Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Method for Exploring the Store Sales Forecasting using Fuzzy Pruning LS-SVM Approach

Intelligent robots, intelligent mobiles, intelligent stores, and so on are just a few of the areas where computer-aided ergonomics is being put to use. Convenience stores (CVS) are adapting to a new era of competition by offering a wider variety of products and services than ever before, such as daily fresh meals, a cafe, ticketing, and a grocery. Therefore, it is becoming increasingly difficult to estimate daily sales of’ fresh commodities due to the impact of both internal and external factors. In the long run, a trustworthy sales-forecasting system is going to be critical for enhancing corporate plans and gaining an edge over the competition. In today's internet age, data production has reached unprecedented levels, well beyond what any single human being can comprehend. This has led to the development of a plethora of machine learning methods. In this proposed approach various machine learning methods are explored for predicting store's sales and evaluate them to find the one that works best for the specific scenario. Training times are reduced and data quality is enhanced with the help of Normalization in the proposed approach. K-Means is a popular feature selection clustering algorithm. Fuzzy Pruning LS-SVM is used in the suggested method for training the model. The proposed model has superior performance on SVM and CNN.
A Novel Deep Learning-Based Malware Detection Scheme Considering Packers and Encryption

With the continuous improvement of the current level of information technology, the malicious software produced by attackers is also becoming more complex. It's difficult for computer users to protect themselves against malicious software attacks. Malicious software can steal the user's privacy, damage the user's computer system, and often cause serious consequences and huge economic losses to the user or the organization. Hence, this research study presents a novel deep learning-based malware detection scheme considering packers and encryption. The proposed model has 2 aspects of innovations: (1) Generation steps of the packer malware is analyzed. Packing involves adding code to the program to be protected, and original program is compressed and encrypted during the packing process. By understanding this step, the analysis of the software will be efficient. (2) The deep learning based detection model is designed. Through the experiment compared with the latest methods, the performance is proven to be efficient.
New Infectious Disease Prevention Technology Applied with IT: Baggage Disinfection Device

Since the COVID-19 pandemic, it has become important to block infectious diseases from abroad at airports and ports. Therefore, not only travelers arriving from abroad, but also their luggage should be disinfected. The purpose of this study is to manufacture a baggage disinfection device and evaluate its sterilization efficiency using bacteria, and implement an intelligent infectious disease control system that applies its. The bacteria were passed baggage disinfection device, what composed with disinfectant water and UV modules. As a result, the elimination of bacteria on sample was 99.26 % effective. This device can disinfect baggage against infectious diseases introduced from abroad, which can be managed and operated by an intelligent control system using MQTT communication
4.9 GHz A1-mode Lamb Wave Resonator based on Partially Released LN Film

This work presents a 4.9 GHz ${A}_{1}$ -mode Lamb wave resonator (LWR) based on partially released Z-cut lithium niobate (LN) thin film, which is aimed at application of 5G communication in N79 band. The designed LWR utilizes the horizontal electric field for efficient excitation of first-order antisymmetric (A1) mode in the acoustic cavity made of LN film. The fabrication process for the designed LWR is described and validated. The measured results show that the partially released devices achieve large electromechanical coupling coefficient (k$_{t}^{2}$=27%), which is comparable to that of reported fully released devices. The measured results are in good agreement with the finite element (FE) simulations. The presented partially released LWRs show prospect to improve the mechanical robustness of such devices without much sacrifice to performance.
Underdetermined DOA Estimation of Moving Sparse Nested Arrays under the Coexistence of Mutual Coupling and Nonuniform Noise

In order to enhance the performance of direction of arrival (DOA) under the coexistence of mutual coupling and unknown nonuniform noise, we propose a moving sparse nested array (MSNA) with limited physical sensors, and then provide an improved DOA estimation algorithm depending on the suppression of nonuniform noise and the processing of the mean value of the precise estimate. Owing to space spacing between adjacent sensors, the proposed array can give larger aperture and reduced mutual coupling compared with the existing nested arrays. For solving the ambiguity for DOA estimation introduced by such spacing and reducing cost, the two-component subarrays of the proposed array are provided by the array motion. Based on it, the improved algorithm can obtain unique and high-accuracy estimation with such suppression and processing. Simulation results verify the superiority of the proposed MSNA and the improved algorithm for high-accuracy DOA estimation with mutual coupling reduction, cost saving and nonuniform noise mitigation.
Condensation Symbol-Level MIMO Precoding for Non-Uniform Constellation in ITCN

Wireless backhaul and inter-tower communications networks (ITCN) were previously proposed as crucial enabling technologies for the next generation of digital broadcasting systems, e.g., the Advanced Television Systems Committee (ATSC) 3.0 system. It is desirable to consider using multi-input multi-output (MIMO) for service delivery in ITCN to achieve better bandwidth efficiency. In ITCN, the transmitter can obtain channel states information (CSI), which could be applied in MIMO precoding. For the first time, this paper introduces the symbol-level precoding (SLP) scheme, which uses CSI and data symbols to precode to ATSC 3.0. Meanwhile, an enhanced SLP method for condensed non-uniform constellation (NUC) in modulation and coding schemes (MCSs) with a low code rate (CR) is proposed. Simulation results of the 64-NUC show that the proposed scheme performs better than Minimum Mean Square Error (MMSE), Zero-forcing (ZF), and ATSC 3.0 MIMO precoding.
A Resonant Accelerometer Based on Piezoelectric TBTF MEMS Resonator and Two-stage Microleverage

In this paper, a resonant accelerometer based on piezoelectric triple-beam tuning fork (TBTF) MEMS resonator is presented, which is implemented in the form of an oscillator. Compared with the conventional resonant accelerometer based on capacitive resonators, the presented resonant accelerometer adopts piezoelectric TBTF resonator as the core sensing unit, which has advantages such as large electromechanical transduction capability, no requirement for high DC voltage bias and vacuum package. The designed TBTF resonator uses out-of-plane vibration mode and thus improves the axial force sensitivity compared to traditional double-ended tuning fork (DETF) device operating in in-plane vibration mode. Also, the piezoelectric transduction of TBTF is generally larger than that of DETF, which benefits attaining higher signal-to-noise ratio at the sensor output. Moreover, a two-stage micro-leverage is herein utilized for force transfer, which amplifies the axial acceleration force by nearly two orders of magnitude, further enhancing the sensitivity of the device. In this work, the frequency response of the TBTF resonator was measured, based on which the equivalent circuit parameters were extracted for design of the oscillator. The reported resonant accelerometer exhibit measured acceleration sensitivity of 18 Hz/g and resolution of 0.293 g at atmospheric pressure.
One-Stage Mobile Palmprint Recognition via Keypoint Detection Network

With the increasing use of digital devices, researches on the security of user authentication have attracted considerable attention. Especially, on mobile devices, various information such as fingerprint and password are utilized for user authentication. However, these contact authentication methods cannot work well in certain environments, such as when the user's fingers are wet or dirty. In addition, by the COVID-19 pandemic, the need for contactless authentication methods has increased, and some studies introduced for contactless palmprint recognition on mobile devices. In this paper, we propose a one-stage palmprint recognition network with keypoint detection network for mobile named MPN. The proposed palmprint detection method is designed with keypoint detection network to predict the information needed for alignment, which is simpler and faster than the previous detection method. Then, we propose an adaptive ROI extraction method to align palm image and extract clear ROI data of the palmprint using the predicted information. By combining our proposed method, we develop a robust and fast network for palmprint recognition.
Equivalent circuit model based on the average period for periodic overlapping circle metallic mesh

The equivalent circuit model based on the average period for periodic overlapping circle metallic mesh is proposed in this paper. The equivalent reactance coefficient b in the equivalent circuit model is revised according to the simulation results of Ansoft designer software. In order to guide the optimization of the electromagnetic shielding effectiveness of periodic overlapping circle mesh, the effects of the diameter 2R and period g on the electromagnetic shielding efficiency were simulated and analyzed. The simulation results show that the electromagnetic shielding effectiveness is better than 22dB in the 2-18GHz band. The periodic overlapping circle mesh can be applied to the area where the strong shielding effectiveness,high optical transmittance and low image-degradation are required simultaneously.
Surgical Instrument Positioning System Based on Binocular Vision

Surgical instrument positioning is the core component of the surgical navigation system, which determines the accuracy of the entire system. Based on the principle of binocular vision, a surgical instrument positioning system is proposed. The system uses colored balls as markers. First, the colored ball area is extracted through color segmentation, then the center coordinates of the markers in the left and right images are obtained through the proposed algorithm based on connected domain extraction and centroid calculation. The algorithm first extracts the connected domain in the image, excludes the non-marker areas by the area of the connected domain, and then calculates the center of the connected domain using the centroid formula. Next, the left and right centers are matched based on spatial position, and the centers' three-dimensional coordinates are obtained through three-dimensional reconstruction. Finally, the three-dimensional coordinates of the surgical instrument tip are indirectly obtained through the center 3D coordinates. The stability and accuracy tests were carried out for the system, the results show that the system has good stability and accuracy.
DRL based Flexible LDM Scheme for Mixed Unicast-Broadcast Transmission in 5G

With the increasing demand for data, radio resources are increasingly scarce. The cooperative transmission mechanism, which allows broadcast and unicast to share the same spectrum, can effectively alleviate this situation. The success of LDM in ATSC 3.0 has inspired many scholars to superimpose broadcast and unicast services with a fixed power injection ratio. To further improve radio resource utilization, we propose a flexible LDM scheme with variable power injection ratios in this paper. Then we formulate the scheduling policy in the MAC layer as a sequential decision problem and prove it has Markov property. We propose a DRL-based algorithm to obtain the optimal scheduling scheme. Simulation results show that the proposed scheme can achieve superior performance in system throughput.
Electroencephalogram Characteristics Induced by Different Magnetic Stimulation Modes of Acupuncture Point

Transcranial magnetic stimulation (TMS) makes use of electromagnetic induction principle to induce electrical fields in the neuronal tissues and has an effect on neuromodulation. Theta burst stimulation (TBS) as a novel pattern of TMS has been shown to have advantages over conventional repetitive TMS (crTMS) in modulating brain cortical excitability. However, to date little is known about the potential of TBS of acupuncture point (acupoint) in modifying the brain activity. An acupoint is a specific point in the human body with nerve trunk and nerve ending through it, which is sensitive to various stimuli. In this paper, different-mode magnetic stimulations including crTMS, continuous TBS (cTBS), intermittent TBS (iTBS), and corresponding Sham iTBS were used to stimulate the Neiguan acupoint (PC6). Electroencephalogram characteristics induced by different-mode magnetic stimulations were compared to investigate whether the mode of magnetic stimulation of acupoint would impact stimulation effects. The results showed that iTBS of acupoint could induce a remarkable evoked potential of P80 component, activate more brain functional areas compared to crTMS, cTBS, and Sham stimulation, which suggested that iTBS of acupoint could induce a powerful brain response and produce a plastic reorganization of the cortical network. This paper proved that iTBS of acupoint with lower stimulation intensity and fewer stimulation pulses may be one more effective stimulation mode than crTMS, which may be clinically useful.
A 28-NM CMOS High PSRR and Stability Low Dropout Regulator Using Feed-Forward with Current Sensing

This paper presents an innovative approach for achieving high power supply rejection ratio (PSRR) and stability in low drop-out (LDO) regulators, which are critical components in system-on-chip (SoC) chips. The feedforward method improves PSRR levels in LDO regulators by adding a compensating signal proportional to power supply noise to the output voltage, thus reducing the impact of the noise on the output voltage. To ensure stability, the LDO regulator is designed with current sensing. The proposed LDO has been fabricated based on 28nm CMOS process and regulates the output at 1V from a 1.2V supply. The measured PSRR has an enhancement of -39dB at 10kHz when compared of an LDO with no enhancement and has low overshoot and undershoot.
Surveillance System using Moving Vehicle Number Plate Recognition

A growing popularity of self-owned vehicles in the modern world has called for an even increased demand for better enforcement of traffic rules, often at the traffic signals and important crosses of crowded livelihoods. Due to the increase in the number of self-owned vehicles there is a huge demand of such systems for the surveillance of the vehicles either it can be or traffic purpose or it can be used as surveillance system near a sensitive zones like military base, parliament and other key headquarters. Proposed road safety personnel are in need of helping hand from the software world to develop tools that can recognize, with very high accuracy, the number plates of the vehicles bring up the details related to their registration number. The above state problem can be resolved using an Automatic Vehicle Number Plate Recognition system (ANPR). There are several research goings on in this field to make ANPR systems more accurate and liable. Although the moto of this study is going to demonstrate simple ANPR system which can detect number plates efficiently using object detection methodology. The given problem is to design a model which would detect a vehicle number plate automatically and in addition to that extract the text and number from the detected image with the help of Optical Character Recognition (OCR). So, it's a combination of both object detection and OCR. YOLO object detection model is used with Py-tesseract engine for text recognition. ANPR system using YOLO detection model has shown significant accuracy in license plate detection and can be used for ANPR systems. Proposed system is detecting license plate with an accuracy of 97.0 %. The other parameters like precision, map, and recall values are also satisfiable.
Robust Visual Odometry On $\text{SE}(3)$: Design and Verification

Visual odometry is a crucial technique for estimating a robotic vehicle's trajectory by analyzing images captured by its onboard camera when the vehicle's attitude cannot be retrieved. However, uncertainties such as modeling errors, measurement noise, misidentification of feature marks, and the switching output arising from visual geometric constraints can all hinder accurate estimations. To address these challenges, this article proposes a robust visual odometry that can be implemented in a sampled-data structure. Comprehensive simulations and experiments are conducted to demonstrate the effectiveness of the proposed design and to explore the relationship between design parameters and estimation performance. In addition, tuning guidelines for visual odometry parameters are provided to help address these uncertainties effectively.
Adaptive control measures and thermal comfort : Input parameters for IoT devices

Thermal comfort can be defined as the ‘condition of mind that expresses satisfaction with the thermal environment. ’, and adaptive control measures comprise an adjustment to indoor environmental conditions based on occupant preferences, behavior, and feedback. The use of Artificial Intelligence (AI) and Internet of Things (IoT) in thermal comfort and adaptive control measures has the potential to significantly improve energy efficiency and occupant comfort in buildings. The adoption of the Working-from-home modality by several institutions and companies during and after the 2020 pandemic, accelerated the development of even more advanced and effective solutions in this area. This study, in its first phase, seeks to identify input parameters related to adaptive control measures adopted by the same occupants in two work environments (office building and residences) to promote personal thermal comfort contributing to the development of individual devices and IoT applications related to thermal comfort monitoring.
Deep learning-based radar-assisted beam prediction

Beam selection in millimeter wave (mmWave) communication systems rely on information about the environment surrounding the communication target, and the use of deep learning methods to analyze sensing data acquired by low-cost radar sensors can effectively reduce communication overhead. In this paper, we further investigate the radar-based beam selection problem using deep learning methods. The beam selection performance of the Feature Pyramid Network (FPN) network and an optimized version of the Residual Networks (Resnet) network is evaluated for a large-scale real-world dataset, DeepSense 6G, and a targeted network is proposed for beam selection. The experimental results show that the accuracy of beam selection is improved by 18.5% compared to the original Lenet network.
Searching for exoplanets with HEPS: I. detection probability of Earth-like planets in multiple systems

The astrometry method has great advantages in searching for exoplanets in the habitable zone around solar-like stars. However, the presence of multiple planets may cause a problem with degeneracy when trying to compute accurate planet parameters from observation data and reduce detectability. The degeneracy problem is extremely critical, especially in a space mission which has limited observation time and cadence. In this series of papers, we study the detectability of habitable Earth-mass planets in different types of multi-planet systems, aiming to find the most favorable targets for the potential space mission–Habitable ExoPlanet Survey (HEPS). In the first paper, we present an algorithm to find planets in the habitable zone around solar-like stars using astrometry. We find the detectability can be well described by planets’ signal-to-noise ratio (SNR) and a defined parameter S = M2/(T1 – T2)2, where M2 and T2 are the mass and period of the second planet, respectively. T1 is the period of the planet in the habitable zone. The parameter S represents the influence of planetary architectures. We fit the detectability as a function of both the SNR of the planet in the habitable zone and the parameter S. An Earth-like planet in a habitable zone is harder to detect (with detectability PHP < 80%) in a system with a hot Jupiter or warm Jupiter (within 2 AU), in which the parameter S is large. These results can be used in target selections and to determine the priority of target stars for HEPS, especially when we select and rank nearby planet hosts with a single planet.
A study of EEG classification based on attention mechanism and EEGNet Motor Imagination

In recent years, with the development of deep learning (DL), deep learning has made great contributions to the improvement of motor imagination brain-computer interface (MI-BCI). At the same time, the proposed attention mechanism also provides a new method for the feature extraction and classification of EEG. CNN has limitations in perceiting global dependencies, and the Attention mechanism can make up for this limitation. Based on this, a method combining convolution and Attention is proposed. The Attention mechanism was added based on the EEGNet model, and the BCI competition-IV 2a data set was used for the experiment. The experimental results show that the accuracy of EEGNet in the four categories of motor imagination can reach 54.50%, and the accuracy can reach 70.49% by adding the multi-head attention mechanism to EEGNet. Due to the characteristics of parallel computing of Attention mechanism, on the server with NVIDIA A100-PCIE 40GB graphics card and CUDA version 11.6, the training time of each epoch is reduced by 3-5s compared with the original time. In recent years, with the development of deep learning (DL), Deep learning has made great contributions to the improvement of the motor imagination brain computer interface (MI-BCI). At the same time, the proposed attention mechanism also provides a new method for the feature extraction and classification of EEG. CNN has limitations in perceiting global dependencies, and the Attention mechanism can make up for this limitation. Based on this, a method combining convolution and Attention is proposed. The Attention mechanism was added based on the EEGNet model, and the BCI competition-IV 2a data set was used for the experiment. The experimental results show that the accuracy of EEGNet in the four categories of motor imagination can reach 54.50%, and the accuracy can reach 70.49% by adding the multi-head attention mechanism to EEGNet. Due to the parallel computing features of the Attention mechanism, on the NVIDIA A100-PCIE 40GB graphics card and CUDA 11.6 server, the training time of each epoch is reduced by 3-5s compared to the original time, reducing the training time.