Miracle-dz/UniFAR-FTUs huggingface dataset:
INVESTIGATING DEEP LEARNING APPLICATIONS IN COMPUTER VISION FOR EFFECTIVE FACIAL MASK DETECTION DURING THE GLOBAL COVID-19

Purpose: This study aims to scrutinize the integration of deep learning algorithms within the sphere of computer vision, with a concentrated focus on proficiently detecting face mask usage amidst the global COVID-19 pandemic. Theoretical Framework: The research is grounded in the theoretical underpinnings of deep learning, a branch of artificial intelligence, and its application in computer vision. It explores the advancements in machine learning algorithms capable of complex image processing and pattern recognition, essential for identifying face mask usage in various settings. Methodology/Approach : The research adopts a methodological approach involving the design and development of a deep learning model. This model is trained on a diverse dataset encompassing images of individuals with and without face masks. Python, along with libraries such as OpenCV, Keras, and TensorFlow, forms the backbone of the implementation, facilitating the processing and analysis of image data. Findings: The study's findings reveal that the developed model demonstrates a high degree of accuracy, with a 99% success rate in test image predictions, showcasing the effectiveness of deep learning in image recognition tasks. This underscores the model's proficiency in identifying face mask usage, a critical factor in controlling the spread of airborne viruses like COVID-19. Research, Practical & Social Implications: This research contributes significantly to the field of computer vision, offering practical applications in public health monitoring and societal well-being. The model's ability to accurately detect face mask usage paves the way for enhanced pandemic management strategies and reinforces the role of technology in public health initiatives. Originality/Value: This study innovates within existing research by applying deep learning in computer vision for addressing the COVID-19 crisis. It uniquely focuses on developing technological solutions for efficient and cost-effective monitoring of face mask usage, emphasizing prevention.
Non-Mixture Cure Model Estimation in Bladder Cancer Patients: A Novel Approach with Exponentiated Weibull Exponential Distribution

Objective: Cure models are frequently used in survival analysis to account for a cured fraction in the data. When there is a cure rate present, researchers often prefer cure models over parametric models to analyse the survival data. These models enable the ability to define the probability distribution of survival durations for patients who are at risk. Various distributions can be considered for the survival times, such as Exponentiated Weibull Exponential (EWE), Exponential Exponential (EE), Weibull and lognormal distribution. The objective of this research is to choose the most appropriate distribution that accurately represents the survival times of patients who have not been cured. This will be accomplished by comparing various non-mixture cure models that are based on the EWE distribution with its sub-distributions, and distributions distinct from those belonging to the EWE distribution family. Material and Methods: A sample of 85 patients diagnosed with superficial bladder tumours was selected to be used in fitting the non-mixture cure model. In order to estimate the parameters of the suggested model, which takes into account the presence of a cure rate, censored data, and covariates, we utilized the maximum likelihood estimation technique using R software version 3.5. 7. Result: Upon conducting a comparison of various parametric models fitted to the data, both with and without considering the cure fraction and without incorporating any predictors, the EE distribution yields the lowest AIC, BIC, and HQIC values among all the distributions considered in this study, (1191.921/1198.502, 1201.692/1203.387, 1195.851/1200.467). Furthermore, when considering a non-mixture cure model utilizing the EE distribution along with covariates, an estimated ratio was obtained between the probabilities of being cured for placebo and thiotepa groups (and its 95% confidence intervals) were 0.76130 (0.13914, 6.81863). Conclusion: The findings of this study indicate that EE distribution is the optimal selection for determining the duration of survival in individuals diagnosed with bladder cancer.
Taming Serverless Cold Start of Cloud Model Inference with Edge Computing

ÐServerless computing is envisioned as the de-facto standard for next-generation cloud computing. However, the cold start dilemma has impeded its adoption by delay-sensitive and burst applications. In this paper, we propose to tame serverless cold start in a cloud inference system with edge computing. Specifically, the proposed solution smooths the serverless cloud workload with user-owned edge computing, reducing the number of cold starts. Leveraging the configurability of requests and serverless functions, the proposed solution further reduces the transmission latency and serverless cost by adapting request configuration (e.g., image resolution) and function configuration (e.g., memory). To alleviate the potential inference accuracy degradation incurred by configuration adaption, we aim to strike a nice balance between inference latency, cost, and accuracy. However, achieving this goal is non-trivial since the underlying optimization is non-convex and involves future uncertain information. To simultaneously address dual challenges, the presented cold-start-aware online algorithms apply the regularization technique to decompose the problem into separate convex subproblems. Then, it applies lazy switching to smooth the number of provisioned functions and thus reduces the cold start. Through rigorous theoretical analysis, realistic prototype evaluations on AWS Lambda, and trace-driven simulations, we comprehensively validate the theoretical and empirical performance of our proposed solution.
A LoRa-Based Monitoring System for Agriculture

In this paper, we present a LoRa-based monitoring system implementing LoRa with simple MAC (medium access control) architecture as a lightweight alternative to more complex LoRaWAN systems for agricultural applications. Our developed system consists of several low-cost and low-power remote sensor nodes with LoRa transceivers in a star topology with a custom-built. NET data-logging and control application acting as the central node. Despite using LoRa without LoRaWAN, we were able to demonstrate the reliable collection of remote sensor data and control of remote nodes through field trials of the novel system conducted on a working cattle farm.
Full-Range ZVS Control Method Based on Near-CRM for T-Type Three-Level Inverter

T-type inverter is attractive due to low conduction losses and low switching voltage, and small size of passive component. Usually, zero-voltage switching (ZVS) without auxiliary circuit can be easily achieved by controlling the inverter to operate in the near critical conduction mode (near-CRM). However, with conventional modulation, T-type inverter require an extremely low switching frequency to achieve near-CRM mode when the AC voltage crosses zero, which is impractical for the digital controller. This paper proposes a new third-mode based modulation method and a variable switching frequency control method to make the T-type inverter into near-CRM. The proposed method is able to make all switches achieve ZVS soft-switching at full-range operation. The effectiveness and performance of the proposed modulation and control are validated by an experimental prototype.
Multi Sensory Distributed Bearing Fault Classification using Wavelet Scattering Transform

Distributed bearing faults are a prevalent issue in the industrial sector, primarily attributed to factors such as inadequate lubrication, contamination, electrical erosion, and propagation of localized faults. This research offers a multi-signal fault diagnosis framework aimed at identifying and categorizing such faults in induction machines. The framework leverages the wavelet scattering transform to extract feature sets from nonstationary signals, including tri-axial vibrations and stray magnetic flux signals. Data for this investigation are collected from two induction motors, with capacities of 3 and 10 horsepower respectively. The investigation includes a comprehensive analysis across 50 unique operating scenarios, covering ten different rotational speeds and five distinct torque levels. To reduce the redundancies among features, two dimension reduction algorithms such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA)are employed and compared. Ensemble classification techniques, namely XGBoost and Random Forest, are used for the classification of distributed faults. Various combinations of dimensionality reduction methods and classifiers are examined to ascertain the most efficient approach in terms of accuracy and training time performance. Additionally, the efficacy of different signal combinations is also studied. Empirical data underscores the effectiveness of combining LDA and Random Forest, achieving an optimal accuracy of 99.4% with a minimum training duration of 15 seconds. Notably, the utilization of vibration signals from the X, Y, and Z axes yielded an accuracy rate above 90%. However, the standalone effectiveness of the stray magnetic flux was found to be minimal.
Service-driven User Plane Architecture for Future Cellular Networks and Multi-access Edge Computing

The proliferation of 5G applications that are delay sensitive has increased demand for computing and storage capability at the network edge. User mobility results in frequent reallocation of application state between edge servers and the UP paths. Currently, there is a complicated interaction between the Control Plane (CP) and User Plane (UP) that reduces the effective utilisation of network and computation resources, and degrades the Quality of Service (QoS). A service-driven UP selection mechanism plays a key role in fulfilling the heteroge-neous service requirements, such as low latency and high speed, and the trade-off between cost and performance. This paper focuses on optimizing Xn-based post-handover UP management in 5G/6G networks by enabling the Uplink Classifier (ULCL) function and Application Function (AF) with refined QoS flow in the integration of 5G/6G with a multi-access edge computing environment. A service-driven UP path reallocation mechanism and architecture are proposed for dynamic UP path management to balance resource cost and performance.
Accurate Analytical eGaN® HEMT Parameterizable Matlab® Model Based on Datasheet from Manufacturer and Its Applications in Optimal Design

This paper presents an accurate and parameterizable analytical modeling for Gallium Nitride high electron mobility transistor (GaN HEMT) implemented in Matlab. GaN HEMTs drive the design of power converters to higher switching frequencies and efficiencies. Reaching highest efficiency, each loss source becomes acknowledgeable and must be accurately evaluated. In GaN HEMT based design, gate resistance, deadtime, package and PCB parasitics, reverse voltage drop, nonlinearity of internal capacitances and thermal evolution of transfer characteristic impact dramatically the losses. The proposed model is based on temporal resolution of an equivalent electrical circuit for both GaN HEMT internal waveforms and converter waveforms. The model is parameterizable in order to quickly and accurately evaluate the impact of each design parameter on losses. The GaN HEMT physical parameters are fitted from datasheet. The model is as accurate as the original LTspice model and can be integrated in an automated design system for power electronic converters.
Performance of downlink MIMO-NOMA system in 5G Networks

The combination of multiple-input multiple-output (MIMO) and non-orthogonal multiple access (NOMA) methods is a cross skill that resolves numerous problems in the 5G network. Contrary to traditional multiple access schemes; NOMA agrees diverse users to share the similar resources with dissimilar power levels. Multiple Input Multiple Output (MIMO) scheme can support various users using multiple antennas at the base station. This causes a data rate and spectrum proficiency increase. Combining NOMA and MIMO methods can offer extreme aptitude for the growing demands of 5G services. In this paper, the performance analysis of NOMA-MIMO scheme for 5G system is presented as a function of SNR and data rate achievement.
Internet of Things technology for efficient fire hydrant management

Efficiency in managing fire hydrants is of paramount importance when it comes to preserving public safety and property protection. These vital devices play a crucial role in the event of a fire, as their proper functioning can make the difference between quickly gaining control of a critical situation and the uncontrolled spread of flames. It is in this context that Internet of Things (IoT) technology offers a revolutionary approach to optimize fire hydrant management. This technology connects these essential elements to a smart network, enabling real-time monitoring, proactive maintenance, and more effective resource management dedicated to fire prevention and control. In this work, we specifically focused on the field of fire safety. To this end, we developed a prototype or IoT platform designed to monitor and enhance the management of parameters such as temperature, humidity, gases, and flames. Monitoring these elements poses one of the significant challenges of IoT. Our solution, in the form of an integrated IoT platform, combines hardware components primarily consisting of microcontroller boards (ESP32) and sensors, with a software component based on Node-Red, allowing remote control and real-time alert reception.
Sequence Residual Frequency Domain Segmentation Temporal Convolutional Network for COVID-19 Prediction

COVID-19 belongs to a vast group of viruses rapidly disseminating worldwide. The substantial surge in infected individuals has resulted in a strain on medical facilities across the globe. Consequently, precise predictions of case numbers and fatalities can assist governments and various organizations in preplanning response strategies, alleviating the burden on the healthcare system. Deep learning introduces a fresh approach to addressing emerging coronavirus outbreaks. In this study, we present an innovative hybrid framework that combines singular spectrum analysis (SSA), empirical fourier decomposition (EFD), and temporal convolutional network (TCN) for the prediction of COVID-19. To leverage COVID-19 time series data to its fullest extent, we have devised a residual frequency domain segmentation technique by merging SSA and EFD. Through this approach, the initial time series, characterized by intricate fluctuations, can be restructured into subsequences featuring more conspicuous patterns of change. TCN-based models utilize dilated causal convolution and residual connections to encompass both immediate and prolonged dependencies within time series data. To showcase the predictive capabilities of our proposed model, we conduct short, medium, and long-term forecasts for weekly new confirmed cases and deaths, respectively. Experimental reveal that our model outperforms the other seven baseline models in terms of root mean squared logarithmic error (RMSLE), mean absolute percentage error (MAPE), coefficient of determination (R2), and pearson correlation coefficient (PCC).
Day-ahead Demand Response Potential Forecasting Model Based on BILSTM for Load Aggregators

With the continuous development of social economy, the exhibition of stable, continuous and efficient power supply has become the focus of the global energy field. However, the traditional load aggregator model can not deal with irregular time series data and variable-length series flexibly, which makes the traditional model face certain difficulties in adapting to practical application scenarios. Therefore, considering the real-time and efficiency of the model, a demand response (DR) potential prediction model based on bidirectional long-term and short-term memory network (BiLSTM) is proposed. Firstly, customers are divided into different types according to their willingness to reduce economic costs and respond, and then a Home Energy Management System (HEMS) model considering electricity price incentives is constructed. Secondly, the Light gradient boosting machine (LightGBM) model is used to rank several influencing features according to their importance, which reduces the redundancy of input features. Finally, on the basis of selecting features, BILSTM model is introduced to predict the DR potential of load aggregators (Las). The results of the simulation-based experimentation unequivocally demonstrate the efficacy of the proposed approach.
Group Counting Using Micro-Doppler Signatures From a 77GHz FMCW Radar

People counting and detection technologies have shown great versatility in various scenarios and have become an important tool for event organizers and city planners to optimize their operations. This paper presents a novel approach for people counting using Micro-Doppler Signatures (MDS) extracted from a Frequency-Modulated Continuous-Wave (FMCW) radar operating at 77GHz. The system utilizes the unique gait model of each individual, which results in a distinct instantaneous velocity over time, to generate the MDS that are later used to classify groups of different sizes with a Convolutional Neural Network (CNN). Those results are compared with using existing CNNs for image classification, in a transferred learning approach. The proposed system overcomes the limitations of existing camera-based people counting techniques such as the need for a clear line of sight and being affected by lighting conditions.
Implantable Antennas for Endoscopy Applications: Current Review and Challenges

Implantable antennas are becoming essential components for the design of sensors within implantable medical devices. They facilitate real-time monitoring and control of vital physiological parameters, by enabling wireless communication and data transmission within the human body. A review of current research on implantable medical devices and capsule endoscopy is discussed in the present work. In particular, significant improvements are shown regarding miniaturization techniques, biocompatibility, sensor capabilities, frequency band, and wireless communication. Current issues and future directions also highlighted. The existing achievements are promising for personalized medicine, early disease detection, and minimally invasive diagnostics, thus leading to a true healthcare revolution.
A Tunable Optoelectronic Oscillator Based on Microwave Phase Shifters

A novel realization of a tunable optoelectronic oscillator (OEO) based on a dual-parallel Mach-Zehnder modulator (DPMZM) and two tunable microwave phase shifters (PS) is experimentally demonstrated. By adjusting the time delay of the microwave signal which is sent to two radio frequency (RF) ports of DPMZM, the center frequency of the multiband microwave photonics filter (MMPF) can be tuned. When the OEO feedback loop is closed, single-frequency microwave signals with a tuning range from 3.6 to 15.6 GHz are generated. The side mode suppression ratio is 67 dB, and the phase noise is measured to be - 104 dBc/Hz when the oscillation frequency is 9.2 GHz. The frequency drift is less than 24 kHz in 1600 s without phase-locked or compensation devices.
Digital Twin Enabled 6G Radio Testing: Concepts, Challenges and Solutions

The sixth-generation (6G) mobile communication is envisioned to be an all connected radio system, which covers the vertical expansion from ground-based networks to spaceborne networks, and the horizontal expansion from ultra-short range to ultra-long range communications. As a result, the number of radio devices and associated test cases and scenarios will grow exponentially, making field trials (i.e., testing in real deployment scenarios) unfeasible due to the cost and time concerns. A digital twin (DT) concept can be adopted to reproduce the field trials, enabling 6G radio testing and development in controllable and repeatable laboratory conditions where the radio devices and network should operate as if they would in real-world conditions. This article investigates DT-enabled 6G radio testing. First, the concept of DT for radio testing is introduced, including information on how DT can capture the radio environment with unique propagation features. Next, three state-of-the-art emulation setups of DT for radio testing are explained. The article also covers challenges introduced by new 6G radio technologies for the DT-enabled 6G radio testing, including potential solutions to tackle those challenges.
5G-Wi-SUN for Building Management System

With higher data rates and lower latency, 5G constitutes a mobile alternative to both wired networks and less capable wireless solutions. However, the performance of 5G requires expensive hardware and high energy consumption, making it less suitable for energy-constrained edge devices. Combining the capabilities of 5G for Wide Area Network (WAN) access, with the benefits of other wireless networks purpose-built for low-power embedded systems, a Building Management System (BMS) based on 5G can be deployed anywhere in a building with 5G coverage extended with a suitable LPWAN connectivity for IoT devices of the BMS. In this paper, the LPWAN of choice is the Wireless Smart Utility Network (Wi-SUN), a low-power sub-GHz connectivity based on IEEE 802.15. 4g, offering mesh and multi-hop features for improved reliability and range, alongside competitive data rates. The performance of a 5G-Wi-SUN hybrid network was evaluated with a fibre-broadband hybrid network, a 5G-only network and a Wi-SUN-only network to determine the feasibility of 5G-Wi-SUN network. Test results show a significant performance of our 5G-WiSUN network, with a round-trip times below half a second in most cases, and a trimean of 115ms, only 27ms more than the fibre broadband-WiSUN network in our experiment. Consequently, the results have demonstrated the ability of a 5G-Wi-SUN network to provide a versatile alternative to fibre broadband-WiSUN, while maintaining a comparable performance.
Detection of Hip Dysplasia in Infants Using Audible-Frequency Acoustic Transmission Measurements

Developmental hip dysplasia (DDH) is present in 0.2-0.3% of human newborns. Diagnosis may be made by skilled physical examination, or more commonly by utilizing hip ultrasound imaging. The latter is limited by availability, cost and skill, especially in low resource setting and in the developing nations. A low-cost, easy to use and sensitive DDH screening method would be helpful to assist with early detection, facilitating early and more effective intervention and thereby minimize chronic disability. The current study investigated the utility of an acoustic screening method for DDH. The primary hypothesis is that audible sound transmission through the hip joint is affected by DDH and that these transmission changes are detectable. The method was tested in a clinical setting using a compact system. This involves inputting a brief acoustic signal at one location and measuring the transmitted signal at another. Two different transmission configurations were tested. A decrease in acoustic transmission at certain frequencies was appreciated in DDH patients at the affected side with sensitivity of 80% and specificity of 50-79% depending on the exciter location. The higher accuracy was achieved when the excitation was performed at the anterior superior iliac spine and the transmitted signal was measured at the trochanter. Soothing the infants (e.g., using a feeding bottle, a pacifier, etc.) significantly helped in avoiding crying and movement artifacts that can interfere with the method.