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7°, 2.3°, and 4.3°, respectively. Given the magnitude of error traditionally reported in joint angles computed from a marker-based optoelectronic system, Pose2Sim is deemed accurate enough for the analysis of lower-body kinematics in walking, cycling, and running.In this paper, we propose a data-driven approach for the reconstruction of unknown room impulse responses (RIRs) based on the deep prior paradigm. We formulate RIR reconstruction as an inverse problem. More specifically, a convolutional neural network (CNN) is employed prior, in order to obtain a regularized solution to the RIR reconstruction problem for uniform linear arrays. This approach allows us to avoid assumptions on sound wave propagation, acoustic environment, or measuring setting made in state-of-the-art RIR reconstruction algorithms. Moreover, differently from classical deep learning solutions in the literature, the deep prior approach employs a per-element training. Therefore, the proposed method does not require training data sets, and it can be applied to RIRs independently from available data or environments. Results on simulated data demonstrate that the proposed technique is able to provide accurate results in a wide range of scenarios, including variable direction of arrival of the source, room T60, and SNR at the sensors. The devised technique is also applied to real measurements, resulting in accurate RIR reconstruction and robustness to noise compared to state-of-the-art solutions.Sugarcane is the main industrial crop for sugar production, and its growth status is closely related to fertilizer, water, and light input. Unmanned aerial vehicle (UAV)-based multispectral imagery is widely used for high-throughput phenotyping, since it can rapidly predict crop vigor at field scale. This study focused on the potential of drone multispectral images in predicting canopy nitrogen concentration (CNC) and irrigation levels for sugarcane. An experiment was carried out in a sugarcane field with three irrigation levels and five fertilizer levels. Multispectral images at an altitude of 40 m were acquired during the elongating stage. Partial least square (PLS), backpropagation neural network (BPNN), and extreme learning machine (ELM) were adopted to establish CNC prediction models based on various combinations of band reflectance and vegetation indices. The simple ratio pigment index (SRPI), normalized pigment chlorophyll index (NPCI), and normalized green-blue difference index (NGBDI) were selected as model inputs due to their higher grey relational degree with the CNC and lower correlation between one another. The PLS model based on the five-band reflectance and the three vegetation indices achieved the best accuracy (Rv = 0.79, RMSEv = 0.11). Support vector machine (SVM) and BPNN were then used to classify the irrigation levels based on five spectral features which had high correlations with irrigation levels. SVM reached a higher accuracy of 80.6%. The results of this study demonstrated that high resolution multispectral images could provide effective information for CNC prediction and water irrigation level recognition for sugarcane crop.Copper ion is closely associated with the ecosystem and human health, and even a little excessive dose in drinking water may result in a range of health problems. However, it remains challenging to produce a highly sensitive, reliable, cost-effective and electromagnetic-interference interference-immune device to detect Cu2+ ion in drinking water. read more In this paper, a taper-in-taper fiber sensor was fabricated with high sensitivity by mode-mode interference and deposited polyelectrolyte layers for Cu2+ detection. We propose a new structure which forms a secondary taper in the middle of the single-mode fiber through two-arc discharge. Experimental results show that the newly developed fiber sensor possesses a sensitivity of 2741 nm/RIU in refractive index (RI), exhibits 3.7 times sensitivity enhancement when compared with traditional tapered fiber sensors. To apply this sensor in copper ions detection, the results present that when the concentration of Cu2+ is 0-0.1 mM, the sensitivity could reach 78.03 nm/mM. The taper-in-taper fiber sensor exhibits high sensitivity with good stability and mechanical strength which has great potential to be applied in the detection of low Cu2+ ions in some specific environments such as drinking water.The consumption of multimedia content is ubiquitous in modern society. This is made possible by wireless local area networks (W-LAN) or wire service systems. Bandpass filters (BPF) have become very popular as they solve certain data transmission limitations allowing users to obtain reliable access to their multimedia content. The BPFs with quarter-wavelength short stubs can achieve performance; however, these BPFs are bulky. In this article, we propose a compact BPF with a T-shaped stepped impedance resonator (SIR) transmission line and a folded SIR structure. The proposed BPF uses a T-shaped SIR connected to a J-inverter structure (transmission line); this T-shaped SIR structure is used to replace the λg/4 transmission line seen in conventional stub BPFs. In addition, a folded SIR is added to the short stubs seen in conventional stub BPFs. This approach allows us to significantly reduce the size of the BPF. The advantage of a BPF is its very small size, low insertion loss, and wide bandwidth. The overall size of the new BPF is 2.44 mm × 1.49 mm (0.068λg × 0.059λg). The proposed BPF can be mass produced using semiconductors due to its planar structure. This design has the potential to be widely used in various areas including military, medical, and industrial systems.In the paper, a 3D knitted fabric is used for the design of a circularly polarized textile-integrated antenna. The role of the radiating element is played by a circular slot etched into the conductive top wall of a textile-integrated waveguide. Inside the circular slot, a cross slot rotated for about 45° is etched to excite the circular polarization. The polarization of the antenna can be changed by the rotation of the cross slot. The antenna has a patch-like radiation pattern, and the gain is about 5.3 dBi. The textile-integrated feeder of the antenna is manufactured by screen printing conductive surfaces and sewing side walls with conductive threads. The antenna was developed for ISM bands 5.8 GHz and 24 GHz. The operation frequency 24 GHz is the highest frequency of operation for which the textile-integrated waveguide antenna has been manufactured.In this work, we propose a novel functionalized carbon nanotube (f-CNT) supporting nanoporous cauliflower-like Pd nanostructures (PdNS) as an enzyme-free interface for glucose electrooxidation reaction (GOR) in a neutral medium (pH 7.4). The novelty resides in preparing the PdNS/f-CNT biomimetic nanocatalyst using a cost-effective and straightforward method, which consists of drop-casting well-dispersed f-CNTs over the Screen-printed carbon electrode (SPCE) surface, followed by the electrodeposition of PdNS. Several parameters affecting the morphology, structure, and catalytic properties toward the GOR of the PdNS catalyst, such as the PdCl2 precursor concentration and electrodeposition conditions, were investigated during this work. The electrochemical behavior of the PdNS/f-CNT/SPCE toward GOR was investigated through Cyclic Voltammetry (CV), Linear Sweep Voltammetry (LSV), and amperometry. There was also a good correlation between the morphology, structure, and electrocatalytic activity of the PdNS electrocatalyst. Furthermore, the LSV response and potential-pH diagram for the palladium-water system have enabled the proposal for a mechanism of this GOR. The proposed mechanism would be beneficial, as the basis, to achieve the highest catalytic activity by selecting the suitable potential range. Under the optimal conditions, the PdNS/f-CNT/SPCE-based biomimetic sensor presented a wide linear range (1-41 mM) with a sensitivity of 9.3 µA cm-2 mM-1 and a detection limit of 95 µM (S/N = 3) toward glucose at a detection potential of +300 mV vs. a saturated calomel electrode. Furthermore, because of the fascinating features such as fast response, low cost, reusability, and poison-free characteristics, the as-proposed electrocatalyst could be of great interest in both detection systems (glucose sensors) and direct glucose fuel cells.Whisker sensors are a class of tactile sensors that have recently attracted attention. Inspired by mammals' whiskers known as mystacial vibrissae, they have displayed tremendous potential in a variety of applications e.g., robotics, underwater vehicles, minimally invasive surgeries, and leak detection. This paper provides a supplement to the recent tactile sensing techniques' designs of whiskers that only sense at their base, as well as the materials employed, and manufacturing techniques. The article delves into the technical specifications of these sensors, such as the resolution, measurement range, sensitivity, durability, and recovery time, which determine their performance. The sensors' sensitivity varies depending on the measured physical quantity; for example, the pressure sensors had an intermediate sensitivity of 58%/Pa and a response time of around 90 ms, whereas the force sensors that function based on piezoelectric effects exhibited good linearity in the measurements with a resolution of 3 µN and sensitivity of 0.1682 mV/µN. Some sensors were used to perform spatial mapping and the identification of the geometry and roughness of objects with a reported resolution of 25 nm. The durability and recovery time showed a wide range of values, with the maximum durability being 10,000 cycles and the shortest recovery time being 5 ms. Furthermore, the paper examines the fabrication of whiskers at the micro- and nanoscales, as well as their contributions to mechanical and thermal behavior. The commonly used manufacturing techniques of 3D printing, PDMS casting, and screen printing were used in addition to several micro and nanofabrication techniques such as photolithography, etching, and chemical vapor deposition. Lastly, the paper discusses the main potential applications of these sensors and potential research gaps in this field. In particular, the operation of whisker sensors under high temperatures or high pressure requires further investigation, as does the design of sensors to explore larger topologies.One of the essential requirements of injection molding is to ensure the stable quality of the parts produced. However, numerous processing conditions, which are often interrelated in quite a complex way, make this challenging. Machine learning (ML) algorithms can be the solution, as they work in multidimensional spaces by learning the structure of datasets. In this study, we used four ML algorithms (kNN, naïve Bayes, linear discriminant analysis, and decision tree) and compared their effectiveness in predicting the quality of multi-cavity injection molding. We used pressure-based quality indexes (features) as inputs for the classification algorithms. We proved that all the examined ML algorithms adequately predict quality in injection molding even with very little training data. We found that the decision tree algorithm was the most accurate one, with a computational time of only 8-10 s. The average performance of the decision tree algorithm exceeded 90%, even for very little training data. We also demonstrated that feature selection does not significantly affect the accuracy of the decision tree algorithm.
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