Miracle-dz/UniFAR-FTUs huggingface dataset:
Resource Allocation for LEO Beam-Hopping Satellites in a Spectrum Sharing Scenario

In recent years, low earth orbit (LEO) satellite constellation systems have been developed rapidly. However, the scarcity of satellite spectrum resources has become one of the major obstacles to this trend. LEO satellite constellation communication systems sharing the spectrum of incumbent geostationary earth orbit (GEO) satellite system is a feasible way to alleviate spectrum scarcity. Therefore, it has practical significance to study the optimization of satellite resources allocation (RA) in a spectrum sharing scenario. This paper focuses on the RA problem that LEO satellites share a GEO high throughput satellite’s spectrum in a beam-hopping (BH) manner. The GEO satellite system is served as the primary system and the LEO satellite constellation system is served as the secondary system whose frequency bands and transmitting power are strictly limited. Compared with conventional multibeam satellites, BH satellites have the advantage of flexibility in the time dimension. Therefore, we make full use of the flexibility of LEO BH satellites to realize the matching of traffic demand and traffic supply. The RA problem is decomposed into three sub-problems, namely, frequency band selection (FBS) problem, illuminated cell selection (ICS) problem, and transmitting power allocation (TPA) problem. We solve each sub-problem in order and finally form a complete RA scheme. The performance evaluation of the proposed RA scheme is carried out in real-time and simulation results show that the LEO BH satellite paired with the RA scheme we proposed has good adaptability to the uneven distribution of traffic demand in the spectrum sharing scenario.
Maximum Entropy Estimation of Density Function Using Order Statistics

The main premise of this article is to develop a maximum entropy estimation of an unknown distribution using order statistics. The exact solution using constraints on the order statistic means is derived. The result is an integral of a rational parametric function whose parameters depend on the order statistic constraints. This integral equation can be solved directly for a small number of order statistic means, e.g., 1, 2, or at most 3, via a multidimensional search in the parameter space. As the number of constraints increases, the search for the optimum parameters becomes formidable. To resolve this problem we have proposed an approximate solution to the resultant integral equation based on Bernstein polynomials and estimates of a desired number of quantiles from available samples. The proposed approximation approach does not require any search in the parameter space and is formulated for any number of samples from an unknown distribution. Performance of this approach is evaluated under various practical conditions by using Kullback-Leibler divergence function.
Toward Safe and Personalized Autonomous Driving: Decision-Making and Motion Control With DPF and CDT Techniques

In this article, a novel approach of decision-making and motion control is designed for realizing safe and personalized driving of autonomous vehicles. A new lane-change intention generation model and a new lane-change decision-making algorithm are proposed. The feature of the proposed decision-making module is that the interactions between the ego vehicle and other surrounding vehicles are represented by the dynamic potential field (DPF) and embedded in the gap acceptance model to ensure the safety and personalization during driving. In addition, an integrated trajectory planning and tracking control algorithm, which incorporates the artificial potential field and constrained Delaunay triangulation (CDT) into the model predictive control framework, is developed. The newly developed integrated controller allows efficient execution of the expected motion. The proposed approach is tested under different driving conditions and further compared with an existing baseline method. The results show that the proposed approach is able to make safe and personalized decisions, and execute motion control more efficiently for automated driving under dynamic situations, validating its feasibility and effectiveness.
Formulation Of A Rational Option Pricing Model using Artificial Neural Networks

This paper inquires on the options pricing modeling using Artificial Neural Networks to price Apple's European Call Options. The model is based on the premise that ANNs can be used as functional approximators and used as an alternative to the numerical methods to some extent, for a viable and faster solution. We evaluate our predictions using the existing numerical solutions for the same, the analytic solution for the Black-Scholes equation, COS-Model for Heston's Stochastic Volatility Model and Standard Heston-Quasi analytic formula. The aim of this study is to find a viable time-efficient alternative to existing quantitative models for option pricing.
Optimizing in the Dark: Learning Optimal Network Resource Reservation Through a Simple Request Interface

Network resource reservation systems are being developed and deployed, driven by the demand and substantial benefits of providing performance predictability for modern distributed applications. However, existing systems suffer limitations: They either are inefficient in finding the optimal resource reservation, or cause private information (e.g., from the network infrastructure) to be exposed (e.g., to the user). In this paper, we design BoxOpt, a novel system that leverages efficient oracle construction techniques in optimization and learning theory to automatically, and swiftly learn the optimal resource reservations without exchanging any private information between the network and the user. In BoxOpt, we first model the simple reservation interface adopted in most reservation systems as a resource membership oracle. Second, we develop an efficient algorithm that constructs a resource separation oracle by a linear number of calls on resource membership oracle. Third, we develop a generic framework to construct a resource optimization oracle by iteratively calling the resource separation oracle, and then develop three novel, efficient algorithms under this generic framework, the best of which computes the optimal resource reservation by a linear number of calls on resource separation oracle. As such, BoxOpt can discover the optimal resource reservation with $O(n^{2})$ calls on the resource membership oracle. We implement a prototype of BoxOpt with and demonstrate its efficiency and efficacy via extensive experiments using real network topology and a 7-day trace from a large operational federation network. Results show that (1) BoxOpt has a 100% correctness ratio by comparing with a state-of-the-art optimization solver, and (2) for 90% of requests, BoxOpt learns the optimal resource reservation within 10 seconds.
Hybrid Model Predictive Control strategy of renewable resources in DC microgrids based on Mixed Logic Dynamics and Measurements

A mixed logical dynamic model is established for hybrid concept by studying the microgrid hybrid property in depth. As a state variable, the energy in the DC bus is defined, the process purposes and constraints are provided with continuous state variables, logical variables and supplementary variables. The model predictive control theory is used in order to minimize battery swapping times among charging and discharge, based on the mixed logical dynamic model. The objective function of the quadric form is configured and an optimum vector sequence of input control is obtained. A simulation is developed, simulation results attest to the suggested approach that can both minimize the fluctuation of DC bus voltage and reduce battery energy storage device switching times.
Research on the Influence Mechanism of Precision Marketing on Online Consumer Behaviour based on the ‘S–O–R’ Paradigm and Eroglu’s (2001) Online Shopping Response Model : Take the TikTok platform as an example

The development of e-commerce and the Internet has promoted the rise of the short video shopping platform called TikTok. The increasing scale of trading on the TikTok platform has aroused the increasing concern of people regarding practice and theory, but the influence mechanism of its precision marketing on consumer buying behaviour remains unclear. Combined with the ‘Stimulus–Organism—Response’ framework and Eroglu’s consumer online shopping response model, this study takes precision marketing as an independent variable and online consumer behaviour as a dependent variable. The perceived value of online shopping consumers is the mediating variable, whereas their extroversion is the moderating variable to establish the model. Through a network questionnaire, information is collected from 251 consumers. The positive correlation between precision marketing and the perceived value of online shopping consumers and purchase behaviour is confirmed by empirical research; the relationship between precision marketing and online consumer behaviour is mediated by consumer perceived value. The extraversion of online shopping consumers has a positive moderating effect on the relationship between their perceived value and precision marketing, including the above mediating effect on such a perceived value. The above conclusions provide theoretical basis and practical enlightenment for the network platform to improve its precision marketing effect.
Review on Machine Learning Frameworks in Drivers’ Physiological Signal Analysis to Detect Stress

Nowadays, the performance of classification methods used in physiological signals for healthcare purposes governs computer-based analysis. A prevalent research area in biosignal analysis with the aim of stress recognition and classification is to design algorithms that outperform established approaches. Accurate stress realization in drivers would address significant costs of driving-induced stress and increase drivers’ safety if unobstructed and fully automated stress detection devices developed. This procedure faces some challenges such as human stress detection and recognition, along with simulating stress in computing devices. For stress computing, the initial step is to detect stress which requires capturing human state data. The next step is to extract the most relevant features to find meaningful patterns to analyze data which could be performed by mathematical analysis and machine learning tools. In this paper, a review of the recent advancement in signal processing, feature extraction, and machine learning methods with a focus on galvanic skin response (GSR) analysis is presented. This review enlightens the reader with common methodologies, issues, and future opportunities in this research area.
Design and Development of an Automated Compression Mechanism for a Bag-Valve-Mask-Based Emergency Ventilator

In response to COVID-19 pandemic, universities and related institutions around the world came up with various mechanical ventilator designs to help cope with the expected shortages of ventilators as the pandemic rages. Many of these designs are based on automating the manual operation of the Bag Valve Mask (BVM), a ubiquitous resuscitator device used for emergency ventilation or resuscitation of patients with breathing problems. In this paper, the mechanical design and development process for a BVM-based emergency ventilator is discussed. In particular, the evolution of the design from a simple, low-cost device to a more sophisticated system acceptable to pulmonologists and related medical practitioners is documented.
Analysis and Evaluating the Effect of Harmonic Distortion Levels in Industry

Power Quality is an escalating concern for power engineers since the use of increased electronic devices. The primary reason for deteriorating Power Quality is high levels of Harmonics. Harmonic glitches in the industry are produced from numerous sources, mainly Variable Frequency Drives and Rectified Loads. Harmonics produce various unwanted effects; consequently, it is imperative to identify harmonic distortions, recognize all effects pertaining to harmonics, and analysis of power system quality with tools and equipment that are available for reliable power system operation. In this study, the harmonic analysis will be presented in Industry using Fluke Energy Analyzer. The measured THD values for the Voltage and current will be used to model and analyze overall harmonics in the complete three-phase distribution system of Textile Plant through simulation using ETAP software. A model in ETAP software is presented to demonstrate the applicability of the proposed techniques for harmonics reduction and Power factor correction (PFC) at the same time.
Simultaneous Bi-Directional Communications and Data Forwarding Using a Single ZigBee Data Stream

With the exponentially increasing number of Internet of Things (IoT) devices and the huge volume of data generated by these devices, there is a pressing need to investigate a more efficient communication method in both frequency and time domains at the edge of IoT networks. In this paper, we present Amphista, a novel cross-layer design for IoT communication and data forwarding that can more efficiently utilize the ever increasingly crowded 2.4 GHz spectrum near the gateway. Specifically, to enable the communication from ZigBee to WiFi, we leverage WiFi’s fine-grained channel state information to extract the concurrently transmitted ZigBee-to-WiFi message from time overlapped ZigBee and WiFi packet. We further leverage this unique feature and design a novel forwarding protocol that can simultaneously forward uplink (e.g., collecting sensing data) and downlink (e.g., disseminating control messages) data by using a single ZigBee data stream. Our extensive experimental results show that Amphista significantly improves throughput (by up to 400x) and reduces the latency.
SR-ScatNet Algorithm for On-device ECG Time Series Anomaly Detection

Anomaly detection of real-time ECG time series is of particular interest for early detection of cardiovascular disease for aging population. To use convolutional neural networks (CNN) for any on-device training or inference, you need GPU-accelerated hardware which will not only increase the hardware cost but also consume higher battery power. We proposed a SR-ScatNet algorithm for on-device application such as smart cloth with ECG monitoring sensors. Two improvements were made. First on spectral residual, we use Fourier Transform of autocorrelation of ECG signals instead of original time series to increase the sensitivity. Second on feature extraction, we use shallow wavelet scattering network (ScatNet) instead of deep CNN network so the on-device training can be performed on a simple Arm Cortex-A53 processor without any GPU-accelerator. These improvements are made to create a compact machine learning model according to the nature of different waves constituting the ECG signals. To verify the proposed method, we use the MIT-BIH Arrhythmia Database. The spectral residual of autocorrelation ECG signals can detect the abnormal ECG signals with over 98% accuracy. The wavelet scattering network can further classify the type of abnormality with over 90% accuracy. We believe the design of ECG monitoring smart cloth can benefit from such SR-ScatNet algorithm.
Power Scaling Laws for Radio Receiver Front Ends

In this paper, we combine practically verified results from circuit theory with communication-theoretic laws. As a result, we obtain closed-form theoretical expressions linking fundamental system design and environment parameters with the power consumption of analog front ends (AFEs) for communication receivers. This collection of scaling laws and bounds is meant to serve as a theoretical reference for practical low power AFE design. We show how AFE power consumption scales with bandwidth, $\mathit {SNDR}$ , and $\mathit {SIR}$ . We build our analysis based on two well established power consumption studies and show that although they have different design approaches, they lead to the same scaling laws. The obtained scaling laws are subsequently used to derive relations between AFE power consumption and several other important communication system parameters, namely, digital modulation constellation size, symbol error probability, error control coding gain, and coding rate. Such relations, in turn, can be used when deciding which system design strategies to adopt for low-power applications. For instance, we show how AFE power scales with environment parameters if the performance is kept constant and we use these results to illustrate that adapting to fading fluctuations can theoretically reduce AFE power consumption by at least 20x.
The Effect of Operational Variables on the Performance of Electrochemical Water Treatment for Drinking Purposes

Electrochemical processes have emerged as novel, convenient, and highly efficient water treatment techniques for the elimination of a wide range of physical, chemical, and biological contaminants from the water. The challenge in designing and scaling up the electrochemical systems lies in the selection of electrode material and suitable operating conditions. This study investigates the effect of three operational variables including treatment time, current density, and inter- electrode spacing on the quality parameters of drinking water. The results show that the technique effectively reduces the level of suspended and dissolved solids. Increasing either operating time or current density and decreasing the interelectrode spacing has a beneficial role in the treatment performance. Further, the ions produced during the electrolysis process can disinfect the biological contaminants thus making the water suitable for drinking purposes. The need for stable electrode materials for the successful application of the treatment method has also been discussed.
Research on Computer-Generated Force Perceptual Attention

Perception model is an important research field of CGF behavior modeling. As the input of the reasoning module, whether the information obtained from the perception model is precise is of great significance for the simulation fidelity. This paper first investigates the related researches on CGF perception model, and focuses on analyzing the attention mechanism, action time, and action way. Besides, a virtual human perception framework based on attention mechanism is proposed by constructing the knowledge base of the attention and modeling of information feedback mechanism. Compared to the traditional perception model, the proposed framework is more effective to capture and deal with the information from simulation environment.
Bucket MapReduce: Relieving the Disk I/O Intensity of Data-Intensive Applications in MapReduce Frameworks

Hadoop MapReduce is a software framework for processing vast amounts of data in parallel on large clusters. As data size increases, a need arises to resolve the significant increase in the disk $I/O$ of reducer nodes, which may cause runtime bottlenecks. In response, we propose Bucket MapReduce, a system that utilizes bucket sort and pipeline parallelism to improve the performance of Hadoop MapReduce. Several parameters may influence the performance in Bucket MapReduce, and thus we will further discuss parameter tuning. In this paper, we perform experiments on TeraSort benchmark. Bucket MapReduce successfully reduces local disk $I/O$ by 61% and improves the runtime by 1. 39x in 800GB TeraSort benchmark.
Development of a Vision Based Parking Monitoring System Using Quadrotor UAV

Parking spaces are now more than ever in demand as the volume of cars increase as the years go by. Different parking spaces apply certain ways to determine whether the parking lot is full or not. Image processing is being used in many applications face detection, objection detection, and many more. Having seen the wide range of uses, image processing can be used to detect cars in a certain parking lot together with a UAV that can hover above a parking lot. A UAV is a type of machine has the flight capability whether it is four motors or six motors. These types of bots are controlled by a pilot in the certain desired area. Different things can be accomplished when combining the two developments in technology the world has available today.
Congestion Detection in Wireless Sensor Networks Based on Artificial Neural Network and Support Vector Machine

A wireless sensor network (WSN) monitors a certain phenomenon through interconnecting voluminous quantity of interconnected sensor nodes that are implemented in the sensing space and event. Transmission reliability is the key contributing factor in of WSN. Network latency, data packet loss, throughput reduction, and low energy efficiency is caused by a congested network. To solve the network congestion issue, a proposed strategy of congestion detection using ANN (artificial neural network) in comparison to SVM (support vector machine) is presented in this paper. The number of sensor nodes, traffic rate, and node retention are the parameter used. These were generated from the WSN setup in the lettuce smart farm located in Rizal, Philippines. Network Simulator 2 (NS-2) is used for network design, traffic generation, and data gathering. The 3-layer feedforward ANN is optimized using scaled conjugate gradient with sigmoidal function as output activation. SVM is configured using box constraint of 564.22 and kernel scale of 0.1475. There is 0.00776625 CE performance (cross-entropy) and 98.8% accuracy through network training. Conversely, SVM shows 95.18% accuracy. Thus, the developed ANN-based WSN congestion detection model with +3.62% accuracy is an effective tool for agricultural wireless network where array of sensors for crop and environmental monitoring is connected.
A Review of Query Optimization Techniques for Complex Event Processing

Complex Event Processing (CEP) Systems is a technology that is used in many modern fields of application such as finance and business analysis. It tracks and analyses data from large information streams pertaining to a string of related or non-related events in order to identify patterns and relations that could be used to derive useful connections among seemingly unrelated factors within its applications. CEP systems make use of pattern queries to match identified events within an event stream. However, due to the generalized nature of CEP query languages and the lack of general structure and semantics, it is difficult to write queries that function optimally to deliver the expected results within the required time frames. This issue is particularly of importance as CEP systems often deal with time sensitive data and hence require rapid processing in order to output useful information and hence, defines the importance and requirement for query optimization techniques that may be applied to CEP systems. This paper focuses on research publications related to the four main pattern query optimization techniques, namely, Multi-Query Optimization, Join Query Optimization, Nested Query Processing techniques and Query Rewriting as well as their applications within modern CEP systems. This study further aims to identify possible limitations within the four techniques mentioned previously and advise on possible measures that may be taken to further improve these techniques in order to offer greater efficiency and stability to pattern query processing within CEP systems.