Miracle-dz/UniFAR-FTUs huggingface dataset:
Trajectory Tracking Control of Nonlinear Singularly Perturbed Systems With Disturbances

In order to solve the control problem of singularly perturbed systems subject to external disturbances, a novel composite control scheme based on the disturbance observer and the singular perturbation technique is proposed. To reduce the system order and facilitate the controller design, the singular perturbation technique is first utilized to de-compose the original high-order system into two lower order subsystems. Subsequently, nonlinear disturbance observers are proposed for reduced order slow subsystem and the reduced order fast subsystem respectively. Using the estimated disturbance information, two composite controllers are developed for the reduced-order subsystems, and the uniformly ultimately boundedness of subsystems is guaranteed. Moreover, the stability of the closed-loop high-order system is also proved via the Lyapunov approach, even though the observers and controllers are developed for the reduced-order subsystems. The effectiveness of the proposed control scheme is demonstrated by simulations.
Design of a Variable Stiffness Actuator Based on Variable Radius Mechanisms*

Variable stiffness actuators (VSAs) can effectively protect humans in case of an accidental collision owing to the adjustable compliance. This paper proposes a variable radius approach to develop a variable stiffness actuator with a large stiffness range based on the antagonism principle. Unlike traditional antagonistic VSAs, the stiffness control and position control are decoupled, and stiffness adjustment is achieved through the simultaneous regulation of spring stiffness and joint radius. Specifically, the regulation of joint radius is achieved by using a variable configuration mechanism (VCM). A quadratic spring is constructed using a variable radius pulley (VRP). A novel profile function of the VRP is proposed, and mathematical models of joint stiffness are established. A VSA prototype is developed to evaluate the performance, and an experiment is carried out to verify the effectiveness of the proposed quadratic spring. Joint position and stiffness are driven by actuators with position control strategies. The results of the stiffness experiment show that the stiffness can be effectively adjusted in a large range by adjusting the joint radius and the stiffness of the quadratic spring.
A Novel Partial Joint Processing Architecture for distributed Massive MIMO

We propose a new partial joint processing architecture for distributed massive multiple-input multiple-output (MIMO) networks. As opposed to the traditional full-joint processing architecture, where the channel coefficients of all the users within the cooperating cluster are learnt at the base stations, in the proposed architecture, we allow each base station to learn the channel coefficients only of the users that maintain a strong average received signal-to-noise ratio to that base station based on a predefined threshold. This threshold provides extra flexibility, trading-off channel estimation for performance. We assume a zero-forcing receiver at the central processing unit using estimated channels and unknown terms are set to zero. We then derive an accurate approximation for the instantaneous received signal-to-interference-and-noise ratio of an arbitrary user. We use this approximation to derive closed-form expressions for the achievable rate and symbol error probability of an arbitrary user. Numerical examples are used to illustrate the accuracy of the analysis.
City Gas Pipeline Leakage Detection Based on Infrared Imaging Studies

Pipelines are one of the most important modes of transportation for urban natural gas, the security is paramount. In order to study on gas leakage problem of city gas pipeline, according to the principle of similarity to build experimental platform, in this paper, an infrared imager was selected for gas leak detection experiments on gas pipelines, control variable method is used to design a set of comparative experiments, by separate changing light conditions, defect size, and temperature conditions, verify the impact of these conditions change detection results of the experiment. Experiments show that the infrared imaging technology can effectively detect the urban natural gas pipeline leakage, and points out the factors that affect the test results.
MetoidS: Hybrid K-Medoids-Meta Heuristic Clustering-Based Routing Optimization in Vehicular Ad-Hoc Networks

Clustering plays a vital role in establishing a more stable global network topology in Vehicular Ad Hoc NETworks (VANETs) and supports Intelligent Transportation Systems (ITS) applications and message routing. However, due to the unstable infrastructure of VANETs, cluster size and geographical span have a significant impact on maintaining cluster stability and network efficiency. Thus, this paper presents a hybrid machine learning (ML) and meta-heuristics (MH) based routing scheme called MetoidS to support scalability, enhance the stability of the network topology, and provide efficient routing. We incorporate vehicle orientation-based unsupervised clustering and population based MH to provide a new direction to the taxonomy of the approaches to handling efficient route discovery and cluster maintenance challenges. To represent a real-world simulation of our approach, we have conducted the experiments using a combination of four frameworks (i.e., OMNeT++, SUMO, VEINS, and INET) that demonstrate better performance in terms of high cluster stability, enhanced throughput, high packet delivery ratio, and minimizes average transmission delay compared to the existing routing protocols used in this research.
Secure Key Exchange and Transmission Design Using Artificial Noise Injection in OFDM Systems

Orthogonal frequency division multiplexing (OFDM) is such a vital waveform scheme that is utilized in fifth-generation (5G) communication systems as well as the previous one. Since the usage scenarios of 5G are quite wide, the security of those systems is currently an open and critical issue. In this paper, a promising paradigm is proposed for enhancing physical layer (PHY) security in OFDM systems. Two independent secret keys are extracted by legitimate users quantizing the channel measurements. Those key sequences are exchanged via a novel artificial noise (AN) injection method which does not result in a key mismatch. Bit error rate (BER) versus signal-to-noise ratio (SNR) performance of the adversary is still low even if the malicious node has better channel than the legitimate user. After agreement upon the same key, both legitimate parties directly employ key sequence and inter-carrier interference matrix caused by the local oscillators to produce a secrecy matrix. This matrix enables secure communication by inserting it into the transmission design.
DC to 12+GHz, +30dBm OIP3, 7.2dB Noise Figure Active Balun in 130nm BiCMOS for RF Sampling Multi-Gbps Data Converters

An active balun using dual stage feedback and distributed feedforward distortion cancellation for use as a driver amplifier for wideband RF sampling ADCs is presented. With a gain of 16.5dB & HD2 of 60dBc while delivering 3dBm (100Ω), the DC coupled balun achieves a linear-bandwidth of >12GHz holding OIP3>27dBm & NF<8dB, and small-signal bandwidth of 18GHz with <±0.5dB amplitude & <±2.5° phase imbalance. Cascaded balun + ADC measurements demonstrate no linearity limitation, while allowing for 19.5dB lower input signal. Occupying 1mm2in a 130nm BiCMOS process, the device consumes 100mA from 5V in a 2×2mm2 flip chip QFN package.
Predictive Model of Student Academic Performance in Private Higher Education Institution (Case in Undergraduate Management Program)

A private university must consider many things in accepting prospective students. Students enrolled are expected to stay until their studies are completed, have good academic performance, and be able to graduate on time. Private universities, from the beginning of the admission of new students, it is necessary to choose which prospective students are accepted to achieve the quality of education goals in the study program. This work aims to study the prediction class and class order of variable importance to students’ length of stay and academic performance labeled graduation. The method adopted falls into a technique called feature extraction. This study uses rank methods information gain and gain ratio to confront other methods χ2 and random forest. A dataset of 7676 observations, spanning the years from 2010-2021, students from a management program of a private university in Indonesia, is used. This study collects data from the faculty-specific department from the university’s academic admissions as inputs. The result of the study shows that all techniques vote IP/GPA (IP) as the most critical feature in predicting length of stay and graduation. Origin of High School, Selection Test Score, and Gender get split votes. This study is unique because it sheds light on the case particularity to Indonesia.
Deep Learning CNN Implementation on Packed Malware for Cloud Cross Domain Solution Filters

This research focuses on Windows Portable Executable (PE) packed malware detection and Deep Learning (DL) using the Convolutional Neural Network (CNN) algorithm. Our primary goal is to improve the usage of DL techniques in Cybersecurity to strengthen the defenses against cyberattacks on U.S. Department of Defense (DoD) systems. According to our hypothesis, existing Cross Domain Solutions (CDSs) can be upgraded to include built-in DL-CNN algorithms for identifying well-crafted packed malware. To put this into perspective, implementing DL-CNN into the Cross Domain Solution (CDS) filter software will significantly enhance the effectiveness and detection of packed malware. CDSs are strategically positioned between unclassified and classified systems, and with DL-CNN capabilities, the CDS virus detection filter will learn to detect malware on its own, regardless of whether the malware is well-crafted, packed, or encrypted. Using our trained model, we were able to identify Windows packed PE malicious executables from Windows packed PE benign executables with an average training accuracy of 94 percent and a validation accuracy of 93 percent. Although the DL-CNN algorithm’s results could be enhanced through further development and refinement using KerasTuner, this research provides a solid foundation. Our experiments were conducted on our lab computer system and in the Amazon SageMaker Studio Lab and Google Collab cloud environments.
Experimental Investigations on Aerodynamic Performances of NACA0012 Airfoil

The aerodynamic characteristics for the various angles of attack at different Reynolds numbers with a view to extending the knowledge of the pressure distribution over the NACA 0012 airfoil have been measured in a low-speed subsonic wind tunnel. Experiments were carried out by varying the angle of attack from −30 to +30 degrees and the velocity varied from 10 m/s to 25 m/s. The lift coefficient and drag coefficient for the NACA 0012 airfoil are obtained by measuring the pressure distribution from the pressure ports situated over the airfoil surface.
Levenberg-Marquardt Backpropagation model augmented with Prim's algorithm approach (LMBP) to minimize power in FSM synthesis

Recent advancements in intelligent learning enhance the logic synthesis domain, especially for low power minimization. This paper presents a low power synthesis by implementing AI with conventional greedy Prim's algorithm. Initially, by applying Prim's algorithm, an MST (Minimum Spanning Tree) has been formed from the undirected state transition graph, which has been obtained from a conventional FSM benchmark with the help of a probability method. After finding the MST, the minimum power is found from a weighted Hamming distance by state encoding from the SIS (synthesis for sequential circuit) tool, which is an NP-Hard problem and thus time-consuming. This motivates us to apply the AI integrated technique with the greedy algorithm. The database has been initially generated, and with that Levenberg-Marquardt back propagation model is trained to replace the SIS tool to predict the power for multi-level realizations. The results are impressive, and the model is even more efficient for prediction. Experimental results can show that the augmented-based framework speed up the process by two times compared to the basic simulation-based process.
A Simple 36-Pulse Rectifier With Passive Pulse-Tripling Circuit at the DC Side

A series-connected 36-pulse rectifier with passive pulse-tripling circuit (PPTC) on the dc side is proposed in this article. The proposed rectifier consists of a series-connected 12-pulse rectifier and a PPTC. The PPTC includes a passive current forming network, a single-phase auxiliary transformer with dual secondary windings and two auxiliary single-phase rectifiers (ASRs). One ASR in PPTC is connected in series with the load to directly modulate the output current of rectifier bridges. The other ASR in PPTC is connected in parallel with the load to modulate the output current of rectifier bridges according to the relationship between its input voltage and load voltage. Composite modulation of the two ASRs increases the pulse number of the rectifier from 12 pulses to 36 pulses. The resulting rectifier obtains an approximate sinusoidal input current with less than 5% THD. Only a few low-capacity passive components are sufficient given the absence of active components in the proposed scheme. The proposed scheme presents advantages of simple circuit structure, easy implementation, and high reliability. Detailed analysis and experimental results are provided to verify the effectiveness of the proposed scheme.
Workflow Scheduling Using Hybrid PSO-GA Algorithm in Serverless Edge Computing for the Internet of Things

In this paper, we design a task scheduling scheme for Internet of Things (IoT) workflow applications in serverless edge computing. Notice the fact that complex applications in traditional serverless computing are decomposed into several stateless, dependent functions, whose execution environments are pre-deployed at the resource-finite edge domain, we model the workflow application as Directed Acyclic Graph (DAG) by considering the distribution of edge resources and the deployment of serverless functions. We further formulate the scheduling problem as a multi-objective optimization problem to reduce the time consumption, energy consumption, and cost simultaneously. Then, considering the diversity of solution space and the fast convergence to optimal solutions, an improved hybrid algorithm that combines Particle Swarm Optimization and Genetic Algorithm (PSO–GA) is introduced and utilized to make the scheduling decision. Finally, extensive simulation experiments are conducted to validate the superiority of the proposed scheme.
An LSTM-based Intent Detector for Conversational Recommender Systems

With the rapid development of artificial intelligence (AI), many companies are moving towards automating their services using automated conversational agents. Dialogue-based conversational recommender agents, in particular, have gained much attention recently. The successful development of such systems in the case of natural language input is conditioned by the ability to understand the users’ utterances. Predicting the users’ intents allows the system to adjust its dialogue strategy and gradually upgrade its preference profile. Nevertheless, little work has investigated this problem so far. This paper proposes an LSTM-based Neural Network model and compares its performance to seven baseline Machine Learning (ML) classifiers. Experiments on a new publicly available dataset revealed The superiority of the LSTM model with 95% Accuracy and 94% F1-score on the full dataset despite the relatively small dataset size (9300 messages and 17 intents) and label imbalance.
Strategic Placement of PV and WTG in Low Power Distribution System for Improving System Efficacy

It is common for distributed generation (DG) systems to have a capacity of several megawatts, which may be utilized to generate electricity close to users. Consider that DGs are linked to the network at ideal locations. As a result, they will reduce network losses, improve voltage profile, increase network reliability, and gain from investment deferral for distribution feeder up-gradation. The simultaneous deployment of wind turbine generators (WTG) and solar photovoltaic (PV) generators are addressed in this work, considering their uncertain power generation characteristics for a day. DGs' placement and sizing issues were then addressed using Tunicate Swarm Algorithm (TSA), an evolutionary algorithm; incorporated into the IEEE 33-bus radial distribution system. Simulated findings demonstrate an improved voltage profile, increased minimum security margin by 7.8%, network loss reduction of 22.7%, and feeder investment deferral by a minimum of 13 years for distribution feeder up-gradation.
Research on Anti-jamming Algorithm for Beidou High-precision Positioning

This work investigates the primary anti-interference adaptive filtering methods in the first place in light of the vulnerability of the Beidou receiver to interference, based on the interference experienced by the Beidou navigation satellite system. Second, various adaptive filtering algorithms' technical features are contrasted. Finally, a study and analysis of the time-space joint anti-jamming method is conducted. Broadband and narrowband interference can be suppressed simultaneously based on the antenna array element.
A Two-Stage Adaptive Channel Estimation Scheme for Millimeter-Wave Massive MIMO Communication

In millimeter-wave (mmWave) communication, accurate acquisition of channel state information is vital in enabling reliable beamforming transmission of multiple data-streams in both single-user and multi-user scenarios. Previously, compressive sensing (CS) techniques have been widely exploited for channel estimation purpose, thanks to the sparse nature of mmWave channels. However, most of CS schemes are non-adaptive and show poor estimation performance when the signal-to-noise ratio is low. Enhanced CS-based schemes, such as multi-stage adaptive CS scheme have also been proposed, yet the achievable performance is still unsatisfactory under multi-path environments. In this paper, we advance the existing studies and propose an improved two-stage adaptive channel estimation (TSACE) scheme. In the first stage, a certain number of random sensing measurements are performed over the entire space. Given the initial measurements, the posterior probability distribution of possible combinations of angular supports for the sparse channel are computed. In the second stage, adapting to the posterior information obtained, narrow beams are then deployed to probe a small set of most likely angular directions, based on which the channel estimation is performed. Numerical results validate the effectiveness of TSACE proposed and confirm its performance gain over a few CS baselines subject to the same total average number of measurements under multi-path environments.
Analyzing the Impact of Age and Gender for Targeted Advertisements Prediction Model

The practice of targeted advertisements has been gaining popularity, especially in this digital era. There are a lot of aspects to take into consideration when creating an efficiently targeted advertisement, such as advertisement details and user backgrounds. Using this information can increase the likelihood of sending the right advertisements to the right demographic. In this paper, we will explore which features have an influence towards the click-through rate of these targeted advertisements. The best models in our experiment are LightGBM and XGBoost with the ROC-AUC score of 0.76 for LightGBM and 0.78 for XGboost. Adding age and gender can improve the results. Our experiment can be insightful for making a better marketing strategy to reach more segmented users in display advertisements.
Parallel Processing Framework for Efficient Computation of Analyst Consensus Estimates and Measurement of Forecast Accuracy

Forecasting of earnings is an integral component in the valuation of companies. Financial analysts provide such forecasts in the form of earnings estimates. Academic study has shown analyst forecasts to be more accurate than timeseries forecasts. Historically this has been based on a consensus forecast computed as the mean of analyst forecasts. In our research we consider alternative methods of aggregating consensus forecasts. We take inspiration from iterative filtering methods from Physics, as applied to other fields such as the aggregation of sensor readings and online reviews. In this paper we discuss the challenges of adapting iterative filtering algorithms to the aggregation of analyst earnings estimates. This encompasses modelling as well as technological challenges. We present our solution to the afore-mentioned challenges and develop a general framework for the systematic assessment of consensus aggregation algorithms. We show that a naïve implementation of this computation takes approximately 4 days to complete. Our framework performing the same computation takes a significantly reduced time of approximately 2 hours. We then apply this framework to the assessment of iterative filtering algorithms in the context of aggregating consensus earnings estimates. We present preliminary results of our study of the application of iterative filtering algorithms against a simple mean consensus.
Deep Learning-Based Signal-to-Noise Ratio Prediction for Realistic Wireless Communication

Artificial intelligence (AI) based channel state information (CSI) prediction for frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems have attracted growing attention recently. Accurate channel prediction can effectively improve the quality of CSI and can help optimize system transmission schemes, such as the throughput and transmission efficiency. The aim of this paper is to propose an efficient deep learning algorithm for signal-to-noise ratio (SNR) prediction in the real world, and a method for measuring SNR data from a universal software radio peripheral (USRP)based software-defined radio platform. The results verify that the proposed channel measurement method is efficient for getting real-world channel data, and the deep learning-based algorithm has a strong ability on the real-world channel prediction.