Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Power Allocation Scheme for NOMA Uplink in Cognitive Radio Networks Using Deep Q Learning

Non-orthogonal multiple access (NOMA) is a promising technique to satisfy a host of access demand and provide higher throughput. It enables that multiple users are multiplexed with different power levels by using superposition coding at the transmitter side and successive interference cancellation at the receiver side. In this paper, we deal with the long-term throughput maximization of an uplink NOMA in the cognitive radio network (CRN). The secondary users (SUs) have limited capacity battery, hence, SUs equipped with an energy harvester can harvest energy from solar sources to prolong their operations. Particularly, a combination of NOMA and time division multiple access (TDMA) is proposed in order to reduce the complexity of massive wireless communication systems. By taking into account the practical applications, a deep Q learning algorithm is employed to maximize the long-term throughput of the system, where the agent (i.e secondary base station (SBS)) can interact with an environment to learn about system dynamics. As a result, the SBS learns how to allocate optimal transmission energy to SUs in each time slot. Simulation results demonstrate that the proposed scheme can achieve better performance than conventional schemes.
Power Control Algorithm for Throughput Enhancement in Full-Duplex Communications

Full-duplex (FD) communication transmits and receives packets simultaneously at the same channel. The FD communication is possible thanks to self-interference cancellation (SIC) technology that has developed. However, in the asymmetric FD pair, the interference should be carefully considered to fully utilize FD communication. In this paper, we propose a power control algorithm to improve throughput performance. The access point (AP) controls the transmission power of the AP and the uplink (UL) node. The simulation results show that the throughput performance of the proposed algorithm is higher than that of other algorithms.
Performance evaluation for 5G FWA in Quasi Static mmWave Channels: OFDM vs SC-QAM

Fixed wireless access (FWA) is an alternative to fiber-to-the-home services thanks to new 5G cellular networks. It is one of the alternatives for the services offered in the new integrated access and backhaul (IAB) architecture. These point-to-point links take place generally in a quasi-static wireless channel. We perform link-level simulations with complete transmitter and receiver signal processing chains to evaluate the performance of orthogonal frequency-division multiplexing (OFDM) and single-carrier quadrature amplitude modulation (SC-QAM) in millimeter wave channels using QuaDRiGa tool for the channel generation. Results from Urban-macro line-of-sight (UMa LOS) channels and the impact of phase noise and faulty elements in 2D antenna-array transmitters are evaluated. The results presented show that SC-QAM is an appealing alternative for rate-hungry FWA links in the years to come.
A Context Information Enhanced Multilevel Beam Search Procedure for mmWave Bands

The mmWave band with its broad unused spectrum find its way into the 5th generation (5G) of communication networks to tackle the traffic demand and need for higher data rates. However, the mmWaves are subjected to high levels of attenuation and this loss can be compensated by making use of highly directional transmissions, this leads to delays that are incurred in searching for the appropriate narrow beam transmission which involves scanning the cell area with narrow directional beams. This paper deals with a beam search algorithm that makes use of user location information in reducing the time taken for obtaining the optimum beam configuration. We investigate the performance of the proposed algorithm in a scenario with location inaccuracies. We also evaluate how the algorithm fare in scenarios involving obstructions in the transmission path.
External Power Gating Technique – An Inappropriate Solution for Low Power Devices

This paper presents an in-sight about one of the most widely used power optimization technique: a power gating. The power gating technique has been used for use cases, where part of a system is required to be (temporally) disconnected from a power source to reduce the overall system's power consumption. However, we discovered an issue which makes a discrete variant (external MOSFET transistors in power supply paths of an integrated circuit) of the power gating technique unsuitable for low power designs such as battery-operated IoT (Internet of Things) nodes.
Localization without Pose by Spherical Image

In this paper we propose a method of localize a mobile robot along a route by processing spherical images. A spherical camera is mounted on a mobile robot. The route is sampled by discrete points which correspond to the places to be localized. Then, the localization problem is formulated as a classification problem, in which a place (a sampling point along a route) corresponds to a class. Since a spherical image has rotational invariance, the content of a spherical image is independent of camera rotation. Since a route is sampled as discrete points at space, the discrete points have a unique label the scenes at the points are different to each other. As shown in the preliminary experimental results, the proposed method achieves a very high accuracy for the localization of a wheelchair robot.
Grape Leaf Multi-disease Detection with Confidence Value Using Transfer Learning Integrated to Regions with Convolutional Neural Networks

Identifying variant diseases in leaves is a significant method for optimizing food production. As the global population continues to arise and agricultural space continues to decline, every possible way of increasing the supply of food in any given condition and limited resources will address the above-mentioned problems. This study proposes a way for detecting three different diseases from grape leaves apart from the healthy leaves and considers the confidence value of the system in correctly identifying the classes. The diseases are namely: Black Rot, Black Measles, and Isariopsis. The system conducted a comparative analysis to determine which among the three pre-trained networks, AlexNet, GoogLeNet, and ResNet-18 will be the most suitable network to be integrated with Regions with Convolutional Neural Networks (RCNN) in performing multiple object detection in a given image. The data used in training the models comprised of annotated image data represented as a ground truth table with image files and their corresponding bounding boxes coordinates. The models evaluated resulted to AlexNet being the best pre-trained network to be working on the RCNN with an accuracy of 95.65%. The other two models from GoogLeNet and ResNet-18 only obtained accuracies of 92.29% and 89.49% respectively.
An efficient approach for paper submission recommendation

Nowadays, there is a rapidly increasing number of conferences and journals in computer science that make a lot of challenges for researchers to find an appropriate venue to submit their scientific work. There is a need for a recommendation system that can support researchers for a better process of paper submission. In this paper, we present an efficient approach for building such a recommendation model by using embedding methods, Global Vector (GloVe) 1 created by Pennington et al. [1] and FastText 2 proposed by Facebook [2], Convolutional Neural Network (CNN) [3], and LSTM. The experimental results show that the combination of CNNs and FastText, CNN + FastText, can achieve the best performance in terms of the Top 1 Accuracy compared with other techniques, including the S2RSCS model, as presented in [4]. Moreover, the performance by using GloVe or FastText is much better, faster, and more stable than S2RSCS in most cases.
An architecture for enabling VNF auto-scaling with flow migration

Network functions virtualization (NFV) together with Software-defined networking (SDN) are complementary technologies that offer elasticity in Network Functions (NFs) control. Due to the changes in network policy, traffic characteristic, or physical topology in Software-Defined NFV (SDNFV) systems, the control plane needs to carry out network updates frequently. Besides, the control plane should enable to orchestrate migrating NF states and redistributing packet processing across multiple instances of NFs to support NFV elasticity control such as NF auto-scaling, NF maintenance. Currently, existing frameworks are proposed to implement these functions separately, thus, there is still a need for a common orchestrator that provides efficient, coordinated control of NF auto-scaling, NF state transfer, and flow migration. In this paper, we discuss the NF states transfer and flow migration method for the high-availability NFV environment. Furthermore, this paper presents a proposal and experimental study of an architecture that supports dynamically state and flow migration to realize NFV elasticity control.
Multidimensional Media - Based Modulation

Media-based modulation (MBM) is a new and promising transmission scheme that employs reconfigurable antennas to create different channel fade realizations and appears as an emerging index modulation (IM) application. In this paper, we present a novel multiple-input multiple-output (MIMO) transmission scheme called multidimensional media-based modulation (M-MBM) by redesigning the classical Alamouti's space-time block coding (STBC) scheme with MBM through the use of the additional dimension of channel states. The error performance of the proposed M-MBM scheme is theoretically analyzed for correlated and uncorrelated channel states and a union bound is obtained for the average bit error probability (ABEP). Moreover, through computer simulations, the superior error performance of the M-MBM scheme over the emerging IM techniques is demonstrated.
Reinforcement Learning Based Adaptive Video Streaming on Named Data Networking

Under complex network conditions, adaptive video streaming requires additional state information for optimal quality selection. In this paper, we present the applicability of reinforcement learning techniques on NDN adaptive streaming. Both buffer-based and throughput-based adaptation are studied and observed their characteristics. The Q-learning algorithm is used to learn state-action values. Based on a greedy policy, the simulation results demonstrate that RL agents tend to choose the best possible bitrate which consequently reduces the quality fluctuation in adaptive streaming.
Control Strategy of Active Commutated Converter for HVDC transmission system under Unbalanced Grid

An active commutated converter (ACC) topology for HVDC transmission system is introduced. Based on the mathematical model of ACC under two-phase static coordinate system, a novel control strategy which adopts the PR controller for negative sequence component of the grid current elimination and unity power factor operation under unbalanced grid situation is proposed. Besides, the maximum active power that can be transmitted during unbalanced grid fault is also proposed. What’s more, the PR controller are designed. Compared with the traditional control methods, the proposed control strategy can be performed at a low switching frequency and without decoupling process. The results of ACC built in PSCAD/EMTDC validate the correctness and effectiveness of the proposed control strategy.
Research on Synthesis of Benzoic Acids Intermediates

This article briefly describes the development of dye intermediates in China and the research and development situation on synthetic 2 - amino-5 - nitrobenzoic acids. It also tries two more new routes to practice synthesis of them, for which it focuses on speculation of the reaction mechanism of the former route and on research of process conditions of the latter, getting the optimal conditions of the synthetic 5 - nitro-isatin. 2 - Amino - 5 - nitro benzoic acid is a versatile organic intermediate, which is seldom reported both home and abroad, but research on its synthesis method and process conditions are significant to the development of China's fine intermediates industry.
Enhancing Prediction of Chlorophyll-a Concentration with Feature Extraction using Higher-Order Partial Least Squares

Harmful algal blooms can cause significant negative impacts on the health of humans and other organisms. It is useful to predict chlorophyll-a concentration accurately for the forecast of algal blooms. While convolutional machine learning models are often used for such prediction, they may not fully consider the relationships among input features. We propose an approach to enhance the prediction by extracting latent features using higher-order partial least squares (HOPLS). The experimental results show that the feature extraction using HOPLS can significantly improve the prediction accuracy, especially in critical cases.
Research and Development of ICT Call Center Data Auxiliary Analysis System Based on Knowledge Discovery

State Grid ICT call center has a large number of data, such as structured work orders and unstructured voice files, and its rich value needs to be developed. Based on the existing cross-media data of State Grid ICT call center, constructing a large-scale knowledge graph, studies data entity extraction technology and entity relationship extraction model, and traces the hot questions of the system according to its data relevance. Auxiliary analysis system of ICT call center data uses hot events to analyze user needs, shortens the time of ICT system fault detection, improves the efficiency of system fault recovery, improves the level of ICT call center intelligence, and guides the optimization direction of service system. Through the pilot application of typical business scenarios such as e-commerce platform, it can effectively improve the answer coverage and answer accuracy of user questions, and improve customer service satisfaction and intelligent analysis level of business system.
DSPBench: A Suite of Benchmark Applications for Distributed Data Stream Processing Systems

Systems enabling the continuous processing of large data streams have recently attracted the attention of the scientific community and industrial stakeholders. Data Stream Processing Systems (DSPSs) are complex and powerful frameworks able to ease the development of streaming applications in distributed computing environments like clusters and clouds. Several systems of this kind have been released and currently maintained as open source projects, like Apache Storm and Spark Streaming. Some benchmark applications have often been used by the scientific community to test and evaluate new techniques to improve the performance and usability of DSPSs. However, the existing benchmark suites lack of representative workloads coming from the wide set of application domains that can leverage the benefits offered by the stream processing paradigm in terms of near real-time performance. The goal of this article is to present a new benchmark suite composed of 15 applications coming from areas like Finance, Telecommunications, Sensor Networks, Social Networks and others. This article describes in detail the nature of these applications, their full workload characterization in terms of selectivity, processing cost, input size and overall memory occupation. In addition, it exemplifies the usefulness of our benchmark suite to compare real DSPSs by selecting Apache Storm and Spark Streaming for this analysis.
Character-word Double-dimensional Semantic Classification Model for Judging Illegal and Irregular Behaviors for Internet Food Safety

It is difficult for Internet food safety supervision departments to automatically identify and classify the illegal and irregular behaviors in sampling inspection, an automatic semantic classification model was established to assist relevant departments in building an intelligent management system, so as to realize scientific decision-making. In this paper, Chinese word vectors and character vectors are used as the model input, CNN (Convolutional Neural Network) model is used to train the character vectors, and Positional Attention (PA) mechanism is introduced to BLSTM (bidirectional long short-term memory) network model to train the word vectors, to construct a character-word double-dimensional semantic classification model, CNN-PA-BLSTM, for judging illegal and irregular behaviors for internet food safety. The experimental results on the sampling inspection dataset show that the accuracy of the CNN-PA-BLSTM model is significantly higher than that of several commonly used deep neural network models, which verifies its rationality and effectiveness.
Adaptive Robust Noise Modeling of Sparse Representation for Bearing Fault Diagnosis

The adoption of vibration signals in sparse representation (SR) modeling is very popular in realizing bearing fault diagnosis in recent years. However, it is still challenging since the feature information in the vibration signal is usually submerged by strong and complex noise. Thus, the noise distribution assumption in the SR model needs to be carefully studied, namely, noise modeling. In this article, we propose a new SR model under the assumption that the noise in the signal obeys a mixture of generalized Gaussian (MoGG) distribution. Also, the $L_{p}$ norm ( $0\leq p \leq 1$ ) is adopted as the regularization to keep the proposed method sufficiently sparse and adjustable. Thus, this new model is named a MoGG noise distribution enabled SR (MoGG-SR) model. The mixed distribution characteristic can make this model more adaptive, and the use of the generalized Gaussian function as the basis function makes it more robust to outliers. Then, an algorithm to solve MoGG-SR is developed based on expectation–maximization (EM) and alternating direction method of multipliers (ADMM). After a series of simulations, all parameters in MoGG-SR are determined, and it has a much better denoising performance compared with other classic sparse models under different noise distribution interferences. Finally, the effectiveness of MoGG-SR in bearing fault diagnosis is verified by extracting fault impulses from the vibration signal of two data sets. The simulation and experiment together demonstrate that MoGG-SR is not only innovative in theory but also valuable in practical bearing fault diagnosis.
Investigation of analog single event transient on low noise amplifier in X and Ka bands

In this paper, we have analyzed the analog single event transient (ASET) performance of MOSFET based LNA circuits at two different frequencies (one in X band and another in Ka-band) to study the frequency dependency. The ASET impact is analyzed in the time and frequency domains under two scenarios, (i) absence of the RF input signal and (ii) the Presence of the RF input signal. In the first case, the collected charge (Qc) is taken as metric in the time domain and the second case, the spectrogram is used to analyze the disturbing presence in the fundamental frequency due to radiation strike. Based on the QC, the LNA used in X-band is more vulnerable compared to LNA used in Ka-band. The spectrogram results show that the LNA output spectrum in the X-band is disturbed more and stronger to the radiation strike compared to the LNA used in Ka-band.
Gamma-Rhythm Oscillations and Synchronization Transition in a Hybrid Excitatory-Inhibitory Complex Network

Spiking Neural Networks(SNN) stands out as a promising solution to perform complex computations or solve pattern recognition tasks, which is based on cerebral cortical dynamics of neuroscience. However, it is challenging for SNN to accurately capture the biological properties, since most SNN algorithms depend on the different variants of Integrate-and-Fire (IF) neuron model, which produces less biophysical properties of neural networks. Learning the mathematical foundations on how mammalian neocortex mechanism is performing in information processing and artificial intelligence is particularly important. This paper investigates the neural dynamics and gamma oscillations in a complex network with balanced excitatory and inhibitory neurons (E-I network), as such networks are ubiquitous in the brain. The network consisting of hybrid regular spiking (RS) and chattering (CH) excitatory neurons and fast spiking (FS) inhibitory neurons emulated by the Izhikevich model, is designed to simulate the cortical regions of the human brain. Besides, the relationship between synchronization and gamma rhythm is explored by adjusting the critical parameters of our method. Experiments visually demonstrate that gamma oscillations are generated by synchronous behaviors of our neural network. We also discover that the CH excitatory neurons can make the system easier to synchronize. These findings shed some light on further enhancements of the human brain and facilitate the development of artificial intelligence.
Feature Extraction Model Based on Inception V3 to Distinguish Normal Heart Sound from Systolic Murmur

In this study, we propose a model for classifying normal and abnormal sounds by extracting characteristics from abnormal heart sounds in which normal and symbolic murmurs appear. Heart sound data obtained through an electronic stethoscope are converted into mel-spectrogram images. The pre-trained Inception V3 model that carries out fine-tuning uses the mel-spectrogram image as input. Convolutional layers of fine-tuning completed Inception V3 models were used as feature extractors. A point-binary correlation analysis technique was used to select effective features for classification from the features extracted through the feature extractor. A crystal coefficient value, which is the square of the correlation coefficient value, is used for an accurate comparison between the features. We used an artificial neural network as a classifier in this experiment. Fine-tuned Inception V3 has an average accuracy of 87.7%. When 5-fold class validation is advanced by selecting the top 30 characteristics with high crystal coefficient values, the accuracy is 97.5%. These results can greatly assist physicians trying to detect a systolic murmur.
Computation Offloading and Content Caching with Traffic Flow Prediction for Internet of Vehicles in Edge Computing

The development of the Internet of Vehicles (IoV) enables numerous emerging in-vehicle applications to accommodate users with various contents, thus enhancing their traveling experiences. In IoV, content decoding tasks are typically offloaded to edge servers for implementation, as edge computing is an admirable paradigm to provide low-latency services. However, as different vehicular users may request the same contents, processing these contents repeatedly leads to the waste of storage, computation and bandwidth resources. Therefore, fine-grained computation offloading and content caching are demanded in IoV. In this paper, a joint optimization method for computation offloading and content caching based on traffic flow prediction, named COC, is proposed. Firstly, traffic flow covered by each edge server is predicted by a modified deep spatiotemporal residual network (ST-ResNet). Secondly, the non-dominated sorting genetic algorithm III (NSGA-III) is leveraged to realize the many-objective optimization to shorten the execution time and reduce the energy consumption of computation and transmission in IoV. Finally, evaluated by real-world big data from Nanjing China, COC shows a great reduction in execution time and energy consumption of transmission and computation compared to other methods.