Swainmccartney6170

ial to differentiate between different human emotions with a decent accuracy of 76% without placing any on-body sensors. Separate analysis for male and female participants reveals that males experience high arousal for happiness while females experience intense fear emotions. For disgust emotion, no large difference is found for male and female participants. To the best of the authors' knowledge, this study presents the first non-invasive approach using the IR-UWB radar for emotion recognition.Rail corrugation appears as oscillatory wear on the rail surface caused by the interaction between the train wheels and the railway. Corrugation shortens railway service life and forces early rail replacement. Consequently, service can be suspended for days during rail replacement, adversely affecting an important means of transportation. We propose an inspection method for rail corrugation using computer vision through an algorithm based on feature descriptors to automatically distinguish corrugated from normal surfaces. We extract seven features and concatenate them to form a feature vector obtained from a railway image. The feature vector is then used to build support vector machine. Data were collected from seven different tracks as video streams acquired at 30 fps. The trained support vector machine was used to predict test frames of rails as being either corrugated or normal. The proposed method achieved a high performance, with 97.11% accuracy, 95.52% precision, and 97.97% recall. Experimental results show that our method is more effective in identifying corrugated images than reference state-of the art works.Microstrip transmission lines loaded with dumbbell defect-ground-structure (DB-DGS) resonators transversally oriented have been exhaustively used in microwave circuits and sensors. Typically, these structures have been modelled by means of a parallel LC resonant tank series connected to the host line. However, the inductance and capacitance of such model do not have a physical meaning, since this model is inferred by transformation of a more realistic model, where the DB-DGS resonator, described by means of a resonant tank with inductance and capacitance related to the geometry of the DB-DGS, is magnetically coupled to the host line. From parameter extraction, the circuit parameters of both models are obtained by considering the DB-DGS covered with semi-infinite materials with different dielectric constant. The extracted parameters are coherent and reveal that the general assumption of considering the simple LC resonant tank series-connected to the line to describe the DB-DGS-loaded line is reasonable with some caution. The implications on the sensitivity, when the structure is devoted to operating as a permittivity sensor, are discussed.Second-order Zeeman frequency shift is one of the major systematic factors affecting the frequency uncertainty performance of cesium atomic fountain clock. ASN007 Second-order Zeeman frequency shift is calculated by experimentally measuring the central frequency of the (1,1) or (-1,-1) magnetically sensitive Ramsey transition. The low-frequency transition method can be used to measure the magnetic field strength and to predict the central fringe of (1,1) or (-1,-1) magnetically sensitive Ramsey transition. In this paper, we deduce the formula for magnetic field measurement using the low-frequency transition method and measured the magnetic field distribution of 4 cm inside the Ramsey cavity and 32 cm along the flight region experimentally. The result shows that the magnetic field fluctuation is less than 1 nT. The influence of low-frequency pulse signal duration on the accuracy of magnetic field measurement is studied and the optimal low-frequency pulse signal duration is determined. The central fringe of (-1,-1) magnetically sensitive Ramsey transition can be predicted by using a numerical integrating of the magnetic field "map". Comparing the predicted central fringe with that identified by Ramsey method, the frequency difference between these two is, at most, a fringe width of 0.3. We apply the experimentally measured central frequency of the (-1,-1) Ramsey transition to the Breit-Rabi formula, and the second-order Zeeman frequency shift is calculated as 131.03 × 10-15, with the uncertainty of 0.10 × 10-15.Information and communication technologies (ICT) are major features of smart cities. Smart sensing devices will benefit from 5 G and the Internet of Things, which will enable them to communicate in a safe and timely manner. However, the need for sustainable power sources and self-powered active sensing devices will continue to be a major issue in this sector. Since their discovery, piezoelectric energy harvesters have demonstrated a significant ability to power wireless sensor nodes, and their application in a wide range of systems, including intelligent transportation, smart healthcare, human-machine interfaces, and security systems, has been systematically investigated. Piezoelectric energy-harvesting systems are promising candidates not only for sustainably powering wireless sensor nodes but also for the development of intelligent and active self-powered sensors with a wide range of applications. In this paper, the various applications of piezoelectric energy harvesters in powering Internet of Things sensors and devices in smart cities are discussed and reviewed.Cleaning is one of the fundamental tasks with prime importance given in our day-to-day life. Moreover, the importance of cleaning drives the research efforts towards bringing leading edge technologies, including robotics, into the cleaning domain. However, an effective method to assess the quality of cleaning is an equally important research problem to be addressed. The primary footstep towards addressing the fundamental question of "How clean is clean" is addressed using an autonomous cleaning-auditing robot that audits the cleanliness of a given area. This research work focuses on a novel reinforcement learning-based experience-driven dirt exploration strategy for a cleaning-auditing robot. The proposed approach uses proximal policy approximation (PPO) based on-policy learning method to generate waypoints and sampling decisions to explore the probable dirt accumulation regions in a given area. The policy network is trained in multiple environments with simulated dirt patterns. Experiment trials have been conducted to validate the trained policy in both simulated and real-world environments using an in-house developed cleaning audit robot called BELUGA.A tyre blow-out can greatly affect vehicle stability and cause serious accidents. In the literature, however, studies on comprehensive three-dimensional vehicle dynamics modelling and stability control strategies in the event of a sudden tyre blow-out are seriously lacking. In this study, a comprehensive 14 degrees-of-freedom (DOF) vehicle dynamics model is first proposed to describe the vehicle yaw-plane and roll-plane dynamics performance after a tyre blow-out. Then, based on the proposed 14 DOF dynamics model, an integrated control framework for a combined yaw plane and roll-plane stability control is presented. This integrated control framework consists of a vehicle state predictor, an upper-level control mode supervisor and a lower-level 14 DOF model predictive controller (MPC). The state predictor is designed to predict the vehicle's future states, and the upper-level control mode supervisor can use these future states to determine a suitable control mode. After that, based on the selected control mode, the lower-level MPC can control the individual driving actuator to achieve the combined yaw plane and roll plane control. Finally, a series of simulation tests are conducted to verify the effectiveness of the proposed control strategy.Since signal-dependent noise in a local weak texture region of a noisy image is approximated as additive noise, the corresponding noise parameters can be estimated from a given set of weakly textured image blocks. As a result, the meticulous selection of weakly textured image blocks plays a decisive role to estimate the noise parameters accurately. The existing methods consider the finite directions of the texture of image blocks or directly use the average value of an image block to select the weakly textured image block, which can result in errors. To overcome the drawbacks of the existing methods, this paper proposes a novel noise parameter estimation method using local binary cyclic jumping to aid in the selection of these weakly textured image blocks. The texture intensity of the image block is first defined by the cumulative average of the LBCJ information in the eight neighborhoods around the pixel, and, subsequently, the threshold is set for selecting weakly textured image blocks through texture intensity distribution of the image blocks and inverse binomial cumulative function. The experimental results reveal that the proposed method outperforms the existing alternative algorithms by 23% and 22% for the evaluative measures of MSE (a) and MSE (b), respectively.With the development of light microscopy, it is becoming increasingly easy to obtain detailed multicolor fluorescence volumetric data. The need for their appropriate visualization has become an integral part of fluorescence imaging. Virtual reality (VR) technology provides a new way of visualizing multidimensional image data or models so that the entire 3D structure can be intuitively observed, together with different object features or details on or within the object. With the need for imaging advanced volumetric data, demands for the control of virtual object properties are increasing; this happens especially for multicolor objects obtained by fluorescent microscopy. Existing solutions with universal VR controllers or software-based controllers with the need to define sufficient space for the user to manipulate data in VR are not usable in many practical applications. Therefore, we developed a custom gesture-based VR control system with a custom controller connected to the FluoRender visualization environment. A multitouch sensor disk was used for this purpose. Our control system may be a good choice for easier and more comfortable manipulation of virtual objects and their properties, especially using confocal microscopy, which is the most widely used technique for acquiring volumetric fluorescence data so far.The emerging literature suggests that implantable functional electrical stimulation may improve gait performance in stroke survivors. However, there is no review providing the possible therapeutic effects of implanted functional electrical stimulation on gait performance in stroke survivors. We performed a web-based, systematic paper search using PubMed, the Cochrane Library, and EMBASE. We limited the search results to human subjects and papers published in peer-reviewed journals in English. We did not restrict demographic or clinical characteristics. We included 10 papers in the current systematic review. Across all included studies, we found preliminary evidence of the potential therapeutic effects of functional electrical stimulation on walking endurance, walking speed, ankle mobility, and push-off force in stroke survivors. However, due to the heterogeneity between the included studies, small sample size, and lack of randomized controlled trials, more studies are critically needed to confirm whether implanted functional electrical stimulation can improve gait performance in stroke survivors.