Sensor-based sorting techniques have the potential to improve ore grades while reducing waste material processing. Previous research has shown that by discarding waste prior to further processing, sensor-based sorting can reduce energy, water, and reagent consumption, as well as fine waste production. Recent studies of sensor-based sorting and the fundamental mechanisms of the main sorting techniques are evaluated in this literature review to inform optimal sensor selection. Furthermore, the fusion of data from multiple sensing techniques is being investigated in order to improve characterization of the sensed material and thus sorting capability. The key to effective sensor-based sorting implementation was discovered to be the selection of a sensing technique capable of sensing a characteristic capable of separating ore from waste with a sampling distribution sufficient for the considered sorting method. Classifications of possible sensor fusion sorting applications in mineral processing are proposed and illustrated with case studies. It was also discovered that the main impediment to implementing sensor fusion is a lack of correlative data on the response of multiple sensing techniques to the same ore sample. To provide data for the evaluation and development of sensor fusion techniques, a combined approach of experimental testing supplemented by simulations is proposed.
The pervasive or ubiquitous use of computing, i.e. the incorporation of sensor information, processing, and communication technology into everyday objects, has been integrated into a wide range of sports and exercise-related areas. Scientific experiments in motion studies conducted under valid ecological conditions; assistive technology assisting recreational and health-conscious athletes in their physical activities; and performance and tactical analyses of association football games providing analysis immediately following the end of a match, to name a few examples. The current state of the art in micro-electromechanical system inertial measurement units (MEMS IMU) and current approaches for data acquisition on human activities and sports are discussed. The integration of these sensors and cloud computing technologies is discussed later in the work, but first we discuss the benefits of wearable devices, which have gained a strong foothold in sport performance analysis when combined with mobile computing. We look at a variety of pervasive computing applications in sports performance and health. Among these applications are advances in computer vision with deep learning algorithms used to evaluate sports skills, promote injury prevention, and provide key performance indicators in a variety of sports. The progress in the integration of various sensors in wearable intelligent monitoring systems is broadly described. Sensor fusion is used specifically in sports and health monitoring to quantify exercise parameters and body response, provide classification of activities and movement patterns, estimate energy expenditure, and assess sleeping patterns.
Because of its growing popularity, the use of a piece of technology known as a digital signature is becoming more common. Its primary responsibilities include detecting and preventing unauthorised changes to the data as well as verifying the identity of the signature. Some of the possible applications for digital signatures include the signing of legally binding contracts, the protection of software updates and the use of digital certificates to ensure the security of online business transactions. Digital signatures have the potential to be used in a wide range of other situations. Because it uses public channels, a public key white box is the single most important example of a public key white box. This is in addition to the process of establishing keys through insecure channels, which is also a critical component of the equation. It is critical for ensuring the security of monetary transactions that take place over open or insecure networks. Digital signature techniques are commonly used in white-box cryptographic protocols. This allows for the provision of services like entity authentication, authenticated key transfer and key agreement.
High-performance Mn-Zn and Ni-Zn ferrites, amorphous, nanocrystalline, and metamaterials have been developed rapidly in recent years to meet the electromagnetic characteristics requirements of WPT systems. This paper begins with a comprehensive review of the magnetic materials used in WPT systems and concludes with cutting-edge WPT technology and the development and application of high-performance magnetic materials. Furthermore, this study provides an exclusive resource for researchers and engineers interested in learning about the technology and highlights critical issues that must be addressed. Finally, the potential challenges and opportunities of WPT magnetic materials are presented, and the technology's future development directions are predicted and discussed.
Because of its high transmission efficiency and acceptable transmission distance, the magnetic coupling resonant wireless power transfer (MCR-WPT) system is regarded as the most promising wireless power transfer (WPT) method. Magnetic cores made of magnetic materials are typically added to MCR-WPT systems to improve coupling performance in order to achieve magnetic concentration. However, as WPT technology advances, traditional magnetic materials gradually become a bottleneck, limiting system power density enhancement.
Ultra-wideband (UWB) sensors use radio frequency technology to precisely determine position by wireless communication between devices. The most recent applications concentrate on locating and collecting sensor data from mobile phones, car keys, and other similar devices. However, this technology is still underutilised in the mining industry. This viewpoint provides implementation options and solutions to bridge this gap. It also assessed the advantages and disadvantages of using ultra-wideband for mining. The provided measurements were made with QORVO two-way ranging sensors and compared to theoretical and existing technological solutions. To ensure that UWB sensors are used optimally, special emphasis was placed on influencing factors such as UWB location methods and factors affecting measurement accuracies such as line of sight, multipath propagation, the effect of shielding, and the ideal measurement setup. An experiment revealed that when there is no multipath propagation and the arriving signal travels directly from the transmitter to the receiver, the results are the most accurate. Ultra-wideband (UWB) is a radio technology that allows for short-range, high-bandwidth communications at very low energy levels while covering a large portion of the radio spectrum. Even though it was introduced in 1901, this technology has many applications today. It is not linked to any frequency, unlike other similar technologies. UWB can also send data by utilising unused frequency capacity and a very wide frequency range. The minimum frequency range for UWB is 500 MHz. In general, the frequency response and pulse width of a signal determine the accuracy that can be achieved. The response frequency of UWB can range between 10 and 40 MHz, and the pulse width can be as short as one nanosecond, giving it a theoretical accuracy of one millimetre.
Several surveys of the findings of research into the power consumption of virtual entities (VEs, also known as virtual machines (VMs) or containers) have been published. Our contribution to this work is a thorough examination of the dynamics of research itself: the challenges, approaches, pitfalls, fallacies, and research gaps, without ignoring the research results. The prospective researcher who wants to understand the dynamics of research into predictive modelling and supporting measurements of power consumption by individual VEs relevant to the telco cloud is our target audience. A thorough frequency analysis is used to characterise dynamics, which we do using a novel method we developed that is unique in its ability to parse research literature. A prospective researcher can obtain a thorough characterization of the problems, approaches, developments, formal methods, pitfalls, fallacies, and research gaps that characterise this research space by using the visual aids we provide and our observations through cross-cutting themes. Among the themes identified by our survey, we noted that all of the problem categories we identified touch on one or more of a set of seven major variables that may affect power consumption by virtual entities and the resulting model representations: workload type, virtualization agent (VM or container) characteristics, host machine resources and architecture, temperature, operating frequency, attribution of a fraction of consumed power.
DOI: 10.37421/ jms.2022.11. 392.
DOI: 10.37421/ jms.2022.11. 393.
DOI: 10.37421/ jms.2022.11. 394.
DOI: 10.37421/ jms.2022.11. 395.
DOI: 10.37421/ jms.2022.11. 396.
Jeffrey E. Arle* and Longzhi Mei
The stability of evolutionary systems is critical to their survival and reproductive success. In the face of continuous extrinsic and intrinsic stimuli, biological systems must evolve to perform robustly within sub-regions of parameter and trajectory spaces that are often astronomical in magnitude, characterized as homeostasis over a century ago in medicine and later in cybernetics and control theory. Various evolutionary design strategies for robustness have evolved and are conserved across species, such as redundancy, modularity, and hierarchy. We investigate the hypothesis that a strategy for robustness is in evolving neural circuitry network components and topology such that increasing the number of components results in greater system stability. As measured by a center of maximum curvature method related to firing rates, the transition of the neural circuitry systems model to a robust state was ~153 network connections (network degree).
DOI: 10.37421/ 0974-7230.2022.15.411
Using machine learning techniques in smart farming is gaining momentum worldwide. The main goal is to achieve better production by predicting right crop considering present conditions of weather and soil. The climatic changes that are being uncertain results in reduced yield when the farmers follow traditional way of growing crops. The features of soil and conditions of the weather change time to time and this criteria when concentrated leads to precision farming. The study implements machine learning techniques to predict the right crop for cultivation, expecting better yield, taking into account the changes in the weather and soil every time, as and when the farmer aims for growing a fresh crop.
DOI: 10.37421/ 0974-7230.2022.15.420
DOI: 10.37421/ jcsb.2022.15.399
DOI: 10.37421/ jcsb.2022.15. 400
DOI: 10.37421/ jcsb.2022.15.398
DOI: 10.37421/ jcsb.2022.15. 397
DOI: 10.37421/ jcsb.2022.15.401
With the increasing dependence on the internet for various activities, ensuring robust security measures has become paramount. Blockchain technology has emerged as a promising solution for enhancing internet security due to its decentralized and immutable nature. This systematic review aims to provide a comprehensive understanding of the role of blockchain technology in strengthening internet security. Through a thorough analysis of existing literature, this review explores the key concepts, applications, and challenges associated with the integration of blockchain in internet security. The findings highlight the potential of blockchain technology in enhancing data integrity, authentication, access control, and privacy protection. Furthermore, this review discusses the limitations and future research directions in this domain.
Deep Neural Networks (DNNs) have achieved remarkable success in various domains, ranging from computer vision to natural language processing. However, their increasing complexity poses challenges in terms of model size, memory requirements, and computational costs. To address these issues, researchers have turned their attention to sparsity, a technique that introduces structural zeros into the network, thereby reducing redundancy and improving efficiency. This research article explores the role of sparsity in DNNs and its impact on performance improvement. We review existing literature, discuss sparsity-inducing methods, and analyze the benefits and trade-offs associated with sparse networks. Furthermore, we present experimental results that demonstrate the effectiveness of sparsity in improving performance metrics such as accuracy, memory footprint, and computational efficiency. Our findings highlight the potential of sparsity as a powerful tool for optimizing DNNs and provide insights into future research directions in this field.
Cloud computing has emerged as a paradigm shift in computing, providing scalable and on-demand access to computing resources. With the increasing demand for cloud services, efficient resource utilization and load balancing have become critical factors for optimizing performance and ensuring cost-effectiveness. This research article explores dynamic load balancing techniques in cloud computing to enhance resource utilization and improve overall system efficiency. We examine various load balancing algorithms and strategies employed in cloud environments, highlighting their advantages, limitations, and areas of application. Through an in-depth analysis of the literature, this article aims to provide valuable insights into the dynamic load balancing techniques that can be leveraged to achieve optimal resource utilization in cloud computing.
Journal of Computer Science & Systems Biology received 2279 citations as per Google Scholar report