Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast Channel Estimation for Massive Machine Type Communications1

For massive machine type communications (mMTC), it is critical to squeeze the transmission overhead as packet length is usually short and power is limited. Reducing preamble/pilot length for channel estimation is thus very important. In this paper, we propose to use short preamble for channel estimation in generalized frequency division multiplexing (GFDM) communication. Based on the short preamble, we can estimate a short channel first. We then show that the conventional zero-padding method for extending short channel to long channel in GFDM is not accurate. A new efficient method to construct the long effective channel from the obtained short channel is proposed. The proposed new method can construct a long channel for GFDM equalization without knowing the time sync error. It is proved theoretically that the constructed channel is correct given that the length of cyclic prefix (CP) and cyclic suffix (CS) are longer than the original channel length plus the time sync error. Simulations at various situations are shown to verify the results.
Battery Management for Warehouse Robots via Average-Reward Reinforcement Learning

In automated warehouses, the battery management strategy of Automated Guided Vehicles (AGVs) can affect the throughput and operational efficiency of the warehouse. In this paper, we first model the battery management problem as a Markov Decision Process (MDP) and adopt the deep reinforcement learning (DRL) algorithm as the battery management strategy. However, discounted reward DRL algorithms ignore long-term benefits, which are not suitable for the strategy since orders arriving at the warehouse at every moment are important and should be treated. In order to solve the above problems, we then introduce the average reward DRL algorithm to focus more on long-term benefits. But the existing average reward DRL algorithms have the problems of low sample utilization and unstable training. Therefore, we present a practical algorithm called average reward TD3 (ARTD3) that learns faster and is more stable. Finally, we conduct extensive experiments to confirm that ARTD3 outperforms discounted reward DRL algorithm and rule-based methods.
A Robust Force Controller of Multi-robot Cooperative Manipulators for Carrying Task

In this paper, the internal force control algorithm of the multi-robot manipulators cooperatively transporting a common object is investigated. A robust force controller is presented by combining admittance control (AC) with sliding mode control to improve the force-tracking performance of AC in unstructured environments. The proposed sliding mode admittance control (SMAC) is robust in complex environments under uncertain stiffness. Then, the SMAC is applied to control the internal force when multi-robot manipulators grasp a common object. Finally, simulations and experiments are conducted to verify the feasibility of the control algorithm proposed in this paper.
Design of Fast Start-up CMOS RC Oscillator Based on Low Temperature Coefficient Current Mode Bandgap Reference

This paper presents a fully integrated 2MHz RC oscillator which is less affected by the fluctuations of power supply voltage and ambient temperature. This design consists of a current mode bandgap reference circuit and an RC oscillation circuit. Furthermore, a digital trimming circuit is used to adjust the frequency back to the ideal one, in order to obtain better accuracy. The proposed current mode bandgap reference has a very low temperature coefficient, which is only 1.66ppm/°C. This RC oscillator can work in the power supply range of l.9V to 4.6V and the ambient temperature of −40°C~+125°C, and has a very fast start-up speed. After digital trimming, the frequency can be adjusted back to the target frequency under the other four process corners, and the circuit supports complete shutdown. Spectre simulation was made based on CSMC’s $0.18\mu \mathrm{m}$ CMOS technology. The final die size is $194\mu \mathrm{m}\times 87\mu \mathrm{m}$.
Development of Boxing Glove Type Sensation Device for Exercise Induction and Continuation

The COVID-19 pandemic caused by a virus called RARS-CoV-2 spread worldwide. As the result, the number of people who are lack exercise has increased. In addition, there are few systems to evaluate exercise movements and facilitate physical exercise at home. In this study, we developed a boxing glove type sensation device an acceleration sensor was installed in a boxing glove to evaluate the power of a punch, and the device has the function of glitz to the punches by producing sound, vibration, and light according to the type and power of the punching motion. An evaluation experiment was conducted to confirm that this device could induce exercise and promote health by having fun. A comparison was made between a normal boxing glove and the boxing glove type sensation device. As the results, we could confirm that, the number of strong punches with high acceleration and the total number of punches increased in the sensation device. In addition, the heart rate after exercise increased significantly compared to before and after exercise. The results also showed an increase in mood elevation and positive emotions.
Prediction the Melting Characteristics of Self-Shielded Flux Cored arc Welding (FCAW-S) with Exothermic Addition (CuO-Al)

Implementation of exothermic addition as an additional heat source is one of advanced directions in the arc welding process energy efficiency improvement. Using of exothermic addition (EA) of the CuO-Al system is important because of the large thermal effect as well as the deposited metal additional alloying. In this article, we studied the effect on some melting characteristics of the composition and proportions of the core filler of flux cored wire electrode components. Mathematical models of the melt off rate (MOR), deposited rate (DR), spattering factor (SF) and deposition efficiency (De) dependence on the responders were received as a result of the investigation composition of core filler. Studies have shown that it is necessary to limit the amount of graphite, as well as to limit the introduction of exothermic addition into the core filler EA~35 [wt. %].
Medical Assistance Robot with capabilities of Mask Detection with Automatic Sanitization and Social Distancing Detection/ Awareness

Healthcare sectors such as hospitals, nursing homes, medical offices, and hospice homes encountered several obstacles due to the outbreak of Covid-19. Wearing a mask, social distancing and sanitization are some of the most effective methods that have been proven to be essential to minimize the virus spread. Lately, medical executives have been appointed to monitor the virus spread and encourage the individuals to follow cautious instructions that have been provided to them. To solve the aforementioned challenges, this research study proposes an autonomous medical assistance robot. The proposed autonomous robot is completely service-based, which helps to monitor whether or not people are wearing a mask while entering any health care facility and sanitizes the people after sending a warning to wear a mask by using the image processing and computer vision technique. The robot not only monitors but also promotes social distancing by giving precautionary warnings to the people in healthcare facilities. The robot can assist the health care officials carrying the necessities of the patent while following them for maintaining a touchless environment. With thorough simulative testing and experiments, results have been finally validated.
FC-DenseNet-Based Photometric Stereo for Endoscopes with Close-range Non-parallel Light Illumination

In medical diagnosis, viewing endoscopic video may miss important pathological conditions in a traditional setting. Clinically, tissues with pathological conditions show unique surface 3D shape and identifying these delicate shapes are very helpful for diagnosis. In this paper, a Photometric Stereo (PS) method based on a modified FC-DenseNet is proposed for endoscopes with close-range non-parallel light illumination to reconstruct the surface 3D shapes. This study uses an endoscope with four separate light sources at the tip. To calibrate the illuminance of the four close-range light sources, a modified light illuminance variation model is proposed. The modified FC-DenseNet takes in four images captured by the endoscopic camera under the four separate light source illuminations and outputs a normal map of the view. To verify the effectiveness of the proposed method, synthetic datasets are built for training, validating, and testing while images with real objects were captured for further testing. The quantitative and qualitative results showed that the proposed method is able to perform effective surface 3D reconstruction for real-world objects, including a 3D-printed phantom and a plastic liver model.
Using Fixed Thresholds with Grouped Data in Structural Equation Modeling

Valuable methods have been developed for incorporating ordinal variables into structural equation models using a latent response variable formulation. However, some model parameters, such as the means and variances of latent factors, can be quite difficult to interpret because the latent response variables have an arbitrary metric. This limitation can be particularly problematic in growth models, where the means and variances of the latent growth parameters typically have important substantive meaning when continuous measures are used. However, these methods are often applied to grouped data, where the ordered categories actually represent an interval-level variable that has been measured on an ordinal scale for convenience. The method illustrated in this article shows how category threshold values can be incorporated into the model so that interpretation is more meaningful, with particular emphasis given to the application of this technique with latent growth models.
A Low-Cost Digital Stethoscope For Normal and Abnormal Heart Sound Classification

Heart disease is a major problem in most deaths. To conquer this situation, heartbeat sound analysis is a convenient method for diagnosing heart disease. Heartbeat sound classification remains a challenging problem in heart sound division and feature extraction. A stethoscope is a medical device widely used by physicians to listen to the heartbeat. An acoustic stethoscope operates on the chest piece to the ears of the listener. The main problem is in listening to heart sounds that the low signal level and are difficult to be analyzed. Adding electronic circuitry and software to acoustic stethoscopes will strengthen the heart rate signal and can minimize error analysis of the state of the patient's heart. Machine learning is used to efficiently analyze and classify heart sounds. Convolutional Neural Network (CNN) models and Support Vector Machine (SVM) with feature extractors were effective methods and were used in this research. First, the Phonocardiogram (PCG) files are fragmented into pieces of equivalent length. Then, we convert the PCG files to a spectrogram. The spectrogram images are fed into a convolutional neural network and support vector machine. The best result is using an Inception V3 model with the CNN classifier which has an accuracy of 0.909, with 0.948 sensitivity and 0.869 specificity.
Quantum theory of multiphoton lasers. II. Systems without detailed balance

A quantum-mechanical model for a multiphoton laser is proposed by an obvious generalization of the usual one-photon laser model. The authors consider a class of lasers which preserve detailed balance. This condition is ensured by assuming a multiphoton loss mechanism for the laser light of the same order as the multiphoton gain from the stimulated emission process. Solutions for the stationary density operator of the light field are obtained by a straightforward generalization of the techniques of Scully and Lamb. The multiphoton laser around threshold exhibits behaviour analogous to a second-order phase transition. A non-thermal distribution of multiphoton laser light is achieved above threshold.
An Intelligent Internet of Things (IoT) based Automatic Dry and Wet Medical Waste Segregation and Management System

Currently, with the pandemic wave, the collecting of medical waste is becoming a problem. Unwanted rubbish dumping on the edges of cities and towns causes damage, and these overflowing landfills are impossible to restore. Moreover, the manual labour required by the current technology may result in the individual using it developing a chronic illness. The suggested system's noble goals include creating an automated system to save the lives of countless people and create a cleaner, greener society. The main goal of this paper is an intelligent Internet of Things (IoT) based dry and wet waste separation and management process of medical waste material. It detects wastes in dustbins with the aid of using sensor devices. As soon as it is detected, its waste substances will be separated with sensors. Right away, information is transferred to cloud databases via IoT, making it easier to efficiently and shrewdly remove trash from the trash can. The proposed approach will prevent harmful wastes from taking numerous lives.
Simulation and Analysis of the Adaptive Power Control System on NOMA VLC

Radiofrequency technology is a commonly used technology. This technology has the disadvantage of not being able to cover the increase in data rate services, especially in terms of 5G technology. Visible Light Communication (VLC), a subset of optical communication systems, can potentially be a solution or even a replacement for radio frequency technology. VLC has various Multiple Access Techniques, one of which is non-orthogonal multiple access, commonly called NOMA. In optical communication, the signal transmitted through the communication channel will always experience changes due to interference from outside and customers who are always moving and sometimes away from the transmitter. As a counterbalance to this, Power Control Techniques are used to overcome the problems that often occur. Simulation with Adaptive Power Control will go beyond the power ratio allocation scheme, fixed power allocation scheme, and Multiple Access schemes regarding time-sharing. Or in other words, the results achieved can improve energy efficiency in the system. The research in this final project shows that the number and location of users in the NOMA VLC Adaptive Power Control technique affect the average value data rate and the average gain value achieved. This study also proves that the value of Adaptive Power Control has an important role in changing the gain value. With Adaptive Power Control, the gain has a value of 60% more efficient in its use
Analyzing Impact of BESS Allocation on Hosting Capacity in Distribution Networks

Given the increasing number of power system reliability problems caused by the high penetration of distributed generation, hosting capacity, which refers to the maximum amount of distributed generation to be accommodated in a power network, is of great importance for power system planning and management. To this end, this paper mainly studies the impact of battery energy storage system (BESS) allocation on hosting capacity in distribution systems. Two types of BESS allocation are considered, i.e., the centralized BESS allocation and the distributed BESS allocation. With respect to the centralized BESS allocation, a large BESS is installed near the feeder head to evaluate the hosting capacity. In contrast, with respect to the distributed BESS allocation, a series of small BESSs are distributed across the distribution network to evaluate the hosting capacity, where the locations and operation status of those small BESSs are determined by the mixed-integer program. Finally, the performance of centralized and distributed BESS allocations is tested on the IEEE 123-bus test system and the results show that installing BESS can greatly increase the system’s maximum hosting capacity. Compared with the centralized BESS allocation, the distributed allocation of BESSs can further improve the system hosting capacity.
A Low BER Adaptive Sequence Detection Method for High-Speed NRZ Data Transmission

To reduce the decision bit error rate (BER) caused by inter symbol interference (ISI) which become more severe as the baud rate of serializer/deserializer (SerDes) interface increases in high-performance computer (HPC) or data center networks, a novel low BER adaptive maximum likelihood sequence estimation (AMLSE) algorithm and its realizable circuit structure for high-speed ADC+DSP SerDes receiver are proposed. In additional, the viterbi algorithm (VA) is adopt to reduce the computational complexity, and the sliding block (SB) technique is proposed to cut the continuous input symbol stream into sliding blocks to achieve parallel decoding and continuous output of symbols in limited resources. An adaptive ISI parameters acquisition algorithm based on an improved zero-forcing (ZF) algorithm is combined to obtain the channel ISI parameters without special training sequence in initialization. The simulation results show that the maximum likelihood sequence estimation (MLSE) method can reduce the BER by up to more than two orders of magnitude and at least by half over DFE for five different fading levels SerDes channels as the baud rates changing from 12G to 56G. While the BER of the AMLSE method is reduced by up to three orders of magnitude and at least by two-thirds relative to ADFE under the same experimental conditions. The adaptive parameters can converge as fast as near 1000 unit interval (UI).
Machine Learning based Prediction of Customer Churning in Banking Sector

The term “customer churn” refers to the process of losing customers over time in the commercial and financial worlds. Businesses are more prepared to take preventative steps against customer turnover when they are aware of which of their customers are most likely to defect. The bank will gain by knowing which customers are most likely to switch banks in the near future both practically and theoretically. This article offers a technique for identifying which banking clients are most likely to move banks by using algorithms created for machine learning. This article shows how, given sufficient customer data such as age, location, gender, credit card information, balance, etc., machine learning models such as Logistic Regression (LR), Naive Bayes' (NB)can accurately predict which customers are most likely to leave the bank in the future. Additionally, this article illustrates how machine learning models like Logistic Regression (LR), Naive Bayes (NB), can accurately predict what customers are most likely toFinally, this research analysisshows that NB is better than LR.
Pose Estimation of Moving Vehicles Based on Heuristic Rules for Autonomous Driving

Estimating the pose information on moving vehi-cles is one of the most fundamental functions of autonomous driving for detecting and tracking moving objects. The current methods are often based on the prior information of the point cloud distribution, however, the distribution in the actual scene changes dynamically, and is easy not to meet a priori. This paper proposes a 3D pose estimation method based on heuristic rules. In a single frame point cloud, the edge length and edge visibility are used to heuristically screen the candidate orientations established by the convex hull sampling method. On consecutive frame point clouds, tracking heuristics are employed to obtain smoother orientation estimation results. The experimental results on the KITTI dataset and the data from the actual scene of vehicles show that the proposed method reduces the average object-orientation error and improves the accuracy of estimation effectively.
Short-term Electricity Price Prediction Based on CEEMD-TCN-ATTENTION

The accuracy of short-term electricity price forecasting has profound influence on the business decisions made by the participants in the electricity market. Thus, we propose a short-term electricity price combination prediction model based on Complete Ensemble Empirical Mode Decomposition(CEEMD) as well as Temporal Convolutional Attention-based Network(TCAN). First, we defined the correlations among the features according to the Pearson Correlation Analysis and selected the desired features for our model. We then defined the single prediction period to be 24 hours based on the Unit Root test. In addition, we decomposed and denoised the data based on CEEMD and built the attention mechanism into the temporal convolution network. Using the expansion cascade network to extract the relationship between the inputs, combined with the self-attention mechanism to extract the internal information and learn the position dependence, effectively improve the model training speed and prediction accuracy. In order to verify the validity of the model, the actual electricity price data of DK1 in the Nordic electricity market from April to May 2020 were forecasted. The results show that the proposed method can effectively improve the accuracy and reliability of electricity price forecasting, and can provide reliable decision-making basis.
Low Power Flip-Flop Design with Reduced Leakage using Sleepy Stack and Stack Power Gating Technique

Leakage power consumption in current CMOS technology remains challenging which in turn contributes to the overall power dissipation. A flip-flop design using sleepy stack inverter pair along with stack power gating technique is proposed inthis paper to attain higher power efficiency with reduced leakage. Unlike conventional flip-flop design, sleepy stack inverter techniqueretains the logic in sleep state and power gating techniques involved results in efficient power consumption. Although the proposed technique provokes area overhead, lowest power consumption along with reduced leakage is acquired. As the proposed flip-flop design incorporates low power and efficient leakage profile, power performance of the sleepy stack inverter with stack power gating has been improved to 50.25% and leakage power has efficient improvisation of 41%.
The Importance of Gender Specification for Detection of Driver Fatigue using a Single EEG Channel

Although detection of the driver fatigue using a single electroencephalography (EEG) channel has been addressed in literature, the gender differentiation for applicability of the model has not been investigated heretofore. Motivated accordingly, we address the detection of driver fatigue based the gender-segregated datasets, where each of them contains 8 subjects. After splitting the EEG signal into its sub-bands (delta, theta, alpha, beta, and gamma) using discrete wavelet transform, the log energy entropy of each band is computed to form the feature vector. Afterwards, the feature vector is randomly split into 50% for training and 50% for the unseen testing, and fed to a support vector machine model. When comparing the classification results of fatigue driving detection between the gender segregated and non-gender segregated datasets, the former achieved the accuracy 78% and 77% for male and female subjects, respectively, than the latter (71%). The obtained results show the importance of gender-specification for the driver fatigue detection.