Entropy-based algorithms for signal processing

Entropy, the key factor of information theory, is one of the most important research areas in computer science. Entropy coding informs us of the formal limits of today’s storage and communication infrastructure. Over the last few years, entropy has become an important trade-off measure in signal processing. Entropy measures especially have been used in image and video processing by applying sparsity and are able to help us to solve several of the issues that we are currently facing. As the daily produced data are increasing rapidly, a more effective approach to encode or compress the big data is required. In this sense, applications of entropy coding can improve image and video coding, imaging, quality assessment in agricultural products, and product inspection, by applying more effective coding approaches. In light of these and many other challenges, a Special Issue of Entropy-Based Algorithms for Signal Processing has been dedicated to address the current status, challenges, and future research priorities for the entropy of signal processing

Editorial for the Special Issue “Advanced Machine Learning for Time Series Remote Sensing Data Analysis”

This Special Issue intended to probe the impact of the adoption of advanced machine learning methods in remote sensing applications including those considering recent big data analysis, compression, multichannel, sensor and prediction techniques. In principal, this edition of the Special Issue is focused on time series data processing for remote sensing applications with special emphasis on advanced machine learning platforms. This issue is intended to provide a highly recognized international forum to present recent advances in time series remote sensing. After review, a total of eight papers have been accepted for publication in this issue

A smart anomaly detection system for industrial machines based on feature autoencoder and deep learning

Machine-health-surveillance systems are gaining popularity in industrial manufacturing systems due to the widespread availability of low-cost devices, sensors, and internet connectivity. In this regard, artificial intelligence provides valuable assistance in the form of deep learning methods to analyze and process big machine data. In diverse industrial applications, gears are considered a condemning element; many contributing failures occur due to an unexpected breakdown of the gears. In recent research, anomaly-detection and fault-diagnosis systems have been the gears’ most contributing content. Thus, in work, we presented a smart deep learning-based system to detect anomalies in an industrial machine. Our system used vibrational analysis methods as a deciding tool for different machinery-maintenance decisions. We will first perform a data analysis of the gearbox data set to analyze the data’s insights. By calculating and examining the machine’s vibration, we aim to determine the nature and severity of the defect in the machine and hence detect the anomaly. A gearbox’s vibration signal holds the fault’s signature in the gears, and earlier fault detection of the gearbox is achievable by examining the vibration signal using a deep learning technique. Therefore, we aim to propose a 6-layer autoencoder-based deep learning framework for anomaly detection and fault analysis using a publically available data set of wind-turbine components. The gearbox fault-diagnosis data set is utilized for experimentation, including collecting vibration attributes recorded using SpectraQuest’s gearbox fault-diagnostics simulator. Through comprehensive experiments, we have seen that the framework gains good results compared to others, with an overall accuracy of 91%

Accelerating Power Grid Monitoring with Flying Robots and Artificial Intelligence

The digital revolution is expected to surpass all previous economic transformations in scale, scope, and complexity. Digital technologies are making electrical grids more connected, more reliable, and sustainable. Several efforts have been made to revamp the electric grid and modernize century-old systems. In the near future, we expect to see a revolution in power grind monitoring with incredible results through artificial intelligence and big data, drones, among others. The business opportunity for using drones in the energy sector is impressive, although very few companies have joined in its implementation. The drone allows the safe remote overflight of high voltage power lines. They can be deployed to detect, inspect, and diagnose the defects of the power line infrastructure. In this article, we provide the state of the art of using drones in smart grid monitoring. We demonstrate and propose an architecture based on Faster R-CNN for detecting ice accretion on power lines. Finally, we shed light on opportunities and future trends of these emerging technologies that can guide future research directions