Assessment of Standing Multi-Frequency Bioimpedance Analyzer to Measure Body Composition of the Whole Body and Limbs in Elite Male Wrestlers.

We investigated differences in body composition measurements for the whole body and limb segments in elite male wrestlers between results of multi-frequency bioelectrical impedance analyses (MFBIA) and dual energy X-ray absorptiometry (DXA). Sixty-six elite male wrestlers from Taiwan were recruited. Wrestlers' body fat percentage (PBFWB), whole body fat-free mass (FFMWB), whole body lean soft tissue mass (LSTMWB), and fat-free mass of arms, legs and trunk (FMArms, FFMLegs, FFMTrunk) were measured by MFBIA and DXA, and analyzed using Pearson correlation coefficient and Bland-Altman plot. Correlations of FFMWB, LSTMWB, and PBFWB between devices were 0.958, 0.954, and 0.962, respectively. Limits of agreement (LOA) of Bland-Altman plot were -4.523 to 4.683 kg, -4.332 to 4.635 kg and -3.960 to 3.802%, respectively. Correlations of body composition parameters FFMArms, FFMLegs and FFMTurnk between devices in each limb segment were 0.237, 0.809, and 0.929, respectively; LOAs were -2.877 to 2.504 kg, -7.173 to -0.015 kg and -5.710 to 0.777 kg, respectively. Correlation and consistency between the devices are high for FFM, LSTM and PBF but relatively low for limb segment FFM. MFBIA may be an alternative device to DXA for measuring male wrestlers' total body composition but limb segment results should be used cautiously.

New scheme of LiDAR-embedded smart laser headlight for autonomous vehicles.

A new scheme of LiDAR-embedded smart laser headlight module (LHM) for autonomous vehicles is proposed and demonstrated. The LiDAR sensor was fabricated by LeddarTech with the wavelength of 905-nm, whereas the LHM was fabricated by a highly reliable glass phosphor material that exhibited excellent thermal stability. The LHM consisted of two blue laser diodes, two blue LEDs, a yellow glass phosphor-converter layer with a copper thermal dissipation substrate, and a parabolic reflector to reflect the blue light and the yellow phosphor light combined into white light. The LHM exhibited a total output optical power of 9.5 W, a luminous flux of 4,000 lm, a relative color temperature of 4,300 K, and an efficiency of 421 lm/W. The high-beam patterns of the LHMs were measured to be 180,000 luminous intensity (cd) at 0° (center), 84,000 cd at ± 2.5°, and 29,600 cd at ± 5°, which met the ECE R112 class B regulation. The low-beam patterns also satisfied the ECE R112 class B regulation as well. Integrating the signals received from the Lidar detection and CCD image by a smart algorithm, we demonstrated the generation of smart on/off signals for controlling the laser headlights. The recognition rate of the objects was evaluated to be more than 86%. This novel LiDAR-embedded smart LHM with the unique highly reliable glass phosphor-converter layer is favorable as one of the most promising candidates for use in the next-generation high-performance autonomous vehicle applications.

Time sequence image analysis of positron emission tomography using wavelet transformation.

This paper presents the time sequence image analysis technique of positron emission tomography (PET) using a wavelet transformation method. The abdominal cavity of a person taking [18F]Fluoro-2-deoxy-2-D-glucose (18F-FDG) was scanned by the dynamic PET. The organ selection with dynamic PET images was conducted by the wavelet transformation to implement automatic selection of the region of interest (ROI). The image segmentation was carried out by the processes of sampling, wavelet transformation, erosion, dilation, and superimposition. Wavelet constructed image (WCI) contours were created by sampling 512 images from 1960 consecutive dynamic sequence PET images. The image segmentation technology developed can help doctors automatically select ROI, accurately identify lesion locations of organs, and thus effectively reduce human operation time and errors.

An intelligent health monitoring system using radio-frequency identification technology.

Long-term care (LTC) for the elderly has become extremely important in recent years. It is necessary for the different physiological monitoring systems to be integrated on the same interface to help oversee and manage the elderly's needs. This paper presents a novel health monitoring system for LTC services using radio-frequency identification (RFID) technology. Dual-band RFID protocols were included in the system, in which the high-frequency (HF) band of 13.56 MHz was used to identify individuals and the microwave band of 2.45 GHz was used to monitor physiological information. Distinct physiological data, including oxyhemoglobin saturation by pulse oximetry (SpO2), blood pressure, blood sugar, electrocardiogram (ECG) readings, body temperature, and respiration rate, were monitored by various biosensors. The intelligent RFID health monitoring system provided the features of the real-time acquisition of biomedical signals and the identification of personal information pertaining to the elderly and patients in nursing homes.