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Recently, Siamese architecture has been widely used in the field of visual tracking, and has achieved great success. Most Siamese network based trackers aggregate the target information of two branches by cross-correlation. However, since the location of the sampling points in the search feature area is pre-fixed in cross-correlation operation, these trackers suffer from either background noise influence or missing foreground information. Moreover, the cross-correlation between the template and the search area neglects the geometry information of the target. In this paper, we propose a Siamese deformable cross-correlation network to model the geometric structure of target and improve the performance of visual tracking. We propose to learn an offset field end-to-end in cross-correlation. With the guidance of the offset field, the sampling in the search image area can adapt to the deformation of the target, and realize the modeling of the geometric structure of the target. We further propose an online classification sub-network to model the variation of target appearance and enhance the robustness of the tracker. Extensive experiments are conducted on four challenging benchmarks, including OTB2015, VOT2018, VOT2019 and UAV123. The results demonstrate that our tracker achieves state-of-the-art performance.A multi-layered interference mitigation approach can significantly improve the performance of Global Navigation Satellite System (GNSS) receivers in the presence of jamming. In this work, three levels of defence are considered including pre-correlation interference mitigation techniques, post-correlation measurement screening and FDE at the Position, Velocity, and Time (PVT) level. The performance and interaction of these receiver defences are analysed with specific focus on Robust Interference Mitigation (RIM), measurement screening through Lock Indicator (LIs) and Receiver Autonomous Integrity Monitoring (RAIM). The case of timing receivers with a known user position and using Galileo signals from different frequencies has been studied with Time-Receiver Autonomous Integrity Monitoring (T-RAIM) based on the Backward-Forward method. From the experimental analysis it emerges that RIM improves the quality of the measurements reducing the number of exclusions performed by T-RAIM. Effective measurements screening is also fundamental to obtain unbiased timing solutions in this respect T-RAIM can provide the required level of reliability.This paper addresses the problem of robust sensor faults detection and isolation in the air-path system of heavy-duty diesel engines, which has not been completely considered in the literature. Calibration or the total failure of a sensor can cause sensor faults. In the worst-case scenario, the engines can be totally damaged by the sensor faults. For this purpose, a second-order sliding mode observer is proposed to reconstruct the sensor faults in the presence of unknown external disturbances. To this aim, the concept of the equivalent output error injection method and the linear matrix inequality (LMI) tool are utilized to minimize the effects of uncertainties and disturbances on the reconstructed fault signals. The simulation results verify the performance and robustness of the proposed method. By reconstructing the sensor faults, the whole system can be prevented from failing before the corrupted sensor measurements are used by the controller.The human immune system is very complex. Understanding it traditionally required specialized knowledge and expertise along with years of study. However, in recent times, the introduction of technologies such as AIoMT (Artificial Intelligence of Medical Things), genetic intelligence algorithms, smart immunological methodologies, etc., has made this process easier. These technologies can observe relations and patterns that humans do and recognize patterns that are unobservable by humans. Furthermore, these technologies have also enabled us to understand better the different types of cells in the immune system, their structures, their importance, and their impact on our immunity, particularly in the case of debilitating diseases such as cancer. The undertaken study explores the AI methodologies currently in the field of immunology. The initial part of this study explains the integration of AI in healthcare and how it has changed the face of the medical industry. It also details the current applications of AI in the different healthcare domains and the key challenges faced when trying to integrate AI with healthcare, along with the recent developments and contributions in this field by other researchers. Navitoclax nmr The core part of this study is focused on exploring the most common classifications of health diseases, immunology, and its key subdomains. The later part of the study presents a statistical analysis of the contributions in AI in the different domains of immunology and an in-depth review of the machine learning and deep learning methodologies and algorithms that can and have been applied in the field of immunology. We have also analyzed a list of machine learning and deep learning datasets about the different subdomains of immunology. Finally, in the end, the presented study discusses the future research directions in the field of AI in immunology and provides some possible solutions for the same.Non-invasive measurement of physiological parameters and indicators, specifically among the elderly, is of utmost importance for personal health monitoring. In this study, we focused on photoplethysmography (PPG), and developed a regression model that calculates variables from the second (SDPPG) and third (TDPPG) derivatives of the PPG pulse that can observe the inflection point of the pulse wave measured by a wearable PPG device. The PPG pulse at the earlobe was measured for 3 min in 84 elderly Korean women (age 71.19 ± 6.97 years old). Based on the PPG-based cardiovascular function, we derived additional variables from TDPPG, in addition to the aging variable to predict the age. The Aging Index (AI) from SDPPG and Sum of TDPPG variables were calculated in the second and third differential forms of PPG. The variables that significantly correlated with age were c/a, Tac, AI of SDPPG, sum of TDPPG, and correlation coefficient 'r' of the model. In multiple linear regression analysis, the r value of the model was 0.308, and that using deep learning on the model was 0.839. Moreover, the possibility of improving the accuracy of the model using supervised deep learning techniques, rather than the addition of datasets, was confirmed.A method of detecting and counting road vehicles using an acoustic sensor placed by the road is presented. The sensor measures sound intensity in two directions parallel and perpendicular to the road. The sound intensity analysis performs acoustic event detection. A normalized position of the sound source is tracked and used to determine if the detected event is related to a moving vehicle and to establish the direction of movement. The algorithm was tested on a continuous 24-h recording made in real-world conditions. The overall results were recall 0.95, precision 0.95, F-score 0.95. In the analysis of one-hour slots, the worst results obtained in dense traffic were recall 0.9, precision 0.93, F-score 0.91. The proposed method is intended for application in a network of traffic monitoring sensors, such as a smart city system. Its advantages include using a small, low cost and passive sensor, low algorithm complexity, and satisfactory detection accuracy.For sports scientists and coaches, its crucial to have reliable tracking systems to improve athletes. Therefore, this study aimed to examine the validity of a wearable real-time tracking system (WRRTS) for the quantification of ski jumping. The tracking system consists of wearable trackers attached to the ski bindings of the athletes and fixed antennas next to the jumping hill. To determine the accuracy and precision of the WRRTS, four athletes of the German A or B National Team performed 35 measured ski jumps. The WRRTS was used to measure the 3D positions and ski angles during the jump. The measurements are compared with camera measurements for the in-flight parameters and the official video distance for the jumping distance to assess their accuracy. We statistically evaluated the different methods using Bland-Altman plots. We thereby find a mean absolute error of 0.46m for the jumping distance, 0.12m for the in-flight positions, and 0.8°, and 3.4° for the camera projected pitch and V-style opening angle, respectively. We show the validity of the presented WRRTS to measure the investigated parameters. Thus, the system can be used as a tracking system during training and competitions for coaches and sports scientists. The real-time feature of the tracking system enables usage during live TV broadcasting.While wireless IOT modules can be made extremely compact, antennas typically protrude from the module, providing the potential to catch near moving/rotating equipment or transfer loads to the PCB through end forces, which can lead to failures. This work explores the use of split-ring resonator (SRR) designs to achieve a planar antenna with a maximum dimension less than a monopole working at the same frequency. The very narrow bandwidth of the SRR required detailed physical models to create printed circuit board (PCB)-based antenna designs that could be used at LoRa frequencies of 433 MHz and 915 MHz. Uncertainty analysis allowed for the impact of geometrical and physical tolerances on the resonant frequency to be evaluated. Nearfield and farfield measurements were performed allowing for the resonant frequency, directionality, and range of the antenna to be evaluated. An unbalanced SMA port was added to the SRR design to allow for the use of a network vector analyser to determine the input impedance of various designs. The optimum design achieved an input resistance of 44 Ω at a resonant frequency of 919 MHz, close to the target values (50 Ω at 915 MHz). Field measurements of the received signal strength from a planar antenna design indicated a gain of 5 dB over a conventional quarter-wave monopole antenna, in a footprint that was 40% smaller than the monopole.Antiresonant hollow core fibers (ARHCFs) have gained some attention due to their notoriously attractive characteristics on managing optical properties. In this work, an inline optical fiber sensor based on a hollow square core fiber (HSCF) is proposed. The sensor presents double antiresonance (AR), namely an internal AR and an external AR. The sensor was designed in a transmission configuration, where the sensing head was spliced between two single mode fibers (SMFs). A simulation was carried out to predict the behaviors of both resonances, and revealed a good agreement with the experimental observations and the theoretical model. The HSCF sensor presented curvature sensitivities of -0.22 nm/m-1 and -0.90 nm/m-1, in a curvature range of 0 m-1 to 1.87 m-1, and temperature sensitivities of 21.7 pm/°C and 16.6 pm/°C, in a temperature range of 50 °C to 500 °C, regarding the external resonance and internal resonance, respectively. The proposed sensor is promising for the implementation of several applications where simultaneous measurement of curvature and temperature are required.