Miracle-dz/UniFAR-FTUs huggingface dataset:
PRECURSOR ROUTE PENTACENE METAL-INSULATOR-SEMICONDUCTOR FIELD-EFFECT TRANSISTORS

Metal‐insulator‐semiconductor field‐effect transistors have been constructed with pentacene as the active semiconductor. The pentacene is processed by spin coating from a soluble precursor. A simple thermal conversion yields transistors with carrier mobilities as high as 9×10−3 cm2 V−1 s−1 and current modulations of the order of 105. Depletion of charge is essential to the device operation. Data for an invertor exhibiting voltage amplification are presented.
SCA:Segmentation based adversarial Color Attack

Deep Neural Networks (DNNs) are widely used nowadays. Some researchers have found an effective way to do harm to the DNNs, which is called adversarial examples. Via the way of generating the adversarial examples can divide the attack into two directions. Part of the researchers is focusing on generating imperceptible images whose noise is restricted by Lp- Norm. However, these adversarial examples have poor transferability. Therefore, some researchers are focusing on generating images with large unrestricted corruption. However, existing unrestricted images are noticeable, arousing human suspicion. Among them, the attack that modifies the color of images may generate images with better image quality. In this work, we proposed an unrestricted adversarial attack that modifies the color of original images with different strengths in each region. The regions are segmented by a segmentation network. The experiment shows that our method has strong attack strength with a quite high image quality compared with existing unrestricted methods.
Miniaturized Dual-Band Half-Mode Vivaldi Antenna

A dual-band antenna with small size is proposed in this communication. The proposed antenna consists of half a palm- shape-edge radiating arm, two rows of metallized vias and a pair of rhombus patches. Based on mirror image theory, the induced current on the electric wall in the antenna system can be equivalent to the current in the symmetric position of the radiation arm. The half-mode (HM) Vivaldi structure can effectively reduce the antenna size. Rhombus patches are introduced to enhance the radiation performance of the dielectric notch radiator. The dual- band operation of the antenna is obtained by loading with dumbbell defect-ground-structure (DB-DGS) resonators under the microstrip line. Rogers RO 4003 is used as the dielectric substrate for this antenna, and the entire size is only 20 × 36mm2. For the simulation results, the operating frequencies of the antenna are 7.03GHz to 10.83GHz and 22.95GHz to 29.89GHz respectively. The proposed antenna achieves the gains of 4.5 dB and 8 dB at low frequency and high frequency, respectively. The overall performance of the antenna is good.
A Systematic Constellation Design for BICM Systems With Geometric Shaping

We investigate the application of geometric constellation shaping for standard bit-interleaved coded modulation (BICM) systems employing Gray labeling and high-order quadrature amplitude modulation (QAM). We focus on the non-uniform Gaussian-like distributions, and analyze their performance in terms of the mutual information as well as more practical bit-wise mutual information (BMI). We also propose a new systematic constellation design approach based on truncated Gaussian distribution, where a single parameter balances the two extreme constellations represented by uniform and Gaussian distributions. We also evaluate the peak-to-average power ratio (PAPR) of the resulting constellations, where a good trade-off between the BMI and PAPR can be achieved by optimizing the associated parameter. Simulation results based on the conventional Gray-labeled BICM with binary turbo codes show that a noticeable shaping gain can be achieved by the proposed constellation design.
AP Connection Method Considering Interference for Maximizing System Throughput Using Potential Game

For multi-transmission rate environments, access point (AP) connection methods have been proposed for maximizing system throughput, which is the throughput of an entire system, on the basis of the cooperative behavior of users. These methods derive optimal positions for the cooperative behavior of users, which means that new users move to improve the system throughput when connecting to an AP. Typically, the arrival of two or more new users causes signal interference for other users. Specifically, traditional methods have not considered interference and the number of allowable mobile positions is so large that it is difficult to find the optimal position to maximize system throughput. In this paper, we propose an AP connection method that maximizes system throughput by considering interference between users, with a potential game model. This game can find at least one optimal user position. Moreover, numerical analyses show the effectiveness of our method.
Remaining Useful Life and State of Health Assessment for Lithium Ion Batteries Using CNN-BILSTM-DNN Hybrid Method

Accurate prediction of Remaining Useful Life (RUL) and State of Health (SOH) of lithium-ion batteries play an increasingly crucial role in intelligent battery health management systems. It also serves as a battery failure early warning system. For electrical vehicles, lithium-ion batteries serve as the primary energy source. Li-ion battery safety requires the use of a battery management system (BMS), which typically rests on RUL and SOH. This work suggests a hybrid method, consisting of a Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Deep Neural Network (DNN), to estimate the remaining useful life (RUL) and state of health (SOH) of the battery. A comparative analysis has been done with another existing hybrid method consisting of a Convolutional Neural Network, Long Short-Term Memory, and Deep Neural Network. Three analytical indices are chosen to evaluate the prediction results numerically. They are MAE, R2, and RMSE. The suggested method is experimented with and validated on the NASA lithium-ion battery health dataset. When compared with the existing method, it is observed that the suggested technique has greater accuracy.
Windage Losses Calculation and Performance Estimation for Wet Airgap PM Machines

With an increased need for high power density machines, especially for the motor-driven pump application, flooded airgap offers a good solution. However, the main disadvantage related to this configuration is the high windage loss generated at the air-gap due to the viscosity of the fluid, which can result in very high temperatures on the rotor surface and drag forces on the rotating components. This required an accurate prediction of windage losses to estimate the reduction in mechanical output torque and to evaluate exactly the heat generated on the rotor surface, which can cause a series of problems like the demagnetization of the magnet in PM machines. Also to accurately estimate the machine's performance. Therefore, this paper investigates the impact of machine design parameters on the windage losses generated inside the airgap of high speed machines, and the resulting variations in the machine efficiency are presented and described. Both theoretical and finite element modeling have been used in the analysis.
Identifying Defective Fruits and Vegetables with Hyper-spectral Images: A Brief Tutorial

Horticulture is the branch of agriculture that addresses the crop of fruits and vegetables. This art of cultivating natural products has been a critical research area that helped to satisfy human's food consumption. The enhancement of quality control has improved the cost-effectiveness and increased the financial profit of the production. This study presents a processing pipeline architecture that assesses the quality of fruits and vegetables through image processing. The solution involves hyper-spectral imaging, image enhancement, image binarization, and median filtering. Our experiments demonstrate that our image processing pipeline proposal successfully identifies the defective regions in a publicly available dataset that contains images of apples and onions.
AI assisted pothole detection and depth estimation

AI-assisted engineering solutions integrated with commercial RGB sensors and computationally intensive Graphical Processing Units (GPUs) promise a low-cost solution, to prevent deterioration of premature pavement disintegration. Potholes a common pavement distress are a severe threat to road safety and demand time and cost-effective state-of-the-art technologies for road inspection and condition monitoring. An intelligent pavement pothole detection system is proposed in this study by modifying the single stage CNN architecture-RetinaNet to detect potholes and perform metrological studies using 3D vision. The photogrammetric technique of structure from motion based on image frames extracted from pavement video recordings is used to model the 3D point cloud structure of potholes to assess the severity of the detected potholes as a function of its depth and is integrated with the CNN based pothole detection system. High F1 scores on benchmark dataset with a high value of 0.98, validate the model’s performance. A mean error below 5% is obtained on the measured depths thus promising an intelligent and practical solution to be implemented as part of a potential pavement health assessment system for future practice.
Musify: An application for Aspect Analysis and Visualization of Spotify’s song data

Spotify is a leading music streaming application, with over 180 million users. Its algorithm is designed in a manner that users never run out of relevant songs. Authors in this research work aim to extract, analyze, and visualize how it handles music charts, playlists and the criteria behind song recommendation and studying various components & parameters of music over the years. In this research work, authors have used the Spotify developer platform and built a new application called "Musify" and took a small dataset to analyze separately. The output is shown in terms of various charts representing the changes in music over the years, the components of a song, the various hits of different artists etc.
Addressing the Unbounded Latency of Best-Effort Device-to-Device Communication with Low Earth Orbit Satellite Support

We propose SAMAC, a Device-to-Device (D2D) communication scheme that can exploit Low Earth Orbit (LEO) satellites as temporarily-available infrastructure with intermediate round-trip-times for supporting D2D medium access of highly mobile ground nodes such as vehicles. We demonstrate how such a scheme can be kept backwards-compatible to, e.g., IEEE 802.11p and we demonstrate analytically and in computer simulations that, compared to unassisted D2D communication or relaying, in situations where a LEO satellite is available it can improve performance in terms of all of throughput, latency bounds, and reliability.
DDOS Attack Detection and Prevention using the Bat Optimized Load Distribution Algorithm in Cloud

Cloud computing provides a great platform for the users to utilize the various computational services in order accomplish their requests. However it is difficult to utilize the computational storage services for the file handling due to the increased protection issues. Here Distributed Denial of Service (DDoS) attacks are the most commonly found attack which will prevent from cloud service utilization. Thus it is confirmed that the DDoS attack detection and load balancing in cloud are most extreme issues which needs to be concerned more for the improved performance. This attained in this research work by measuring up the trust factors of virtual machines in order to predict the most trustable VMs which will be combined together to form the trustable source vector. After trust evaluation, in this work Bat algorithm is utilized for the optimal load distribution which will predict the optimal VM resource for the task allocation with the concern of budget. This method is most useful in the process of detecting the DDoS attacks happening on the VM resources. Finally prevention of DDOS attacks are performed by introducing the Fuzzy Extreme Learning Machine Classifier which will learn the cloud resource setup details based on which DDoS attack detection can be prevented. The overall performance of the suggested study design is performed in a Java simulation model to demonstrate the superiority of the proposed algorithm over the current research method.
A Review on Machine Learning Techniques for DDoS Attack Detection in IoT

In today’s era, the demand for global connectivity has made researchers establish such a wireless network that provides around the clock connectivity for information exchange and maximum coverage. Here, the coverage involves both large and small-scale organizations. This coverage has given a chance to malicious practitioners to steal sensitive information by performing network attacks. Amongst this large pool of network attacks, this paper identifies that, the distributed denial of service (DDoS) attack is such an attack that goes undetected. In recent years, various researchers have come up with different solutions to detect this attack. So, this paper reviews the existing solutions proposed by the research community to detect such attacks using modern technology such as deep learning and supervised machine learning techniques. Further, this paper performs the comparative analysis of the existing techniques in terms of accuracy, where deep learning models were found to be effective in detecting such attacks.
A Q-learning-based Multipath Scheduler for Data Transmission Optimization in Heterogeneous Wireless Networks

In the era of 5G and beyond, mobile devices usually can access several heterogeneous wireless networks (e.g., Wi-Fi and 5G). To simultaneously and efficiently utilize the accessible network resources, muli path transport protocols, such as MPTCP and MPQUIC, have shown much potential. In these protocols, scheduling is one of the critical processes to ensure the performance of the multipath transmission. Although there have been many proposed multipath schedulers in the literature, they have not performed well in heterogeneous networks, especially when the network conditions vary (i.e., dynamicity). In this paper, we propose a novel Q-learning-based Multipath scheduler for data transmission optimization (Q-SAT), aiming to bypass the existing limitation. By leveraging the self-learning ability of reinforcement learning, Q-SAT can instantly observe environmental changes and deploy appropriate path selection to optimize data transmission time. As a result, Q-SAT efficiently schedules multipath communication in heterogeneous wireless networks with different dynamicity levels. We have implemented Q-SAT with MPQUIC and extensively evaluated Q-SAT in an emulated environment and a real network. The evaluation results show that Q-SAT improves the data transmission time by at least 10% in the emulation and 26% in the actual deployment compared to the state-of-the-art schedulers.
An approach for efficient vehicular tracking in internet of vehicles

The Internet of Vehicles (IoV) is a use case of the Internet of Things (IoT), where the urban vehicle fleet forms a worldwide network. Quality of Service (QoS) optimization in IoV is a challenging task, since vehicles are in continuous movement, which causes the network to be unstable and in continuous topology change, this creates a hostile environment with many variables that should be taken in consideration. Location based routing protocols have proven to be the most performing in such an environment, they offer high packet delivery with low delay, but they also encounter some issues such as routing loops and bandwidth overload. In the present paper, we study existing solutions and propose a novel optimal location based routing algorithm named Minimum Hops Routing (MHR). For experimental study, we compared the proposed MHR algorithm with existing solutions from the state of the art. The experimental results show that the proposed MHR solution is more efficient than existing solutions in terms of reliability enhancement and delay.
Dialectical thinking is linked with smaller bilateral nucleus accumbens and right amygdala: the mediating role of sensitivity to reward

Our current work examined the interface of thinking style and mental health at both behavioral and neuropsychological levels which describe a predisposition to psychopathology. Thirty-nine Chinese participants were divided into high and low holistic thinkers based on the triad task scores, completed the Sensitivity to Punishment and Sensitivity to Reward Questionnaire (SPSRQ), and performed
Indoor Localization Based on Sparse TDOA Fingerprints

Fingerprint-based indoor localization methods usually use received signal strength (RSS) and channel status information (CSI) as the localization fingerprint, which suffers from time-consuming and labor-intensive site survey. In this paper, we propose an indoor localization method based on sparse time difference of arrival (TDOA) fingerprints. This method constructs the localization fingerprints by TDOA, which is calibrated by the straight line fitting method and the beacon estimation method. In order to get the dense fingerprint database, we propose a TDOA interpolation method based on distance relation. Experiments on field measurements validate the effectiveness of the proposed method. In the case of only sampling three reference points (RPs), the average localization error (ALE) of the proposed method reaches 0.824 m, which obtains a 48.8 % improvement compared with the traditional TDOA algorithm,
Multi-UAV Assisted Network Coverage Optimization for Rescue Operations using Reinforcement Learning

Mobile communication networks could make a significant difference in rescuing affected people in post-disaster scenarios. However, the existing communication infrastructures tend to be out of service in such scenarios. To solve this issue, Unmanned Aerial Vehicles (UAVs) could be launched as flying base stations to provide the required coverage to Rescue Members (RMs) and allow them to communicate and transmit crucial information through the established links. Meanwhile, with the unpredictable movements of RMs, three serious issues are affecting the deployment of UAVs: (i) the control of their mobility, (ii) their limited energy capacity, and (iii) their restricted communication ranges. Aiming to address these issues, we propose deploying an intelligent connected group of energy-efficient UAVs assisting RMs and providing them communication coverage in the long run. These requirements are satisfied using a deep reinforcement learning strategy to learn the environment dynamics and make good trajectory decisions. Simulation experiments have demonstrated the potential of our framework compared to baseline methods to provide temporary communication networks for emergency response teams during disaster relief missions.
A driver-based approach for DMA transfer between FPGA-GPU

Many recent HPC clusters employ GPUs as accelerators to improve their performance per watt, but the power wall is still there. The use of FPGAs draws attention to achieving more power efficiency because the users can design their application-specific custom accelerator. The hybrid approach with GPUs and FPGAs is also a hopeful solution. Still the data transfer between FPGA and GPU is a significant bottleneck because there is no native support for direct FPGA-GPU DMA transfers. This paper shows the implementation and evaluation of two direct FPGA-GPU data transfer methods. By mapping FPGA memory on the BAR address space, the GPU's DMA gained only 300 MB/s, which was slower than indirect transfer through the host's main memory. The second method maps GPU memory on the BAR address space by NVIDIA GPUDirect API, and the FPGA's DMA initiates data transfer. Although GPU-to-FPGA data transfer is not still succeeded with this method, the FPGA-to-GPU bandwidth on the Gen3 x4 bus reached 3.25 GB/s: more than 80% of peak bandwidth. This result outperformed FPGA-Host-GPU transfer by 1.6x, even faster than Host-GPU DMA transfer. Since the second method is implemented by a small extension on Xilinx's XDMA driver, it is expected to be portable for other FPGA DMA controllers.
Customized Deep CNN for Foliar Disease Prediction Based on Features Extracted from Apple Tree Leaves Images

India is an agricultural country, almost 80% of rural people are depending on agriculture only. From the years a lot of technologies evolved that are used in designing various machineries, identifying the suitable crops based on the soil by testing the soil etc. Even though a lot of information technology-based evolvements done in the agriculture field, still it needs to be improved to have a better crop yield, to identify various diseases due to which the crop effects, fertilizers and pesticides to be used etc. Apple is one of the natural crop products, which is effected with various diseases causing the reduction in the crop yielding. If the diseases are identified at early stages and treated properly, the crop yielding can be increased by that price can be reduced and it can make available to even poor people. A customized deep CNN was developed to predict the foliar diseases in apple trees by studying various features in the leaf images. The dataset consists a total of 3642 apple leaf images belongs to 4 classes. Healthy, Multiple disease, Rust and Scab are the class labels. The efficiency of developed customized deep convolutional neural network was studied by implementing the network with various epoch values. The proposed customized CNN model performs with accuracies 0.9376 and 0.9290 on training and validation sets with the number of epochs 30 respectively. From the results obtained, it is noticeable that the trained model predicts the foliar disease in apple tree leaves correctly. In future, the process of predicting the foliar disease in apple tree leaves can be automated by the development of mobile apps and by incorporating the model in IOT.
Blockage Prediction Using Exhaustive Beam-Pair Scan in mmWave Networks: An Experimental Study

This paper experimentally demonstrates a novel blockage prediction based on exhaustive beam-pair scans in millimeter wave (mmWave) networks toward 6G. In particular, the prediction uses the signal strength of all transmit/receive beam-pairs through a two-sided scan rather than that of all transmit beams through the one-sided scan assumed in existing studies. It is also possible to predict future line-of-sight (LOS) blockages. The mapping from the signal strength of all beam pairs to future blockages was learned with LightGBM. The experimental results confirm that the blockage prediction through a two-sided scan of all beam pairs is more accurate than that through a one-sided scan.
On-line Estimation Method of Junction Temperature Based on SiC Module Aging Parameter Identification

Accurate evaluation of junction temperature is extremely important to improve the safety and reliability of SiC modules. However, it is difficult to obtain the internal junction temperature of SiC modules in real time, limited by sensors and testing methods. The junction temperature estimation based on the thermal network model combined with an integrated negative thermal coefficient (NTC) thermistor is an effective evaluation method. The existing thermal network models omit effects of the solder layer fatigue aging due to thermal stress, resulting in junction temperature estimation error. In this paper, a junction temperature estimation method is proposed, which establish the relationship between NTC sensor temperature and the thermal resistances of chip solder layer and bottom layer. The junction temperature and NTC sensor temperature of the SiC module is expressed using the equation of state analysis to obtain a discrete-time system of temperature. The aging thermal resistance parameters of the thermal network model can be identified online, and the accurate online estimation of different aging conditions inside the module can be realized.