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Sacral neural stimulator achievement after filum part regarding refractory dysfunctional voiding.
Interaction sites with the Abcg2 gene promoter of these four selected regulators were clarified by progressive deletions and mutation assays. This study shed some light on the regulatory mechanisms involved in chicken Abcg2 gene expression and the results may have far-reaching significance regarding the usage and development of veterinary drugs.Sensor differential signals are widely used in many systems. The tracking differentiator (TD) is an effective method to obtain signal differentials. Differential calculation is noise-sensitive. There is the characteristics of low-pass filter (LPF) in the TD to suppress the noise, but phase lag is introduced. For LPF, fixed filtering parameters cannot achieve both noise suppression and phase compensation lag compensation. We propose a fuzzy self-tuning tracking differentiator (FSTD) capable of adaptively adjusting parameters, which uses the frequency information of the signal to achieve a trade-off between the phase lag and noise suppression capabilities. Based on the frequency information, the parameters of TD are self-tuning by a fuzzy method, which makes self-tuning designs more flexible. Simulations and experiments using motion measurement sensors show that the proposed method has good filtering performance for low-frequency signals and improves tracking ability for high-frequency signals compared to fixed-parameter differentiator.Traditional calibration method is usually performed with expensive equipments suchas three-axis turntable in a laboratory environment. However in practice, in order to ensure theaccuracy and stability of the inertial navigation system (INS), it is usually necessary to recalibratethe inertial measurement unit (IMU) without external equipment in the field. In this paper, anew in-field recalibration method for triaxial accelerometer based on beetle swarm antenna search(BSAS) algorithm is proposed. Firstly, as a new intelligent optimization algorithm, BSAS algorithmand its improvements based on basic beetle antennae search (BAS) algorithm are introduced indetail. Secondly, the nonlinear mathematical model of triaxial accelerometer is established forhigher calibration accuracy, and then 24 optimal measurement positions are designed by theoreticalanalysis. In addition, the calibration procedures are improved according to the characteristics of BSASalgorithm, then 15 calibration parameters in the nonlinear method are optimized by BSAS algorithm.Besides, the results of BSAS algorithm and basic BAS algorithm are compared by simulation, whichshows the priority of BSAS algorithm in calibration field. Finally, two experiments demonstrate thatthe proposed method can achieve high precision in-field calibration without any external equipment,and meet the accuracy requirements of the INS.Person re-identification (re-ID) is among the essential components that play an integral role in constituting an automated surveillance environment. Majorly, the problem is tackled using data acquired from vision sensors using appearance-based features, which are strongly dependent on visual cues such as color, texture, etc., consequently limiting the precise re-identification of an individual. BGJ398 mouse To overcome such strong dependence on visual features, many researchers have tackled the re-identification problem using human gait, which is believed to be unique and provide a distinctive biometric signature that is particularly suitable for re-ID in uncontrolled environments. However, image-based gait analysis often fails to extract quality measurements of an individual's motion patterns owing to problems related to variations in viewpoint, illumination (daylight), clothing, worn accessories, etc. To this end, in contrast to relying on image-based motion measurement, this paper demonstrates the potential to re-identify an individual using inertial measurements units (IMU) based on two common sensors, namely gyroscope and accelerometer. The experiment was carried out over data acquired using smartphones and wearable IMUs from a total of 86 randomly selected individuals including 49 males and 37 females between the ages of 17 and 72 years. The data signals were first segmented into single steps and strides, which were separately fed to train a sequential deep recurrent neural network to capture implicit arbitrary long-term temporal dependencies. The experimental setup was devised in a fashion to train the network on all the subjects using data related to half of the step and stride sequences only while the inference was performed on the remaining half for the purpose of re-identification. The obtained experimental results demonstrate the potential to reliably and accurately re-identify an individual based on one's inertial sensor data.Abnormal falls in public places have significant safety hazards and can easily lead to serious consequences, such as trampling by people. Vision-driven fall event detection has the huge advantage of being non-invasive. However, in actual scenes, the fall behavior is rich in diversity, resulting in strong instability in detection. Based on the study of the stability of human body dynamics, the article proposes a new model of human posture representation of fall behavior, called the "five-point inverted pendulum model", and uses an improved two-branch multi-stage convolutional neural network (M-CNN) to extract and construct the inverted pendulum structure of human posture in real-world complex scenes. Furthermore, we consider the continuity of the fall event in time series, use multimedia analytics to observe the time series changes of human inverted pendulum structure, and construct a spatio-temporal evolution map of human posture movement. Finally, based on the integrated results of computer vision and multimedia analytics, we reveal the visual characteristics of the spatio-temporal evolution of human posture under the potentially unstable state, and explore two key features of human fall behavior motion rotational energy and generalized force of motion. The experimental results in actual scenes show that the method has strong robustness, wide universality, and high detection accuracy.
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