Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-function intelligent robotic in metals detection applications

Recent technologies for robotics have been offered an effective and efficient solution to safeguard workers from risks in their work environments. These risks involve radioactive, toxic, explosive and mines. In this paper, design and implement computer robot based on metal detection as well as avoiding obstacles automatically. The proposed wireless controlled robotic vehicle can be used in metal detection applications such as landmine detection, obstacles avoidance, selecting best routing without imposing human's harms and workforce aspects. The robotic wheel can sense the obstacles that positioning at ahead of its path, and also avoids the obstacles forward, left and right of its routes. The robot is controlled by using Bluetooth wireless communication to interface between the controller and the implemented robot. Furthermore, sensor IR (FC-03) for the metal detector and used ultrasonic sensor (HC-SR04) for objects or obstacles sensing. The presented controlled robotic designed for desert and dry soil that can replace the human role in avoiding obstacles and metal detection capabilities. The produced robot was useful due to it can detect metals and avoiding obstacles consecutively besides it was effective to select the best route based on the intelligent technique that adopted, the predefined metals by using an intelligent decision maker for route finder in a flat surface environment.
Neural Brdf Plugin for Unsupervised Photometric Stereo

Recovering surface normal under unknown general reflectance is challenging for photometric stereo. Supervised learning-based methods implicitly handle general reflectances by learning from training data. However, it suffers from a demand for a large number of rendered images as training data and also from a domain gap due to the biased shape distribution in the training set. Unsupervised methods mitigate these issues by assessing image re-rendering losses, but their performance is limited primarily due to the fact that multiple combinations of reflectance parameters can explain the same appearance. We combine the merits of these two approaches by introducing a BRDF plugin, a neural network-based BRDF dictionary that is trained using a measured BRDF dataset, offering a data prior to effectively constrain reflectances. Specifically, it maps each measured BRDF to a unique latent code, forming a latent BRDF space that serves as the data prior for reflectances. Building upon the BRDF plugin, we develop a new unsupervised photometric stereo method to recover both surface normal and reflectance, which exhibits superior accuracy than previous methods in experiments on synthetic and real-world datasets 11Project done in Osaka University.
Coverage and Cell Capacity optimization in Private LTE network based on Position and Expected Channel Knowledge

This paper presents an innovative approach to maximize cell coverage and capacity in a private LTE network by optimizing resource block (RB) and power allocation. The proposed method decomposes the cell into sub-cells and employs two possible solutions: genetic algorithm (GA) and a water-filling based algorithm to find optimal resource distribution for each sub-cell. A priori knowledge of the expected channel gain is assumed. Both algorithms explore an efficient RB and power allocation, based on expected channel conditions and capacity requirements. The reported numerical results validate the effectiveness of the proposed approach, offering valuable insights for enhancing private LTE network performance through optimized resource allocation.
Etude théorique et expérimentale de la turbulence homogène en rotation

Une turbulence en rotation solide est generee par le passage d'un ecoulement d'air a travers un cylindre rotatif contenant une structure alveolaire dense et une grille. Le champ de vitesse a l'aval de ce dispositif est sonde au moyen de fils chauds. A travers la mesure des grandeurs statistiques qui caracterisent le champ fluctuant, on montre que la rotation affecte surtout les composantes normales a l'axe de rotation et que ces effets se declenchent quand les nombres de rossby batis sur les grandeurs turbulentes macroscopiques sont inferieurs a l'unite. Une approche spectrale permet alors de relier ces resultats aux mecanismes d'une transition vers la turbulence bidimensionnelle
A Short-Term Prediction Method for Photovoltaic Power Based on Similar Daily Set Training of Meteorological Data

Accurate prediction of photovoltaic power can make the formulation of grid scheduling plan more flexible, which plays an important role in enhancing the security and stability of the power system. To firstly, the C fuzzy mean clustering method was used to cluster the meteorological data and irradiance data separately and the intersection of the clustering results was taken as the similar day set. Irradiance was then predicted using an autoregressive sliding average model based on similar day sets. Finally, the irradiance prediction results are used to find similar days for the prediction of PV output using grey prediction method. In order to avoid the influence due to the strong volatility and randomness of weather conditions, this paper also establishes an error prediction model to correct the prediction results to improve the prediction accuracy. The short-term prediction errors of PV power for different weather types are statistically evaluated with an actual PV power plant as an example to verify the feasibility and effectiveness of the proposed PV output prediction method.
Brain Stroke Prediction Using Machine Learning Techniques

Machine learning (ML) techniques have gained prominence in recent years for their potential to improve healthcare outcomes, including the prediction and prevention of stroke. The primary objective of this study is to develop and validate a robust ML model for the prediction and early detection of stroke in the brain. This study aimed to address some of the limitations of previous studies by utilizing a representative dataset and applying proper feature engineering techniques. The results showed that the Artificial Neural Network (ANN) model with synthetic minority oversampling technique (SMOTE) (ratio =0.3) and hyperparameter turning performs the best in terms of average precision, which is crucial for making accurate predictions in clinical settings. By adjusting the threshold for classification, the model can be tailored to achieve the desired balance between precision and recall, depending on the specific requirements of a clinical workflow.
Tidal currents and Kuroshio transport variations in the Tokara Strait estimated from ferryboat ADCP data

From 2003 through 2011, current surveys, using an acoustic Doppler current profiler (ADCP) mounted on the Ferry Naminoue, were conducted across the Tokara Strait (TkS). 1,234 of the resulting velocity sections were used to estimate major tidal current constituents in the TkS. The semi-diurnal M2 tidal current (maximum amplitude 27 cm s−1) was dominant among all the tidal constituents, and the diurnal K1 tidal current (maximum amplitude 21 cm s−1) was the largest among all the diurnal tidal constituents. Over the section, the ratios, relative to M2, of averaged amplitudes of M2, S2, N2, K2, K1, O1, P1, and Q1 tidal currents were 1.00:0.44:0.21:0.12:0.56:0.33:0.14:0.10. Tidal currents estimated from the ship-mounted ADCP data were in good agreement with those from the mooring ADCP data. Their root-mean-square difference for the M2 tidal current amplitude was 2.0 cm s−1. After removing the tidal currents, the annual-mean of the net volume transport (NVT) through the TkS ± its standard derivation was 23.03 ± 3.31 Sv (Sv = 106 m3 s−1). The maximum (minimum) monthly-mean NVT occurred in July (November) with 24.60 (21.47) Sv. NVT values from the ship-mounted ADCP were in good agreement with previous geostrophic volume transports calculated from conductivity temperature depth data, but the former showed much finer temporal structure than those from the geostrophic calculation. This article is protected by copyright. All rights reserved.
Sum-Rate Maximization for RSMA-Enabled Energy Harvesting Aerial Networks With Reinforcement Learning

In this paper, we propose a joint power and precoder design framework for energy-harvesting aerial networks, where an aerial base station serves multiple users via rate-splitting multiple access (RSMA) using harvested energy. To maximize the sum-rate from the long-term perspective, we utilize a deep reinforcement learning (DRL) approach to allocate optimal transmission power at each time based on the randomness property of the channel environment, harvested energy, and battery power information. Moreover, we employ sequential least squares programming (SLSQP) to design the RSMA precoder maximizing the sum-rate with the allocated power. Numerical results show the superiority of the proposed scheme over baseline methods in terms of the average sum-rate performance.
An Improved Delay Cell with Low Power Consumption and Strong Driving Capability

In this paper, a novel modified low-power and strong driving capability delay cell has been proposed, which belongs to one of the three important modules of the MMY-FIO-S40LL-1p2V IP library. The delay cell include DLY3 and DLY5 cells, which can be created as customized standard cell that can used by P&R (Placement & Routing) CAD tool. The input signal and output signal of the delay cell are both 50/50 duty cycle. DLY3 supports 3ns delay with 32-level fine-tuned delay-value adjustment. DLY5 supports 5ns delay with 16-level tuned delay-value adjustment. It has the advantages of strong driving ability, low current, low power consumption, simple wiring, regular array layout, small area, and high reliability. The layout area of DLY3 is 45.574um × 58.123um. The layout area of DLY3 is 50.594um × 31.171um.
0.85 mW, 8-bit, 1GS/s, 58dB SFDR Cryogenic DAC for Superconducting Qubit Control Applications

This work reports a 8-bit cryogenic digital-to-analog converter (DAC) for qubit control applications. To mitigate the excessive process variations of transistor at cryogenic temperature, we propose a capacitive DAC (CDAC) to utilize the temperature-invariant and better matching properties of MOM-cap devices. Our simulation results showed that SFDR of the CDAC only drops by 1.9dB (from 59.9dBc at 300K to 58dBc at 4K) which is much smaller than 8.5dB change for current steering DAC. This suggests that our proposed CDAC is a promising candidate for cryogenic qubit control applications.
Study on Test Plan Establishment for Enhancing Trustworthiness of Artificial Intelligence Systems

Compared to traditional software, artificial intelligence systems (AIS) include uncertainty in the inference results during the training process, which is one of the factors that could undermine the trustworthiness of the system. To overcome this uncertainty of AIS, a trustworthiness test plan establishment that accounts for the complexity and operational environment is required. In this study, four requirements that must be considered by the system designers, developers, and quality managers are proposed for establishing the test plan of the trustworthiness of AIS. If AISs satisfy the proposed requirements, it will help secure safety and transparency among the AI trustworthiness factors.
A DC Offset Cancellation Circuit Using Digital Assistance Technique and Self-Calibrating Comparator for RF Transceiver

This paper describes a dc offset cancellation (DCOC) circuit using digital assistance technique. This DCOC includes a self-calibrating comparator, a counter, and a 8-bit binary weighted current DAC. It is used in amplifiers for RF transceivers. The DCOC in this paper calibrates the offset by injecting current into the resistor of the amplifier. This method does not cause damage to the desired signal. It also avoids the use of large capacitors and resistors, with an area of 0.004mm2 and power consumption of 12. 1μW. With the self-calibration, the comparator has an input offset of less than 505μV. The Monte Carlo simulation shows that the 3σ value of the output residual offset of the amplifier is 1.64mV.
Analysis for binary chaotic sequences generated by cascade chaotic maps

This paper analyzes the characteristics of sequences generated using cascade chaotic maps employing two or three seed maps. We begin by comparing their Lyapunov exponents and also propose a new conjecture for the Lyapunov exponent of a cascade chaotic map using three seed maps. Furthermore, their real-valued output sequences are translated into binary sequences using two distinct binary mapping methods. Subsequently, we compare the correlation and balance properties of each converted binary sequence, and show the results of statistically validating these sequences by NIST SP 800-22.
Crosstalk-Based Hardware Trojan In Low Power Designs

In this paper, we present a novel approach that demonstrates how an attacker can exploit the crosstalk phenomenon between critical nets and power supply networks, in designs featuring multiple voltage domains. By capitalizing on switching a power domain, the assailant can obscure trojan-triggering signals or manipulate the system’s functionality maliciously. Our findings shed light on the potential vulnerabilities arising from power domain interactions and underscore the importance of robust physical design methodologies for ensuring system security and trustworthiness. Simulation results are presented to demonstrate how the functionality of the design can be altered.
Efficient Multi-Stage Detection of Malicious Windows Executable During Execution

Machine learning methods are commonly used for malware detection to respond to the ever-increasing number of malicious programs. Detection models are usually trained on full execution logs and are unsuitable for real-time malware detection during execution. We build a real-time malware detection system based on process behavior combined with artifacts from process memory that aims to show that real-time malware detection using machine learning is feasible. We optimize our system by separating detection into two stages, one stage based on classical machine learning models and the other on a deep neural network. We show how we train our models and how we evaluate them. We introduce a novel way to evaluate detection models that aims to improve system performance.
Improved GAI-BP Detection for MIMO Systems Based on Message Post-Processing

Multiple-input multiple-output serves as a key technique for modern wireless communication systems but brings big challenges in data detection. Belief propagation (BP) detection enjoys advances for its near-optimal error rate performance and strong robustness. However, the state-of-art BP detection algorithms suffer from either an exponentially increasing computational complexity or sub-optimal error rate performance. To address this issue, this paper proposes an efficient variant based on the real-domain GAI BP (RD-GAI-BP), called the improved RD-GAI-BP, achieving a better error rate performance at the expense of acceptable complexity increments. Numerical results demonstrate that, in the MIMO scenario with Nr = 16, Nt = 8 and 64-QAM modulation, the proposed improved RD-GAI-BP earns more than 2 dB SNR gains at the BER of 10-3 when compared with the state-of-art RD-GAI-BP detection.
Research on Surface Defect Detection in Submersible Pump Impellers Based on the Novel MXNet Deep Learning Model

In industrial production, surface defects of submersible pump impellers often have a significant impact on equipment performance and lifespan. However, the current defect detection is mainly performed manually, which is time-consuming, inefficient, and prone to human errors. Furthermore, the existing mainstream automatic detection methods have limitations, such as limited accuracy, complexity of the model, and low generalization, which make them difficult to promote widely. Therefore, an automated technology that can improve the efficiency and accuracy of the detection process is urgently needed. This study aims to develop an automatic defect detection method based on a Convolutional Neural Network (CNN) called MXNet to ensure superior product quality and reduce manual labor. This model, derived from the Xception architecture, has been specifically tailored for the task with custom convolutional and fully connected layers for precise classification, data augmentation techniques and an incorporated attention module to highlight vital regions in input images, coupled with the utilization of binary cross-entropy loss and custom callback functions, and a strategy for dynamically adjusting the learning rate. To demonstrate the superiority of this model, we compared it with four mainstream defect detection models. Experimental results show that the average accuracy and average recall rate of MXNet on the validation set reached 99.23% and 98.95% respectively, outperforming the comparative models in all performance indicators with shorter runtime and fewer parameters. This suggests that MXNet can not only achieve high-accuracy defect detection but also has high practicality, and is expected to have broad application potential in future in-dustrial production practices.
Investigating Adoption and User Experience of Digital Health Technologies Amongst Elderly Population

One in five Malaysians will be 60 years of age or older by 2030 [2], according to estimations, which indicates that the country is seeing a significant rise in its senior population. The challenges presented by this demographic shift include the requirement for robust digital health solutions that can support elderly individuals throughout their daily lives. This study uses bibliometric analyses to explore challenges encountered by the elderlies when using health technologies, additionally, the study investigates factors affection the adoption of technologies. Bibliometric analysis techniques are utilised to systematically review literature on the novel science of Gerontechnology, identifying trends, patterns and gaps that provides insight into the adoption of digital health technologies among the ageing demographics.
Mitigating Write Amplification of Dual-mode Flash Memory

To improve the write performance of MLC flash memory, a conventional approach is to convert MLC blocks to SLC-mode blocks. Although the SLC-mode block has better write performance, it sacrifices half storage capacity. If the SLC-mode blocks are exhausted, they must be converted back to MLC blocks, which incurs the write amplification issue. To address this issue, we proposed a novel design to mitigate write amplification with the dual-mode block allocator and low latency programming scheme. The dual-mode block allocator rethinks the block allocation strategy and redesigns the traditional programming method of MLC flash memory with a low latency programming method. The experiment results showed that the proposed design could reduce write amplification up to 55%. Furthermore, the average write response time could be reduced up to 51%.
QFO-UCNN: Quantizable Feature Oriented Utilitarian Convolutional Neural Network for Sar Recognition

Synthetic aperture radar (SAR) imagery is susceptible to the non-systematic motion error, which will defocus the data in the training set. Also, traditional convolutional neural networks (CNN) are random in the features utilized, many of which are useless for practical target recognition. To this end, quantizable feature oriented utilitarian CNN (QFO-UCNN) is proposed in this paper, where the defocusing can be alleviated via a quantizable entropy norm regularizer. To overcome the non-convexity of the optimization function, a tight surrogate function is designed to ensure the closed-form solution of the focusing feature. Besides, the dominant feature of sparsity is encoded by a l1 norm, where the sparse feature is quantified via a closed-form soft-thresholding operator. In such cases, two useful features are quantified and constrained in the CNN, which improves the intentionality of the intended SAR target recognition. The canonical MSTAR dataset is employed to verify the effectiveness of the proposed algorithm, when the data encounters the non-systematic and multiplicative phase error.
Species diversity in the phytoplankton of some Finnish lakes

The relations and seasonal variations of some diversity indices of the phyto plankton were studied in different types of lakes. The indices compared were the species richness index (d = S—1/ln N),_Shannon and Wiener's index (H = £ pi In pi), the evenness index (e = H/ln S) and the index of the concen tration of dominance (c = 2 (ni/N)2. The indices were correlated with each other and with the species richness and biomass, but the slopes of the correlation lines varied between the seasons and the different lake types.
Remote Heart Ailment Detection using Eulerian Video Magnification

Our main objective through this is using the Eulerian Video Magnification computer vision algorithm to use a standard video sequence recorded from any source and process it on our board to reveal minute details not visible to the human eye like the flow of blood, using which we’ll be able to detect things like heart-rate etc. in real-time. A webpage/webapp remotely hosted on our IoT board will be created to act as an easily accessible and reliable way for the user/patient to use their readily available devices (such as laptop, smartphones) with a camera. Using various medical APIs, the raw data related to the user that is collected after the processing of the algorithm will be used to check for various ailments and direct the patient to more medical information and show them a way to diagnose their illness further.
Short-Term Prediction of Water Level on Ciliwung River with Hybrid Neural Network

Floods, common natural disasters in Indonesia, bring substantial damage and economic losses. Jakarta, Indonesia's capital, faces frequent flooding, mainly due to the overflowing Ciliwung River. Effective flood prevention, like timely warnings, is crucial to reduce losses. Yet, the existing flood early warning system managed by the Ciliwung-Cisadane River Center has limitations. Its hydrological models for short-term predictions lack accuracy, leading to prolonged computational times. To enhance this system, a machine learning approach was introduced for better water level prediction. This model utilizes rainwater and runoff data from four river segments upstream. Two neural network methods, Adaptive Neuro-Fuzzy Inference System (ANFIS) and Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM), were compared. RNN-LSTM outperformed ANFIS, displaying lower error rates and faster computation times. It excelled in predictive capabilities for three of four river segments, evident through improved Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). Despite a minor setback in the third segment's performance, both methods scored well in the coefficient of determination (R2). RNN-LSTM emerged superior due to its minimal error and efficient computational speed, making it the preferred choice for water level prediction. Additionally, RNN-LSTM showcases enhanced predictive ability concerning water level fluctuations, as reflected in its larger standard deviation.