For smartphone indoor localization, an INS/WiFi hybrid localization system is proposed in this paper. Acceleration and angular velocity are used to estimate step lengths and headings. The problem with INS is that positioning errors grow with time. Using radio signal strength as a fingerprint is a widely used technology. The main problem with fingerprint matching is mismatching due to noise. Taking into account the different shortcomings and advantages, inertial sensors and WiFi from smartphones are integrated into indoor positioning. For a hybrid localization system, pre-processing techniques are used to enhance the WiFi signal quality. An inertial navigation system limits the range of WiFi matching. A Multi-dimensional Dynamic Time Warping (MDTW) is proposed to calculate the distance between the measured signals and the fingerprint in the database. A MDTW-based weighted least squares (WLS) is proposed for fusing multiple fingerprint localization results to improve positioning accuracy and robustness. Using four modes (calling, dangling, handheld and pocket), we carried out walking experiments in a corridor, a study room and a library stack room. Experimental results show that average localization accuracy for the hybrid system is about 2.03 m.

The rain removal task is to restore a clean image from the contaminated image by separating the background. Since the rise of deep learning in 2016, the task of image deraining has also stepped into the era of deep learning. Numerous researchers have devoted themselves to the field of computer vision and pattern recognition. However, there is still a lack of comprehensive review papers focused on using deep learning to perform rain removal tasks. In this paper, we present a comprehensive review of single image deraining based on deep learning over the past ten years. Two categories of deraining methods are discussed: the data-driven approach and the data-model-based approach. For the first type, we compare the existing network structures and loss functions. For the second type, we analyze the combination of different deraining models with deep learning, and each branch method is introduced in detail. Additionally, we quantitatively investigate the performances of the existing state-of-the-art methods on both publicly synthetic and real datasets. The trend of image deraining is also discussed.

In recent years, using smartphones for indoor positioning has become increasingly popular with consumers. This paper presents an integrated localization technique for inertial and magnetic field sensors to challenge indoor positioning without Wi-Fi signals. For dead-reckoning (DR), attitude angle estimation, step length calculation, and step counting estimation are introduced. Dynamic time warping (DTW) usually calculates the distance between the measured magnetic field and magnetic fingerprint in the database. For DR/Magnetic matching (MM), we creatively propose 3-dimensional dynamic time warping (3DDTW) to calculate the distance. Unlike traditional DTW, 3DDTW extends the original one-dimensional signal to a two-dimensional signal. Finally, the weighted least squares further improves indoor positioning accuracy. In the three different experimental scenarios—teaching building, study room, office building—DR/MM hybrid positioning accuracy is about 3.34 m.

Location-based services for smartphones are becoming more and more popular. The core of location-based services is how to estimate a user’s location. An INS/floor-plan indoor localization system, using the Firefly Particle Filter (FPF), is proposed to estimate a user’s location. INS includes an attitude angle module, a step length module and a step counting module. In the step length module, we propose a hybrid step length model. The proposed step length algorithm reasonably calculates a user’s step length. Because of sensor deviation, non-orthogonality and the user’s jitter, the main bottleneck for INS is that the error grows over time. To reduce the cumulative error, we design cascade filters including the Kalman Filter (KF) and FPF. To a certain extent, KF reduces velocity error and heading drift. On the other hand, the firefly algorithm is used to solve the particle impoverishment problem. Considering that a user may not cross an obstacle, the proposed particle filter is proposed to improve positioning performance. Results show that the average positioning error in walking experiments is 2.14 m.

Vehicular ad hoc networks (VANETs) have recently drawn a large amount of attention because of their enormous potential in road safety improvement and traffic management as well as infotainment service support. As the standard of medium access control (MAC) and physical (PHY) layers for VANETs, IEEE 802.11p has been proposed for more than a decade. Though performance analyses of IEEE 802.11p MAC have been performed, the existing analytical methods still need to be improved. In this paper, to assess the saturated throughput and the average packet delay of IEEE 802.11p MAC in VANETs, a two-dimensional (2-D) Markov model is introduced by considering the capture effect under Nakagami-m fading channel. Moreover, the closed-form expressions of successful transmission, collided transmission, saturated throughput, and average packet delay are carefully derived. Finally, the simulation results are demonstrated to verify the accuracy of the proposed analytical model, which also proves that this analytical model is more precise than the existing ones in terms of saturated throughput and average packet delay.

This paper explores a system that integrates non-orthogonal multiple access (NOMA) with mobile edge computing (MEC), supported by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) in energy splitting (ES) mode. We aim to address the challenge of minimizing energy consumption over the long-term by modeling it as a non-convex optimization problem. To simplify the resolution of this complex issue, we introduce a decomposition approach using a multi-agent deep reinforcement learning (DRL) technique. Specifically, we decompose the problem into the sub-problems of computational resource and power allocation, and phase shifting, amplitude coefficient, offloading ratio, and time optimization. Based on theoretical derivations, we determine the optimal computational resource and power allocation for the given phase shifting, amplitude coefficient, offloading ratio, and time. Then, we propose a multi-agent twin delayed deep deterministic policy gradient (TD3) algorithm to jointly optimize phase shifting, amplitude coefficient, offloading ratio, and time for each time slot. Simulation outcomes demonstrate that the decomposition-based multi-agent TD3 (DB-MATD3) algorithm achieves faster convergence compared to conventional centralized DRL approaches. Compared with other existing methods, the proposed task offloading strategy, which employs the ES-based STAR-RIS, achieves energy savings of 28.7% to 40.8%.