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12We present our latest work on in-vivo skin capacitive imaging analysis by using grey 13 level co-occurrence matrix (GLCM). The in-vivo skin capacitive images were taken 14 by a capacitance based fingerprint sensor, the skin capacitive images were then 15

Interviews from strength and conditioning coaches across all levels of athletic competition identified their two biggest concerns with the current state of wearable technology: (a) the lack of solutions that accurately capture data “from the ground up” and (b) the lack of trust due to inconsistent measurements. The purpose of this research is to investigate the use of liquid metal sensors, specifically Liquid Wire sensors, as a potential solution for accurately capturing ankle complex movements such as plantar flexion, dorsiflexion, inversion, and eversion. Sensor stretch linearity was validated using a Micro-Ohm Meter and a Wheatstone bridge circuit. Sensors made from different substrates were also tested and discovered to be linear at multiple temperatures. An ankle complex model and computing unit for measuring resistance values were developed to determine sensor output based on simulated plantar flexion movement. The sensors were found to have a significant relationship between the positional change and the resistance values for plantar flexion movement. The results of the study ultimately confirm the researchers’ hypothesis that liquid metal sensors, and Liquid Wire sensors specifically, can serve as a mitigating substitute for inertial measurement unit (IMU) based solutions that attempt to capture specific joint angles and movements.

Collision avoidance is a critical task in many applications, such as ADAS (advanced driver-assistance systems), industrial automation and robotics. In an industrial automation setting, certain areas should be off limits to an automated vehicle for protection of people and high-valued assets. These areas can be quarantined by mapping (e.g., GPS) or via beacons that delineate a no-entry area. We propose a delineation method where the industrial vehicle utilizes a LiDAR (Light Detection and Ranging) and a single color camera to detect passive beacons and model-predictive control to stop the vehicle from entering a restricted space. The beacons are standard orange traffic cones with a highly reflective vertical pole attached. The LiDAR can readily detect these beacons, but suffers from false positives due to other reflective surfaces such as worker safety vests. Herein, we put forth a method for reducing false positive detection from the LiDAR by projecting the beacons in the camera imagery via a deep learning method and validating the detection using a neural network-learned projection from the camera to the LiDAR space. Experimental data collected at Mississippi State University's Center for Advanced Vehicular Systems (CAVS) shows the effectiveness of the proposed system in keeping the true detection while mitigating false positives.

Enforcing protection of medical content becomes a major issue of computer security. Since medical contents are more and more widely distributed, it is necessary to develop security mechanism to guarantee their confidentiality, integrity and traceability in an autonomous way. In this context, watermarking has been recently proposed as a complementary mechanism for medical data protection. In this paper, we focus on the verification of medical image integrity through the combination of digital signatures with such a technology, and especially with Reversible Watermarking (RW). RW schemes have been proposed for images of sensitive content for which any modification may affect their interpretation. Whence, we compare several recent RW schemes and discuss their potential use in the framework of an integrity control process in application to different sets of medical images issued from three distinct modalities: Magnetic Resonance Images, Positron Emission Tomography and Ultrasound Imaging. Experimental results with respect to two aspects including data hiding capacity and image quality preservation, show different limitations which depend on the watermark approach but also on image modality specificities.

4OBJECTIVE: Skin imaging plays a key role in many clinical studies. We have used 5 many skin imaging techniques, including the recently developed capacitive contact 6 skin imaging based on fingerprint sensors. The aim of this study is to develop an 7 effective skin image retrieval technique using Gabor wavelet transform, which can 8 be used on different types of skin images, but with a special focus on skin 9 capacitive contact images. 10 11 METHODS: Content-based image retrieval (CBIR) is a useful technology to retrieve 12 stored images from database by supplying query images. In a typical CBIR, images 13 are retrieved based on colour, shape, and texture, etc. In this paper, texture feature 14 is used for retrieving skin images, and Gabor wavelet transform is used for texture 15 feature description and extraction. 16 17 RESULTS:The results show that the Gabor wavelet texture features can work 18 efficiently on different types of skin images. Although Gabor wavelet transform is 19

The Mars Mineralogical Spectrometer (MMS) is a hyperspectral imager onboard the Mars orbiter of Tianwen-1, China’s first Mars exploration mission. MMS consists of 4 subassemblies: an Optical Sensor Unit (OSU), an Electronics Unit (EU), a Calibration Unit (CU), and a Thermal Control Accessories (TCA). With a 0.5 mrad IFOV and a 416-sample cell array for nadir observation, MMS can map the spectral and spatial information of the Martian surface through push-broom scanning, and it can transmit scientific data by hyperspectral mode or multispectral imaging mode through spatial and spectral combination. MMS can perform multi-sample hyperspectral imaging at full spectral resolution (0.379–1.076 μm with 2.73 nm/band, 1.033–3.425 μm at 7.5 nm/band, both spectral ranges at 2.1 km/pixel at 265 km). For the wavelength region of interest, the multispectral mapping mode provides additional options, a subset of 72 bands that are binned to minimum pixel footprints of 265 m/pixel. The major objective of the MMS is to analyze the compositions and distributions of the minerals on Martian surface, in order to characterize its evolution.