Digital Signal Processing (DSP) is a very significant and active research area. High throughput is a requirement for most wireless communication systems. The critical bottleneck that affects communication ability is the Fast Fourier Transform (FFT), which is the essence of most modulators. Currently, Floating point FFT processors have been used in Radar signal processing, fast convolution, Spectrum

As a side effect caused by new technologies, supraharmonics attracted the attention of researchers in recent years. Mitigation and analysis of supraharmonics distortions are of great importance to guarantee the power quality (PQ) available in the power grids since such distortions can cause serious damage to the grid-connected equipment. However, there are few works in literature reporting techniques

In order to resolve the problem of excessive processing time and inadequate accuracy caused by existing algorithms in robot vision image reconstruction, a block variable step size adaptive compression sensor reconstruction algorithm is proposed. The algorithm integrates the regularized orthogonal matching pursuit technique in a seamlessly efficient manner to obtain consistent and accurate signal reconstruction

Recent studies have shown that cyberbullying is a rising youth epidemic. In this paper, we develop a novel automated classification model that identifies the cyberbullying texts without fitting them into large dimensional space. On the other hand, a classifier .cannot provide a limited convergent solution due to its overfitting problem. Considering such limitations, we developed a text classification

This research proposed the data acquisition system (DAS), which has a capability to collect real-time voltage and current at variable load resistance during an experimental characterization analysis of 3 × 3 size, photo voltaic (PV) system, under partial shading conditions (PSCs). In addition, the system is economical and minimizes the testing period for PV system characterization relative to traditional

With improvements in human living standard, people’s demands on food quality are getting higher and higher. Effective food recognition algorithms are needed to obtain more useful food information. In order to solve the problem of low accuracy and slow speed of food recognition algorithms, a new food recognition algorithm based on CNN algorithm is proposed. First, the proposed algorithm preprocess the

Recently, great progress has been witnessed in the application of mobile cloud computing in the field of health care such as online medical inquiries. However, due to the limitation of cognitive intelligence, QoE (Quality of Experience) is hampered by two problems, the first of which is that the traffic pressure of the core network cannot well meet the requirements of delay-sensitive emotional services

Long short-term memory (LSTM) is one of the most widely used recurrent neural network. Traditionally, it adopts an offline batch mode for model training. To be updated with new data, the network has to be re-trained with merged data using both old and new data, which is very time-consuming and causes catastrophic forgetting. To address this issue, we proposed an incremental ensemble LSTM model-IncLSTM

Road scene semantic segmentation often requires a deeper neural network to obtain higher accuracy, which makes the segmentation model more complex and slower. In this paper, we use shallow neural networks to achieve semantic segmentation for intelligent transportation system. Specifically, we propose a lightweight semantic segmentation model. First, the image features are extracted by using a simple

Multi-target multi-camera tracking systems track many pedestrians through videos taken from multiple cameras. Generally, multi-target multi-camera tracking comprises three steps, namely, detection, feature extraction, and data association. It also involves a number of marginal post-processing procedures, such as pruning and interpolating. The task is a complicated and challenging problem. In this work

In this era of modernization, digital technology plays a major role in all facets of life. We are accustomed to using computers and smartphones to access information, create, express, communicate, and collaborate, contributing to our personal, social, and professional survival at a comfortable level. As the legitimate use of digital space has grown, so have the prospects for malice via swindlers, blackmailers

Neural encoder–decoder architectures have been used extensively for image captioning. Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) are popularly used in encoder and decoder models. Recurrent Neural Networks are popular architectures in natural language processing used for language modeling, but they are sequential in nature. The transformer model can solve this problem of

Identification of flicker sources is necessary to find who is responsible for the measured flicker and improve power quality. This paper puts forward a new method for identifying flicker sources with minimum measurement units. Contrary to the previous works where flicker sources are considered a single-frequency signal, the autoregressive moving average (ARMA) is used to model active and reactive power

Internet of Things (IoT) has been a major influence in Agriculture since its application to the sector. This paper provides an extensive review of the use of smart technologies in agriculture and elaborates the state-of-the-art technologies for smart agriculture including, Internet of Things, cloud computing, machine learning, and artificial intelligence. The application of smart farming to crop and

The fundamental challenge of data analytics scheduling is the heterogeneity of both data analytics jobs and resources. Although many scheduling solutions have been developed to improve the efficiency of data analytics frameworks (e.g., Spark), they either (1) focus on the scheduling of a single type of resource, without considering the coordination between different resources; or (2) schedule multiple

Large-scale particle image velocimetry (LSPIV) has been increasingly applied in whole-field flow measurement for its non-contact operation mode. However, due to the complicated flow conditions and optical disturbances on the water-air interface, commonly-used imaging technologies are degenerated by the low visibility and image distortion. There are also increased challenges for control point calibration

With the increasing maturity of Internet of Things technology, temperature sensors based on Zigbee, LoRa and other transmission modes have successively appeared. However, due to factors such as outdoor environment and transmission distance, the temperature measurement data under different transmission methods may be inaccurate and the update of background data may be delayed. To master the temperature

The Industrial Internet of Things (IIoT) has revolutionized the industrial sector by providing advanced and intelligent applications. The objects and nodes communicate with one another to collect, exchange, and analyze a large amount of sensing data using techno-social systems, thereby challenging the security and trustworthiness of the data. To achieve effective communication in IIoT, trustworthy

Photovoltaic (PV) systems exhibit non-linear output power voltage (P-V) characteristics for varying input irradiation and temperature. Due to this, the Maximum Power Point Tracker (MPPT), a widely used optimization technique is indispensable in PV power systems. This non-linearity gets more complicated when PV panels experience partial shading in an array. During partial shading, the panels receive

It is quite challenging to improve the accuracy of the measurement point coordinate when using the double forward intersection method to measure the coordinates of ground objects in the traditional digital photogrammetry system. In this paper, a method is proposed to improve the coordinate accuracy of measuring points using multi-slice forward intersection mapping. The intersection images are provided

The stator current signals of mining asynchronous motor are often non-stationary, making it challenging to extract fault features in the time domain. Therefore, this paper proposes a rotor fault diagnosis method based on the combination of Modified Ensemble Empirical Mode Decomposition (MEEMD) energy entropy and Artificial Neural Network (ANN). Firstly, the stator current signals are decomposed into

A Computational Grid (CG) consists of networks that are linked to implementing grid computing. Here, a huge computational task is shared amongst the individual machines, where the calculations are executed in parallel and the final results are combined and returned to the original computer. One of the most critical issues in the CG is to measure the performance, which is handled by the task scheduling

Deep learning has gained popularity for feature extraction in the field of Network Intrusion Detection and Prevention System (NIDPS) to extract the patterns matching and secure the networks by detecting the unknown and malicious activities. The malicious activities and security attacks are disturbing the normal operations of networks. The new attacks are difficult to monitor due to their new features

With the recent progress of science and the development of society, the development of artificial intelligence technology and robot theory have become increasingly mature. Compared to the development of Internet, robot industry is a branch of traditional industry, and the popularization of social robots has always been the bottleneck. Robot intelligence is an important part of artificial intelligence

In recent years, the manufacturing industry has faced various global challenges. One of these challenges is the increasing frequent adjustment and reconfiguration of production lines, necessitated by the diversification of customer demands. To improve the processing efficiency after production line reconfiguration, this paper puts forward a group learning architecture towards intelligent equipment

Due to limited computation and storage resources of industrial internet of things (IoT) edge devices, many emerging intelligent industrial IoT applications based on deep neural networks (DNNs) heavily depend on cloud computing for computation and storage. However, cloud computing faces technical issues in long latency, poor reliability, and weak privacy, resulting in the need for on-device computation

In this paper, we propose a deep multimodal feature learning (DMFL) network for RGB-D salient object detection. The color and depth features are firstly extracted from low level to high level feature using CNN. Then the features at the high layer are shared and concatenated to construct joint feature representation of multi-modalities. The fused features are embedded to a high dimension metric space