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For this purpose, an example of a vehicle state estimation with a focus on the tire-road friction coefficient is used, which represents a challenging problem for classical analysis and filter parameterization.In the modern era, deep learning techniques have emerged as powerful tools in image recognition. Convolutional Neural Networks, one of the deep learning tools, have attained an impressive outcome in this area. Applications such as identifying objects, faces, bones, handwritten digits, and traffic signs signify the importance of Convolutional Neural Networks in the real world. The effectiveness of Convolutional Neural Networks in image recognition motivates the researchers to extend its applications in the field of agriculture for recognition of plant species, yield management, weed detection, soil, and water management, fruit counting, diseases, and pest detection, evaluating the nutrient status of plants, and much more. The availability of voluminous research works in applying deep learning models in agriculture leads to difficulty in selecting a suitable model according to the type of dataset and experimental environment. In this manuscript, the authors present a survey of the existing literature in applying deep Convolutional Neural Networks to predict plant diseases from leaf images. Cytoskeletal Signaling inhibitor This manuscript presents an exemplary comparison of the pre-processing techniques, Convolutional Neural Network models, frameworks, and optimization techniques applied to detect and classify plant diseases using leaf images as a data set. This manuscript also presents a survey of the datasets and performance metrics used to evaluate the efficacy of models. The manuscript highlights the advantages and disadvantages of different techniques and models proposed in the existing literature. This survey will ease the task of researchers working in the field of applying deep learning techniques for the identification and classification of plant leaf diseases.In this study, we propose a highly sensitive transparent urea enzymatic field-effect transistor (EnFET) point-of-care (POC) diagnostic test sensor using a triple-gate amorphous indium gallium zinc oxide (a-IGZO) thin-film pH ion-sensitive field-effect transistor (ISFET). The EnFET sensor consists of a urease-immobilized tin-dioxide (SnO2) sensing membrane extended gate (EG) and an a-IGZO thin film transistor (TFT), which acts as the detector and transducer, respectively. To enhance the urea sensitivity, we designed a triple-gate a-IGZO TFT transducer with a top gate (TG) at the top of the channel, a bottom gate (BG) at the bottom of the channel, and a side gate (SG) on the side of the channel. By using capacitive coupling between these gates, an extremely high urea sensitivity of 3632.1 mV/pUrea was accomplished in the range of pUrea 2 to 3.5; this is 50 times greater than the sensitivities observed in prior works. High urea sensitivity and reliability were even obtained in the low pUrea (0.5 to 2) and high pUrea (3.5 to 5) ranges. The proposed urea-EnFET sensor with a triple-gate a-IGZO TFT is therefore expected to be useful for POC diagnostic tests that require high sensitivity and high reliability.In this study, polycrystalline lead magnesium niobate-lead titanate (PMN-PT) was explored as an alternative piezoelectric material, with a higher power density for energy harvesting (EH), and comprehensively compared to the widely used polycrystalline lead zirconate titanate (PZT). First, the size distribution and piezoelectric properties of PZT and PMN-PT raw powders and ceramics were compared. Thereafter, both materials were deposited on stainless-steel substrates as 10 μm thick films using the aerosol deposition method. The films were processed as 3-1-mode cantilever-type EH devices using microelectromechanical systems. The films with different annealing temperatures were characterized by scanning electron microscopy, energy-dispersive X-ray spectroscopy, and dielectric behavior measurements. Furthermore, the mechanical and electrical properties of PMN-PT- and PZT-based devices were measured and compared. The PMN-PT-based devices showed a higher Young’s modulus and lower damping ratio. Owing to their higher figure of merit and lower piezoelectric voltage constant, they showed a higher power and lower voltage than the PZT-based devices. Finally, when poly-PMN-PT material was the active layer, the output power was enhanced by 26% at the 0.5 g acceleration level. Thus, these devices exhibited promising properties, meeting the high current and low voltage requirements in integrated circuit designs.This paper presents a new configuration of a slotted waveguide antenna (SWA) array aimed at the X-band within the desired band of 9.38~9.44 GHz for shipboard marine radars. The SWA array, which typically consists of a slotted waveguide, a polarizing filter, and a metal reflector, is widely employed in marine radar applications. Nonetheless, conventional slot array designs are weighty, mechanically complex, and geometrically large to obtain high performances, such as gain. These features of the conventional SWA are undesirable for the shipboard marine radar, where the antenna rotates at high angular speed for the beam scanning mechanism. The proposed SWA array herein reduces the conventional design’s size by 62% using a tapered dielectric-inset guide structure. It shows high gain performance (up to 30 dB) and obtains improvements in radiation efficiency (up to 80% in the numerical simulations) and weight due to the use of loss and low-density dielectric material.Fragile X Syndrome (FXS), the leading form of inherited intellectual disability and autism, is characterized by specific musculoskeletal conditions. We hypothesized that gait analysis in FXS could be relevant for the evaluation of motor control of gait, and help the understanding of a possible correlation between functional and intellectual abilities. Typical deficits in executive control and hyperactivity have hampered the use of standard gait analysis. The aim of our study was to quantitatively assess musculoskeletal alterations in FXS children in standard ambulatory conditions, in a friendly environment. Ten FXS children and sixteen controls, with typical neurodevelopment, were evaluated through four synchronized video cameras and surface electromyography; lower limb joints rotations, spatiotemporal parameters, duration of muscle contraction, activation timing and envelope peaks were determined. Reliability and repeatability of the video based kinematics analysis was assessed with respect to stereophotogrammetry.