ECM-integrin signalling instructs cellular position-sensing to pattern the early mouse embryo

Development entails patterned emergence of diverse cell types within the embryo. In mammals, cells positioned inside the embryo give rise to the inner cell mass (ICM) that eventually forms the embryo proper. Yet the molecular basis of how these cells recognise their ‘inside’ position to instruct their fate is unknown. Here we show that provision of extracellular matrix (ECM) to isolated embryonic cells induces ICM specification and alters subsequent spatial arrangement between epiblast (EPI) and primitive endoderm (PrE) cells that emerge within the ICM. Notably, this effect is dependent on integrin β1 activity and involves apical to basal conversion of cell polarity. We demonstrate that ECM-integrin activity is sufficient for ‘inside’ positional signalling and it is required for proper EPI/PrE patterning. Our findings thus highlight the significance of ECM-integrin adhesion in enabling position-sensing by cells to achieve tissue patterning.

Sensorless Control of Asynchronous Motor at Low speed

The subject of the submitted paper is to provide a detailed description of simulation control of rotor position sensing of an asynchronous motor by an injection method of high-frequency analogue signal on supply signal frequency of up to 5 Hz, i.e., at zero and low speed. In general, contrary to discrete signal injection, the implementation of the method appears to be simpler for continual signal injection aimed at monitoring of asymmetry of rotary electric machines, however, the process of information gathering related to position of monitored asymmetries during signal reaction processing is more complicated. Genuine verification of the method requires designing a mathematical model of a motor including asymmetries caused by rotor grooving and by magnetic core saturation. The asymmetries occurring in asynchronous motors considerably influence the instant value of a stator induction LS. Asymmetries caused by magnetic circuit saturation were identified and eliminated because of inducing the measured signal distortion. The elimination method LMDEM is the method proposed for repressing the asymmetries. The asymmetries caused by rotor grooving are intended to detect the rotor position. In final part, mathematical functions will be used for converting the signal to rotor position.

Automated Bowel Polyp Detection Based on Actively Controlled Capsule Endoscopy: Feasibility Study

This paper presents an active locomotion capsule endoscope system with 5D position sensing and real-time automated polyp detection for small-bowel and colon applications. An electromagnetic actuation system (EMA) consisting of stationary electromagnets is utilized to remotely control a magnetic capsule endoscope with multi-degree-of-freedom locomotion. For position sensing, an electronic system using a magnetic sensor array is built to track the position and orientation of the magnetic capsule during movement. The system is integrated with a deep learning model, named YOLOv3, which can automatically identify colorectal polyps in real-time with an average precision of 85%. The feasibility of the proposed method concerning active locomotion and localization is validated and demonstrated through in vitro experiments in a phantom duodenum. This study provides a high-potential solution for automatic diagnostics of the bowel and colon using an active locomotion capsule endoscope, which can be applied for a clinical site in the future.

A Position Sensing Glove to Aid Ankle-Foot Orthosis Diagnosis and Treatment

A position sensing glove called SmartScan, which creates a 3D virtual model of a real object, is presented. The data from the glove is processed by a volume minimization algorithm to validate the position sensor data. This allows only data from the object’s surface to be retained. The data validation algorithm allows the user to progressively improve an image by repeatedly moving their hand over the object. In addition, the user can choose their own balance between feature resolution and invalid data rejection. The SmartScan glove is tested on a foot model and is shown to be robust against motion artifacts, having a mean accuracy of 2.9 mm (compared to a 3D model generated from optical imaging) without calibration.

TORQUE RIPPLE REDUCTION OF A BRUSHLESS DC MOTOR USING Y-SOURCE CONVERTER

The brushless DC motor (BLDC) is a low cost, reliable and efficient motor for low power applications. In general, the speed, torque and current of the BLDC motor are controlled using a well tuned PI controller in the inner and outer control loops. This controller will be effective in reducing the dynamic speed error, but will produce large current ripples. This reference current when given to the inner control loop and controlled using hall-effect position sensing technique, leads to comparatively large ripples in the torque. Because of large dynamic behavior of dc link voltage when nominal rating capacitor is used, there will be torque ripples and reduction in rotor speed from the reference current value. Hence, to mitigate this torque ripples in BLDC motor a fast acting adjustable dc link voltage like chopper is generally used. The effective dc link voltage control with voltage boosting and controlling action is observed with Y-source converter and is compared with a Z-source converter in this paper. The Y-source converter is designed in such a way that, it will effectively control the speed and also produces lesser current ripples reference. Further, the inverter topology uses a six switch basic configuration but with a new switching strategy. The results are compared with a Z-source converter with the proposed Y-source converter under variable load torque and variable speed cases in MATLAB/ SIMULINK environment. It is found that, the torque ripples are reduced effectively without much change in the reference speed. Also, even at higher rotor speeds, the torque ripples and surges are also lesser.

Global Position Prediction for Interactive Motion Capture

We present a method for reconstructing the global position of motion capture where position sensing is poor or unavailable. Capture systems, such as IMU suits, can provide excellent pose and orientation data of a capture subject, but otherwise need post processing to estimate global position. We propose a solution that trains a neural network to predict, in real-time, the height and body displacement given a short window of pose and orientation data. Our training dataset contains pre-recorded data with global positions from many different capture subjects, performing a wide variety of activities in order to broadly train a network to estimate on like and unseen activities. We compare training on two network architectures, a universal network (u-net) and a traditional convolutional neural network (CNN) - observing better error properties for the u-net in our results. We also evaluate our method for different classes of motion. We observe high quality results for motion examples with good representation in specialized datasets, while general performance appears better in a more broadly sampled dataset when input motions are far from training examples.

A Position Sensing Glove to Aid Ankle-Foot Orthosis Diagnosis and Treatment

A position sensing glove, called SmartScan, that creates a 3D virtual model of a real object is presented. The data from the glove is processed by a volume minimization algorithm to validate the position sensor data. This allows only data from the object’s surface to be retained. The data validation algorithm allows the user to progressively improve an image by repeatedly moving their hand over the object. In addition, the user can choose their own balance between feature resolution and invalid data rejection. The SmartScan glove is tested on a foot model and is shown to be robust against motion artifacts, and has a mean accuracy of 2.9 mm (compared to a 3D model generated from optical imaging) without calibration.

Current Progress of Magnetoresistance Sensors

Magnetoresistance (MR) is the variation of a material’s resistivity under the presence of external magnetic fields. Reading heads in hard disk drives (HDDs) are the most common applications of MR sensors. Since the discovery of giant magnetoresistance (GMR) in the 1980s and the application of GMR reading heads in the 1990s, the MR sensors lead to the rapid developments of the HDDs’ storage capacity. Nowadays, MR sensors are employed in magnetic storage, position sensing, current sensing, non-destructive monitoring, and biomedical sensing systems. MR sensors are used to transfer the variation of the target magnetic fields to other signals such as resistance change. This review illustrates the progress of developing nanoconstructed MR materials/structures. Meanwhile, it offers an overview of current trends regarding the applications of MR sensors. In addition, the challenges in designing/developing MR sensors with enhanced performance and cost-efficiency are discussed in this review.

Reflective Noise Filtering of Large-Scale Point Cloud Using Multi-Position LiDAR Sensing Data

Signals, such as point clouds captured by light detection and ranging sensors, are often affected by highly reflective objects, including specular opaque and transparent materials, such as glass, mirrors, and polished metal, which produce reflection artifacts, thereby degrading the performance of associated computer vision techniques. In traditional noise filtering methods for point clouds, noise is detected by considering the distribution of the neighboring points. However, noise generated by reflected areas is quite dense and cannot be removed by considering the point distribution. Therefore, this paper proposes a noise removal method to detect dense noise points caused by reflected objects using multi-position sensing data comparison. The proposed method is divided into three steps. First, the point cloud data are converted to range images of depth and reflective intensity. Second, the reflected area is detected using a sliding window on two converted range images. Finally, noise is filtered by comparing it with the neighbor sensor data between the detected reflected areas. Experiment results demonstrate that, unlike conventional methods, the proposed method can better filter dense and large-scale noise caused by reflective objects. In future work, we will attempt to add the RGB image to improve the accuracy of noise detection.