Advances in computing technology have fostered the development of new and powerful deep learning (DL) techniques, which have demonstrated promising results in a wide range of applications. Particularly, DL methods have been successfully used to classify remotely sensed data collected by Earth Observation (EO) instruments. Hyperspectral imaging (HSI) is a hot topic in remote sensing data analysis due to the vast amount of information comprised by this kind of images, which allows for a better characterization and exploitation of the Earth surface by combining rich spectral and spatial information. However, HSI poses major challenges for supervised classification methods due to the high dimensionality of the data and the limited availability of training samples. These issues, together with the high intraclass variability (and interclass similarity) –often present in HSI data– may hamper the effectiveness of classifiers. In order to solve these limitations, several DL-based architectures have been recently developed, exhibiting great potential in HSI data interpretation. This paper provides a comprehensive review of the current-state-of-the-art in DL for HSI classification, analyzing the strengths and weaknesses of the most widely used classifiers in the literature. For each discussed method, we provide quantitative results using several well-known and widely used HSI scenes, thus providing an exhaustive comparison of the discussed techniques. The paper concludes with some remarks and hints about future challenges in the application of DL techniques to HSI classification. The source codes of the methods discussed in this paper are available from: https://github.com/mhaut/hyperspectral_deeplearning_review .

Deep learning classifiers for hyperspectral imaging: A review

Comprehensive Survey of Image Steganography: Techniques, Evaluations, and Trends in Future Research

Background/objectives The Internet of Things (IoT) can create disruptive innovation in healthcare Thus, during COVID-19 Pandemic, there is a need to study different applications of IoT enabled healthcare For this, a brief study is required for research directions Methods Research papers on IoT in healthcare and COVID-19 Pandemic are studied to identify this technology’s capabilities This literature-based study may guide professionals in envisaging solutions to related problems and fighting against the COVID-19 type pandemic Results Briefly studied the significant achievements of IoT with the help of a process chart Then identifies seven major technologies of IoT that seem helpful for healthcare during COVID-19 Pandemic Finally, the study identifies sixteen basic IoT applications for the medical field during the COVID-19 Pandemic with a brief description of them Conclusions In the current scenario, advanced information technologies have opened a new door to innovation in our daily lives Out of these information technologies, the Internet of Things is an emerging technology that provides enhancement and better solutions in the medical field, like proper medical record-keeping, sampling, integration of devices, and causes of diseases IoT’s sensor-based technology provides an excellent capability to reduce the risk of surgery during complicated cases and helpful for COVID-19 type pandemic In the medical field, IoT’s focus is to help perform the treatment of different COVID-19 cases precisely It makes the surgeon job easier by minimising risks and increasing the overall performance By using this technology, doctors can easily detect changes in critical parameters of the COVID-19 patient This information-based service opens up new healthcare opportunities as it moves towards the best way of an information system to adapt world-class results as it enables improvement of treatment systems in the hospital Medical students can now be better trained for disease detection and well guided for the future course of action IoT’s proper usage can help correctly resolve different medical challenges like speed, price, and complexity It can easily be customised to monitor calorific intake and treatment like asthma, diabetes, and arthritis of the COVID-19 patient This digitally controlled health management system can improve the overall performance of healthcare during COVID-19 pandemic days

Internet of Things (IoT) enabled healthcare helps to take the challenges of COVID-19 Pandemic

Using a single sensor to determine the pose estimation of a device cannot give accurate results. This paper presents a fusion of an inertial sensor of six degrees of freedom (6-DoF) which comprises the 3-axis of an accelerometer and the 3-axis of a gyroscope, and a vision to determine a low-cost and accurate position for an autonomous mobile robot. For vision, a monocular vision-based object detection algorithm speeded-up robust feature (SURF) and random sample consensus (RANSAC) algorithms were integrated and used to recognize a sample object in several images taken. As against the conventional method that depend on point-tracking, RANSAC uses an iterative method to estimate the parameters of a mathematical model from a set of captured data which contains outliers. With SURF and RANSAC, improved accuracy is certain; this is because of their ability to find interest points (features) under different viewing conditions using a Hessain matrix. This approach is proposed because of its simple implementation, low cost, and improved accuracy. With an extended Kalman filter (EKF), data from inertial sensors and a camera were fused to estimate the position and orientation of the mobile robot. All these sensors were mounted on the mobile robot to obtain an accurate localization. An indoor experiment was carried out to validate and evaluate the performance. Experimental results show that the proposed method is fast in computation, reliable and robust, and can be considered for practical applications. The performance of the experiments was verified by the ground truth data and root mean square errors (RMSEs).

/pdf/pose-estimation-of-a-mobile-robot-based-on-fusion-of-imu-44r71oehjx.pdf

Pose Estimation of a Mobile Robot Based on Fusion of IMU Data and Vision Data Using an Extended Kalman Filter.

Cloud gaming is a new way to deliver high-quality gaming experience to gamers anywhere and anytime. In cloud gaming, sophisticated game software runs on powerful servers in data centers, rendered game scenes are streamed to gamers over the Internet in real time, and the gamers use light-weight software executed on heterogeneous devices to interact with the games. Due to the proliferation of high-speed networks and cloud computing, cloud gaming has attracted tremendous attentions in both the academia and industry since late 2000’s. In this paper, we survey the latest cloud gaming research from different aspects, spanning over cloud gaming platforms, optimization techniques, and commercial cloud gaming services. The readers will gain the overview of cloud gaming research and get familiar with the recent developments in this area.

https://ir.nctu.edu.tw:443/bitstream/11536/145505/1/007df636d72b23b15d610d0c56e730b5.pdf

A Survey on Cloud Gaming: Future of Computer Games

Purpose Many investigations are going on in monitoring, contact tracing, predicting and diagnosing the COVID-19 disease and many virologists are urgently seeking to create a vaccine as early as possible Even though there is no specific treatment for the pandemic disease, the world is now struggling to control the spread by implementing the lockdown worldwide and giving awareness to the people to wear masks and use sanitizers The new technologies, including the Internet of things (IoT), are gaining global attention towards the increasing technical support in health-care systems, particularly in predicting, detecting, preventing and monitoring of most of the infectious diseases Similarly, it also helps in fighting against COVID-19 by monitoring, contract tracing and detecting the COVID-19 pandemic by connection with the IoT-based smart solutions IoT is the interconnected Web of smart devices, sensors, actuators and data, which are collected in the raw form and transmitted through the internet The purpose of this paper is to propose the concept to detect and monitor the asymptotic patients using IoT-based sensors Design/methodology/approach In recent days, the surge of the COVID-19 contagion has infected all over the world and it has ruined our day-to-day life The extraordinary eruption of this pandemic virus placed the World Health Organization (WHO) in a hazardous position The impact of this contagious virus and scarcity among the people has forced the world to get into complete lockdown, as the number of laboratory-confirmed cases is increasing in millions all over the world as per the records of the government Findings COVID-19 patients are either symptomatic or asymptotic Symptomatic patients have symptoms such as fever, cough and difficulty in breathing But patients are also asymptotic, which is very difficult to detect and monitor by isolating them Originality/value Asymptotic patients are very hazardous because without knowing that they are infected, they might spread the infection to others, also asymptotic patients might be having very serious lung damage So, earlier prediction and monitoring of asymptotic patients are mandatory to save their life and prevent them from spreading

Detection and monitoring of the asymptotic COVID-19 patients using IoT devices and sensors

Design of cognitive image filters for suppression of noise level in medical images

In this paper, an improved fuel consumption and emissions control strategy based on a mathematical and heuristic approach is presented to optimize Parallel Hybrid Electric Vehicles (HEVs). The well-known Sequential Quadratic Programming mathematical method (SQP-Hessian approach) presents some limitations to achieve fuel consumption and emissions control optimization, as it is not able to find the global minimum, and it generally shows efficient results in local exploitation searches. The usage of a combined Modified Artificial Bee Colony algorithm (MABC) with the SQP approach is proposed in this work to obtain better optimal solutions and overcome these limitations. The optimization is performed with boundary conditions, considering that the optimized vehicle performance has to satisfy Partnership for a New Generation of Vehicles (PNGV) constraints. The weighting factor of the vehicle’s performance parameters in the objective function is varied, and optimization is carried out for two different driving cycles, namely Federal Test Procedure (FTP) and Economic commission Europe—Extra Urban Driving Cycle (ECE-EUDC), using the MABC and MABC with SQP approaches. The MABC with SQP approach shows better performance in terms of fuel consumption and emissions than the pure heuristic approach for the considered vehicle with similar boundary conditions. Moreover, it does not present significant penalties for final battery charging and it offers an optimized size of the key vehicle’s components for different driving cycles.

A Modified ABC-SQP-Based Combined Approach for the Optimization of a Parallel Hybrid Electric Vehicle

https://robots.iopscience.iop.org/article/10.1088/1757-899X/925/1/012062/pdf

Comparative Analysis to Identify Efficient Technique for Interfacing BCI System

Biometric Recognition and Authentication is used in many applications for the secured identification of the persons. Several Researches has been carried out to strengthen the security algorithms through which the identification can be done in secured manner. With this objective, a new algorithm called Hybrid Adaptive Fusion(HAF) has been proposed which works on the principle of hybrid fusion of two feature inputs such as Hand geometry and iris of the users. As mentioned, the proposed algorithm uses the novel and hybrid fusion of feature extraction along with the accurate machine learning classifier. Effective Linear Binary Patterns (ELBP) and Scale Invariant Fourier Transform (SIFT) are stored in the databases for the further verification. The features stored are fed into the Extreme Learning machines for the detection of the verified users. This algorithm has been tested with the CASIA Image Datasets and with the different classifiers such as Neural Networks, Baiyes Networks. The proposed algorithm with ELM has better accuracy of 98.5% when compared with the other machine learning algorithms.

S. Prabu

Papers

Detection and monitoring of the asymptotic COVID-19 patients using IoT devices and sensors

Design of cognitive image filters for suppression of noise level in medical images

A Modified ABC-SQP-Based Combined Approach for the Optimization of a Parallel Hybrid Electric Vehicle

Comparative Analysis to Identify Efficient Technique for Interfacing BCI System

A Multimodal Authentication for Biometric Recognition System using Intelligent Hybrid Fusion Techniques.