This study synthesized pristine and aluminum (Al)-doped zinc oxide (Al:ZnO) nanostructures through a simplistic low-temperature ultrasonicated solution immersion method. Al:ZnO nanostructures were synthesized as a sensing material using different immersion times varying from two to five hours. The Al:ZnO nanostructured-based flexible humidity sensor was fabricated by employing cellulose filter paper as a substrate and transparent paper glue as a binder through a simplistic brush printing technique. XRD, FESEM, HRTEM, EDS, XPS, a two-probe I–V measurement system, and a humidity measurement system were employed to investigate the structural, morphological, chemical, electrical, and humidity-sensing properties of the pristine ZnO and Al:ZnO nanostructures. The structural and morphological analysis confirmed that Al cations successfully occupied the Zn lattice or integrated into interstitial sites of the ZnO lattice matrix. Humidity-sensing performance analysis indicated that the resistance of the Al:ZnO nanostructure samples decreased almost linearly as the humidity level increased, leading to better sensitivity and sensing response. The Al:ZnO-4 h nanostructured-based flexible humidity sensor had a maximum sensing response and demonstrated the highest sensitivity towards humidity changes, which was noticeably superior to the other tested samples. Finally, this study explained the Al:ZnO nanostructures-based flexible humidity sensor sensing mechanism in terms of chemical adsorption, physical adsorption, and capillary condensation mechanisms.

Functional electrical stimulation (FES) has been widely used to treat spinal cord injury (SCI) patients. Many research studies employ a closed-loop FES system to monitor the stimulated muscle response and provide a precise amount of charge to the muscle. However, most closed-loop FES devices perform poorly and sometimes fail when muscle nonlinearity effects such as fatigue, time delay response, stiffness, spasticity, and subject change happen. The poor performance of the closed-loop FES device is mainly due to discrepancies in the feedback control algorithms. Most of the existing feedback control algorithms were not designed to adapt to new changes in patients with different nonlinearity effects, resulting in early muscle fatigue. Therefore, this research proposes an adaptive sliding mode (SM) feedback control algorithm that could adapt and fine-tune internal control settings in real-time according to the current subject’s nonlinear and time-varying muscle response during the rehabilitation (knee extension) exercise. The adaptive SM feedback controller consists mainly of system identification, direct torque control, and tunable feedback control settings. Employing the system identification unit in the feedback control algorithm enables real-time self-tuning and adjusting of the SM internal control settings according to the current patient’s condition. Initially, the patient’s knee trajectory response was identified by extracting meaningful information, which included time delay, rise time, overshoot, and steady-state error. The extracted information was used to fine-tune and update the internal SM control settings. Finally, the performance of the proposed adaptive SM feedback control algorithm in terms of system response time, stability, and rehabilitation time was analysed using a nonlinear knee model. The findings from the simulation results indicate that the adaptive SM feedback controller demonstrated the best control performance in accurately tracking the desired knee angle movement by having faster response times, smaller overshoots, and lower steady-state errors when compared with the conventional SM across four reference angle settings (20°, 30°, 40°, and 76°). The adaptive feedback SM controller was also observed to compensate for muscle nonlinearities, including fatigue, stiffness, spasticity, time delay, and other disturbances.

This study fabricated tantalum (Ta)-doped titanium dioxide with a unique nanorod-assembled actinomorphic-flower-like microsphere structured film. The Ta-doped TiO₂ actinomorphic-flower-like microsphere (TAFM) was fabricated via the solution immersion method in a Schott bottle with a home-made improvised clamp. The samples were characterised using FESEM, HRTEM, XRD, Raman, XPS, and Hall effect measurements for their structural and electrical properties. Compared to the undoped sample, the rutile-phased TAFM sample had finer nanorods with an average 42 nm diameter assembled to form microsphere-like structures. It also had higher oxygen vacancy sites, electron concentration, and mobility. In addition, a reversed double-beam photoacoustic spectroscopy measurement was performed for TAFM, revealing that the sample had a high electron trap density of up to 2.5 μmolg⁻¹. The TAFM showed promising results when employed as the resistive-type sensing film for a humidity sensor, with the highest sensor response of 53,909% obtained at 3 at.% Ta doping. Adding rGO to 3 at.% TAFM further improved the sensor response to 232,152%.

A foggy environment may cause digitally captured images to appear blurry, dim, or low in contrast. This will impact computer vision systems that rely on image information. With the need for real-time image information, such as a plate number recognition system, a simple yet effective image enhancement algorithm using a hardware implementation is very much needed to fulfil the need. To improve images that suffer from low exposure and hazy, the hardware implementations are usually based on complex algorithms. Hence, the aim of this paper is to propose a less complex enhancement algorithm for hardware implementation that is able to improve the quality of such images. The proposed method simply combines brightness and contrast manipulation to enhance the image. In order to see the performance of the proposed method, a total of 100 vehicle registration number images were collected, enhanced, and evaluated. The evaluation results were compared to two other enhancement methods quantitatively and qualitatively. Quantitative evaluation is done by evaluating the output image using peak signal-to-noise ratio and mean-square error evaluation metrics, while a survey is done to evaluate the output image qualitatively. Based on the quantitative evaluation results, our proposed method outperforms the other two enhancement methods.