Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint optimization scheme based on beam selection and interference cancellation in lens millimeter wave NOMA systems

Non-orthogonal multiple access (NOMA) has been integrated in millimeter-wave (mmWave) Massive MIMO systems to further enhance the spectrum efficiency, but NOMA has not been fully considered in lens mmWave systems. The fusion of these two technologies requires the joint design of beam selection and interference cancellation. In addition, when users follow the spatial cluster distribution, although the user clustering schemes based on K-means algorithm have been applied, the influence of the virtual and actual cluster center users on achievable sum rate has not been differentiated and analyzed in detail. To solve the above problems, a joint optimization scheme is proposed to maximize the achievable sum rate. The optimization problem is NP-hard, which is solved by using the divide-and-conquer approach. Concretely, based on the signal power loss analysis of directional deviation, a beam selection algorithm is designed for inter-cluster interference cancellation based on the K-means algorithm. Further for intra-cluster interference cancellation, a power allocation algorithm based on the bisection method is designed to guarantee the fairness of users in each cluster. The simulation results show that the proposed scheme offers a significant performance improvement in terms of both achievable sum rate and energy efficiency, and it is suitable for the large-scale user scenario due to its low complexity.
FPL-UDA: Filtered Pseudo Label-Based Unsupervised Cross-Modality Adaptation for Vestibular Schwannoma Segmentation

Automatic segmentation of Vestibular Schwannoma (VS) from Magnetic Resonance Imaging (MRI) will help patient management and improve clinical workflow. This paper aims to adapt a model trained with annotated ceT1 images to segment VS from hrT2 images, without annotations of the latter. The proposed method is named as Filtered Pseudo Label-based Unsupervised Domain Adaptation (FPL-UDA) and consists of three components: 1) an image translator converting hrT2 images to pseudo ceT1 images, where a two-stage translation strategy is proposed to deal with images with VS in various sizes, 2) a pseudo label generator trained with ceT1 images to provide pseudo labels for the pseudo ceT1 images, where a GAN-based data augmentation method is proposed to deal with the domain gap between them, and 3) a final segmentor trained with hrT2 images and the corresponding pseudo labels, where an uncertainty-based filtering is used to select high-quality pseudo labels to improve the segmentor’s robustness. Experimental results with a public VS dataset showed that our method achieved an average Dice of 81.52% for VS segmentation from hrT2 images, which outperformed existing unsupervised cross-modality adaptation methods.
Intelligent task offloading and collaborative computation in multi-UAV-enabled mobile edge computing

This article establishes a three-tier mobile edge computing (MEC) network, which takes into account the cooperation between unmanned aerial vehicles (UAVs). In this MEC network, we aim to minimize the processing delay of tasks by jointly optimizing the deployment of UAVs and offloading decisions, while meeting the computing capacity constraint of UAVs. However, the resulting optimization problem is nonconvex, which cannot be solved by general optimization tools in an effective and efficient way. To this end, we propose a two-layer optimization algorithm to tackle the non-convexity of the problem by capitalizing on alternating optimization. In the upper level algorithm, we rely on differential evolution (DE) learning algorithm to solve the deployment of the UAVs. In the lower level algorithm, we exploit distributed deep neural network (DDNN) to generate offloading decisions. Numerical results demonstrate that the two-layer optimization algorithm can effectively obtain the near-optimal deployment of UAVs and offloading strategy with low complexity.
Satellite Image Segmentation using Modified U-Net Convolutional Networks

The object detection in satellite imagery is a primary and elaborate one receiving lot of interest in latest years and performs an essential function for wide range of applications. After the massive fulfillment of Deep learning techniques in computer imaginative and prescient discipline, they're presently being studied in the context of satellite imagery for unique functions like object identification, object tracking, object classification, semantic segmentation of aerial/satellite images. Although diverse assessment research associated with object detection from satellite/aerial imagery are carried out, this observation provides an assessment of the latest development in the discipline of object detection from satellite imagery with the use of deep learning. This paper elaborates the detection of roads, buildings, solar panels and vehicles using Modified U-Net Convolutional networks and achieves more accuracy compared to the previous ones.
Ensembling Framework for Pneumonia Detection in Chest X-ray images

Pleural effusion, a condition of fluid filled lungs is caused by pneumonia. This results in difficulty in breathing. Early pneumonia diagnosis is essential for curative treatment and to decrease mortality rates. The most commonly used technique for pneumonia diagnosis is chest X-ray imaging. Chest X-ray analysis is a demanding task as it requires high level of expertise. Various deep learning approaches including Convolutional Neural Network (CNN), Capsule Network (CAPSNet), VDSNet and Visual Geometry Group (VGG) are used for lung disease identification. CNN performs poorlyin case of tilted, rotated, or other irregular image orientation. Therefore, in this paper, an ensemble framework is proposed with two classifiers VDSNet and CAPSNet using a snapshot ensemble scheme with cyclic cosine learning rate. The proposed ensemble framework is evaluated on Kermany data set. From the results, it is evident that the proposed snapshot ensembling model performs better than individual CNN models in detecting pneumonia from chest X-ray images.
A Novel Multi-task Learning Method with Attention Mechanism for Wind Turbine Blades Imbalance Fault Diagnosis

With the rapid development of wind power generation in recent years, wind power plays a more and more important role in the modern power system. Accurate and efficient condition monitoring and fault diagnosis of wind turbines (WTs) is essential to ensure the safe and stable operation of wind farm and the stability of power system. Because of the characteristic of wind power, the imbalance fault caused by ice accretion on WT blades poses a threat to the reliable operation of WT. To this end, a multi-task learning method with attention mechanism has been proposed in this paper to address the problem of imbalance fault diagnosis of WT. The proposed model can complete the diagnosis of fault type and ice accretion level at the same time. The performance of the proposed model is evaluated on a simulation dataset. The diagnosis results show that the proposed framework can reach a state-of-the-art performance compared with other approaches, which can strongly illustrate that the proposed multi-task framework with attention mechanism can improve the effectiveness of each task.
A Deep Learning Based Approach for Detection of Face Mask Wearing using YOLO V3-tiny Over CNN with Improved Accuracy

Aim: The objective is to build an efficient face mask detector using YOLO V3-tiny. Materials and Methods: The algorithm used to detect face masks is novel YOLO V3-tiny in comparison with Convolutional Neural Network (CNN), the dataset used was (“Facemask Detection Dataset”) the sample size was 136. Results: Novel YOLO V3-tiny gets accuracy of 95% and for CNN it was 84%. On the basis of the network’s original two-scale prediction target, a scale is added to create a three-scale prediction, which can improve the accuracy of detecting small targets such as masks. The YOLO V3-tiny and CNN have a statistically significant independent sample t-test value (p0.001) with a 95 percent confidence level. Conclusion: face mask detection in YOLO V3-tiny has a significantly better accuracy than CNN.
The Golden Ratio in Machine Learning

Gradient descent has been a central training prin-ciple for artificial neural networks from the early beginnings to today's deep learning networks. The most common imple-mentation is the backpropagation algorithm for training feed-forward neural networks in a supervised fashion. A drawback of backpropagation has been the search required to find optimal values of two important training parameters, learning rate and momentum weight. The learning rate specifies the step size towards a minimum of the loss function when following the gradient, while the momentum weight considers previous weight changes when updating current weights. Using both parameters in conjunction with each other generally improves training, although their specific values do not follow immediately from standard backpropagation theory. This paper proposes a new information-theoretical loss function based on cross-entropy for which it derives a specific learning rate and momentum weight. Many training procedures based on backpropagation use cross-entropy directly as their loss function. Instead, this paper investigates a dual process model with two processes, in which one process minimizes the Kullback-Leibler divergence while its dual counterpart minimizes the Shannon entropy. The golden ratio plays an important role here, allowing to derive theoretical values for the learning rate and momentum weight, matching closely the values traditionally used in the literature, which are determined empirically. To validate this information-theoretical approach further, classification results for a handwritten digit recognition task are presented, showing that the proposed loss function, in conjunction with the derived learning rate and momentum weight, works in practice.
Comparative Analysis of Simulators for IoT Applications in Fog/Cloud Computing

In order to make people's life easier, a significant number of Internet of Things (IoT) applications have become a part of a global world. The internet of things is strongly intertwined to cloud computing, and sensor data handling is one of the most urgent issues with cloud computing. Sensors and other cloud-connected smart devices are frequently used to operate IoT-Cloud systems; as a result, these devices generate a large volume of data that must be preserved and processed efficiently. Simulators have the advantage of allowing complex systems to be explored without the requirement to purchase and install physical resources. The purpose of this article is to look at several cloud implementation simulators and see which ones are best for today's IoT demands. With the moving of the trend and computing needs, this paper provides a brief examination of the different most widely used simulators in the research field by categorising the simulators into three categories: cloud, edge, and fog simulators. Finally, various future research direction in accordance with the simulator's evolution is given.
Prediction of Cognitive Scores by Movie-Watching FMRI Connectivity and Eye Movement Via Spectral Graph Convolutions

Brain functional connectivity has been demonstrated to serve as a "fingerprint" to predict individual behaviors and phenotypes. A precise mapping between them could provide insightful clues to brain architectures and the generation of cognition. In this context, the naturalistic paradigm provides more engaging conditions and richer fMRI information, and both preserves or even enhances individual features and increases sensitivity to phenotypic measures, compared with other functional MRI modalities including resting-state and task paradigms. However, to the best of our knowledge, only linear methods were developed for predicting phenotypic measures from brain activity under naturalistic stimulus, while the brain activity is highly dynamic and nonlinear. Hence, we adopted the nonlinear graph convolutional network (GCN) to predict cognition-related phenotypic score from brain functional connectivity under naturalistic stimulus, where subjects are the nodes and functional connectivity is node feature. The behavior patterns of eye movement were integrated into this method to estimate similarity across subjects and define the graph edges. A few nodes are labeled by their phenotypic score, and the model is trained to predict the scores of those unlabeled nodes. The prediction accuracy of this method outperforms those from the linear classification method, resting-state based functional node feature and random edge tests.
Optimal Scheduling of Electricity-Gas Integrated Energy System Considering Wind Power Uncertainty

In recent years, the proportion of wind power in power grid has increased rapidly, and the uncertainty of wind power brings challenges to the smooth operation of power grid. This paper establishes an optimal dispatch model for an electricity-gas integrated energy system (EGIES) with power-to-gas (P2G) technique considering the uncertainty of wind power by minimizing the operating cost of the system. Firstly, the wind power probability model is established based on nonparametric kernel density estimation. The wind power scenario is generated by Monte Carlo simulation method, and the typical wind power scenario is obtained by backward scenario reduction technology. Secondly, an optimization model is established to minimize the operation cost of EGIES. Finally, a six-bus power system and a seven-node natural gas system are used as the coupling system, and the gas turbine and the P2G are used as the coupling nodes. The necessity and economy of considering the uncertainty of wind power in the optimal scheduling of EGIES are verified.
A Multi-dimensional Copula Wind Speed Correction Method for Ultra-Short-Term Wind Power Prediction

The focus of this paper is how to make comprehensive use of numerical weather prediction (NWP) and real power data to correct the forecasted wind speed, to improve the accuracy of ultra-short-term wind power prediction. Firstly, the multi-dimensional Copula model is built by 3 time series: current wind speed, previous wind speed error and current wind speed error. Determinate the first two values, and the current wind speed error can be obtained by gradually solving the conditional probability. Then, use the current wind speed error to correct and replace forecasted wind speed in NWP. Finally, take corrected NWP as the input of Long Short-Term Memory (LSTM), and take real power as the output. After selecting appropriate parameters to train the LSTM model, this hybrid model can be used to predict the ultra-short-term wind power. Compared with Autoregressive Integrated Moving Average (ARIMA), which only considers the time-series characteristics of wind speed error, this hybrid model can bring 2.3% reduction of mean absolute percentage error in prediction.
Kevert szövegelemzési módszertan alkalmazása gazdasági és társadalmi jelenségek vizsgálatához - Online CSR-kommunikáció vizsgálata tartalomelemzéssel és diskurzuselemzéssel

A vallalati tarsadalmi felelőssegvallalas kerdese egy fontos es időszerű tema, ami erzekelhetően jelen van a hazai gazdasagi eletben is. Azonban ha melyebben meg szeretnenk vizsgalni a jelenseget, szamos kerdessel lehet talalkozni. Az egyik az, hogy mennyire altalanos jelenseg ez a hazai kozep- es nagyvallalatok honlap-kommunikaciojaban, illetve, hogy mit is takar ez a vallalati felelőssegvallalasra, a vallalatok tarsadalmi szerepere valo hivatkozas. Ez a kerdesfelvetes jol vizsgalhato a szovegelemzes modszereivel. A szerző a cikkben bemutatja, hogy a kevert modszertanu kutatas a kvantitativ tartalomelemzessel ragadja meg azt, hogy mit kommunikalnak a hazai kozep- es nagyvallalatok a tarsadalmi felelőssegvallalasuk kapcsan es erre milyen tenyezők vannak hatassal. Tovabba a kvalitativ diskurzuselemzes modszerevel vizsgalja, hogy milyen tarsadalmi szerepeket tematizalnak, milyen szerepkeszleteket es diskurziv eljarasokat hasznalnak a honlap-kommunikaciojukban.
Enhancement of Slotted Circular Patch Antenna ith Different Substrate

:-This paper presents the design and simulation of broadband microstrip patch antenna using High Frequency Structure Simulator (HFSS). The two different configuration of broadband microstrip patch antenna, rectangular patch antenna & circular patch antenna are analyzed. The performance of designed antennas are analyzed in terms of return loss and gain. The substrate used in these two configurations is RT Duroid having dielectric constant 2.2. The result calculated is return loss of rectangular patch & C slot rectangular patch are -10.768 db& -13.4457db and gain of rectangular patch & C shape patch are 5.6197db& 5.7457db respectively. The calculated result for return loss of circular patch & C slot circular patch are -27.7852 db& -32.4685 db and gain of circular patch & C shape patch are 11.0708 db& 11.1834db respectively.
Reconstruction of Resting State FMRI Using LSTM Variational Auto-Encoder on Subcortical Surface to Detect Epilepsy

Functional MRI offers unique insights for the characterization and presurgical evaluation of people with epilepsy (PWE). In this paper, we develop a graph-based variational auto-encoder (gVAEs) to 1) learn the patterns of resting state functional MRI (rsfMRI) within the brain’s subcortical structures in healthy subjects and 2) reconstruct it in PWE to identify findings unique to patients with epilepsy. The gVAE was enriched with Sequential Long Short Term Memory (LSTM) and perceptual loss to learn temporal rsfMRI features and smooth the reconstructed signals. Using a cross-validation approach on healthy controls, our best model yielded an average spatial correlation of 0.791 and an average temporal correlation of 0.793. When applied to PWE, the average and spatial correlation decreased to 0.752 and 0.750 respectively. Our findings pave the path to the development of a whole brain data-driven tool that may be valuable for the characterization of abnormalities within the epileptic brain. This may advance our understanding as to how these abnormalities are related to the location of seizure onset and can inform the care of patients with epilepsy. The code is available at: GitHub
Intrusion Detection System in Network using Decision Tree

In recent decades, researchers have been working on improving intrusion detection systems (IDS). Computer networks can be detected as potentially harmful by using Genetic Algorithms (GAs). A series of classification rules has been generated based on various features of connection data, such as duration and types of connections. Detection of data originating from attackers or originals is easily done in this proposed work. This proposed work is to detect the data which is received from attacker or non-attacker node.
Modelling the effect of individual hearing impairment on sound localisation in sagittal planes

Normal-hearing (NH) listeners use monaural spectral cues to localize sound sources in sagittal planes, including up-down and front-back directions. The salience of monaural spectral cues is determined by the spectral resolution and the dynamic range of the auditory system. Both factors are commonly degraded in impaired auditory systems. In order to simulate the effects of outer hair cell (OHC) dysfunction and loss of auditory nerve (AN) fibres on localisation performance, we incorporated a well-established model of the auditory periphery [Zilany et al., 2014, J. Acoust. Soc. Am. 135] into a recent model of sound localisation in sagittal planes [Baumgartner et al., 2014, J. Acoust. Soc. Am. 136]. The model was evaluated for NH listeners and then applied on conditions simulating various degrees of OHC dysfunction. The predicted localisation performance is hardly affected by a moderate OHC dysfunction but drastically degrades in case of a severe OHC dysfunction. When further applied on conditions simulating loss of AN fibres with specific spontaneous rates (SRs), predicted localisation performance degrades if only high-SR fibres are preserved.
Integrated Different Fingerprint Identification and Classification Systems based Deep Learning

Fingerprint classification plays a fundamental role in fingerprint identification systems because it dramatically reduces the time spent identifying an individual. This classification can be based on gender, as fingerprints can be used to identify and differentiate between genders. The gender classification based on fingerprints is a crucial step in different applications such as airports, banks, explosions and natural disasters, forensic anthropology for identifying the gender of the offender and reducing the list of suspects' searches. It helps in efficiently analyzing the data and helping to sort the data. This study proposes a new idea based on integrating the Wiener filter, multi-level Histogram techniques with three Convolutional Neural Networks for classifying and identifying fingerprints in terms of gender, hand, and fingers utilizing a publicly accessible benchmark of a SOCOFing dataset. The fingerprint dataset augmentation and pre-processing are applied for expanding and enhancing the dataset. Then, the features were extracted using convolutional neural networks followed by Softmax as a classifier. It is worth mentioning that the dropout layer is used to prevent overfitting. Evaluation results have proven the high performance of accuracies: 99.96% for identifying the gender (Male or Female), 99.93% for identifying the hand type (Right or Left), and 99.90% for identifying the fingers.
Thermal modeling of RF FinFET PAs through temperature-dependent X-parameters extracted from physics-based simulations

FinFET RF technology is increasingly investigated for the deployment of 5G communication systems and beyond. Thermal management represents one of the main problems that must be addressed for the successful design of FinFET-based microwave power amplifiers. In fact, the peculiar 3D device structure inhibits the efficient heat dissipation through the substrate, requiring an accurate temperature-dependent nonlinear device model. In this paper we extract the FinFET X-parameters from accurate temperature dependent physics-based simulations. The T -dependent X-parameters are then included into Keysight ADS using a dedicated isolated port to represent the device junction temperature. With this modeling approach, we present the T -dependent analysis of a low-power FinFET amplifier at 70 GHz, showing that with T increasing from 300 K to 390 K we have more than 15% Psat loss, 2 dB gain degradation and 10 percentage points efficiency loss.
A Deep Learning Based Novel Approach for Detection of Face Mask Wearing using Enhanced Single Shot Detector (SSD) over Convolutional Neural Network (CNN) with Improved Accuracy

The objective is to build an efficient face mask detector using Single Shot Detector (SSD). The algorithm used for face mask detection was a novel SSD and with the comparison of Convolutional Neural Network (CNN). The face mask detection dataset was usedand the ability of the algorithm was measured with the sample size of 136. SSD has achieved accuracy of 92.25% and for CNN it was 82.6%. By using a base architecture of VGG-16, SSD was able to outperform other object detectors like CNN without compromising speed and accuracy. The SSD and CNN are statistically satisfied with the independent sample t-test value (p<0.05) with a confidence level of 95%. Face mask detection using SSD was significantly better accurate than CNN.