Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards smart helmet for motorcyclists: automatic stress level detection using wearable accelerometer sensor system

In the modern world, stress is a common occurrence that alters how people behave and how they physically react to certain situations. However, Consistent or prolonged stress can put our well-being at risk and disrupt our everyday tasks. Assessing stress and other psychological factors in humans is difficult because it involves self-reporting, which can be subjective, and there can be differences both among and within individuals. The advancement of Internet of Things (IoT) sensors has enabled the tracking of a wide range of human behavior patterns, including stress-related behavior that can be quantified objectively. This study makes use of a wearable accelerometer sensor that was used to collect information on a real-world scenario concerning a motorcyclist’s mental health. In this study, a wearable single triaxial accelerometer sensor was placed inside the motorcycle rider’s helmet to collect data on his physiological and behavioral characteristics. Using various machine learning methods such as Decision Trees (DT), Discriminant Analysis (DA), Support Vector Machines (SVM), Nearest Neighbor Classifiers (k-NN), and Ensemble Classifiers, the accuracy of motorcyclist stress was assessed and compared. By employing classification methods, the Quadratic Support Vector Machine (QSVM) exhibited the highest average accuracy of 83.3% and an average F-score of 91.3% overall. In contrast, the Quadratic Discriminant (QD) analysis demonstrated the lowest average accuracy of 62.5% and an average F-score of 70.7%. A questionnaire that measured privacy concerns on a scale of 1- 5 was used. The findings indicate that our approach has the potential to deliver precise identification of stress levels among motorcyclists, hence demonstrating its usefulness for future adoption.
Numerical investigation of transient response of a coupled two-degrees-of-freedom symmetric airfoil before flutter

In this study, the numerical investigation of free dynamic response of a rigid, symmetric airfoil having two coupled degrees of freedom is performed. The airfoil is excited using two different mechanisms. Firstly, a mechanical excitation scenario is simulated using initial conditions for both the degrees of freedom. Dynamic airfoil response in terms of amplitudes of oscillation is recorded under no flow as well as uniform upstream flow conditions. In the second scenario, an upstream flow perturbation is introduced in the form of a gust superimposed on the otherwise uniform in-flow. Transient energy amplification behavior of the airfoil under both scenarios is monitored. The findings are presented in terms of dimensionless aerodynamic coefficients, oscillations amplitudes, and total energy of the airfoil. Detailed comparisons of the experimental data and results obtained using analytical models are developed and discussed using a very simple simulation methodology.
Real-time Lane Detection and Departure caution Gadget for Automobiles based on Raspberry pi

The advanced driver assistance machines critical aspect is lane detection (ADAS). even through several methologies have been put out via researches up so far, correctly detecting the street lanes below numerous environmental instances stays difficult trouble. The devices number one aim is to perceive lane departures to prevent injuries on the road and ensure pedestrain safety.To pick out and tune the road borders, cameras accumulated snapshots function as the devices number one entry. On raspberry pi hardware, the open CV library, the use of python is used to achive this concept of photograph processing this method can efficiently understand the roadways in a selection of tough occasions.
Prediction of uterine fibroid outcomes based on DenseNet and multiple MRI sequences

High-intensity focused ultrasound (HIFU) is a surgical method commonly used to treat uterine fibroids, but not all patients are suitable for this treatment. Therefore, effective postoperative outcome prediction can improve surgical success rate. In this study, a data-driven prediction model for HIFU treatment outcome was developed based on patients' MRI sequences and DenseNet network. We used N4 bias correction, SimpleITK intensity normalization, image registration and other preprocessing methods to improve image quality. We also introduced data augmentation and transfer learning methods to solve the problem of small dataset and integrated multimodal information through feature fusion method. In conclusion, the fusion model's Accuracy was 0.769, AUC value was 0.806, which was better than the prediction accuracy of single sequence, and the experimental results showed that the model had good predictive performance.
Differences and Trial-to-Trial Reliability of Vertical Jump Heights Assessed by Ultrasonic System, Force-Plate, and High-Speed Video Analyses

Background: Jumpanalysesarefrequentlyconductedinthesportandrehabilitationsectorbydiﬀerentmethods. Thisstudyaimed to compare a new ultrasonic system (US) measuring the distance between standing and the highest point of the jump with (a) the impulse-momentum (FP_IM), double integration (FP_DI), and ﬂight-time (FP_FT) methods using a force plate and (b) the rise-time (VA_RT) and vertical distance (VA_VD) methods of the ankle using a high-speed video analysis. Additionally, the trial-to-trial reliability of each method was examined. Methods: Eighteen participants (25.8 ± 5.9 years) performed three countermovement jumps. The jump height was determined by thesixmethods. One-wayrepeatedmeasuresANOVAwithpost-hoccomparisonswereusedtoevaluatesystematicbias. Pearsoncor-relationcoeﬃcientsandtypicalerrorsbetweenallthemethodswerecalculatedtodeterminerandomerrors. Trial-to-trialreliability was assessed with intraclass correlation coeﬃcients (ICC). Results: A systematic bias existed between the US ( ≤ 15.4 cm) as well as FP_DI ( ≤ 14.0 cm) and all the other methods (all P < 0.05). Moreover, the vertical jump heights of FP_FT were 1.7 and 1.4 cm greater than the heights of FP_IM and VA_VD, respectively (all P < 0.05). Correlation coeﬃcients and typical errors between all methods were ≥ 0.91 and ≤ 2.6 cm, respectively. The ICCs for the US, FP_IM, FP_DI, FP_FT, VA_VD, and VA_RT were 0.96, 0.96, 0.93, 0.95, 0.94, and 0.90, respectively. Conclusions: All methods showed a high trial-to-trial reliability, conﬁrming their general usefulness. However, the systematic dif-ferencesbetweenthejumpheightsof themethodsneedtobeconsidered. Consequently,regressionequationsallowtheconversion of countermovement jump heights between the six methods with small typical errors of estimate.
A Comparative Analysis of Fraud Detection in Healthcare using Data Balancing & Machine Learning Techniques

Frauds in healthcare sector has become a major problem and is widespread now. It is reported that total healthcare expenditure is increasing exponentially due to fraudulent claims in health insurance sector. It is a planned crime that include providers, doctors/physicians and beneficiaries etc, who together perform certain actions to make fraud claims. Most vulnerable institutions due to such frauds are the insurance companies. In this work as large data set is used so we have performed Exploratory Data Analysis (EDA) and then data preprocessing & feature engineering to create a feasible dataset for further analysis. In this proposed methodology, we have depicted a comparative analysis of results of different machine learning models using two data balancing techniques, i.e Class Weighing Scheme (CWS) and Adaptive Synthetic Oversampling (ADASYN) for oversampling along with the results of these models with unbalanced data set. Our objective is to verify the efficacy of various machine learning models in this domain.
Investigating the Resolution-Performance Trade-off of Object Detection Models in Support of the Sustainable Development Goals

Satellite imagery provides impartial, inclusive, and timely geospatial data regarding global issues, supplementing traditional ground-based methods. The volume, velocity, and variety of satellite imagery present novel capabilities to support the United Nations' Sustainable Development Goals by providing repeatable data to assess progress and support reliable statistics connecting policy with evidence. Currently, the relationship between spatial resolution and accurate detection of sub-meter- to meter-scale objects supporting policy-making remains unexplored. This study investigates the resolution-performance trade-off by comparing results of object detection models and their implications for two sustainability-motivated use cases. These include vehicle detection, used to inform traffic flow and density for understanding infrastructure needs and lockdown policies, and solar panel detection, where accuracy is crucial in monitoring green energy production and sustainable energy access. The results show a largely linear degradation of object detection models using imagery at 30, 40, 50, and 60 cm resolutions simulated from 7 cm aerial imagery. Object detection models using 30 cm imagery improve F$_{1}$ score by 65% compared to 60 cm models. Further investigation into a preprocessing step using 30 cm imagery to generate 15 cm imagery shows an average F$_{1}$ score increase by 5% for detection of small objects across multiple use cases. The results and analysis provide a basis for more robust investigations into the capabilities of very high resolution satellite imagery to support the Sustainable Development Goals.
A Low-Power Hardware Accelerator of MFCC Extraction for Keyword Spotting in 22nm FDSOI

With the development of artificial intelligence, the real-time feature extraction of acoustic signals is required in a wide variety of applications, such as keyword spotting and speech recognition. Feature extraction based on Mel-frequency cepstral coefficients (MFCCs) is one of the most significant methods thereinto. A software implementation of the MFCC extraction results in relatively high power consumption and computational time limitation, often making it unsuitable for tiny battery powered devices. Therefore, an on-chip accelerator of MFCC extraction is of interest in cutting-edge scenarios. This paper presents a fixed-point low-power hardware accelerator of MFCC feature extraction implemented in 22nm FDSOI technology. It consumes an average power of 2. 78µW for 1024-sample frame at a clock frequency of 1MHz. For keyword spotting, the quantized accelerator achieves an average accuracy of around 96% working along with different classification networks.
Research on Improving Gray Wolf Algorithm Based on Multi-Strategy Fusion

To address the shortcomings of the basic Gray Wolf Optimization (GWO) algorithm in solving complex problems, such as relying on the initial population, converging too early, and easily falling into local optimality, a chaotic reverse learning initialization strategy, a nonlinear control parameter convergence strategy, and a dynamic position update strategy are introduced to develop a multi-strategy fusion Improved Gray Wolf Optimization (IGWO) algorithm, and this method is used to solve function optimization problems. First, a chaotic backward learning initialization strategy, based on logistic mapping and backward learning, is adopted to improve the random initialization of the GWO algorithm and enhance the traversal and diversity of the initial population. Second, a nonlinear control parameter for local perturbation is constructed to avoid the problem of premature convergence of the GWO algorithm due to linear convergence and to balance the exploration and exploitation ability of the GWO algorithm. Finally, a location guidance strategy based on dynamic weights and individual memory is proposed to effectively improve the algorithm’s optimization accuracy and computational efficiency; meanwhile, the Gaussian-Cauchy mutation strategy of superior selection is introduced to optimize the location update of optimal individual $\alpha $ wolves and improve the ability of the population to jump out of local extremes. Simulation experiments are conducted for 11 classical test functions, and the results show that the proposed improved algorithm IGWO for gray wolves is superior to 10 other standard swarm intelligence optimization algorithms and 4 other improved optimization algorithms in terms of solution accuracy, convergence speed, and algorithm stability. It provides a new optimization algorithm for solving complex optimization problems.
Advanced Optical Modulators for Sub-THz-to-Optical Signal Conversion

The use of sub-terahertz (sub-THz) and/or THz bands is a method of achieving some attractive applications such as future large-capacity radio over fiber (RoF) networks. However, in the current scenario, the performance of devices operating in the THz band is considerably worse than that of devices operating in the microwave band. An optical modulator is a device that converts electrical signals such as microwave, sub-THz, and THz signals to optical signals, and their conversion efficiency decreases when they are operated at higher frequencies. In this article, we investigate two types of optical modulators. One is a long effective-length modulator to maximize its responsivity in the >100 GHz range. It has an advantage for band-limited applications such as RoF. The other is a broadband modulator integrated with an electro-optic (EO) frequency-domain equalizer. The fabricated modulator achieved an over 110-GHz 3-dB bandwidth by customizing the optical circuit diagram in a traveling-wave modulator, and in a numerical estimation, the 3-dB bandwidth reached sub-THz. We also investigated the modulation distortions of the modulator with the equalizer. Using the measurement results, the optical crosstalk in the EO equalizer of the fabricated modulator was estimated to be less than −30 dB, and the distortion attributable to the EO equalizer in the modulator was sufficiently small to be negligible. We also measured a third-order intermodulation distortion, and the results showed that integration of the equalizer does not cause a degradation of modulation linearity. The obtained spurious-free dynamic range was as high as 83.3 dB.
3D Semantic Understanding of Large-Scale Urban Scenes from LiDAR Point Clouds

We studied the semantic segmentation of point clouds in large-scale real-world 3D scenarios. In particular, the urban LiDAR point cloud is characterized by complex environment, diverse structure and unbalanced sample distribution, which make it difficult for machine to effectively understand the scenes. In this work, we propose a new deep learning network for 3D semantic understanding of large-scale urban scenes. Compared with other works, we designed a new multichannel encoding model to enrich the local features. At the same time, we used the point-based cross-stage local area network to preserve the feature information of each layer to the greatest extent. Experiments showed that our work achieved on-par or better performance than state-of-the-art methods on two benchmarks and significantly outperformed other works for semantic categories with small sample sizes, proving the effectiveness of our network.
Evaluation of Power Consumption in 5G Networks at sub-6 GHz and mmWave

Being able to estimate the power consumption of a 5G network deployment before installation allows operators to identify the best technological solution for the investment. This operation is of high relevance in 5G networks, for both legacy sub-6 GHz frequencies and, most importantly, for the incoming millimeter wave (mmWave) ones. Indeed, the consumption of the latter has not been fully investigated yet. In this research work, we study the power consumption of 5G sub-6 GHz and mmWave networks for different cell architectures (macrocells, microcells, femtocells, streetmacro cells) and technologies (fiber, radio, copper) used in fronthaul and backhaul. The originality of this work resides in a thorough study of power consumption for sub-6 GHz networks across a variety of different configurations and, as main novelty, we investigate the overall consumption of a fully mmWave-based 5G network, showing how it represents an energy efficient solution to cope with the increase traffic demand.
Bit-Offsetter: A Bit-serial DNN Accelerator with Weight-offset MAC for Bit-wise Sparsity Exploitation

With the rapid evolution of deep neural networks (DNNs), the massive computational burden brings about the difficulty of deploying DNN on edge devices. This situation gives rise to specialized hardware aiming at exploiting the sparsity of DNN parameters. Bit-serial architectures (BSAs) possess great performance potential by leveraging the abundant bit-wise sparsity. However, the distribution of effective bits of weights confines the performance of BSA designs. To improve the efficiency of BSA, we propose a weight-offset multiply-accumulation (MAC) scheme and an associated hardware design called Bit-offsetter in this paper. Weight-offsetting not only significantly boosts bit-wise sparsity but also brings out a more balanced distribution of essential bits. For Bit-offsetter, aside from leveraging the abundant bitwise sparsity induced by weight-offsetting, it’s also equipped with a load-balancing scheduler to reduce idle cycles and mitigate utilization degradation. According to our experiment on a series of DNN models, weight-offsetting can increase bit-wise sparsity for pre-trained weight up to 77.4% on average. The weight-offset MAC scheme associated with Bit-offsetter achieves 3.28×/2.94× speedup/energy efficiency over the baseline.
Data-driven-based Predictive Optimal for a class of Iterative Learning Control by Q-learning method

The paper investigates predictive optimal for P-type iterative learning control (lLC) under unknown system's dynamic. First, with an Δuk+l-based discounted linear quadratic cost function of control errors in ILC, we formulate the predictive optimal problem for P-type ILC and present a solution for such a problem in a discrete algebraic Riccati equation (DARE) with known system's dynamic, by which a predictive optimal algorithm can be established. Second, a reinforcement Q-Iearning method is proposed to find predictive optimal solution for P-type ILC under unknown system's dynamic, which is intended to develop a model-free and iterative learning algorithm to implement ILC with a higher convergence speed only by utilizing the input and output data of ILC. Based on the performance function, a Q-function is elaborately chosen in an iterative formulation with trials of Δuk+l and control errors for critic P-type ILC performance with its updated converge rated. Third, the convergence of the algorithm is proved and discussed with it conditions. Last, an illustrative digital simulation at MATLAB verifies the effectiveness of the proposed algorithm and shows its advantages over the traditional method.
Filtration and Deposition Rate of Viscose Fiber in Textile Industry

In Textile Industry, Cotton is the essential source of processing. Nowadays, Different types of Cotton are used for clothing like viscose, rayon and so on. During processing, storage and transport of such viscose fibers makes the surrounding environment dirty by floating in air and sticking it on other objects. It majorly affects the body parts like ear, eyes, nose and mouth even though workers are wearing masks. Continuous exposure to cotton breathing could lead to various lung diseases like asthma, byssinosis, chest tightness and further can cause bronchitis. Exhaust fan does not remove the dust effectively and cannot be able to remove as much viscose which is grounded. Also, there is a possibility of increase in wastage of such type of viscose fiber which is expensive in the market. So the above constraints lead us to propose a product which will be able to reduce such viscose fiber waste and it is used to collect the floating viscose fiber by moving and collecting it using a cotton fiber filter. The accumulation of such viscose fiber is determined by either thickness or weight to provide the deposited rate at display and to reduce the wastage. The viscose suction vehicle is movable. Due to that, it can be able to collect viscose fiber more effectively and productively in all perspective environments. It can reduce the majority of the wastage of viscose fiber. The movable fiber vehicle helps to prevent inhaling viscose fiber and by drawing most of the viscose fiber to the fiber filter.
Hammerstein system identification using robust estimator based on quantized observation

Hammerstein system is the most popular block-oriented model, which can represent a large number of nonlinear model features. With the rapid development of science and technology, quantization communication has become a hot topic in data transmission field based on quantized sensor. Because Hammerstein system can describe an actuator in series with linear system, the quantized Hammerstein system are receiving more and more attention. In this context, this work discusses the identification of the Hammerstein system in the presence of quantitative observation. To obtain the effective system data, a constant filter gain is used to filter the system input and output data. Then, the multi innovation theory is introduced to expand the prediction error data into matrix form. Based on the above matrices, the augmented parameter error data can be gained. Right after, the robust estimator is given by using the augmented parameter error data. Finally, we apply the quantized example to examine the performance of the proposed estimator.
In-memory Activation Compression for GPT Training

Recently, a large number of parameters in Transformer-based language models have caused memory short-ages during training. Although solutions such as mixed precision and model parallelism have been proposed, they have the limitation of inducing communication overhead and requiring modification of the model by a programmer. To address this issue, we propose a scheme that compresses activation data in memory, enabling the reduction of memory usage during training in a user-transparent manner. The compression algorithm gathers activation data into a block and compresses it, using base-delta compression for the exponent and bit-plane zero compression for the sign and mantissa. Then, the important bits are arranged in order, and LSB truncation is applied to fit the target size. The proposed compression algorithm achieves a compression ratio of 2.09 for the sign, 2.04 for the exponent, and 1.21 for the mantissa. A compression ratio of 3.2 is obtained by applying up to the truncation, and we confirm the convergence of GPT-2 training with the compression.
Design of AlGaN/GaN HEMT Device with InGaN Back Barrier for RF Applications

This paper reports a double hetero-junction AlGaN/GaN/InGaN HEMT. A 3nm layer of InGaN is introduced in a conventional AlGaN/GaN HEMT structure. Considering both the proposed HEMT structure and conventional HEMT a performance comparison and detailed analysis is done. A potential barrier is created as a result of InGaN layer insertion leading to improved 2DEG carrier confinement. This in turn leads to enhanced DC and RF performance of the device. Better carrier confinement in the quantum well will result in high drain current and transconductance eventually leading to high frequency performance. The proposed device reported a drain current of 450mA/mm and transconductance of 200mS/mm at Vds=2V. The RF characteristics reported a cut-off frequency of 25GHz. The additional potential barrier, created at the InGaN/GaN hetro-junction, improves the modulation efficiency of the channel carriers which leads to minimization of gate leakage current and spilling over of 2DEG carriers into the buffer region. This is reflected in the low noise figure of the reported device. This enhances the device’s performance in terms of DC and RF characteristics thus making it a suitable candidate for RF applications.