The authors have applied a systems analysis approach to describe the musculoskeletal system as consisting of a stack of superimposed kinematic hier-archical segments in which each lower segment tends to transfer its m...
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The authors have applied a systems analysis approach to describe the musculoskeletal system as consisting of a stack of superimposed kinematic hier-archical segments in which each lower segment tends to transfer its motion to the other superimposed segments. This segmental chain enables the derivation of both conscious perception and sensory control of action in space. This applied systems analysis approach involves the measurements of the complex motor behavior in order to elucidate the fusion of multiple sensor data for the reliable and efficient acquisition of the kinetic, kinematics and electromyographic data of the human spatial behavior. The acquired kinematic and related kinetic signals represent attributive features of the internal recon-struction of the physical links between the superimposed body segments. In-deed, this reconstruction of the physical links was established as a result of the fusion of the multiple sensor data. Furthermore, this acquired kinematics, kinetics and electromyographic data provided detailed means to record, annotate, process, transmit, and display pertinent information derived from the musculoskeletal system to quantify and differentiate between subjects with mobility-related disabilities and able-bodied subjects, and enabled an inference into the active neural processes underlying balance reactions. To gain insight into the basis for this long-term dependence, the authors have applied the fusion of multiple sensor data to investigate the effects of Cerebral Palsy, Multiple Sclerosis and Diabetic Neuropathy conditions, on biomechanical/neurophysiological changes that may alter the ability of the human loco-motor system to generate ambulation, balance and posture.
Piezoresistive-type, capacitive-type, and optical-type sensors have mainly been used for measuring a strain. However, in building a sensor network for remote monitoring using these conventional sensors there are disad...
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Piezoresistive-type, capacitive-type, and optical-type sensors have mainly been used for measuring a strain. However, in building a sensor network for remote monitoring using these conventional sensors there are disadvantages such as the complexity of a measuring system including wireless communication circuitry and high cost. In this paper, we demonstrates a highly-sensitive surface acoustic wave (SAW) strain sensor which is advantageous to harsh environments and wireless network. We designed and fabricated the SAW strain sensor. The SAW strain sensor attached on a specimen was tested with a tensile tester. The strain on the sensor surface was measured with a commercial strain gauge and compared with that obtained from strain analysis. The central frequency shift of the SAW sensor was measured with a network analyzer. The sensitivity of the SAW strain sensor is 134 Hz/μe which is high compared to previous results.
Multiple single-units recorded from chronically-implanted microelectrode arrays frequently exhibit variability in their spike waveform features and firing characteristics, making it challenging to ascertain the identi...
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Multiple single-units recorded from chronically-implanted microelectrode arrays frequently exhibit variability in their spike waveform features and firing characteristics, making it challenging to ascertain the identity of recorded neurons across days. In this study, we present a fast and efficient algorithm that tracks multiple single-units, recorded in a nonhuman primate performing brain control of a robotic arm, across days based on features extracted from units' average waveforms. Furthermore, the algorithm does not require long recording duration to perform the analysis and can be applied at the start of each recording session without requiring the subject to be engaged in a behavioral task. The algorithm achieves a classification accuracy of up to 92% compared to experts' manual tracking.
Complex inorganic brown pigments having a high near infrared solar reflectance have been synthesized. Fe2O3 is the host component and mixtures of Sb2O3, SiO2, Al2O3, and TiO2 were used as the guest components. Sb2O3, ...
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Complex inorganic brown pigments having a high near infrared solar reflectance have been synthesized. Fe2O3 is the host component and mixtures of Sb2O3, SiO2, Al2O3, and TiO2 were used as the guest components. Sb2O3, SiO2, Al2O3, and TiO2 were mixed into 40 different compositions. It was found that a sample, denoted by S31, with a composition of Fe2O3, Sb2O3, SiO2, Al2O3, and TiO2 of 70, 10, 12, 2 and 6 wt% respectively, gives a maximum near infrared solar reflectance of 46.7%. Brown pigment colours were measured in CIE L*a*b* colour index. The S31 pigment powder was then prepared as a reflective coating material with different amounts of pigment powder from 4-8 g in 100 g of ceramic glaze. The prepared materials were sprayed on the biscuit clay tiles for reflectance measurements. It was found that 5 g of S31 pigment powder mixed with 100 g of ceramic glaze gives the highest near-infrared reflectance value of 41.3%. The newly synthesized pigment is a suitable ceramic roof coating for its high reflectance performance and the durability performing once the ceramic roof installed on house.
We characterize k–colorability of a simplicial graph via the intrinsic algebraic structure of the associated right-angled Artin group. As a consequence, we show that a certain problem about the existence of homomorph...
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This paper introduces a fast algorithm for simultaneous inversion and determinant computation of small sized matrices in the context of fully Polarimetric Synthetic Aperture Radar (PolSAR) image processing and analysi...
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At the Unit Laka Lantas Polres Lhokseumawe determining accident-prone areas on roads in Lhokseumawe still using manual systems. Determination of accident-prone areas are less effective because the police Unit Laka Lan...
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This paper presents a methodology for the segmentation and analysis of the tissues in color images of leg ulcers. The segmentation is obtained through an automatic analysis made in the RGB and SI channels by changing ...
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ISBN:
(纸本)0769511139
This paper presents a methodology for the segmentation and analysis of the tissues in color images of leg ulcers. The segmentation is obtained through an automatic analysis made in the RGB and SI channels by changing from the RGB color space to the HSI color space. The aim is to determine which of the five channels have the characteristic that will make the segmentation process more efficient. After the analysis, the selected channel is segmented and used as a mask over the original image, allowing that only the inner tissues of the wound be analyzed. The analysis is done through predetermined functions that attribute membership grade for each processed pixel as well as its level of engagement in a specified tissue class. The article also shows the results obtained from both the segmentation and analysis of tissues using the proposed method.
Our surroundings’ auditory landscapes are a wealth of knowledge, providing insights into a range of outdoor pursuits. The automatic classification of these actions using audio event detection and classification (AEDC...
Our surroundings’ auditory landscapes are a wealth of knowledge, providing insights into a range of outdoor pursuits. The automatic classification of these actions using audio event detection and classification (AEDC) systems has a great deal of potential applications in environmental monitoring, security, surveillance, and driverless cars. Even though AEDC has advanced significantly in indoor settings, difficulties still exist outside because of the variable and changing acoustic conditions brought on by elements like the weather, different sound sources, and ambient noise from traffic and industry. This study suggests an outdoor audio event categorization model based on convolutional neural networks (CNNs). The suggested model shows passable accuracy by utilizing adaptation to the downstream task using the ESC-50 dataset and transfer learning from a pre-trained model. The effectiveness of multi-class audio classification models in downstream tasks is analyzed in this paper, with an emphasis on the effect of an increasing number of output classes on accuracy. Three models—three, four, and five classes—with different output class configurations are used in the study, and their training and validation accuracies are assessed. Although the accuracy scores above 80% are commendable, the data show a discernible reduction in performance as the number of classes rises. Notably, the three-class model attains a validation accuracy exceeding 90%, whereas the four-class and five-class models exhibit reduced accuracies, falling below 90% and 85%, respectively.
The synthesis reaction used in esterification needs high energy consumption and a precise processing time to get the best yield of target. In this study, a model was formulated to optimize glycerol esterification proc...
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ISBN:
(纸本)9781509003648
The synthesis reaction used in esterification needs high energy consumption and a precise processing time to get the best yield of target. In this study, a model was formulated to optimize glycerol esterification process by minimizing the time needed for the process and maximizing the yield of Mono-glycerides. This optimization has gained importance for boosting the esterification industry and improving the production efficiency. Optimization through adaptive monitoring and control has provided significant advances in the process efficiency, a lower energy consumption and a better product quality. This paper presents the optimization with a computational algorithm in real time and adaptive control (RTAC), as compared to the conventional (traditional) methods to monitor and control of glycerol esterification processes. The identification of esterification status based on temperature and time are evaluated to strengthen the optimization. An adaptive method as feature selection to select wavelength IR sensors at specified intervals was carried out with Relief algorithm and Adaptive Pillar K-means clustering method to set the parameter control was proposed in this paper. Many combinations were evaluated from real time condition process, to achieve the best optimization results. The experimental results demonstrate that real time adaptive control can be achieved by using three clusters, which are heating up, stabilizing and finishing. In RTAC, each cluster has its own parameter to set the control point by the servo motor that was attached to magnetic stirrer-heater. By using optimization parameter for each cluster, esterification process time can be shortened by 15-20 minutes with a higher yield (7% or more), lower range stirrer rotation (300rpm-450 rpm) and a lower final temperature of 200℃ - 210℃.
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