Notes

Association of hepatic steatosis based on ultrasound exam as well as quantitative MRI with prediabetes within the general population.
Despite recent stereo matching algorithms achieving significant results on public benchmarks, the problem of requiring heavy computation remains unsolved. Most works focus on designing an architecture to reduce the computational complexity, while we take aim at optimizing 3D convolution kernels on the Pyramid Stereo Matching Network (PSMNet) for solving the problem. In this paper, we design a series of comparative experiments exploring the performance of well-known convolution kernels on PSMNet. Our model saves the computational complexity from 256.66 G MAdd (Multiply-Add operations) to 69.03 G MAdd (198.47 G MAdd to 10.84 G MAdd for only considering 3D convolutional neural networks) without losing accuracy. On Scene Flow and KITTI 2015 datasets, our model achieves results comparable to the state-of-the-art with a low computational cost.The introduction of various networks into automotive cyber-physical systems (ACPS) brings great challenges on security protection of ACPS functions, the auto industry recommends to adopt the hardware security module (HSM)-based multicore ECU to secure in-vehicle networks while meeting the delay constraint. However, this approach incurs significant hardware cost. Consequently, this paper aims to reduce security enhancing-related hardware cost by proposing two efficient design space exploration (DSE) algorithms, namely, stepwise decreasing-based heuristic algorithm (SDH) and interference balancing-based heuristic algorithm (IBH), which explore the task assignment, task scheduling, and message scheduling to minimize the number of required HSMs. Experiments on both synthetical and real data sets show that the proposed SDH and IBH are superior than state-of-the-art algorithm, and the advantage of SDH and IBH becomes more obvious as the increase about the percentage of security-critical tasks. For synthetic data sets, the hardware cost can be reduced by 61.4% and 45.6% averagely for IBH and SDH, respectively; for real data sets, the hardware cost can be reduced by 64.3% and 54.4% on average for IBH and SDH, respectively. Furthermore, IBH is better than SDH in most cases, and the runtime of IBH is two or three orders of magnitude smaller than SDH and state-of-the-art algorithm.Several studies have shown the importance of proper chewing and the effect of chewing speed on the human health in terms of caloric intake and even cognitive functions. This study aims at designing algorithms for determining the chew count from video recordings of subjects consuming food items. A novel algorithm based on image and signal processing techniques has been developed to continuously capture the area of interest from the video clips, determine facial landmarks, generate the chewing signal, and process the signal with two methods low pass filter, and discrete wavelet decomposition. Peak detection was used to determine the chew count from the output of the processed chewing signal. The system was tested using recordings from 100 subjects at three different chewing speeds (i.e., slow, normal, and fast) without any constraints on gender, skin color, facial hair, or ambience. The low pass filter algorithm achieved the best mean absolute percentage error of 6.48%, 7.76%, and 8.38% for the slow, normal, and fast chewing speeds, respectively. The performance was also evaluated using the Bland-Altman plot, which showed that most of the points lie within the lines of agreement. However, the algorithm needs improvement for faster chewing, but it surpasses the performance of the relevant literature. This research provides a reliable and accurate method for determining the chew count. The proposed methods facilitate the study of the chewing behavior in natural settings without any cumbersome hardware that may affect the results. This work can facilitate research into chewing behavior while using smart devices.With the emerging interest of autonomous vehicles (AV), the performance and reliability of the land vehicle navigation are also becoming important. Generally, the navigation system for passenger car has been heavily relied on the existing Global Navigation Satellite System (GNSS) in recent decades. However, there are many cases in real world driving where the satellite signals are challenged; for example, urban streets with buildings, tunnels, or even underpasses. In this paper, we propose a novel method for simultaneous vehicle dead reckoning, based on the lane detection model in GNSS-denied situations. The proposed method fuses the Inertial Navigation System (INS) with learning-based lane detection model to estimate the global position of vehicle, and effectively bounds the error drift compared to standalone INS. The integration of INS and lane model is accomplished by UKF to minimize linearization errors and computing time. The proposed method is evaluated through the real-vehicle experiments on highway driving, and the comparative discussions for other dead-reckoning algorithms with the same system configuration are presented.In this article, changes in NiTi alloy (Flexinol) electrical resistance during cyclic stretching with small elongation were investigated. A dedicated test stand consisting of motorized vertical test stand, force gauge, and electric resistance measuring device with an accuracy of 0.006 Ω was developed. A dedicated control algorithm was developed using LabVIEW software. Selleckchem Sodium Pyruvate Changes in electrical resistance were investigated for the 0.1 mm Flexinol wire with length of 120 mm. Testing was performed in the elongation range between 0.25% and 1.5% in martensite phase. Tested samples were subjected to 30 stretching cycles with a movement speed of 10 mm/min. Obtained results show that the cyclic stretching of Flexinol wire reduces its electrical resistance with each stretching cycle. Moreover, it was noted that changes in Flexinol electrical resistance during cycling stretching depend on the assumed elongation and number of the already performed stretching cycles. The observed electrical resistance change decreases with each stretching cycle. Thus, the observed changes are greater during the first stretching cycles. For elongations exceeding 1%, the Flexinol electrical resistance in the first stretching cycle increases. In each subsequent cycle, electrical resistance decreases, as in the case of the smallest value of assumed elongation. In almost all tested cases (except in the case with 1.5% of assumed elongation), Flexinol electrical resistance after 30 stretching cycles was smaller than before the test.The bearings of rotating machinery often fail, leading to unforeseen downtime of large machines in industrial plants. Therefore, condition monitoring can be a powerful tool to aid in the quick identification of these faults and make it possible to plan maintenance before the fault becomes too drastic, reducing downtime and cost. Predictive maintenance is often based on information gathered from accelerometers. However, these sensors are contact-based, making them less attractive for use in an industrial plant and more prone to breakage. In this paper, condition monitoring based on ultrasound is researched, where non-invasive sensors are used to record the noise originating from different defects of the Machinery Fault Simulator. The acoustic data are recorded using a sparse microphone array in a lab environment. The same array was used to record real spatial noise in a fully operational plant which was later added to the acoustic data containing the different defects with a variety of Signal To Noise ratios. In this paper, we compare the classification results of the noisy acoustic data of only one microphone to the beamformed acoustic data. We do this to investigate how beamforming could improve the classification process in an ultrasound condition-monitoring application in a real industrial plant.The purpose of this paper is to present a newly developed process for the fabrication of multilayer circuits based on the pad-printing technique. Even though the maturity level, in terms of accuracy, substrate type and print size of several printing industrial processes is relatively high, the fabrication complexity of multilayer printed electronics remains relatively high. Due to its versatility, the pad-printing technique allows the superposition of printed conductive and insulating layers. Compared to other printing processes, its main advantage is the ability to print on various substrates even on flexible, curved or irregular surfaces. Silver-based inks were used for the formulation of conductive layers while UV inks were employed to fulfil the functionality of the insulating layers. To demonstrate the functionality of the pad-printing results, a multilayer test pattern has been designed and printed on Kapton®. Furthermore, to demonstrate the efficacy of this approach, a multilayer circuit composed of three stacked layers has been designed and printed on various substrates including Kapton®, paper and wood. This electronic circuit controls an array of LEDs through the manipulation of a two-key capacitive touch sensor. This study, allowed us to define recommendations for the different parameters leading to high printing quality. We expect a long-term beneficial impact of this study towards a low-cost, fast, and environmental-friendly production of printed electronics.Amyotrophic Lateral Sclerosis (ALS) is one of the most aggressive neurodegenerative diseases and is now recognized as a multisystem network disorder with impaired connectivity. Further research for the understanding of the nature of its cognitive affections is necessary to monitor and detect the disease, so this work provides insight into the neural alterations occurring in ALS patients during a cognitive task (P300 oddball paradigm) by measuring connectivity and the power and latency of the frequency-specific EEG activity of 12 ALS patients and 16 healthy subjects recorded during the use of a P300-based BCI to command a robotic arm. For ALS patients, in comparison to Controls, the results (p less then 0.05) were an increment in latency of the peak ERP in the Delta range (OZ) and Alpha range (PO7), and a decreased power in the Beta band among most electrodes; connectivity alterations among all bands, especially in the Alpha band between PO7 and the channels above the motor cortex. The evolution observed over months of an advanced-state patient backs up these findings. These results were used to compute connectivity- and power-based features to discriminate between ALS and Control groups using Support Vector Machine (SVM). Cross-validation achieved a 100% in specificity and 75% in sensitivity, with an overall 89% success.A microfluidic film bulk acoustic wave resonator gas sensor (mFBAR) adapted specifically as an in-line detector in gas chromatography was described. This miniaturized vapor sensor was a non-destructive detector with very low dead volume (0.02 μL). It was prepared by enclosing the resonator in a microfluidic channel on a chip with dimensions of only 15 mm × 15 mm × 1 mm. The device with polymer coating showed satisfactory performance in the detection of organophosphorus compound, demonstrating a very low detection limit (a dozen parts per billion) with relatively short response time (about fifteen seconds) toward the simulant of chemical warfare agent, dimethyl methylphosphonate. The in-line detection of the mFBAR sensor with FID was constructed and employed to directly measure the concentration profile on the solid surface by the mFBAR with the controlled concentration profile in the mobile phase at the same time. The difference of peak-maximum position between mobile phase and solid phase could be a convenient indicator to measure mass transfer rate.
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