Miracle-dz/UniFAR-FTUs huggingface dataset:
Survey of approaches for targeting quasars

The study of quasars is of great importance to the formation and evolution of galaxies and the early history of the universe, especially high redshift quasars. With the development and employment of large sky spectroscopic survey projects (e.g. 2dF, SDSS), the number of quasars increases to more than 200,000. For improving the efficiency of high-cost telescopes, careful selecting observational targets is necessary. Therefore various targeting quasar algorithms are used and developed based on different data. We review them in detail. Some statistical approaches are based on photometric color, variability, UV-excess, BRX, radio properties, color-color cut and so on. Automated methods include support vector machines (SVMs), kernel density estimation (KDE), artificial neural networks (ANNs), extreme-deconvolution method, probabilistic principal surfaces (PPS) and the negative entropy clustering (NEC), etc. In addition, we touch upon some quasar candidate catalogues created by different algorithms.
Band-Oriented Predistorter for Fully Connected Hybrid Antenna Arrays

Fully connected hybrid antenna arrays (FC-HA) are attractive, low-complexity alternatives to the fully digital array architecture. However, the sub-array architecture performance is limited by reduced beamforming gain because of array partitioning. In this study, we provide a theoretical analysis of the connection between a wideband digital predistorter (DPD) and its equivalent for a fully connected antenna array (AA). Then, we propose new hardware-efficient band-oriented architecture by moving all AA-specific DPD calculations into the analog radio frequency site. To our knowledge, this is the first band-oriented DPD architecture for FC-HA. In the proposed DPD, the unified digital cores are common for all power amplifiers (PAs) in the array. This enables a set of PAs to be linearized with a single, multi-core DPD. The performances of the proposed band-oriented DPD were evaluated with the help of computer simulation. The simulation results demonstrate that both the in-band and the out-of-band intermodulation distortion for all PAs in the array can be successively suppressed with a single multi-core DPD regardless of the individual beam’s directions. Unlike current all-digital state-of-the-art solutions, the proposed band-oriented DPD has a significantly low digital hardware complexity because of the digital core unification, thus reducing its power consumption requirement.
Congestion Study of Transport Layer in DL 5G Communication Systems

Fifth generation (5G) radio systems represent a predictable evolution of the mobile network to achieve new capabilities. As the previous generation, 5G mobile networks will provide high data rate connectivity for smartphones, tablets, and IoT as well. 5G takes the traditional mobile broadband to the extreme in terms of data rates, capacity, spectrum efficiency and availability. For these targets, 5G requires various protocols to support its multiples features, such as Transmission Control Protocol (TCP), which provides high-speed data rates. In this paper we analyze the performances of TCP on top of File Transfer Protocol (FTP) model 2. User throughput, total cell throughput, Round Trip Time (RTT), delay and Physical Resource Block (PRB) utilization are the analyzed metrics for FTP protocol.
An Integrated Neural Unmanned System for Multi-Domain Perception and Decision-Making

Most recent intelligent systems study the perception or policy modules independently but ignore the inter-connection between them, which results in low information efficiency and high network redundancy, in the end, leads to the incomplete utilization of network potential and poor system-level end-to-end intelligence. This paper proposes an integrated end-to-end network that adapts to multi-domain inputs and multi-function outputs. “Integration” is defined in this paper in mainly three aspects: network structure level, optimization level, and functional level. The integrated network architecture is capable of fusing multiple raw sensory inputs and outputting multiple perceptions and decisions simultaneously. We first implement the integrated system in a virtual simulation environ-ment and investigate the domain transformation methods to real world. Comprehensive experiment results in both virtual and real environments show that our proposed system can behave normally under exploration, target-driven navigation and target shooting tasks.
Analysis of Solar Photovoltaic Powered Neutral Point Clamped Single Phase and Three Phase Five Level Inverters

Multilevel inverter (MLI) has several benefits over the conventional two-level inverter bridge like lower voltage stresses across the switches, lower harmonics, near to sinusoidal output and improved efficiency. These benefits enable inverter design to use a lower rating of devices thus reducing the cost of the inverter even though the number of devices increases. This paper compares the performance of single-phase and three-phase neutral point clamped (NPC) inverters up to a five-level configuration with solar power as input. The inverters are designed to measure their total voltage and current harmonics, voltage stress across each switch and efficiency of the inverters using MATLAB Simulink. Moreover, both square wave and sinusoidal pulse width modulation (PWM) methods are applied on both pure resistive (R) and resistive-inductive (RL) loads for a detailed comparative analysis of the inverters. The simulation results suggest that the sinusoidal PWM method for all types of inverters gives better performance in terms of reduction in harmonics however with increased burden of computation. This paper suggests a five-level NPC inverter for most solar power applications due to its superior performance.
Row-sparsity Binary Feature Learning for Open-set Palmprint Recognition

Binary feature representation methods have received increasing attention due to their high efficiency and great robustness to illumination variation. However, most of them are hand-designed feature descriptors that generally require much prior knowledge in their design. This paper introduces a Row-sparsity Binary Feature Learning (Rs-BFL) method to adaptively learn and encode palmprint features for open-set palmprint recognition. Given the training palmprint images, RsBFL jointly learns a bank of linear projection functions that transform the informative texture features into discriminative binary codes. Afterwards, we calculate the block-wise histograms of each feature map and concatenate them as the final feature representation. Based on the pre-trained projection matrix, we mapped the palmprint texture features of the test samples into binary features for matching. For RsBFL, we enforce three criteria: 1) the quantization error between the projected real-valued features and the binary features is minimized, at the same time, the projection noise is minimized; 2) the latent label semantic information is utilized to minimize the distance of the within-class samples and simultaneously maximize the distance of the between-class samples; 3) the $l_{2,1}$ norm is used to make the projection matrix to extract more discriminative features. Extensive experimental results on two publicly accessible palmprint datasets demonstrated the effectiveness and powerful learning capability of the proposed method.
Cavity-Backed Double H-Slot Antenna With IPMC Flaps for Designing Frequency-Switchable On/In-Metal Semi-Active Tag

A frequency-switchable semiactive tag antenna that can perform well on both the on- and in-metal platforms is proposed. A double H-slot antenna has been employed for designing the tag, and it can be easily made using a copper-clad polyimide film. The proposed antenna structure has a backing metallic cavity, which has made the read performance very robust even being used for the in-metal platform. To achieve the frequency reconfiguration, the ionic polymer–metal composite (IPMC) actuators are coated with metal, and they are tactfully integrated with a double H-slot antenna for changing its shape. The IPMC flaps can be actuated with a low operating voltage for switching the tag antenna across the U.S. and EU radio frequency identification (RFID) passbands. A water-embedded substrate that consists of a polydimethylsiloxane (PDMS) container filled up with water has also been employed for miniaturizing the antenna. Good maximum read ranges (> 10 m) have been achieved for all cases.
Comparative Analysis of Different Piezoelectric materials in Design of Microgrippers

Due to the growing demand and wide application of microdevices, there is an increase in the demand of microgrippers that can perfectly carry and place the microparts in MEMS Devices. In this paper, grippers are designed in micro dimensions for micro and nano devices. The piezoelectric actuation is used for analyses of designed precise microgrippers. Different piezoelectric materials such as PZT5A, PZT7, Barium Titanate, Barium Sodium Niobate, and Lithium Niobate are modelled and analysed in terms of displacement of arms with stress observation at the actuator ends. PZT5A proves the best material for microgripping effect. COMSOL Multiphysics is the FEA tool used for the design and analysis of microdevices.
Development of Libelium-based Reconfigurable Solutions For Smart City Applications

As the number of internet-connected devices, Internet of Things (IoT) devices are overgrowing, and thus, the collection and the process of enormous volumes of data generated from these devices is becoming increasingly difficult. The evolving edge computing models help process data stored near the sources to ensure the lowest latency and combine with cloud computing models to compute and handle many complex new data parameters. In this research, we focus on studying the application of the reconfiguration methods to remotely reconfigure the Libelium sensors by referring to related studies on the reconfiguration solutions. The reconfiguration solutions will be applied with the User-driven Adaptive Sampling Algorithm (UDASA) to optimize the device's power consumption time to meet the demand for both precision and latency of real-time data, solving the problem of sensors located in remote locations and hard-to-reach terrain. The feasibility of the research is studied by deploying Libelium devices - a modern IoT solution applied to today's large IoT models, to be able to read and send sensor data to the gateway over WiFi protocol. The sensor data is then stored in the Cloud to serve the following sampling time calculation to reconfigure the device, significantly reducing the energy consumption of the sensor device and opening a new development direction for a reconfiguration solution that applies algorithms to the fields of energy research and new reconfiguration for IoT devices.
MCascade R-CNN: A Modified Cascade R-CNN for Detection of Calcified on Coronary Artery Angiography Images

Among cardiovascular diseases, coronary artery calcification (CAC) is a high-risk factor for worsening protopathy and increased mortality. However, the coronary artery an-giogram, which is the main approach for CAC diagnosis, suffers from plenty of photographing noise. This brings difficulties to detect calcification from the background. In this paper, a modified Cascade R-CNN (MCascade R-CNN) network is proposed to deal with the problem of calcium detection in angiograms. In the proposed network, we propose an innovative balanced aggregation pyramid structure, integrating multi-level features of every depth in the feature map, based on enhanced propagation of strong semantic features. In addition, a new convolutional attention mechanism is designed to improve the performance of the detector. Experiments show that the proposed method enjoys better performance in detecting and marking CAC in angiograms,
The Future of Brain–Computer Interfaces

Operating a drone, playing video games, or controlling a robot simply by thinking are exciting applications of brain–computer interfaces (BCIs) that pave the way for more mind-bending breakthroughs. Crucially, BCIs, which enable the brain to exchange signals with an outside device, also represent a powerful tool to restore movement, speech, touch, and other functions to patients with brain damage. Despite recent progress in the field, technological innovation is needed and plenty of scientific and ethical questions remain unanswered. Still, researchers say BCIs hold great promise for patients with the most severe impairments—and that major breakthroughs are within reach.
The effect of visual cognition on the fear caused by pain recall

In this study, we investigated ‘pain recall’ that results from showing a painful image and evoking pain without actually giving any pain, that is said to be similar to the brain activity that actually causes pain. The experiment involved 12 students showing three short videos in which a child, a female, or a male was being injected. We measured the degree of emotional changes by watching the painful scene in three ways: emotion estimation by facial expression, GSR (Galvanic Skin Response) and Eye tracking. The results showed that subjects felt the same fear and tension as when feeling pain. On the other hand, subjects felt less painful emotions when they looked at the scene that a man with solid arms was injected. The degree of emotion in pain recall varied depending on who received the injection in the short videos. These results suggest that pain may be reduced by showing some body images as visual information. It is possible to alleviate actual pain by applying how to reduce ‘pain recall’.
Low Switching Frequency Operation of a Two-Level Voltage Source Inverter Using Imperialist Competitive Algorithm and Hardware Realization

To cut down switching losses, the low switching frequency pulse width modulation (LSF-PWM) approach is preferred for medium voltage and high power applications. The major challenge with these strategies is obtaining the switching instants by solving a series of non linear transcendental equations in entire modulation index range. The number of switching angles employed determines how many harmonics are controlled from the output. Furthermore, across various ranges of modulation index, the system of equations reveals several solutions, no solution, or a single solution. The numerical and algebraic techniques does not provide any solution in modulation index range of beyond 0.9 and therefore restricts the maximum achievable output voltage in the entire range. In this paper, minimization of harmonics based on Imperialist Competitive Algorithm in a three phase two-level voltage source inverter is proposed to obtain the optimum solution in the modulation index range of 0.8 to 1. A DSP controller based hardware implementation of a three phase two-level voltage source inverter designed with intelligent power module (IPM) is provided in the research paper to validate the proposed idea.
Movement Analysis of the Aircraft Optoelectronic Tracking System in Lateral Plane

The paper describes the analysis of the movement transformation of the tracking optoelectronic system of a fast aircraft. The transformation is performed in the selected phase of flight in the lateral planes. The mathematical model of the aircraft was extended by an optoelectronic tracking system model and a weapon movement model. Mathematical models of the proposed integrated system are described by transfer functions, which are transferred to the state space with evaluation in the simulation environment. Control of the automatic tracking system on the aircraft was designed with the ability to control the tracking line on the selected tracking object in order to reduce the impact of the error in the dynamics of the aircraft movement, the movement of the monitored object and the optoelectronic tracking system. The laws of control of the optoelectronic system and the aircraft have been defined. The optimization of the proposed methods was realized under changing flight conditions of the aircraft and the tracking object as optimal gains. Optimal gains are determined by the values given in the difference speed and altitude for aircraft flight.
An efficient deep learning algorithm for predicting market trends using cyclic long short-term memory network

The stock market's role in the economy has attracted researchers. Many researchers analysed stock market trend and price prediction. Previous study used neural network and statistical models to predict experimental results. Deep learning has a tremendous learning capacity and is appropriate for complicated financial time series. The cyclic long short-term memory (LSTM) network is well-suited for theoretical financial time series prediction. This study proposes an efficient deep learning approach for stock market trends prediction. This deep learning framework includes data processing, deep learning models, and prediction optimization. An optimizer namely whale optimization algorithm (WOA) enhance the RNN-LSTM network-based deep learning network prediction. Comparative models demonstrated that the proposed framework is efficient. Testing and data analysis showed that the proposed framework is effective at predicting stock market trends.
A Heterogeneous RISC-V SoC for Confidential Computing and Password Recovery

In today’s silicon design, there are far more processors than the main ones. Domain-specific accelerators and coprocessors are widely used to continuing to increase the performance and energy efficiency of computing systems. Focused on the security domain, this paper presents a heterogeneous ASIC SoC built with the most popular open-source RISC-V ISA, a coprocessor providing the trusted execution environment for confidential computing and an energy efficient rule-based password generation accelerator. Thanks to its continuously completing ecosystem, the RISC-V core provides application interfaces and task management for the whole SoC, through which we validated the security mechanism for confidential computing in the coprocessor and tested the performance of the password generation accelerator. In addition to verifying the functionality of our design in GlobalFoundries 22FDX technology, the evaluation results also show that our password recovery system is able to process about 0.97 passwords per cycle at 500 MHz and is $25.4 \times$ more energy efficient than that of FPGA implementation.
Exit Criteria for Newton–Cotes Quadrature Rules

A common procedure for deciding when to exit from a quadrature routine is to repeatedly double the number of nodes and stop when the difference between successive approximations is less than a preassigned tolerance. This is shown to be a valid procedure for any Newton–Cotes compound rule when the appropriate higher derivative of the integrand has constant sign. This result is shown to be sharp in the sense that an exit rule based on a weaker inequality will fail for some function in the above class. Conditions are also given under which this exit criterion is asymptotically valid.
Electronic structure at highly ordered organic/metal interfaces : Pentacene on Cu(110)

The electronic structure at highly ordered pentacene monolayer prepared on Cu(110) substrate was studied by angle-resolved ultraviolet photoemission spectroscopy. The valence-level photoemission line shape showed the evidences of (i) formation of the interface states and (ii) two-dimensional energy-band dispersion of the resultant interface states. The lattice constant deduced from the observed energy-band dispersion is consistent with the reported one based on the low-energy electron diffraction experiments. Thus, the observed energy-band dispersion can be ascribed to the in-plane intermolecular energy-band dispersion in the pentacene monolayer on Cu(110). These phenomena may originate from the hybridization between the molecular orbital and the wave function of the substrate surface. Furthermore, work-function change of about $\ensuremath{-}0.9\phantom{\rule{0.3em}{0ex}}\mathrm{eV}$ by adsorption of pentacene was observed from the shift of the secondary-electron cutoff. Such a decrease of the work function indicates the formation of a dipole layer at the interface with the molecule positively charged. This direction is opposite to the naive expectation from the electron transfer from the substrate to the molecule, which was suggested from the previous work of core-level photoemission spectroscopy [McDonald et al., Surf. Sci. 600, 1909 (2006)]. This unexpected result may originate from the charge redistribution at the interface due to the induced image charge in the metal and the push back of electrons spilled out from the metal surface by the adsorbed molecules, which may overwhelm the effect of electron transfer.
Outage Probability of Indoor-outdoor C-NOMA Enabled UAV-Relay Over κμ Fading

In this paper, a downlink cooperative non-orthogonal multiple accesses (C-NOMA) system assisted with a decode and forward (DF) UAV-relay is developed, where the channel gains follow the $\kappa-\mu$ generalized fading model to evaluate its system performance. The study involves two scenarios in which the NOMA signal is propagated through the outdoor-to-indoor channel and the outdoor-to-outdoor channels, where the practical successive interference cancellation (SIC) is addressed. In particular, exact closed-form expressions are derived for the outage probability (OP) of indoor-outdoor NOMA users on $\kappa-\mu$ fading channels. To obtain more insight into how channel fading parameters affect OP performance, asymptotic expression is also evaluated. The results provide insight into the impact of key parameters, such as power allocation and channel fading parameters, on system performance. Ultimately, Monte Carlo simulations were used to validate the analytical expressions.
Anomaly Detection in Cloud-Native Systems

Companies develop cloud-native systems deployed on public and private clouds. Since private clouds have limited resources, the systems should run efficiently by keeping performance related anomalies under control. The goal of this work is to understand whether a set of five performance-related KPIs depends on the metrics collected at runtime by Kafka, Zookeeper, and other tools (168 different metrics). We considered four weeks worth of runtime data collected from a system running in production. We trained eight Machine Learning algorithms on three weeks worth of data and tested them on one week’s worth of data to compare their prediction accuracy and their training and testing time. It is possible to detect performance-related anomalies with a very high level of accuracy (higher than 95% AUC) and with very limited training time (between 8 and 17 minutes). Machine Learning algorithms can help to identify runtime anomalies and to detect them efficiently. Future work will include the identification of a proactive approach to recognize the root cause of the anomalies and to prevent them as early as possible.
Quality control strategy in supply chain under asymmetric information

This paper is essentially concerned with the supplier and the buyer's quality control decision in supply chain with asymmetric information. The paper presents principal-agent models regarding the buyer's quality evaluation level and the supplier's quality prevention level decision problems. The paper then focuses on the supplier and the buyer's optimisation problems being turned into optimal control problems under asymmetric information. Maximal principle is used to get the solution. And comparison analyses are made between two different information settings. At last, numerical examples are presented to prove our models and analyses.