Pressure- and 3D-derived coronary Flow Reserve with Hydrostatic Pressure Correction - A Validation by Intracoronary Doppler Measurements

Purpose: To develop a method of coronary flow reserve (CFR) calculation derived from threedimensional (3D) coronary angiographic parameters and intracoronary pressure data during fractional flow reserve (FFR) measurement. Methods: Altogether 19 coronary arteries of 16 native and 3 stented vessels were reconstructed in 3D. The measured distal intracoronary pressures were corrected to the hydrostatic pressure based on the height differences between the levels of the vessel orifice and the sensor position. Classical fluid dynamic equations were applied to calculate the flow during the resting state and vasodilatation on the basis of morphological data and intracoronary pressure values. 3D-derived coronary flow reserve (CFR p-3D ) was defined as the ratio between the calculated hyperemic and the resting flow and was compared to the CFR values simultaneously measured by the Doppler sensor (CFR Doppler ). Results: Haemodynamic calculations using the distal coronary pressures corrected for hydrostatic pressures showed a strong correlation between the individual CFR p-3D values and the CFR Doppler measurements (r=0.89, p<0.0001). Hydrostatic pressure correction increased the specificity of the method from 46.1% to 92.3% for predicting an abnormal CFR Doppler <2. Conclusions : CFR p-3D calculation with hydrostatic pressure correction during FFR measurement facilitates a comprehensive haemodynamic assessment, supporting the complex evaluation of macro- and microvascular coronary artery disease.

Robotic Forklift for Stacking Multiple Pallets with RGB-D Cameras

We propose a robotic forklift system for stacking multiple mesh pallets. The stacking of mesh pallets is an essential task for the shipping and storage of loads. However, stacking, the placement of pallet feet on pallet edges, is a complex problem owing to the small sizes of the feet and edges, leading to a complexity in the detection and the need for high accuracy in adjusting the pallets. To detect the pallets accurately, we utilize multiple RGB-D (RGB Depth) cameras that produce dense depth data under the limitations of the sensor position. However, the depth data contain noise. Hence, we implement a region growing-based algorithm to extract the pallet feet and edges without removing them. In addition, we design the control law based on path following control for the forklift to adjust the position and orientation of two pallets. To evaluate the performance of the proposed system, we conducted an experiment assuming a real task. The experimental results demonstrated that the proposed system can achieve a stacking operation with a real forklift and mesh pallets.

Soft Array Surface-Changing Compound Eye

The field-of-view (FOV) of compound eyes is an important index for performance evaluation. Most artificial compound eyes are optical, fabricated by imitating insect compound eyes with a fixed FOV that is difficult to adjust over a wide range. The compound eye is of great significance in the field of tracking high-speed moving objects. However, the tracking ability of a compound eye is often limited by its own FOV size and the reaction speed of the rudder unit matched with the compound eye, so that the compound eye cannot better adapt to tracking high-speed moving objects. Inspired by the eyes of many organisms, we propose a soft-array, surface-changing compound eye (SASCE). Taking soft aerodynamic models (SAM) as the carrier and an infrared sensor as the load, the basic model of the variable structure infrared compound eye (VSICE) is established using an array of infrared sensors on the carrier. The VSICE model is driven by air pressure to change the array surface of the infrared sensor. Then, the spatial position of each sensor and its viewing area are changed and, finally, the FOV of the compound eye is changed. Simultaneously, to validate the theory, we measured the air pressure, spatial sensor position, and the FOV of the compound eye. When compared with the current compound eye, the proposed one has a wider adjustable FOV.

Optimal Experimental Design for Inverse Identification of Conductive and Radiative Properties of Participating Medium

The conductive and radiative properties of participating medium can be estimated by solving an inverse problem that combines transient temperature measurements and a forward model to predict the coupled conductive and radiative heat transfer. The procedure, as well as the estimates of parameters, are not only affected by the measurement noise that intrinsically exists in the experiment, but are also influenced by the known model parameters that are used as necessary inputs to solve the forward problem. In the present study, a stochastic Cramér–Rao bound (sCRB)-based error analysis method was employed for estimation of the errors of the retrieved conductive and radiative properties in an inverse identification process. The method took into account both the uncertainties of the experimental noise and the uncertain model parameter errors. Moreover, we applied the method to design the optimal location of the temperature probe, and to predict the relative error contribution of different error sources for combined conductive and radiative inverse problems. The results show that the proposed methodology is able to determine, a priori, the errors of the retrieved parameters, and that the accuracy of the retrieved parameters can be improved by setting the temperature probe at an optimal sensor position.

Analisis Kinerja Sistem Pendingin pada Mesin Toyota Avanza Tipe K3-Ve Menggunakan Scanner Lauch Thinkdiag Easydiag 4.0

Setiap mesin mobil dilengkapi sistem pendingin yang berfungsi untuk menjaga temperature mesin saat bekerja agar tidak terjadi over heating atau terlalu panas dan tentunya menjaga kondisi mesin agar tetap baik. Tujuan dilakukannya penelitian ini untuk mengetahui bagaimana kerja sistem pendingin pada mesin toyota avanza K31-VE dan suhu dari cairan pendingin saat mesin menyala. Penelitian ini menggunakan metode deskriptif dengan pengambilan data dari scanner launch thinkdiag easydiag 4.0. Langkah pertamanya menyalakan mesin dalam jangka waktu total 11 menit dan discan menggunakan scanner, saat mesin posisi stationer pada scanner menunjukan suhu coolant temperature 161.6 Degree F, engine speed 998 rpm, dan throttle sensor position 0%. Dilanjut dengan memainkan pedal gas hingga hasil akhir dari penelitian yang didapat dari scanner adalah coolant temperature 208.4 Degree F, engine speed 751 rpm, dan throttle sensor position 0%.

Ambient light level varies with different locations and environmental conditions: Potential to impact myopia

Purpose Considering that time spent outdoors is protective for myopia, we investigated how ambient light levels reaching the eye varies across 9 outdoor and 4 indoor locations in 5 different environmental conditions. Methods Illuminance (lux) was recorded using a lux meter under conditions of weather (sunny/cloudy), time of a day (7:00,10:00,13:00, and 16:00 hours), seasons (summer/winter), and sun protection (hat and cap) in outdoor and indoor locations. Nine outdoor locations were “open playground”, “under a translucent artificial-shade”, “under a porch facing east”, “under a porch facing south”, “under a big tree”, “between three buildings”, “within 4 buildings”, and “canopy”. As a ninth outdoor location, “Under a glass bowl” in the outdoor location was used as a simulation for “glass classroom model” and measurement was taken at the floor level only to determine in overall the illuminance conditions with glass covered on all sides. The 4 indoor locations included “room with multiple large windows”, “room with combination light source”, “room with multiple artificial lights”, and “room with single artificial light”. Results The overall median illuminance level (median; Q1-Q3) recorded in 9 outdoor locations was 8 times higher than that of all indoor locations (1175;197–5400 lux vs. 179;50–333 lux). Highest illuminance in outdoor locations was recorded in “open playground” (9300;4100–16825 lux), followed by “under a translucent artificial shade (8180;4200–13300 lux) and the lowest in “within 4 buildings” (11;6–20 lux). Illuminance under ‘Canopy’, ‘between three buildings’ and ‘within four buildings’ was similar to that of indoor locations (<1000 lux). Time of the day, weather, season, sensor position and using sun protection did not alter illuminance to change from high to low level (>1000 to <1000 lux). Among indoor locations, illuminance in “room with multiple large windows” crossed 1000 lux at a specific time points on both sunny and cloudy days. Conclusions Illuminance levels in outdoors and indoors varied with location type, but not with other conditions. Given the variation in illuminance in different locations, and the impact it may have on myopia control, appropriate detailed recommendations seems necessary while suggesting time outdoors as an anti-myopia strategy to ensure desired outcomes.

Evaluating Pedometer Algorithms on Semi-Regular and Unstructured Gaits

Pedometers are popular for counting steps as a daily measure of physical activity, however, errors as high as 96% have been reported in previous work. Many reasons for pedometer error have been studied, including walking speed, sensor position on the body and pedometer algorithm, demonstrating some differences in error. However, we hypothesize that the largest source of error may be due to differences in the regularity of gait during different activities. During some activities, gait tends to be regular and the repetitiveness of individual steps makes them easy to identify in an accelerometer signal. During other activities of everyday life, gait is frequently semi-regular or unstructured, which we hypothesize makes it difficult to identify and count individual steps. In this work, we test this hypothesis by evaluating the three most common types of pedometer algorithm on a new data set that varies the regularity of gait. A total of 30 participants were video recorded performing three different activities: walking a path (regular gait), conducting a within-building activity (semi-regular gait), and conducting a within-room activity (unstructured gait). Participants were instrumented with accelerometers on the wrist, hip and ankle. Collectively, 60,805 steps were manually annotated for ground truth using synchronized video. The main contribution of this paper is to evaluate pedometer algorithms when the consistency of gait changes to simulate everyday life activities other than exercise. In our study, we found that semi-regular and unstructured gaits resulted in 5–466% error. This demonstrates the need to evaluate pedometer algorithms on activities that vary the regularity of gait. Our dataset is publicly available with links provided in the introduction and Data Availability Statement.

Investigation on the Sampling Frequency and Channel Number for Force Myography Based Hand Gesture Recognition

Force myography (FMG) is a method that uses pressure sensors to measure muscle contraction indirectly. Compared with the conventional approach utilizing myoelectric signals in hand gesture recognition, it is a valuable substitute. To achieve the aim of gesture recognition at minimum cost, it is necessary to study the minimum sampling frequency and the minimal number of channels. For purpose of investigating the effect of sampling frequency and the number of channels on the accuracy of gesture recognition, a hardware system that has 16 channels has been designed for capturing forearm FMG signals with a maximum sampling frequency of 1 kHz. Using this acquisition equipment, a force myography database containing 10 subjects’ data has been created. In this paper, gesture accuracies under different sampling frequencies and channel’s number are obtained. Under 1 kHz sampling rate and 16 channels, four of five tested classifiers reach an accuracy up to about 99%. Other experimental results indicate that: (1) the sampling frequency of the FMG signal can be as low as 5 Hz for the recognition of static movements; (2) the reduction of channel number has a large impact on the accuracy, and the suggested channel number for gesture recognition is eight; and (3) the distribution of the sensors on the forearm would affect the recognition accuracy, and it is possible to improve the accuracy via optimizing the sensor position.