datasets: SEED-IV and SEED-V. Our approach achieved an average accuracy of 67.3% and 72.3% for SEED-IV and SEED-V, respectively. Our results demonstrate the potential of multimodal deep learning models for subject-independent emotion recognition using EEG and eye movement data, which can have important implications for assessing emotional regulation in clinical and research settings.
Driver-Drowsiness Detection System Using Deep Learning (CNN)

Drowsiness and fatigue of drivers are amongst the significant causes of road accidents. Every year, this factor increases the amounts of deaths and fatalities globally. When drivers are drowsy or fatigued, the frequency of yawning is different from those in the normal state. This behavior can determine whether the drivers are fatigued or not. It's critical to use technologies to create and build systems that can detect and monitor drivers' levels of attention throughout the driving process, whether they're alert or sleepy. In this study, A high-precision artificial intelligence model was developed using deep learning to detect driver drowsiness during driving to enhance road safety statistics. The model was trained using a dataset containing pictures of people yawning and blinking. The model is then integrated with mobile applications for ease of use with smartphones for drowsiness detection. The application includes authentication, camera stream, trip history, trip statistics, alarm and notification, and emergency modules. Similar systems proposed by prior researchers using hardware devices such as webcam, smartwatch, and many more to predict drowsiness. The results achieved model's accuracy ranges from 62-87%, depending on the frames input from the camera stream and device performance. The mAP and the average recall of the model were 0.55 and 0.662, respectively. The testing and evaluation phase is divided into three parts: unit testing, integration testing, and user acceptance testing. The results of unit testing and integration testing indicate that the application is working as intended, with high accuracy in detecting driver drowsiness. User acceptance testing is done using the System Usability Scale questionnaire, which shows that the application is perceived to be easy to use, effective, and satisfactory. The proposed system is expected to reduce the statistics of road accidents by detecting drowsy drivers during their driving trips, and it is widely and easily used by road drivers.
Ordered Choice Modeling of Farmers' Decision to Obtain Commercial Banks' Credit in Kano State

he research seeks to empirically analyze those factors T affecting farmers' decision to obtain commercial banks' credit in Kano State with emphasis on personal attributes of the farmers. A sample of 294 registered farmers was used for the purpose of the research. Ordered choice models were employed where outcome of the research shows that while age, gender and geographical location of farmers significantly affect their decision to borrow from commercial banks, their level of formal education and experience in farming profession were insignificant in determining their decision to borrow. Post estimation test indicates that there is no specification bias in the models. However, the presence of heteroscedasticity made it necessary to employ the White's Robust Standard Errors (VCE). The study highly recommends among other things that commercial banks' loan classification should reflect farmers' individual attributes for example; the youth, women and rural farmers should be targeted whenever it comes to agricultural lending.
Continuously Tunable Chiral Sound Field in a Non-Hermitian Ring Cavity

Chiral exceptional points (CEPs) in travelling-wave resonators provide promising ways to generate and control chiral modes. This paper presents a possible means of continuously tuning the chirality of excited sound field in a loss-biased non-Hermitian ring cavity by exploiting chirality-reversal radiation and varying between two CEPs with opposite chirality.