Reduced reference image and video quality assessments: review of methods

With the growing demand for image and video-based applications, the requirements of consistent quality assessment metrics of image and video have increased. Different approaches have been proposed in the literature to estimate the perceptual quality of images and videos. These approaches can be divided into three main categories; full reference (FR), reduced reference (RR) and no-reference (NR). In RR methods, instead of providing the original image or video as a reference, we need to provide certain features (i.e., texture, edges, etc.) of the original image or video for quality assessment. During the last decade, RR-based quality assessment has been a popular research area for a variety of applications such as social media, online games, and video streaming. In this paper, we present review and classification of the latest research work on RR-based image and video quality assessment. We have also summarized different databases used in the field of 2D and 3D image and video quality assessment. This paper would be helpful for specialists and researchers to stay well-informed about recent progress of RR-based image and video quality assessment. The review and classification presented in this paper will also be useful to gain understanding of multimedia quality assessment and state-of-the-art approaches used for the analysis. In addition, it will help the reader select appropriate quality assessment methods and parameters for their respective applications

An Improved Acoustic Scene Classification Method Using Convolutional Neural Networks (CNNs)

Predicting acoustic environment by analyzing and classifying sound recording of the scene is an emerging research area. This paper presents and compares different acoustic scene classification (ASC) methods to differentiate between different acoustic environments. In particular, two deep learning techniques of classifica-tion i.e. Deep Neural Network (DNN) and Convolution Neural Network (CNN) have been applied using a combination of Mel-Frequency Cepstral Coefficients (MFCCs) and Log Mel energies as features. DNN and CNN are state-of-the-art techniques which are being used widely in speech recognition, computer vision, and natural language processing applications. These techniques have recently achieved great success in the field of audio classification for various applications. Both techniques have been implemented and tuned by performing a variety of experiments with different hyper parameters, hidden layers and units on public benchmark datasets provided in the DCASE 2017 challenge. The proposed method uses frame level randomization of the combined acoustic features i.e. MFCC and log mel energy, for training of model to achieve higher accuracy with DNN and CNN. It has reported higher accuracy than the previous work done on public benchmark datasets provided in the DCASE 2017 challenge. It is observed that DNN achieved 83.45% and CNN achieved 83.65% accuracy that is higher than the previous work done on public benchmark datasets provided in the DCASE 2017 challenge

A Comparative Study of Predicting Student’s Performance by use of Data Mining Techniques

Educational systems need innovative ways to improve quality of education to achieve the best results and decrease the failure rate.  Educational Data Mining (EDM) has boomed in the educational systems recently as it enables to analyze and predict student performance so that measures can be taken in advance. Due to lack of prediction accuracy, improper attribute analysis, and insufficient datasets, the educational systems are facing difficulties and challenges exist to effectively benefit from EDM. In order to improve the prediction process, a thorough study of literature and selection of the best prediction technique is very important. The main objective of this paper is to present a comparative study of various recently used data mining techniques, classification algorithms, their impact on datasets as well as the prediction attribute’s result in a clear and concise way. The paper also identifies the best attributes that will help in predicting the student performance in an efficient way

Reduced reference image and video quality assessments: review of methods

With the growing demand for image and video-based applications, the requirements of consistent quality assessment metrics of image and video have increased. Different approaches have been proposed in the literature to estimate the perceptual quality of images and videos. These approaches can be divided into three main categories; full reference (FR), reduced reference (RR) and no-reference (NR). In RR methods, instead of providing the original image or video as a reference, we need to provide certain features (i.e., texture, edges, etc.) of the original image or video for quality assessment. During the last decade, RR-based quality assessment has been a popular research area for a variety of applications such as social media, online games, and video streaming. In this paper, we present review and classification of the latest research work on RR-based image and video quality assessment. We have also summarized different databases used in the field of 2D and 3D image and video quality assessment. This paper would be helpful for specialists and researchers to stay well-informed about recent progress of RR-based image and video quality assessment. The review and classification presented in this paper will also be useful to gain understanding of multimedia quality assessment and state-of-the-art approaches used for the analysis. In addition, it will help the reader select appropriate quality assessment methods and parameters for their respective applications

Performance Evaluation of Rake Receiver for UWB Systems Using Measured

Abstract-The industrial environments are an important scenario for ultra wideband (UWB) communication systems. However, due to large number of metallic scatterers in the surroundings, the multipath offered by UWB channels is dense with significant energy. In this paper, the performance of RAKE receivers operating in a non line-of-sight (NLOS) scenario in these environments is evaluated. The channels used for the evaluation are measured in a medium-sized industrial environment. In addition, a standard IEEE 802.15.4a channel model is used for comparison with the results of the measured data. The performance of partial RAKE (PRake) and selective RAKE (SRake) is evaluated in terms of uncoded bit-error-rate (BER) using different number of fingers. The performance of maximal ratio combining (MRC) and equal gain combining (EGC) is compared for the RAKE receiver assuming perfect knowledge of the channel state. Finally, based on the simulation results, conclusions are drawn considering the performance and complexity issues for system design in these environments

Development and Optimization of Deep Learning Models for Weapon Detection in Surveillance Videos

Featured Application This work has applied computer vision and deep learning technology to develop a real-time weapon detector system and tested it on different computing devices for large-scale deployment. Weapon detection in CCTV camera surveillance videos is a challenging task and its importance is increasing because of the availability and easy access of weapons in the market. This becomes a big problem when weapons go into the wrong hands and are often misused. Advances in computer vision and object detection are enabling us to detect weapons in live videos without human intervention and, in turn, intelligent decisions can be made to protect people from dangerous situations. In this article, we have developed and presented an improved real-time weapon detection system that shows a higher mean average precision (mAP) score and better inference time performance compared to the previously proposed approaches in the literature. Using a custom weapons dataset, we implemented a state-of-the-art Scaled-YOLOv4 model that resulted in a 92.1 mAP score and frames per second (FPS) of 85.7 on a high-performance GPU (RTX 2080TI). Furthermore, to achieve the benefits of lower latency, higher throughput, and improved privacy, we optimized our model for implementation on a popular edge-computing device (Jetson Nano GPU) with the TensorRT network optimizer. We have also performed a comparative analysis of the previous weapon detector with our presented model using different CPU and GPU machines that fulfill the purpose of this work, making the selection of model and computing device easier for the users for deployment in a real-time scenario. The analysis shows that our presented models result in improved mAP scores on high-performance GPUs (such as RTX 2080TI), as well as on low-cost edge computing GPUs (such as Jetson Nano) for weapon detection in live CCTV camera surveillance videos.open access</p

Smart Warehouse Management System: Architecture, Real-Time Implementation and Prototype Design

The world has witnessed the digital transformation and Industry 4.0 technologies in the past decade. Nevertheless, there is still a lack of automation and digitalization in certain areas of the manufacturing industry; in particular, warehouse automation often has challenges in design and successful deployment. The effective management of the warehouse and inventory plays a pivotal role in the supply chain and production. In the literature, different architectures of Warehouse Management Systems (WMSs) and automation techniques have been proposed, but most of those have focused only on particular sections of warehouses and have lacked successful deployment. To achieve the goal of process automation, we propose an Internet-of-Things (IoT)-based architecture for real-time warehouse management by dividing the warehouse into multiple domains. Architecture viewpoints were used to present models based on the context diagram, functional view, and operational view specifically catering to the needs of the stakeholders. In addition, we present a generic IoT-based prototype system that enables efficient data collection and transmission in the proposed architecture. Finally, the developed IoT-based solution was deployed in the warehouse of a textile factory for validation testing, and the results are discussed. A comparison of the key performance parameters such as system resilience, efficiency, and latency rate showed the effectiveness of our proposed IoT-based WMS architecture

Smart Warehouse Management System: Architecture, Real-Time Implementation and Prototype Design

The world has witnessed the digital transformation and Industry 4.0 technologies in the past decade. Nevertheless, there is still a lack of automation and digitalization in certain areas of the manufacturing industry; in particular, warehouse automation often has challenges in design and successful deployment. The effective management of the warehouse and inventory plays a pivotal role in the supply chain and production. In the literature, different architectures of Warehouse Management Systems (WMSs) and automation techniques have been proposed, but most of those have focused only on particular sections of warehouses and have lacked successful deployment. To achieve the goal of process automation, we propose an Internet-of-Things (IoT)-based architecture for real-time warehouse management by dividing the warehouse into multiple domains. Architecture viewpoints were used to present models based on the context diagram, functional view, and operational view specifically catering to the needs of the stakeholders. In addition, we present a generic IoT-based prototype system that enables efficient data collection and transmission in the proposed architecture. Finally, the developed IoT-based solution was deployed in the warehouse of a textile factory for validation testing, and the results are discussed. A comparison of the key performance parameters such as system resilience, efficiency, and latency rate showed the effectiveness of our proposed IoT-based WMS architecture

A Comparative Analysis of Various Controller Techniques for Optimal Control of Smart Nano-Grid Using GA and PSO Algorithms

A nano-grid is an independent hybrid sustainable framework that utilizes non-renewable and renewable power resources for supplying continuous electrical energy to the load. Considering this scenario, in this research work, photovoltaic (PV) array, wind turbine, and fuel cell are taken as the three generation resources that have been used in the nano-grid. The active and reactive power of the all three generation resources is controlled using various controllers, i.e. integral, proportional-integral, proportional derivative, proportional integral derivative, fractional-order proportional-integral, fractional order proportional integral derivative (FOPID) and sliding mode controller (SMC). An advanced optimization technique based on a genetic algorithm (GA) and particle swarm optimization (PSO) algorithm has been utilized to optimize all of these controllers. The integral square error is taken as the objective function for both optimization algorithms. Finally, a graphical and tabular comparative analysis of all optimized controllers along with their control parameters and performance indexes is evaluated to find the best optimal solution. The performance of SMC has surpassed the performance of all other optimized controllers for power stability. In less than 0.267 seconds, the actual power yielded by using SMC is within 1% of the desired power. PSO algorithm has performed better than GA algorithm with all controllers. The worst performance is by FOPID controller with a steady state error of 6071.3W using GA algorithm and have a high magnitude of overshoot and undershoot. Moreover, a smart switching algorithm has been introduced for switching between the generation resources in accordance with the load demand and cost of the system in order to operate the nano-grid more economically. Finally, a case study has been performed in which the smart switching algorithm is utilized to switch to the best available generation resource in case of any fault at the generation side to provide uninterrupted power to the attached loads

Reduced reference image and video quality assessments : review of methods

With the growing demand for image and video-based applications, the requirements of consistent quality assessment metrics of image and video have increased. Different approaches have been proposed in the literature to estimate the perceptual quality of images and videos. These approaches can be divided into three main categories; full reference (FR), reduced reference (RR) and no-reference (NR). In RR methods, instead of providing the original image or video as a reference, we need to provide certain features (i.e., texture, edges, etc.) of the original image or video for quality assessment. During the last decade, RR-based quality assessment has been a popular research area for a variety of applications such as social media, online games, and video streaming. In this paper, we present review and classification of the latest research work on RR-based image and video quality assessment. We have also summarized different databases used in the field of 2D and 3D image and video quality assessment. This paper would be helpful for specialists and researchers to stay well-informed about recent progress of RR-based image and video quality assessment. The review and classification presented in this paper will also be useful to gain understanding of multimedia quality assessment and state-of-the-art approaches used for the analysis. In addition, it will help the reader select appropriate quality assessment methods and parameters for their respective applications.open access</p