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With the advent of new technologies and endeavours for automation in almost all day-to-day activities, the recent discussions on the metaverse life have a greater expectation. The metaverse enables people to communicate with each other by combining the physical world with the virtual world. However, realizing the Metaverse requires symmetric content delivery, low latency, dynamic network control, etc. Industry 5.0 is expected to reform the manufacturing processes through human-robot collaboration and effective utilization of technologies like Artificial intelligence for increased productivity and less maintenance. The metaverse with Industry 5.0 may have tremendous technological integration for a more immersive experience and enhanced productivity. In this review, we present an overview of the metaverse and Industry 5.0, focusing on key technologies that enable the industrial metaverse, including virtual and augmented reality, 3D modeling, artificial intelligence, edge computing, digital twins, blockchain, and 6G communication networks. The article then discusses the metaverse's diverse applications across various Industry 5.0 sectors, such as agriculture, supply chain management, healthcare, education, and transportation, illustrated through several research initiatives. Additionally, the article addresses the challenges of implementing the industrial metaverse, proposes potential solutions, and outlines directions for future research.

In this paper, we propose a low-density parity check (LDPC) code design scheme that improves the performance of the existing genetic algorithm-based LDPC scheme. In particular, we enhance the performance of the LDPC code by removing the girth-4 property of the parity check matrix and utilizing the min-sum decoding algorithm instead of the belief propagation decoding algorithm. In addition, we consider different short block-length scenarios, including 64-bit and 128-bit block length. Then, we evaluate the block error rate (BLER) of the LDPC code over the binary input additive white Gaussian noise (BI-AWGN) channel. Finally, extensive simulation results indicate that our proposed approach achieves more than 11% gain in terms of BLER compared with the benchmarked schemes.

Around 2020, 5G began its commercialization journey, and discussions about the next-generation networks (such as 6G) emerged. Researchers predict that 6G networks will have higher bandwidth, coverage, reliability, energy efficiency, and lower latency, and will be an integrated “human-centric” network system powered by artificial intelligence (AI). This 6G network will lead to many real-time automated decisions, ranging from network resource allocation to collision avoidance for self-driving cars. However, there is a risk of losing control over decision-making due to the high-speed, data-intensive AI decision-making that may go beyond designers’ and users’ comprehension. To mitigate this risk, explainable AI (XAI) methods can be used to enhance the transparency of the black-box AI decision-making process. This paper surveys the application of XAI towards the upcoming 6G age, including 6G technologies (such as intelligent radio and zero-touch network management) and 6G use cases (such as industry 5.0). Additionally, the paper summarizes the lessons learned from recent attempts and outlines important research challenges in applying XAI for 6G use cases soon.

Semantic segmentation of aerial and satellite images plays a crucial role in a wide range of applications and services, catering to the increasing needs of environmental resource management, urban planning, and traffic safety. Many efficient semantic segmentation methods have been proposed by exploiting deep learning techniques with convolution neural networks (CNNs) architectures and self-attention mechanisms to achieve superior accuracy if compared with conventional machine learning-based approaches. In this article, we introduce a hybrid bilateral segmentation network (HBSeNet), a novel semantic segmentation architecture. Inspired by the success of dual-path network models that replace conventional single-branch encoder–decoder architectures, we construct a model with the core idea of combining the context path and the spatial path to optimize both the accuracy and the complexity of deep learning model in the field of remote sensing image segmentation. Moreover, HBSeNet innovates with auxiliary modules designed to enhance its performance, such as sequential atrous convolution, information synthesis module, and bridge for efficient multiscale feature extraction, fusion, and integration. In simulations, our model achieves a global accuracy of 92.04%, a mean intersection-over-union of 83.57%, and a mean boundary-F1-score of 90.23% when evaluated on the ISPRS Potsdam dataset, surpassing the state-of-the-art segmentation models, such as DeepLabV3+, SwinCNN, and ST-UNet.

Abstract This article presents a direction‐finding method based on phase difference measurement for wide‐ranging applications of electronic intelligence. By including three processing steps of rotating, re‐scaling, and rounding of phase plane, the proposed method is able to obtain the actual phase shift of the longest baseline from the ambiguous phase differences measured from incoming signals with low computational cost. The method's performance is analysed and evaluated in terms of angle of arrival (AOA) estimation accuracy and probability of unambiguity by several interferometers of long baselines designed by multiple antenna elements. Consequently, an optimised array configuration of four antenna elements is taken into account to obtain high AOA estimation accuracy via several productive simulations and intensively validated with device measurement in the practical environment. Regarding the simulation of angle estimation accuracy, the proposed method remarkably outperforms two other existing methods, including second‐order difference array and correlative interferometers. In the experimental measurement, high AOA determination performance is achieved with the transmitter located around 35.5 m far from an antenna array and the small average angle estimation error of 1.86° at the direction of 20°. For various directions from −60° to 60° with the steps of 5°, the results satisfy the actual directions of the transmitter with a standard deviation of less than 0.5°.

The rapid progress in digitalization and automation have led to an accelerated growth in healthcare, generating novel models that are creating new channels for rendering treatment at reduced cost. The Metaverse is an emerging technology in the digital space which has huge potential in healthcare, enabling realistic experiences to the patients as well as the medical practitioners. The Metaverse is a confluence of multiple enabling technologies such as artificial intelligence, virtual reality, augmented reality, internet of medical devices, robotics, quantum computing, etc. through which new directions for providing quality healthcare treatment and services can be explored. The amalgamation of these technologies ensures immersive, intimate and personalized patient care. It also provides adaptive intelligent solutions that eliminates the barriers between healthcare providers and receivers. This article provides a comprehensive review of the Metaverse for healthcare, emphasizing on the state of the art, the enabling technologies to adopt the Metaverse for healthcare, the potential applications, and the related projects. The issues in the adaptation of the Metaverse for healthcare applications are also identified and the plausible solutions are highlighted as part of future research directions.

In recent years, the increasing popularity of unmanned aerial vehicles (UAVs) has arisen from the emergence of cutting-edge technologies deployed in small and low-cost devices. With the great capability of friendly uses and wide applications for multiple purposes, amateur drones can be piloted to effortlessly access any geographical area. This poses some difficulties in monitoring and managing drones that may invade private or limited-access areas. In this paper, we propose a radio-frequency (RF)-based surveillance solution to effectively detect and classify drones, and recognize operations by leveraging a high-performance convolutional neural network. The proposed network, namely RF-UAVNet, is specified with grouped one-dimensional convolution to significantly reduce the network size and computational cost. Besides, a novel structure of multi-level skip-connection, for the preservation of gradient flow, incorporating multi-level pooling, for the collection of informative deep features, is proposed to achieve high accuracy via learning efficiency improvement. In the experiments, RF-UAVNet yields the accuracy of 99.85% for drone detection, 98.53% for drone classification, and 95.33% for operation mode recognition, numbers which outperform the current state-of-the-art deep learning-based methods on DroneRF, a publicly available dataset for RF-based drone surveillance systems.

Since the beginning of the COVID-19 pandemic, the demand for unmanned aerial vehicles (UAVs) has surged owing to an increasing requirement of remote, noncontact, and technologically advanced interactions. However, with the increased demand for drones across a wide range of fields, their malicious use has also increased. Therefore, an anti-UAV system is required to detect unauthorized drone use. In this study, we propose a radio frequency (RF) based solution that uses 15 drone controller signals. The proposed method can solve the problems associated with the RF based detection method, which has poor classification accuracy when the distance between the controller and antenna increases or the signal-to-noise ratio (SNR) decreases owing to the presence of a large amount of noise. For the experiment, we changed the SNR of the controller signal by adding white Gaussian noise to SNRs of −15 to 15 dB at 5 dB intervals. A power-based spectrogram image with an applied threshold value was used for convolution neural network training. The proposed model achieved 98% accuracy at an SNR of −15 dB and 99.17% accuracy in the classification of 105 classes with 15 drone controllers within 7 SNR regions. From these results, it was confirmed that the proposed method is both noise-tolerant and scalable.

Automatic modulation classification (AMC), which aims to blindly identify the modulation type of an incoming signal at the receiver in wireless communication systems, is a fundamental signal processing technique in the physical layer to improve the spectrum utilization efficiency. Motivated by deep learning (DL) high-impact success in many informatics domains, including radio signal processing for communications, numerous recent AMC methods exploiting deep networks have been proposed to overcome the existing drawbacks of traditional approaches. DL is capable of learning the underlying characteristics of radio signals effectively for modulation pattern recognition, which in turn improves the modulation classification performance under the presence of channel impairments. In this work, we first provide the fundamental concepts of various architectures, such as neural networks, recurrent neural networks, long short-term memory, and convolutional neural networks as the necessary background. We then convey a comprehensive study of DL for AMC in wireless communications, where technical analysis is deliberated in the perspective of state-of-the-art deep architectures. Remarkably, several sophisticated structures and advanced designs of convolutional neural networks are investigated for different data types of sequential radio signals, spectrum images, and constellation images to deal with various channel impairments. Finally, we discuss some primary research challenges and potential future directions in the area of DL for modulation classification.

Intelligent signal processing for wireless communications is a vital task in modern wireless systems, but it faces new challenges because of network heterogeneity, diverse service requirements, a massive number of connections, and various radio characteristics. Owing to recent advancements in big data and computing technologies, artificial intelligence (AI) has become a useful tool for radio signal processing and has enabled the realization of intelligent radio signal processing. This survey covers four intelligent signal processing topics for the wireless physical layer, including modulation classification, signal detection, beamforming, and channel estimation. In particular, each theme is presented in a dedicated section, starting with the most fundamental principles, followed by a review of up-to-date studies and a summary. To provide the necessary background, we first present a brief overview of AI techniques such as machine learning, deep learning, and federated learning. Finally, we highlight a number of research challenges and future directions in the area of intelligent radio signal processing. We expect this survey to be a good source of information for anyone interested in intelligent radio signal processing, and the perspectives we provide therein will stimulate many more novel ideas and contributions in the future.

The very first infected novel coronavirus case (COVID-19) was found in Hubei, China in Dec. 2019. The COVID-19 pandemic has spread over 214 countries and areas in the world, and has significantly affected every aspect of our daily lives. At the time of writing this article, the numbers of infected cases and deaths still increase significantly and have no sign of a well-controlled situation, e.g., as of 13 July 2020, from a total number of around 13.1 million positive cases, 571,527 deaths were reported in the world. Motivated by recent advances and applications of artificial intelligence (AI) and big data in various areas, this paper aims at emphasizing their importance in responding to the COVID-19 outbreak and preventing the severe effects of the COVID-19 pandemic. We firstly present an overview of AI and big data, then identify the applications aimed at fighting against COVID-19, next highlight challenges and issues associated with state-of-the-art solutions, and finally come up with recommendations for the communications to effectively control the COVID-19 situation. It is expected that this paper provides researchers and communities with new insights into the ways AI and big data improve the COVID-19 situation, and drives further studies in stopping the COVID-19 outbreak.

This paper proposes a novel vehicular ad hoc network-based traffic light (TL) scheduling scheme for reducing CO2 emissions from vehicles and improving the safety of road intersections where yellow-light-dilemma (YLD)-related accidents frequently occur. The proposed scheme uses adaptive TL scheduling and optimal speed advisory methods to increase traffic throughput and reduce CO2 emissions of traveling vehicles. In addition, the proposed scheme employs various distributed protocols and algorithms to tackle the YLD problem, which has not been addressed by the previous works in the literature. When the YLD problem arises at a signalized intersection, vehicles in heavily weighted dilemma zones are provided extending yellow signal time to stop or pass through the intersection. A mathematical analysis proves that the proposed protocols and algorithms have low communication overhead and low computational complexity which are equal to O(N). Furthermore, four simulation works are conducted to evaluate the efficiency of the proposed scheme. The outstanding results have demonstrated that the proposed scheme outperforms other existing approaches by approximately 81.9% in terms of the average performance.

Due to the variety of background model in the real world, detecting changes in a video cannot be addressed exhaustively by a simple background subtraction method, especially with several motion detection challenges, such as dynamic background, camera jitter, intermittent object motion, and so on. In this paper, we propose an efficient background subtraction method, namely locally statistical dual-mode (LSD), for detecting moving objects in video-based surveillance systems. The method includes a local intensity pattern comparison algorithm for foreground segmentation by analyzing the homogeneity of intensity patterns of the input frame and the background model, in which the homogeneity is calculated by the mean and standard deviation of pixel intensity. Besides that, a dual-mode scheme is developed to temporally update the background model for the short- and long-term scenarios corresponding to sudden and gradual changes in the background. The advantage of this scheme is the allowance of updating the model in both pixel- and frame-wise manners simultaneously. The parameters used in both the local intensity pattern comparison algorithm and the dual-mode background model updating scheme are estimated for every input frame consecutively based on local and global statistical information of segmentation result. In experiments, the proposed LSD method is extensively evaluated on the Wallflower and CDnet2014 datasets; and remarkable performance demonstrates its preeminence to the many state-of-the-art background subtraction approaches in terms of segmentation accuracy and computational complexity.

CCTV-based behavior recognition systems have gained considerable attention in recent years in the transportation surveillance domain for identifying unusual patterns, such as traffic jams, accidents, dangerous driving and other abnormal behaviors. In this paper, a novel approach for traffic behavior modeling is presented for video-based road surveillance. The proposed system combines the pachinko allocation model (PAM) and support vector machine (SVM) for a hierarchical representation and identification of traffic behavior. A background subtraction technique using Gaussian mixture models (GMMs) and an object tracking mechanism based on Kalman filters are utilized to firstly construct the object trajectories. Then, the sparse features comprising the locations and directions of the moving objects are modeled by PAMinto traffic topics, namely activities and behaviors. As a key innovation, PAM captures not only the correlation among the activities, but also among the behaviors based on the arbitrary directed acyclic graph (DAG). The SVM classifier is then utilized on top to train and recognize the traffic activity and behavior. The proposed model shows more flexibility and greater expressive power than the commonly-used latent Dirichlet allocation (LDA) approach, leading to a higher recognition accuracy in the behavior classification.

Personalized emotion recognition provides an individual training model for each target user in order to mitigate the accuracy problem when using general training models collected from multiple users. Existing personalized speech emotion recognition research has a cold-start problem that requires a large amount of emotionally-balanced data samples from the target user when creating the personalized training model. Such research is difficult to apply in real environments due to the difficulty of collecting numerous target user speech data with emotionally-balanced label samples. Therefore, we propose the Robust Personalized Emotion Recognition Framework with the Adaptive Data Boosting Algorithm to solve the cold-start problem. The proposed framework incrementally provides a customized training model for the target user by reinforcing the dataset by combining the acquired target user speech with speech from other users, followed by applying SMOTE (Synthetic Minority Over-sampling Technique)-based data augmentation. The proposed method proved to be adaptive across a small number of target user datasets and emotionally-imbalanced data environments through iterative experiments using the IEMOCAP (Interactive Emotional Dyadic Motion Capture) database.

The most significant barrier to success in human activity recognition is extracting and selecting the right features. In traditional methods, the features are chosen by humans, which requires the user to have expert knowledge or to do a large amount of empirical study. Newly developed deep learning technology can automatically extract and select features. Among the various deep learning methods, convolutional neural networks (CNNs) have the advantages of local dependency and scale invariance and are suitable for temporal data such as accelerometer (ACC) signals. In this paper, we propose an efficient human activity recognition method, namely Iss2Image (Inertial sensor signal to Image), a novel encoding technique for transforming an inertial sensor signal into an image with minimum distortion and a CNN model for image-based activity classification. Iss2Image converts real number values from the X, Y, and Z axes into three color channels to precisely infer correlations among successive sensor signal values in three different dimensions. We experimentally evaluated our method using several well-known datasets and our own dataset collected from a smartphone and smartwatch. The proposed method shows higher accuracy than other state-of-the-art approaches on the tested datasets.

There is sufficient evidence proving the impact that negative lifestyle choices have on people’s health and wellness. Changing unhealthy behaviours requires raising people’s self-awareness and also providing healthcare experts with a thorough and continuous description of the user’s conduct. Several monitoring techniques have been proposed in the past to track users’ behaviour; however, these approaches are either subjective and prone to misreporting, such as questionnaires, or only focus on a specific component of context, such as activity counters. This work presents an innovative multimodal context mining framework to inspect and infer human behaviour in a more holistic fashion. The proposed approach extends beyond the state-of-the-art, since it not only explores a sole type of context, but also combines diverse levels of context in an integral manner. Namely, low-level contexts, including activities, emotions and locations, are identified from heterogeneous sensory data through machine learning techniques. Low-level contexts are combined using ontological mechanisms to derive a more abstract representation of the user’s context, here referred to as high-level context. An initial implementation of the proposed framework supporting real-time context identification is also presented. The developed system is evaluated for various realistic scenarios making use of a novel multimodal context open dataset and data on-the-go, demonstrating prominent context-aware capabilities at both low and high levels.

Predicting age and gender through images is a common computer vision problem with many practical applications. However, this problem faces many difficulties because a person's age can be affected by genetics, living environment, diet, health, gender, and other factors. Therefore, the accuracy of the prediction model may decrease due to the enormous diversity and variability in the data. In this study, we use three models, including Unet, MobileNets, and EfficientNets, to test the performance of predicting a person's age and gender through their photos. In addition, we also adjust the learning rate parameter to find optimal performance. The best results for gender prediction are achieved by the Unet model with the highest accuracy of 97.22 %, and the MobileNets model gives age prediction results with MAE = 2.248, learning rate 0.001 for optimal performance in the models of our study. [ABSTRACT FROM AUTHOR]