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Earlier toxicities of ultrahypofractionated stereotactic entire body radiotherapy with regard to advanced danger local cancer of prostate using cone-beam worked out tomography as well as real-time three-dimensional transperineal ultrasound overseeing.
The model is compared against experimental data obtained from the transmission of packets that are fragmented using a SCHC over LoRaWAN implementation. This modeling provides a relationship between the channel occupancy efficiency, the spreading factor of LoRa™, and the probability of an error of a SCHC message. The results show that the model correctly predicts the efficiency in channel occupation for all spreading factors. Furthermore, the SCHC ACK-on-Error mode implementation for the upstream channel has been made fully available for further use by the research community.Approaches are being developed to create composite materials with a fractal-percolation structure based on intercalated porous matrices to increase the sensitivity of adsorption gas sensors. Porous silicon, nickel-containing porous silicon, and zinc oxide have been synthesized as materials for such structures. Using the impedance spectroscopy method, it has been shown that the obtained materials demonstrate high sensitivity to organic solvent vapors and can be used in gas sensors. A model is proposed that explains the high sensitivity and inductive nature of the impedance at low frequencies, considering the structural features and fractal-percolation properties of the obtained oxide materials.High-precision position estimations of agricultural mobile robots (AMRs) are crucial for implementing control instructions. Although the global navigation satellite system (GNSS) and real-time kinematic GNSS (RTK-GNSS) provide high-precision positioning, the AMR accuracy decreases when the signals interfere with buildings or trees. An improved position estimation algorithm based on multisensor fusion and autoencoder neural network is proposed. The multisensor, RTK-GNSS, inertial-measurement-unit, and dual-rotary-encoder data are fused with Extended Kalman filter (EKF). To optimize the EKF noise matrix, the autoencoder and radial basis function (ARBF) neural network was used for modeling the state equation noise and EKF measurement equation. A multisensor AMR test platform was constructed for static experiments to estimate the circular error probability and twice-the-distance root-mean-squared criteria. Dynamic experiments were conducted on road, grass, and field environments. To validate the robustness of the proposed algorithm, abnormal working conditions of the sensors were tested on the road. The results showed that the positioning estimation accuracy was improved compared to the RTK-GNSS in all three environments. When the RTK-GNSS signal experienced interference or rotary encoders failed, the system could still improve the position estimation accuracy. The proposed system and optimization algorithm are thus significant for improving AMR position prediction performance.This work explores the effects of embedded software-driven measurements on a sensory target when using a LED as a photodetector. Water turbidity is used as the sensory target in this study to explore these effects using a practical and important water quality parameter. Impacts on turbidity measurements are examined by adopting the Paired Emitter Detector Diode (PEDD) capacitive discharge technique and comparing common embedded software/firmware implementations. The findings show that the chosen software method can (a) affect the detection performance by up to 67%, (b) result in a variable sampling frequency/period, and (c) lead to an disagreement of the photo capacitance by up to 23%. Optimized code is offered to correct for these issues and its effectiveness is shown through comparative analyses, with the disagreement reduced significantly from 23% to 0.18%. Overall, this work demonstrates that the embedded software is a key and critical factor for PEDD capacitive discharge measurements and must be considered carefully for future measurements in sensor related studies.Unmanned aircraft systems are expected to provide both increasingly varied functionalities and outstanding application performances, utilizing the available resources. In this paper, we explore the recent advances and challenges at the intersection of real-time computing and control and show how rethinking sampling strategies can improve performance and resource utilization. We showcase a novel design framework, cyber-physical co-regulation, which can efficiently link together computational and physical characteristics of the system, increasing robust performance and avoiding pitfalls of event-triggered sampling strategies. A comparison experiment of different sampling and control strategies was conducted and analyzed. We demonstrate that co-regulation has resource savings similar to event-triggered sampling, but maintains the robustness of traditional fixed-periodic sampling forming a compelling alternative to traditional vehicle control design.Lung or heart sound classification is challenging due to the complex nature of audio data, its dynamic properties of time, and frequency domains. It is also very difficult to detect lung or heart conditions with small amounts of data or unbalanced and high noise in data. Furthermore, the quality of data is a considerable pitfall for improving the performance of deep learning. In this paper, we propose a novel feature-based fusion network called FDC-FS for classifying heart and lung sounds. The FDC-FS framework aims to effectively transfer learning from three different deep neural network models built from audio datasets. The innovation of the proposed transfer learning relies on the transformation from audio data to image vectors and from three specific models to one fused model that would be more suitable for deep learning. We used two publicly available datasets for this study, i.e., lung sound data from ICHBI 2017 challenge and heart challenge data. We applied data augmentation techniques, such as noise distortion, pitch shift, and time stretching, dealing with some data issues in these datasets. Importantly, we extracted three unique features from the audio samples, i.e., Spectrogram, MFCC, and Chromagram. Finally, we built a fusion of three optimal convolutional neural network models by feeding the image feature vectors transformed from audio features. We confirmed the superiority of the proposed fusion model compared to the state-of-the-art works. WZ4003 AMPK inhibitor The highest accuracy we achieved with FDC-FS is 99.1% with Spectrogram-based lung sound classification while 97% for Spectrogram and Chromagram based heart sound classification.This paper investigates the sensor fault detection and fault-tolerant control (FTC) technology of a variable-structure hypersonic flight vehicle (HFV). First, an HFV nonlinear system considering sensor compound faults, disturbance, and the variable structure parameter is established, which is divided into the attitude angle outer and angular rate inner loops. Then a nonlinear fault integrated detector is proposed to detect the moment of fault occurrence and provide the residual to design the sliding mode equations. Furthermore, the sliding mode method combined with the virtual adaptive controller constitutes the outer loop FTC scheme, and the adaptive dynamic surface combined with the disturbance estimation constitutes the inner loop robust controller; these controllers finally realize the direct compensation of the compound sensor faults under the disturbance condition. This scheme does not require fault isolation and diagnosis observer loops; it only uses a variable structure FTC with a direct estimation algorithm and integrated residual to complete the self-repairing stable flight of variable-structure HFV, which exhibits a high reliability and quick response. Lyapunov theory proved the stability of the system, and numerical simulation proved the effectiveness of the FTC scheme.The impact of airborne molecular contaminants (AMCs) on the lifetime of fused silica UV optics in high power lasers (HPLs) is a critical issue. In this work, we demonstrated the on-line monitoring method of AMCs concentration based on the Sagnac microfiber structure. In the experiment, a Sagnac microfiber loop with mesoporous silica coating was fabricated by the microheater brushing technique and dip coating. The physical absorption of AMCs in the mesoporous coating results in modification of the surrounding refractive index (RI). By monitoring the spectral shift in the wavelength domain, the proposed structure can operate as an AMCs concentration sensor. The sensitivity of the AMCs sensor can achieve 0.11 nm (mg/m3). By evaluating the gas discharge characteristic of four different low volatilization greases in a coarse vacuum environment, we demonstrated the feasibility of the proposed sensors. The use of these sensors was shown to be very promising for meeting the requirements of detecting trace amounts of contaminants.Machine Learning (ML) methods have become state of the art in radar signal processing, particularly for classification tasks (e.g., of different human activities). Radar classification can be tedious to implement, though, due to the limited size and diversity of the source dataset, i.e., the data measured once for initial training of the Machine Learning algorithms. In this work, we introduce the algorithm Radar Activity Classification with Perceptual Image Transformation (RACPIT), which increases the accuracy of human activity classification while lowering the dependency on limited source data. In doing so, we focus on the augmentation of the dataset by synthetic data. We use a human radar reflection model based on the captured motion of the test subjects performing activities in the source dataset, which we recorded with a video camera. As the synthetic data generated by this model still deviates too much from the original radar data, we implement an image transformation network to bring real data close to their synthetic counterpart. We leverage these artificially generated data to train a Convolutional Neural Network for activity classification. We found that by using our approach, the classification accuracy could be increased by up to 20%, without the need of collecting more real data.This paper proposes a high-precision LDO with low-temperature drift suitable for sensitive time-domain temperature sensors. Its topology is based on multiple feedback loops and a novel approach to frequency compensation, that allows the LDO to maintain a large DC gain while handling capacitive loads that vary over a wide range. The key design constraints are derived by using a simplified, yet intuitive and effective, small-signal analysis devised for LDOs with multiple feedback loops. Simulation and measurement results are presented for implementation in a standard 130 nm CMOS process the LDO outputs a stable 1 V voltage, when the input voltage varies between 1.25 V to 1.5 V, the load current between 0 and 100 mA, and the load capacitor between zero and 400 pF. It exhibits a DC load regulation of 1 µV/mA, a 288 µV output offset with a standard deviation of 9.5 mV. A key feature for the envisaged application is the very low thermal drift of the output offset only 14.4 mV across the temperature range of -40 °C to +150 °C. Overall, the LDO output voltage stays within +/-3.5% of the nominal DC value over the entire line voltage, load, and temperature ranges, without trimming. The LDO requires only 1.4µA quiescent current, yet it provides excellent responses to load transients. The output voltage undershoot and overshoot caused by the load current jumping between 0 and 100 mA in 1 µs are 10%/22% for CL = 0 and 12%/16% for CL = 400 pF, respectively. A comparative analysis against seven LDOs published in the last decade, designed for similar levels of supply voltage and output voltage and current, shows that the LDO presented here is the best option for supplying sensitive time-domain temperature sensors. The smallest thermal drift of the output offset, smaller than +/-15 mV, that is, 6.7 times smaller than its closest competitor, and the best overall performance when PSR up to 1 kHz, was considered.
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