There are different applications of computer vision and digital image processing in various applied domains and automated production process. In textile industry, fabric defect detection is considered as a challenging task as the quality and the price of any textile product are dependent on the efficiency and effectiveness of the automatic defect detection. Previously, manual human efforts are applied in textile industry to detect the defects in the fabric production process. Lack of concentration, human fatigue, and time consumption are the main drawbacks associated with the manual fabric defect detection process. Applications based on computer vision and digital image processing can address the abovementioned limitations and drawbacks. Since the last two decades, various computer vision-based applications are proposed in various research articles to address these limitations. In this review article, we aim to present a detailed study about various computer vision-based approaches with application in textile industry to detect fabric defects. The proposed study presents a detailed overview of histogram-based approaches, color-based approaches, image segmentation-based approaches, frequency domain operations, texture-based defect detection, sparse feature-based operation, image morphology operations, and recent trends of deep learning. The performance evaluation criteria for automatic fabric defect detection is also presented and discussed. The drawbacks and limitations associated with the existing published research are discussed in detail, and possible future research directions are also mentioned. This research study provides comprehensive details about computer vision and digital image processing applications to detect different types of fabric defects.

Technology has played a vital part in improving quality of life, especially in healthcare. Artificial intelligence (AI) and the Internet of Things (IoT) are extensively employed to link accessible medical resources and deliver dependable and effective intelligent healthcare. Body wearable devices have garnered attention as powerful devices for healthcare applications, leading to various commercially available devices for multiple purposes, including individual healthcare, activity alerts, and fitness. The paper aims to cover all the advancements made in the wearable Medical Internet of Things (IoMT) for healthcare systems, which have been scrutinized from the perceptions of their efficacy in detecting, preventing, and monitoring diseases in healthcare. The latest healthcare issues are also included, such as COVID-19 and monkeypox. This paper thoroughly discusses all the directions proposed by the researchers to improve healthcare through wearable devices and artificial intelligence. The approaches adopted by the researchers to improve the overall accuracy, efficiency, and security of the healthcare system are discussed in detail. This paper also highlights all the constraints and opportunities of developing AI enabled IoT-based healthcare systems.

Wireless based sensor networks contain sensors for environment monitoring but have restricted resources. Many clustering protocols are designed to prolong network lifetime but have problems of inadequate cluster head selection criteria, fixed clustering, and static rounds which consume more energy. It is needed to develop an adaptive clustering strategy for better CH selection and load balancing. In this article, we introduced an energy-efficient mobility based cluster head selection mechanism to overcome these limitations. CH selection is based on dedicated parameters that have a huge impact on the sensor energy consumption. The weightage of each node is calculated on the base of the node’s mobility level, residual energy, distance to sink, and density of neighbors. Inter-cluster communication uses single-hop/multi-hop. MATLAB is used to perform simulations. Results show that the proposed approach EEMCS performs better as compared to the existing algorithms CRPD, LEACH, and MODLEACH in terms of load balancing, network stability, energy depletion, and throughput. Energy utilization in the case of EEMCS is much less and the network lifetime is greater than other existing protocols.

Diffraction gratings have been widely used in wavelength-controlled non-mechanical laser beam steering for high data-rate indoor optical wireless communications (OWC). Existing free-space diffraction gratings suffer from inherent difficulties of limited diffraction efficiency, bulky configuration, high cost and significant coupling loss with optical fiber links. In this work, a new optical approach for highly efficient, compact and fiber compatible laser beam steering using an in-fiber diffraction grating is proposed and experimentally demonstrated for the first time to our best knowledge. In-fiber diffraction is made possible based on a 45° tilted fiber grating (TFG), where wavelength dependent lateral scattering is obtained due to the strongly tilted grating structure. Improved diffraction efficiency of 93.5% has been achieved. In addition, the 45° TFG works perfectly for both light emission and reception, enabling full-duplex optical wireless transmission. Utility of the 45° TFG in all-fiber laser beam steering for multi-user full duplex optical wireless communications has been verified in experiments. 1.4 m free-space full-duplex wireless transmission has been demonstrated with data rate up to 12 Gb/s per beam using 2.4 GHz bandwidth OFDM signals.

Cloud computing is a long-standing dream of computing as a utility, where users can store their data remotely in the cloud to enjoy on-demand services and high-quality applications from a shared pool of configurable computing resources. Thus, the privacy and security of data are of utmost importance to all of its users regardless of the nature of the data being stored. In cloud computing environments, it is especially critical because data is stored in various locations, even around the world, and users do not have any physical access to their sensitive data. Therefore, we need certain data protection techniques to protect the sensitive data that is outsourced over the cloud. In this paper, we conduct a systematic literature review (SLR) to illustrate all the data protection techniques that protect sensitive data outsourced over cloud storage. Therefore, the main objective of this research is to synthesize, classify, and identify important studies in the field of study. Accordingly, an evidence-based approach is used in this study. Preliminary results are based on answers to four research questions. Out of 493 research articles, 52 studies were selected. 52 papers use different data protection techniques, which can be divided into two main categories, namely noncryptographic techniques and cryptographic techniques. Noncryptographic techniques consist of data splitting, data anonymization, and steganographic techniques, whereas cryptographic techniques consist of encryption, searchable encryption, homomorphic encryption, and signcryption. In this work, we compare all of these techniques in terms of data protection accuracy, overhead, and operations on masked data. Finally, we discuss the future research challenges facing the implementation of these techniques.