Infrared thermal imaging presents significant potential in various domains. However, the widespread development of this technology is hindered by the high cost associated with acquiring high-quality thermal imaging sensors. To overcome this challenge, super-resolution techniques have emerged as a viable solution for extracting valuable information from low-resolution thermal images. While generative adversarial networks (GANs) have been widely adopted for thermal imaging super-resolution, their performance is limited by the inherent lack of detail in low-resolution training images, resulting in reduced fidelity and accuracy in generating high-resolution reconstructions. To tackle this challenge, this letter introduces FEGAN, a novel approach that enhances the performance of GANs by incorporating a feature-oriented enhanced (FE) mechanism within the generative network (GN). The FE plays a pivotal role in extracting high-frequency texture and edge details from lowresolution inputs and reconstructing them into enhanced images. This process substantially improves textures and edges within the training set of thermal images. Furthermore, refinements have been applied to both the GN and the discriminative network (DN) to enhance feature extraction efficiency. The experimental findings unequivocally demonstrate the superior performance of FEGAN compared to state-of-the-art methods. FEGAN achieves impressive performance metrics, including PSNR of 27.18, SSIM of 0.6523, FSIM of 0.5500, and LPIPS of 0.1221, highlighting its remarkable capabilities in the realm of thermal image superresolution.

Thiswork leverages an Intelligent Reflecting Surface (IRS) to improve throughput performance of a wireless powered communication network (WPCN). A novel hybrid multiple access strategy is conceived, where the IRS set the time-division multiple access (TDMA) to allocate its phase shifts, each of which performs the non-orthogonal multiple access for multiple Internet of Things (IoT) devices. These devices firstly collect energy in a non-linear manner disseminated by a power station (PS) at downlink wireless energy transfer (WET) stage. The gathered energy is then utilized by these devices to convey data information to an access point (AP) by employing the TDMA protocol with flexible time slots at the uplink wireless information transfer (WIT) stage. During each time slot, the NOMA protocol is utilized for all IoT devices’ data transmission, thus creating a hybrid TDMA-NOMA framework. To assess the overall performance of the system model under investigation, we aim to maximize the network throughput, subject to the constraints of the downlink WET and uplink WIT time scheduling, the power allocation for each time slots, and unit-modulus IRS phase shifts of downlink WET and uplink WIT, thereby highlighting energy harvesting and information transmission capabilities. The formulated problem is inherently not jointly convex due to the coupled nature of the variables, making it difficult to resolve directly. To deal with this challenge, a power allocation factor is first introduced for each IoT device, and then an alternating optimization (AO) algorithm is proposed to jointly design the power allocation, IRS phase shifts, and time scheduling in an alternated fashion. To be specified, the optimal power allocation factor can be obtained using interior-point methods for given time scheduling and IRS phase shifts. Also, the time slots of downlink WET and uplink WIT stages are theoretically derived in closed-form expressions using the Lagrange dual method and the Karush–Kuhn–Tucker (KKT) conditions. Additionally, the semi-closed-form expressions of IRS phase shifts during WET and WIT stages are iteratively achieved through Riemannian Manifold Optimization (RMO) and quadratic transformation (QT)-based Alternating Direction Method of Multipliers (ADMM) algorithms. Finally, the numerical results are demonstrated to highlight the effectiveness of the proposed algorithm, the optimal IRS phase shift design, and the optimal WET time scheduling design in comparison to the benchmarks.

With the development of indoor location-based services (ILBS), the dual foot-mounted inertial navigation system (DF-INS) has been extensively used in many fields involving monitoring and direction-finding. It is a widespread ILBS implementation with considerable application potential in various areas such as firefighting and home care. However, the existing DF-INS is limited by a high inaccuracy rate due to the highly dynamic and non-stable stride length thresholds. The system also provides less clear and significant information visualization of a person’s position and the surrounding map. This study proposes a novel wearable-foot IOAM-inertial odometry and mapping to address the aforementioned issues. First, the person’s gait analysis is computed using the zero-velocity update (ZUPT) method with data fusion from ultrasound sensors placed on the inner side of the shoes. This study introduces a dynamic minimum centroid distance (MCD) algorithm to improve the existing extended Kalman filter (EKF) by limiting the stride length to a minimum range, significantly reducing the bias in data fusion. Then, a dual trajectory fusion (DTF) method is proposed to combine the left- and right-foot trajectories into a single center body of mass (CBoM) trajectory using ZUPT clustering and fusion weight computation. Next, ultrasound-type mapping is introduced to reconstruct the surrounding occupancy grid map (S-OGM) using the sphere projection method. The CBoM trajectory and S-OGM results were simultaneously visualized to provide comprehensive localization and mapping information. The results indicate a significant improvement with a lower root mean square error (RMSE = 1.2 m) than the existing methods.

Airflows, heat plumes, and gas leaks are ubiquitous yet invisible to both human eyes and commodity cameras, limiting visual sensing of environmental dynamics critical to safety, comfort, and automation. Traditional Schlieren imaging reveals air refractive-index variations but requires bulky, expensive optics and high-end cameras, restricting its use to controlled lab settings. We present MoiréLens, a practical Schlieren imaging system that works in real-world environments by capturing Moiré interference with a low-cost embedded camera. By embedding high-frequency, human-imperceptible Moiré stimuli into everyday wallpaper, MoiréLens converts subtle refractive-index variations into measurable Moiré phase shifts formed between the background and the camera’s color-filter array. An automatic calibration module maintains stable, high-sensitivity Moiré pattern generation under camera viewpoint misalignment by dynamically adjusting the displayed Moiré stimulus. A color Moiré extraction and Moiré-to-Schlieren conversion pipeline isolates Moiré patterns from wallpaper and reconstructs refractive-index variations with adaptive spatial-temporal sensitivity. Extensive experiments show that MoiréLens achieves high-sensitivity Schlieren imaging for both regional airflows and global plumes. Using a 1-megapixel camera, it localizes a 1.5 hPa gas leak at 0.8 m and butane from an unlit lighter at 1 m, extending the sensing range by 3 × over background-oriented Schlieren. Leveraging heat-plume visualization, MoiréLens further classifies dry- and moist-heat cooking with \(99.8\%\) accuracy and estimates oil temperature with an average error of 7.10°C under unseen heating powers.