Miracle-dz/UniFAR-FTUs huggingface dataset:
Soil moisture estimation using Simulated NISAR Dual Polarimetric GRD Product over croplands

Synthetic Aperture Radar (SAR) has immense potential in estimating soil moisture with high-resolution imaging capability and cloud independent acquisition ability. Nevertheless, estimation of soil moisture under vegetation cover is a challenging task. Notably, existing literature used ancillary data sources, such as optical data, to segregate the vegetation contribution in the backscatter. In this study, we propose a new Ground Range Detected (GRD) radar vegetation index for dual-pol data, DpRVIc that overcomes the typical shortcomings (such as cloud cover, asynchronous observations and saturation effect for denser canopies) associated with different optical data derived indices. This proposed descriptor jointly utilizes the copol purity of the wave and normalized co-pol intensity parameter. We then use this index in the Water Cloud Model to estimate soil moisture over croplands. Furthermore, the performance of DpRVIc is compared with Normalized Difference Vegetation Index (NDVI) by utilizing the simulated NISAR L-band dual-pol data (VV-VH, HH-HV) over a Canadian test site. The proposed method has proven to be a potential alternative to synergetic approaches with Root Mean Square Error (RMSE) ranging from 5.6% to 6.0% with DpRVIc as a vegetation descriptor. Thus, the proposed vegetation descriptor provides new insights to quantify the vegetation using dual-pol GRD SAR data. Further, the adapted soil moisture technique has opened up a new avenue for soil moisture estimation using dual-pol GRD SAR data in the presence of vegetation.
Transfer learning on VGG16 for the Classification of Potato Leaves Infected by Blight Diseases

According to the FAO of the UN, availability, access, utilization, and stability are the four pillars of food security that largely depend on sufficient, safe, and nutritious food. Detecting plant disease in advance might be a measure of resilience to the future disruption or unavailability of food supply. Due to the notable performance through highly accurate mechanization, deep learning-based methods have been applied to automatically identify and diagnose plant disease that can improve efficiency and productivity. The work prioritizes Transfer Learning of VGG16 for predicting potato blight disease. The model’s weights are pretrained on ImageNet, which can be extracted from specific features of small datasets. The implemented approach presents a significant performance improvement on a self-prepared dataset. After completing the necessary training and testing process, 96.88% accuracy was achieved by the model. Experimental results are compared with well-established models, which concludes that the model performs better in classifying potato leaves blight diseases.
Novel Clustering Technique for Monitoring Crop Phenology Using SAR Data

Unsupervised clustering is essential when in-hand information about crop phenological stages are not available. In addition, this technique assists in discriminating crop growth stages concerning the changes in the scattering mechanisms. Newly proposed parameter $\theta_{FP}$ for full polarimetric Synthetic Aperture Radar (SAR) data is shown to be enhancing the target characterization capability over the existing parameter such as $\overline{\alpha}$. Following this, the existing clustering technique by combinedly utilizing the Barakat degree of polarization and elements of the Kennaugh matrix along with scattering entropy HFP is effective over land cover classes. The clustering scheme is consisting of 12 zones where zones (Zl, Z2, Z3), (Z10, Zll, Z12), and (Z4, Z5, Z6, Z7, Z8, Z9) represent even, odd and multiple scattering types, respectively. In this study, we use the RADARSAT-2 FP SAR data from Jun to Nov 2019 acquired over the Indian test site of Vijayawada under the Joint Experiment for Crop Assessment and Monitoring (JECAM) initiative. The changes and shifts of the clusters are exciting according to the phenological development of rice fields. At the initial stage, dense clusters are evident in Z10, Zll, and Z12, while, during the advanced tillering stage, more data points are found in the Z3 zone. Following this, clusters in zone Z5, Z6, Z8 and Z9 are evident during the maturity stage.
Current Insights into the Investigations on a New Motor Principle with Radially Laminated Stator Sheets, Concentrated Windings and Flux Barriers

In the wake of the Paris Climate Agreement and the world wide obligation to reduce CO2 emissions, products and their manufacturing processes must be optimized in order to reduce resource consumption. This particularly concerns the topic of electric drives and machines. Regarding machines, one possibility to achieve this goal is the use of so-called flux barriers. This working concept offers further possibilities for structural optimization, such as radial lamination of the sheets, which then allows the use of grain-oriented electrical steel. Both mentioned principles increase the efficiency of the machine to a high degree and reduce manufacturing costs, as the iron losses are lessened and the material utilization is raised. This paper presents recent investigations of the use of grain-oriented electrical steel sheets for novel, radially laminated stators for electric motors with concentrated windings and flux barriers. The focus is on the analysis of the electromagnetic properties of the electrical sheets before and after forming, their coating and the operating characteristics of the planned demonstrator machine. Recent findings in the area of the technical implementation of the demonstrator machine and its electromagnetic design with the aid of FEM simulation are presented. Furthermore, challenges are evaluated with regard to eddy currents that arise due the innovative design. Finally, recommendations for further proceeding are derived and the next steps are explained.
Model Predictive Control for a Multisource Inverter in Electrical Vehicle Applications

The classical voltage source inverter (VSI) is a suitable topology for electrical vehicles (EVs) due to low cost, high power density, and simple control. However, VSI indicates some restrictions due to the wide range of electrical machine performance and high power demand requirements. Recently, one concept called multisource inverter (MSI) has been introduced, which uses two independent DC sources to respond to different ranges of electrical vehicle speed. In this research, a model predictive control (MPC) is applied to the MSI that drives a permanent magnet synchronous machine (PMSM). The simulation results show that the proposed MPC to drive EVs that use MSI as a traction inverter possesses good advantages such as quick performance without any distortion in speed signal and also MPC uses a good transition to reach high voltage levels to respond to the high speeds of EV.
A Comprehensive Survey on Cooperative Intersection Management for Heterogeneous Connected Vehicles

Nowadays, with the advancement of technology, world is trending toward high mobility and dynamics. In this context, intersection management (IM) as one of the most crucial elements of the transportation sector demands high attention. Today, road entities including infrastructures, vulnerable road users (VRUs) such as motorcycles, moped, scooters, pedestrians, bicycles, and other types of vehicles such as trucks, buses, cars, emergency vehicles, and railway vehicles like trains or trams are able to communicate cooperatively using vehicle-to-everything (V2X) communications and provide traffic safety, efficiency, infotainment and ecological improvements. In this paper, we take into account different types of intersections in terms of signalized, semi-autonomous (hybrid) and autonomous intersections and conduct a comprehensive survey on various intersection management methods for heterogeneous connected vehicles (CVs). We consider heterogeneous classes of vehicles such as road and rail vehicles as well as VRUs including bicycles, scooters and motorcycles. All kinds of intersection goals, modeling, coordination architectures, scheduling policies are thoroughly discussed. Signalized and semi-autonomous intersections are assessed with respect to these parameters. We especially focus on autonomous intersection management (AIM) and categorize this section based on four major goals involving safety, efficiency, infotainment and environment. Each intersection goal provides an in-depth investigation on the corresponding literature from the aforementioned perspectives. Moreover, robustness and resiliency of IM are explored from diverse points of view encompassing sensors, information management and sharing, planning universal scheme, heterogeneous collaboration, vehicle classification, quality measurement, external factors, intersection types, localization faults, communication anomalies and channel optimization, synchronization, vehicle dynamics and model mismatch, model uncertainties, recovery, security and privacy.
Hierarchical Risk Parity and Minimum Variance Portfolio Design on NIFTY 50 Stocks

Portfolio design and optimization have been always an area of research that has attracted a lot of attention from researchers from the finance domain. Designing an optimum portfolio is a complex task since it involves accurate forecasting of future stock returns and risks and making a suitable tradeoff between them. This paper proposes a systematic approach to designing portfolios using two algorithms, the critical line algorithm, and the hierarchical risk parity algorithm on eight sectors of the Indian stock market. While the portfolios are designed using the stock price data from Jan 1, 2016, to Dec 31, 2020, they are tested on the data from Jan 1, 2021, to Aug 26, 2021. The backtesting results of the portfolios indicate while the performance of the CLA algorithm is superior on the training data, the HRP algorithm has outperformed the CLA algorithm on the test data.
Dead-beat Predictive Control of Permanent Magnet Synchronous Motors with Improved Dead-time Compensation

Deadbeat predictive control has attracted wide attention in recent years due to its advantages of fast dynamic response. In order to improve the low-speed steady-state performance of dead-beat predictive control for permanent magnet synchronous machines(PMSMs), a novel dead-time compensation method that can simultaneously compensate the dead-time error voltage and eliminate the phenomenon of zero-current clamping was proposed in this paper. Firstly, the voltage error caused by the dead-time and the nonlinearity of inverter were analyzed; secondly, a low-pass filter was used to eliminate the high-frequency noise signal in the phase current; finally, the compensation voltage was calculated by combining the current polarity judgment and optimized sinusoidal switching function. In this paper, an experimental comparison study is carried out. The results show that the proposed method can effectively reduce the current distortion caused by the dead-time and suppress the zero-current clamping phenomenon.
Enhancing Peer Assessment Validity with Engagement Behaviors: A Structural Equation Modeling Approach

Peer Assessment is an essential activity in massive open online courses (MOOCs) to provide students with evaluation and feedback in a timely manner. However, the lack of instructor involvement and the diverse student population have raised the concern regarding its credibility, and there has been insufficient validity evidence to support the interpretation and refinement of current peer assessment approach. The present paper proposed a comprehensive framework to examine the validity evidence of peer assessment scores in MOOCs by analyzing the interplay among peer assessment ratings, instructor ratings, and various peer engagement behaviors in MOOCs. The study results can extend our understanding of peer assessment approach and inform its future usage and refinement in MOOCs.
Research on Locomotive Speed Sensor Fault Diagnosis Method Based on Neural Network

For fault diagnosis of locomotive speed sensor,a predictor method based on RBF neural network is established,and diagnostic decision method is also proposed.The RBF neural network predictor and three kinds of fault mode of locomotive speed sensor are simulated by MATLAB.The simulation results indicate that this method can diagnose faults of the speed sensor online exactly,and provides a new idea for locomotive speed sensor fault diagnosis.
A Multiple Time-Scales Based Multi-state Co-estimation Method for Lithium-ion Battery

As the core power source of electric vehicles (EVs), the lithium-ion batteries are supposed to work in safe and efficient condition, and an effective and stable battery management system (BMS) is the premise. In order to assure the batteries are working in an appropriate condition, the BMS need to be designed with the capability of monitoring the working information and internal states of battery. In this paper, considering the variation characteristics of the state of charge (SOC), the state of health (SOH) and the state of power (SOP) at different time scales and the coupling relationship between them, a multiple time scales based multi-state estimation method for lithium-ion batteries is proposed. First, an integrated model-based SOC estimation method is used for real time battery parameters identification and SOC estimation. Second, the capacity is estimated in a large time scale based on the combination of coulomb counting method and the SOC estimation method. Then, on the premise of knowing the internal parameters and states of the battery, the instantaneous peak charge/discharge power and continuous charge/discharge power are estimated under a variety of constrains. Finally, the urban dynamometer driving schedule (UDDS) test is carried out for verification. Results show that the proposed method can estimate battery states in high accuracy.
Dynamic Time Division Duplexing for Green Internet of Things

Motivated by the promising benefit of time division duplexing (TDD) in interference mitigation, this paper investigates the dynamic TDD problem for green Internet of Things (IoT), where IoT devices and access points (APs) are distributed in an indoor network coverage area and the network traffic is time-varying. The problem is formulated in which each AP optimizes its radio frame configuration (RFC) to maximize a network utility in terms of the throughput in the long term. Considering the time-varying network traffic of uplink and downlink transmissions, we approach with deep reinforcement learning (DRL), an emerging tool in wireless network control, to train the optimal RFC policy for each AP. Simulation results reveal that our approach improves the data rate by approximately 30%, compared to those of existing schemes.
Reinforcement Learning Based Dynamic Adaptive Video Streaming for Multi-client over NDN

The performance of Dynamic Adaptive Streaming (DAS) in multi-client scenarios can be improved by taking advantage of the aggregation capability of Named Data Networking (NDN). In this paper, we propose a client-side reinforcement learning based (RL) ABR algorithm for NDN that can achieve proactive aggregation of requests among clients as much as possible without requiring coordinating with other clients or scheduling by a central controller. We model the interaction process between the DAS client and the network as a Markov decision process. Then, the appropriate states and rewards are selected to decide on the Markov decision process through the reinforcement learning algorithm. Through constant training, the reinforcement learning algorithm is able to guide the client to request the same video bitrate, namely request aggregation, thereby reducing repetitive traffic and achieving fairness. Compared with the existing solutions, through experiments in multi-client video distribution scenarios, the RL algorithm performs well in the overall Quality of Experience (QoE), fairness, and aggregation rate, etc.
NCA: Accelerating Network Caching with eXpress Data Path

In-memory network caching is a common solution to relieve pressure on databases and applications. However, the in-memory caching applications such as redis and memcached cannot achieve maximum performance due to the bottleneck inherent in the linux network stack. While most of the solutions today rely on kernel-bypass approaches or reengineering of applications, little attention has been given to in-kernel processing. In this article, we present NCA (Network Caching Accelerator), an in-kernel cache to release overhead brought by the linux network stack, enabling flexibility and customization without bypassing the kernel that was unthinkable before. NCA can process the cache requests as early as possible in the linux network, bringing significant performance gains without modifying the cache application itself. It works in concert with the linux kernel, enabling reuse of the security model of linux kernel. Additionally, NCA uses little CPU resources, leaving valuable CPU cycles for other applications and processes on the host. We evaluate NCA using redis as an example, and find that redis with NCA can achieve three times the throughput of the original redis. Besides, other caching applications can also use NCA to improve their performance.
Retinal Blood Vessels Segmentation of Diabetic Retinopathy

Now a day Diabetic Retinopathy (DR) could be a common eye infection in diabetic patients. Because DR is a type of diabetes that can cause vision loss, it is critical to ensure early detection and legitimate treatment. The Location of these injuries plays a critical part in early determination of DR. This work proposes a differential evolution algorithm and robotized injury discovery conspire which comprises of the four primary Pre-processing, candidate injury detection, and post-processing are the steps involved in vessel extraction and optic plate expulsion. The optic circle and the blood vessels are smothered to encourage preparing. Curvelet change is utilized for dim injury improvement and Matched filter is utilized for shinning injury improvement. To determine the best values for the parameters and portion the candidate areas, an ANFIS method calculation is used. The diabetic retinopathy images were collected from DRIVE the ANFIS (Adaptive Neuro Fuzzy Interference system) strategy is analyzed utilizing the measurements of Precision. In comparison to the results of the Mixture model-based clustering, Logistic regression classifier algorithm, the proposed method for detection of retinal blood vessel segmentation had a recognition accuracy value of 98.5 percent. Using the ANFIS method, the Logistic regression classifier algorithm outperformed the Mixture model-based clustering in detecting diabetic retinopathy. Micro aneurysm detection, one of the first symptoms of Diabetic Retinopathy, can be predicted and compared in future work. This detection technique is useful for diabetic patients.
STING: Self-attention based Time-series Imputation Networks using GAN

Time series data are ubiquitous in real-world applications. However, one of the most common problems is that the time series could have missing values by the inherent nature of the data collection process. So imputing missing values from multivariate (correlated) time series is imperative to improve a prediction performance while making an accurate data-driven decision. Conventional works for imputation simply delete missing values or fill them based on mean/zero. Although recent works based on deep neural networks have shown remarkable results, they still have a limitation to capture the complex generation process of multivariate time series. In this paper, we propose a novel imputation method for multivariate time series, called STING (Self-attention based Time-series Imputation Networks using GAN). We take advantage of generative adversarial networks and bidirectional recurrent neural networks to learn the latent representations of time series. In addition, we introduce a novel attention mechanism to capture the weighted correlations of a whole sequence and avoid the potential bias brought by unrelated ones. The experimental results on three real-world datasets demonstrate that STING outperforms the existing state-of-the-art methods in terms of imputation accuracy as well as downstream tasks with the imputed values therein.
Combustion Modeling for Real-Time Simulation of Diesel Engine

Through the analysis of the combustion process,a reasonable combustion model is built for 4JB1 diesel engine,which is part of the work to develop a hardware in-the-loop real-time simulation system.For the real-time purpose,its proposed as a zero dimension model based on Wiebe model and Watson model,which makes it easy to figure out the parameters and proves to be consistent with the real heat release rate.The comparison between the measured data and simulation results demonstrates that the model works well in the real-time simulation system.
Continuous Multi-modal Emotion Prediction in Video based on Recurrent Neural Network Variants with Attention

Automatic perception and understanding of human emotion is becoming an increasingly attractive research field in artificial intelligence and human-computer interaction. Emotion portrayal within conversation plays a significant role in the semantics of a sentence. However, emotion is not only biologically determined but is also influenced by the environment. Therefore, cultural differences exist in some aspects of emotions, and it is important for the next generation of computer systems to adapt the cross-cultural difference in order to enable more naturalistic interactions between humans and machines. In this paper, we investigate the suitability of state-of-the-art deep learning architectures based on recurrent neural network (RNN) variants with explicit attention modelling to bridge the gap across different cultures (German and Hungarian) for emotion prediction in video. Three different attention based network architectures are proposed in this work:- early attention fusion, extended multi-attention fusion and attention-based encoder-decoder. Our RNN variants with explicit attention modelling approach achieves very promising Concordance Correlation Coefficient results, which outperform the baseline on Arousal of 0.637 vs. 0.614 (baseline), for Valence of 0.689 vs. 0.615 and for Liking of 0.625 vs. 0.222.
Real-time EEG-based Driver's Fatigue Detection System using Deep Neural Network

Driver fatigne is often the direct cause of many road accidents. Therefore, developing an accurate system that senses and informs the driver of his inadequate psychophysical situation is necessary. The Electroencepbalogram (EEG)-based two-channel real-time test was conducted on 30 healthy subjects on a driving simulator to monitor the significant change in the subject's EEG signal, which happens during the transition of the subject's normal state to a normal drowsiness state. To discriminate the correct parts of the signal from the acquired EEG signal, we use preprocessing. The features were extracted using deep neural networks and defining an optimal set of characteristics and representing the signal in the frequency-time domain. The k-NN (k-Nearest neighbor), and SVM (Support vector machine) classification methods are used to achieve high classification performance, considering the differences between the controllers. The algorithm we propose in this paper uses a discrete wavelet transform to eliminate noise in the data. The classification accuracy achieved was 79.7% using the proposed system. Hence, there is a possibility to identify and distinguish the state of drowsiness of the driver. This advantage contributes towards safe and more relaxed driving.