Miracle-dz/UniFAR-FTUs huggingface dataset:
Light-induced atom desorption for cesium loading of a magneto-optical trap: Analysis and experimental investigations

With the help of light-emitting diodes at the center wavelength of 470 nm, we demonstrate that light-induced atom desorption LIAD can be used for flexibly controlling the loading of magneto-optical traps MOT of cesium atoms. Under an ultralow background pressure in a quartz cell without any wall coating, we show that low intensity blue light can be used to control the loading rates from 200 to 4000 atoms/s and the number of cesium atoms on the order of 10 4 in our experiment in a MOT without the use of dispensers or any secondary atom source. A theoretical model based on an atom loading rate equation is built which can simulate the magneto-optical trap loading process with LIAD. The theoretical results are in agreement with experimental data. Some important parameters of the vacuum system are determined accordingly. The decay time of the vacuum pressure is about 70 ms, which is much shorter than the usual vacuum system for experiments on atomic ensembles. It is very difficult to measure such short times by the traditional fluorescence detection of cold atoms due to the slow loading process. The change of desorption rate on the order of 10 13 atoms/s for the different desorption intensities is also determined based on the background pressure caused by noncesium atoms in the cell. According to the experimental data and theoretical calculations, we obtain a partial pressure of about 1.410 10 Torr for the untrapped cesium atoms and about 6.410 10 Torr for the noncesium atoms at the moment the desorption light is turned off. The system is almost an ideal vacuum system for transferring atoms into another region for further experiments.
Ensemble Pre-Trained Deep Convolutional Neural Network Model for Classifying Medical Image Datasets

Over the last few years, Deep Learning models have shown prominent results in medical image analysis especially to predict disease at the earlier stages. Since Deep Neural Network require more training data for better prediction, it needs more computational time for training. Transfer learning is a technique which uses the learned knowledge to perform the classification task by minimizing the number of training data and training time. To increase the accuracy of a single classifier, ensemble learning is used as a meta-learner. This research work implements a framework Ensemble Pre-Trained Deep Convolutional Neural Network using Resnet50, InceptionV3 and VGG19 pre-trained Convolutional Neural Network models with modified top layers to classify the disease present in the medical image datasets such as Covid X-Rays, Covid CT scans and Brain MRI with less computational time. Further, these models are combined using stacking and bagging ensemble approach to increase the accuracy of single classifier. The datasets are distributed as train, test and validation data and the models are trained and tested for four epochs. All the models are evaluated using validation data and the result shows that the ensemble learning approach increases the prediction accuracy when compared to the single models for all the datasets. In addition, this experiment reveals that the stacked model attains higher test accuracy of 99% for chest X-Ray images, 100% for chest CT scan images and 98% for brain MRI, compared to the bagged models.
Quality of Service Analysis of 5G Telkomsel Network Technology

5G cellular network technology is the latest generation cellular network technology with various advantages from the previous 4G technology which is expected to be able to become the most reliable in providing connectivity that can connect everyone with everything anywhere and anytime. 5G cellular technology as a technology that has just been implemented in Indonesia, its performance is still not optimal for users due to several obstacles, including the service area which is still very limited. Therefore, this study aims to conduct field measurements of the Quality of Service (QoS) with the parameters of latency, packet loss, download and upload speed of the 5G network from the operator Telkomsel. Data collection is carried out at Pantai Indah Kapuk and Kelapa Gading using the drive test method. The results of the field measurement data analysis show that Telkomsel's 5G network has a latency value that is below the minimum standard, while for packet loss, it gets a very good value of 0%. While the data throughput for Download and Upload speed gets enough results to meet the minimum standards of 5G network service.
Industrial applications of temperature and species concentration monitoring using laser diagnostics

Industrial applications of laser diagnostics have been demonstrated for the purpose of clarifying combustor chemical reaction mechanisms, as well as temperature and harmful substance monitoring in large-scale burners and commercial plant exhaust ducts, and the combustion control of commercial plants. Laser induced fluorescence (LIF), laser induced breakdown spectroscopy (LIBS), and tunable diode laser absorption spectroscopy (TDLAS) have accordingly been applied in various industrial fields. In this study, temperature and species concentration were measured inside gas turbine combustors, a diesel engine, and a large-scale industrial burner using LIF. This technique introduces a new tool with respect to practical combustors for the analysis of NO formation characteristics, turbulent flame front structure, and differences between standard and improved combustors. On-line monitoring of trace elements to the ppb level was also successfully demonstrated using LIBS. The automated LIBS unit was found to be capable of monitoring trace element concentration fluctuations at ppb levels with a 1 min detection time under actual plant conditions. In addition, real-time measurement of O2 and CO concentrations in a commercial incinerator furnace was performed using TDLAS to improve the combustion control. By using the multiple-point laser measurement results to control secondary air allocation, higher secondary combustion efficiency was achieved, and CO concentration (considered to be a substitute indicator for dioxins) was reduced from 11.9 to 8.0 ppm.
Massive SIMO System Based on Energy Difference Detection in Rician Channels

Under the condition of limited number of antennas in current noncoherent single-input-multiple-output (SIMO), the key problems of serious mutual interference and small number of supported users are still difficult to overcome. In this paper, we proposed a multi-user massive SIMO based on energy difference detection (EDD) in Rician channels. The proposed method only needs to simply estimate the line-of-sight (LOS) amplitude gain of users. The cross-product interference caused by energy detection is eliminated by designing complementary amplitude constellations and performing energy difference detection at the base station (BS). It reduces the number of antennas required by the BS to distinguish data streams from different users. In addition, we also analyze the impact on the system performance when users move, and adopt the low-complexity approach of re-estimating the moving user’s coefficient to maintain a good performance. Simulation results show that the proposed method can use the LOS component of the Rician channels to support more concurrent users, and obtain a better performance than existing methods.
Research on Optimization of Customized Bus Dispatching by Genetic Algorithm and Intelligent Information System

In order to solve the problem of the "last mile" of passengers traveling between their residences and transfer stations in low-density travel areas, this paper discusses the optimization of customized bus dispatching. Incorporate the effective connection of the customized bus and the main line bus operation into the optimization category, consider the impact of the joint dispatch of multiple vehicle types on the operation of the enterprise and the impact of the formation time reliability on the passenger satisfaction, in the known fleet size and passenger travel Under the premise of demand, the optimization goal is to construct a customized bus dispatching optimization model that considers passenger satisfaction with the maximum operating cost and minimum passenger satisfaction, and at the same time, a genetic algorithm solution model is designed. The research results show that the vehicle configuration scheme that adopts the joint scheduling of multiple vehicle types has a better operation effect, indicating that the model has a certain feasibility.
Low-loss metamaterial electromagnetically induced transparency based on electric toroidal dipolar response

In this paper, a low-loss and high transmission analogy of electromagnetically induced transparency based on electric toroidal dipolar response is numerically and experimentally demonstrated. It is obtained by the excitation of the low-loss electric toroidal dipolar response, which confines the magnetic field inside a dielectric substrate with toroidal geometry. The metamaterial electromagnetically induced transparency (EIT) structure is composed of the cut wire and asymmetric split-ring resonators. The transmission level is as high as 0.88, and the radiation loss is greatly suppressed, which can be proved by the surface currents distributions, the magnetic field distributions, and the imaginary parts of the effective permeability and permittivity. It offers an effective way to produce low-loss and high transmission metamaterial EIT.
Skeleton Features-based Drowsiness Detection for a Semi-Autonomous Driving System in Real Road Conditions

In semi-autonomous, a system has difficulty obtaining a driver’s facial features as the driver is allowed to look away. This paper presents a drowsiness detection system that only uses the posture information of the driver. We assumed that driver drowsiness is reflected by the way of body swaying as well as head swaying which is conventionally utilized. We collected data comprising posture information from 18 drivers in real road conditions. We created indices, such as the position, variation, and frequency characteristics of the entire upper body including the trunk and head, with different tendencies of drowsiness. The accuracy of the drowsiness estimation model combining these indicators was 82%. This accuracy is as high as that of the conventional facial expression– based drowsiness estimation model with adequate facial-feature acquisition.
Channel Group-wise Drop Network with Global and Fine-grained-aware Representation Learning for Palm Recognition

As a relatively new biometric modality, palmprint attracts much attention for its rich intrinsic features and high commercial prospects. Most existing palmprint recognition methods only extract features from the region of interest (ROI) and neglect the features of other regions. Hence, the recognition performance of these methods highly relies on ROI localization, and it requires users' high cooperation. To relieve these problems, we propose a novel end-to-end recognition framework for contactless whole-palm region-based recognition in this paper, dubbed as Channel Groupwise Drop Network (CGDNet). CGDNet consists of a trunk branch and a part exciting branch. The trunk branch extracts the global representations, and the global scale (GS) module is designed to measure the similarity from feature positions to obtain global knowledge. In the part exciting branch, the features are split into two fine-grained parts equally along the horizontal direction. Then, the channel group-wise drop (CGD) module is designed to aggregate the same part features of the object and cooperates with the CGD mask, which randomly drops the same region in the channel group to excite diverse fine-grained features for learning. Finally, fine-grained features and the global features are concatenated as the final feature. The proposed CGDNet achieves competitive performance in two bench-mark datasets compared with the state-of-the-art methods.
Deep Reinforcement Learning for Downlink Resource Allocation in Vehicular Small Cell Networks

It becomes very common to use cell phones in public transportation and the cars. Vehicular networking has a major problem which is the degradation of signal quality due to interference and the large number of mobile devices. Artificial intelligence (AI) is a promising technique for next-generation wireless networks. Deep learning is a type of AI derived from machine learning; here the machine can learn by itself, unlike programming where it is content to execute rules to the letter predetermined. In addition, AI can be explored in order to solve various problems. In this paper, we tackle the problem of resource allocation in a vehicular small cell network (VSCN). Indeed, we propose a new mechanism based on deep reinforcement learning denoted Resource Allocation based Deep Reinforcement Learning (RA-DRL). The main goal of our proposed method is to maximize the total system sum rate (throughput) while guaranteeing minimum interferences, Quality of Service (QoS) and the demand for all users. Simulation results demonstrate that our proposed RA-DRL algorithm exhibits better performance comparing to the other methods, by maximizing the total system sum rate while maintaining inter-VSCs interferences and a minimum latency
Exciton fine-structure splitting in GaN/AlN quantum dots

Exciton bright-state fine-structure splitting (FSS) in single GaN/AlN quantum dots (QDs) is reported, presenting an important step toward the realization of room temperature single-qubit emitters for quantum cryptography and communication. The FSS in nitride QDs is up to 7 meV and thus much larger than for other QD systems. We find also a surprising dependence of FSS on the QD size, inverse to that of arsenide QDs. Now we are able to explain why FSS can only be observed in small QDs of high-emission energies. Our calculations reveal a shape/strain anisotropy as origin of the large FSS allowing different approaches to control FSS in nitrides.
Design and Implementation of SEPIC Converter for PV System Based Smart Home Application

This paper presents the design, simulation and analysis of one of the least understood Switch mode power converter i.e., Single-ended primary-inductor converter (SEPIC). This dc-dc converter can be used as a buck-boost converter by varying the duty cycle of the switching element. SEPIC has distinct advantage of providing positive polarity output voltage and ripple free input and output current with less stress on components. This is in comparison to traditional buck-boost converter which has negative polarity output voltage besides having highly discontinuous currents and more stress on the components. This makes it ideal for industrial and domestic applications like smart home appliances based on PV energy. Extensive simulation has been carried out in continuous conduction mode (CCM) using SIMULINK-MATLAB under different input conditions. PI controller has been incorporated to fine-tune the response, minimize the steady state error while maintaining the constant output voltage. The hardware realization of SEPIC converter validates the simulation results.
QUEST-Based Kalman Filter and LQR for Satellite Attitude Control

This paper presents the design and implementation of accurate attitude estimation and control using low-cost Inertial Measurement Unit (IMU) sensors based on a satellite quaternion model. Mathematical models of the proposed algorithms are outlined and discussed extensively. A two-step optimal estimator from vector observations is proposed. QUEST algorithm is used to produce simultaneous non-collinear vector measurements of the sensors which are then fed into the Kalman filter to obtain better attitude estimates. Linear-Quadratic Regulator control is implemented using a reduced quaternion satellite model. Simulation results show that the two-step filter approach performs satisfactorily in eliminating the bias and error that is present in IMU sensor measurements. The optimal attitude is used to simulate quaternion-based attitude and angular velocity responses to LQR. The results show that the control strategy performs satisfactorily in making sure that there is a minor difference between the reference input attitude and estimated attitude (from the Kalman filter).
3D Point Cloud Image Instance Segmentation System Based on Hierarchical Aggregation

In this paper, 3D point cloud image instance segmentation system based on hierarchical aggregation is proposed in this paper. Instance segmentation on point clouds is an important task in 3D scene perception. This paper applies a clustering-based architecture to fully exploit the spatial relationships of points and point sets. Considering the cluster-based methods may lead to over-segmentation or under-segmentation, this method presents hierarchical aggregation to gradually generate instance segmentation. Point aggregation can be preliminarily clustered into a set and a set aggregation of complete instances can be generated from the set. The subnetwork of intra-instance prediction is used for noise filtering and mask quality scoring after obtaining the full 3D instance.
A Novel High-Gain PAM4 Baud-Rate Phase Detector for ADC-Based CDR

In this letter, we propose a novel high-gain four-level pulse amplitude modulation (PAM4) baud-rate phase detector (BRPD) for the ADC-based high-speed clock and data recovery (CDR) circuit. The PAM4 phase decisions are generated by three adjacent data and three adjacent error types. In addition, a linear model of the proposed PAM4 BRPD is developed for the first time to precisely calculate the gain, which is also critical for analyzing the key characteristics of the PAM4 BRPD-based CDR. Moreover, such a CDR is designed with 12nm CMOS technology and supplied with 0.8 voltage. The transistor simulation results show that the CDR can operate at 112 Gb/s, the RMS jitter of the recovered clock is 523 fs, and the CDR’s jitter tolerance meets the CEI-112G-LR-PAM4 mask.
Comprehensive Review on Aviation Operator’s Total Loads

In the aviation environment and air transport operations, safety is the most crucial element of the system; maintaining safe aeronautical operations is highly dependent on the aviation operators’ actions and reactions. Cognitive loads reflect the human contribution to the system; mental, communication, information, task, and workload shape the aviation total loads. The number of aviation accidents and incidents caused by human factors increases rapidly due to intensive technology and automation in aviation. Such recent examples of human-contributed aviation accidents can be given like (i) Casa 212-200, N497CA Flight accident, (ii) Ethiopian Airlines, ET-343 flight accident. Many previous studies focused on measuring the total load of pilots and ATCOs through different processes and flight scenarios using multiple sensors & devices. This paper reviews a variety of experimental studies differentiating the methodologies, scope, and outcomes. Eventually, the paper introduces a compromised and novel approach for measuring aviation operator loads in multiple scenarios.
Capacitor Voltage Balancing in Two-Level Inverter with Split Capacitor Topology for DSTATCOM Application using Model Predictive Control

A Distribution Static Compensator (DSTATCOM) with split capacitor configuration is implemented in this paper to adjust reactive power requirements of various loads, harmonics, and unbalanced load factor compensation. The most serious issue with this structure is capacitor voltage imbalances, which cause the voltage source inverter (VSI) tracking performance to degrade. In the literature, the proportional integral (PI) controller and model predictive controller (MPC) are employed to suppress voltage divergence. However, the aforementioned controller is effective for three-phase diode bridge rectifier loads as well as unbalanced linear loads. Capacitor voltage balancing is not accomplished in half wave rectifier load because of the presence of its dc component. To provide capacitor voltage balancing for all combinations of loads such as linear unbalanced, half wave rectifier, and three phase diode bridge rectifier loads functioning alone or collectively, a two quadrant chopper circuit is employed across the split capacitors. The switching pulses of chopper switches conventionally generated using a hysteresis current controller, while the switching states of chopper and VSI switches are obtained purely using MPC in this paper. To validate the suggested control strategy, the generated model was studied and results were obtained using detailed simulation using MATLAB.
EPI-CGAN: Robust EEG-based Person Identification Using Conditional Generative Adversarial Network *

Person identification based on Electroencephalogram (EEG) signals has been studied through feature engineering, channel selection methods, and non-neural-network/deep models. Existing studies map signals from 3-D distributed channels into channel-time matrices, leading to spatial information loss. In this paper, we design an EEG-based person identification model EPI-CGAN, which combines the Convolution Neural Network (CNN) with conditional generative adversarial network (CGAN). More specifically, we map handcrafted temporal features into a 3-D feature tensor and apply CNN to extract spatial features. We leverage both handcrafted features and extracted features to improve model performance and robustness. Experiment results demonstrate that EPI-CGAN keeps a Correct Recognition Rate (CRR) over 98.5% on datasets that vary in participant conditions, scale, and EEG acquisition protocols.
3D Deployment of Dynamic UAV Base Station based on Mobile Users

The deployment of multiple dynamic rotary-wing UAV base stations is studied for moving users. The cumulative utility of the communication network is maximized by adding and applying the three paremeters of the UAV base stations in terms of total channel transmission rate, mobile energy consumption, and coverage utility. The UAV base station performs different motions in different time slots, which translates the process into a Markov decision process. Since the user moves randomly within the time slot, the reward function value of the “next two steps” of the dynamic UAV base station is considered. The mobility prediction of the user is taken into account to some extent, and finally the mobile strategy of the UAV base station within the current time slot is selected comprehensively. The simulation results show that the improved algorithm is effective in achieving the best coverage of the UAV base station.
SoK: A Survey on Redundant Execution Technology

Redundant execution technology is one of the effective ways to improve the safety and reliability of computer systems. By rationally configuring redundant resources, adding components with the same function, using the determined redundant execution logic to coordinate and efficiently execute synchronously can effectively ensure high availability of the machine and system. Fault-tolerant is based on redundant execution, which is the primary method of dealing with system hardware failures. Recently, multi-threading redundancy has realized the continuous development of fault-tolerant technology, which makes the processing granularity of the system tolerate random failure factors gradually reduced. At the same time, intrusion tolerant technology has also been continuously developed with the emergence of multi-variant execution technology. It mainly uses the idea of dynamic heterogeneous redundancy to construct a set of variants with equivalent functions and different structures to complete the detection and processing of threats outside the system. We summarize the critical technologies of redundant execution to achieve fault tolerance and intrusion tolerance in recent years, sorts out the role of redundant execution in the development process from fault tolerance technology to intrusion tolerance technology, classify redundant execution technologies at different levels, finally point out the development prospects of redundant execution technology in multiple application fields and future technical research directions.