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Narrative Vs. Common of Treatment Communications: Testing Just how Conversation Can easily Positively Impact Teens with Your body.
In this study, we propose a method to reduce noise from speech obtained from a general microphone using the information of a throat microphone. A throat microphone records a sound by detecting the vibration of the skin surface near the throat directly. Therefore, throat microphones are less prone to noise than ordinary microphones. However, as the acoustic characteristics of the throat microphone differ from those of ordinary microphones, its sound quality degrades. To solve this problem, this study aims to improve the speech quality while suppressing the noise of a general microphone by using the information recorded by a throat microphone as reference information to extract the speech signal in general microphones. In this paper, the framework of the proposed method is formulated, and several experiments are conducted to evaluate the noise suppression and speech quality improvement effects of the proposed method.Low-power wide-area networks (LPWANs), such as LoRaWAN, play an essential role and are expanding quickly in miscellaneous intelligent applications. However, the collision problem is also expanding significantly with the mass promotion of LPWAN nodes and providing collision-resilient techniques that are urgently needed for these applications. This paper proposes BackLoRa, a lightweight method that enables collision-resilient LoRa transmission with extra propagation information provided by backscatter tags. BackLoRa uses several backscatter tags to create multipath propagation features related to the LoRa nodes' positions and offers a lightweight algorithm to extract the feature and correctly distinguish each LoRa node. Further, BackLoRa proposes a quick-phase acquisition algorithm with low time complexity that can carry out the iterative recovery of symbols for robust signal reconstructions in low-SNR conditions. Finally, comprehensive experiments were conducted in this study to evaluate the performance of BackLoRa systems. The experimental results show th compared with the existing scheme, our scheme can reduce the symbol error rate from 65.3% to 5.5% on average and improve throughput by 15× when SNR is -20 dB.The fault diagnosis of power transformers is a challenging problem. The massive multisource fault is heterogeneous, the type of fault is undetermined sometimes, and one device has only met a few kinds of faults in the past. We propose a fault diagnosis method based on deep neural networks and a semi-supervised transfer learning framework called adaptive reinforcement (AR) to solve the above limitations. The innovation of this framework consists of its enhancement of the consistency regularization algorithm. The experiments were conducted on real-world 110 kV power transformers' three-phase fault grounding currents of the iron cores from various devices with four types of faults Phases A, B, C and ABC to ground. We trained the model on the source domain and then transferred the model to the target domain, which included the unbalanced and undefined fault datasets. The results show that our proposed model reaches over 95% accuracy in classifying the type of fault and outperforms other popular networks. Our AR framework fits target devices' fault data with fewer dozen epochs than other novel semi-supervised techniques. Combining the deep neural network and the AR framework helps diagnose the power transformers, which lack diagnosis knowledge, with much less training time and reliable accuracy.In this study, an unmanned aerial vehicle (UAV) with a camera and laser ranging module was developed to inspect bridge cracks. Four laser ranging units were installed adjacent to the camera to measure the distance from the camera to the object to calculate the object's projection plane and overcome the limitation of vertical photography. The image processing method was adopted to extract crack information and calculate crack sizes. The developed UAV was used in outdoor bridge crack inspection tests; for images taken at a distance of 2.5 m, we measured the crack length, and the error between the result and the real length was less than 0.8%. The developed UAV has a dual-lens design, where one lens is used for bridge inspections and the other lens is used for flight control. The camera of the developed UAV can be rotated from the horizontal level to the zenith according to user requirements; thus, this UAV achieves high safety and efficiency in bridge inspections.Access to graphical information plays a very significant role in today's world. Access to this information can be particularly limiting for individuals who are blind or visually impaired (BVIs). In this work, we present the design of a low-cost, mobile tactile display that also provides robotic assistance/guidance using haptic virtual fixtures in a shared control paradigm to aid in tactile diagram exploration. This work is part of a larger project intended to improve the ability of BVI users to explore tactile graphics on refreshable displays (particularly exploration time and cognitive load) through the use of robotic assistance/guidance. The particular focus of this paper is to share information related to the design and development of an affordable and compact device that may serve as a solution towards this overall goal. The proposed system uses a small omni-wheeled robot base to allow for smooth and unlimited movements in the 2D plane. Sufficient position and orientation accuracy is obtained by using a low-cost dead reckoning approach that combines data from an optical mouse sensor and inertial measurement unit. A low-cost force-sensing system and an admittance control model are used to allow shared control between the Cobot and the user, with the addition of guidance/virtual fixtures to aid in diagram exploration. Preliminary semi-structured interviews, with four blind or visually impaired participants who were allowed to use the Cobot, found that the system was easy to use and potentially useful for exploring virtual diagrams tactually.A fiber optic sensing system consisting of a fiber Bragg grating (FBG) sensor, optical circulator, optical band pass filter and photodetector is developed to monitor the real-time temperature response of a structure under a dynamic thermal loading. The FBG sensor is surface-bonded on a test specimen and integrated with an optical band pass filter. As a broadband light source transmits into a FBG sensor, a specific wavelength is reflected and transmitted into an optical band pass filter. The reflected wavelength is significantly affected by the temperature, while the output light power from the optical band pass filter is dependent on the wavelength. By measuring the light power with a photodetector, the wavelength can be demodulated, resulting in the determination of the temperature. In this work, the proposed optical sensing system was utilized to monitor the dynamic temperature change of a steel beam under a thermal cycling loading. To verify the accuracy of the fiber optic sensor, a thermocouple was adopted as the reference. The experimental results illustrate a good agreement between the fiber optic sensor and thermocouple. Electronic packages composed of various components such as a solder joint, silicon die, mold compound, and solder mask are frequently subjected to a thermal cycling loading in real-life applications. Temperature variations' incorporation with mismatches of coefficients of thermal expansion among the assembly components leads to crack growth, damage accumulation and final failure. It is important to monitor the temperature to prevent a thermal fatigue failure. A fast response and easy implementation of the fiber optic sensing system was proposed for the real-time temperature measurement under thermal cycling loading.In recent years, the unmanned aerial vehicle (UAV) remote sensing technology has been widely used in the planning, design and maintenance of urban distributed photovoltaic arrays (UDPA). However, the existing studies rarely concern the UAV swarm scheduling problem when applied to remoting sensing in UDPA maintenance. In this study, a novel scheduling model and algorithm for UAV swarm remote sensing in UDPA maintenance are developed. Firstly, the UAV swarm scheduling tasks in UDPA maintenance are described as a large-scale global optimization (LSGO) problem, in which the constraints are defined as penalty functions. Secondly, an adaptive multiple variable-grouping optimization strategy including adaptive random grouping, UAV grouping and task grouping is developed. Finally, a novel evolutionary algorithm, namely cooperatively coevolving particle swarm optimization with adaptive multiple variable-grouping and context vector crossover/mutation strategies (CCPSO-mg-cvcm), is developed in order to effectively optimize the aforementioned UAV swarm scheduling model. 4-Aminobutyric clinical trial The results of the case study show that the developed CCPSO-mg-cvcm significantly outperforms the existing algorithms, and the UAV swarm remote sensing in large-scale UDPA maintenance can be optimally scheduled by the developed methodology.Extreme weather phenomena are on the rise due to ongoing climate change. Therefore, the need for irrigation in agriculture will increase, although it is already the largest consumer of water, a valuable resource. Soil moisture sensors can help to use water efficiently and economically. For this reason, we have recently presented a novel soil moisture sensor with a high sensitivity and broad measuring range. This device does not measure the moisture in the soil but the water available to plants, i.e., the soil water potential (SWP). The sensor consists of two highly porous (>69%) ceramic discs with a broad pore size distribution (0.5 to 200 μm) and a new circuit board system using a transmission line within a time-domain transmission (TDT) circuit. This detects the change in the dielectric response of the ceramic discs with changing water uptake. To prove the concept, a large number of field tests were carried out and comparisons were made with commercial soil water potential sensors. The experiments confirm that the sensor signal is correlated to the soil water potential irrespective of soil composition and is thus suitable for the optimization of irrigation systems.Parkinson's disease is characterized by abnormal gait, which worsens as the condition progresses. Although several methods have been able to classify this feature through pose-estimation algorithms and machine-learning classifiers, few studies have been able to analyze its progression to perform stage classification of the disease. Moreover, despite the increasing popularity of these systems for gait analysis, the amount of available gait-related data can often be limited, thereby, hindering the progress of the implementation of this technology in the medical field. As such, creating a quantitative prognosis method that can identify the severity levels of a Parkinsonian gait with little data could help facilitate the study of the Parkinsonian gait for rehabilitation. In this contribution, we propose a vision-based system to analyze the Parkinsonian gait at various stages using linear interpolation of Parkinsonian gait models. We present a comparison between the performance of a k-nearest neighbors algorithm (KNN), support-vector machine (SVM) and gradient boosting (GB) algorithms in classifying well-established gait features.
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