Gas recognition is essential in an electronic nose (Enose) system, which is responsible for recognizing multivariate responses obtained by gas sensors in various applications. Over the past decades, classical gas recognition approaches such as principal component analysis (PCA) have been widely applied in E-nose systems. In recent years, artificial neural network (ANN) has revolutionized the field of E-nose, especially spiking neural network (SNN). In this paper, we investigate recent gas recognition methods for E-nose, and compare and analyze them in terms of algorithms and hardware implementations. We find each classical gas recognition method has a relatively fixed framework and a few parameters, which makes it easy to be designed and perform well with limited gas samples, but weak in multi-gas recognition under noise. While ANN-based methods obtain better recognition accuracy with flexible architectures and lots of parameters. However, some ANNs are too complex to be implemented in portable E-nose systems, such as deep convolutional neural networks (CNNs). In contrast, SNN-based gas recognition methods achieve satisfying accuracy and recognize more types of gases, and could be implemented with energy-efficient hardware, which makes them a promising candidate in multi-gas identification.
The average age of population increases worldwide, so does the number of total hip replacement surgeries. Total hip replacement, however, often involves a risk of dislocation and prosthetic impingement. To minimize the risk after surgery, we propose an instrumented hip prosthesis that estimates the relative pose between prostheses intraoperatively and ensures the placement of prostheses within a safe zone. We create a model of the hip prosthesis as a ball and socket joint, which has four degrees of freedom (DOFs), including 3-DOF rotation and 1-DOF translation. We mount a camera and an inertial measurement unit (IMU) inside the hollow ball, or "femoral head prosthesis," while printing customized patterns on the internal surface of the socket, or "acetabular cup." Since the sensors were rigidly fixed to the femoral head prosthesis, measuring its motions poses a sensor ego-motion estimation problem. By matching feature points in images of the reference patterns, we propose a monocular vision based method with a relative error of less than 7% in the 3-DOF rotation and 8% in the 1-DOF translation. Further, to reduce system power consumption, we apply the IMU with its data fused by an extended Kalman filter to replace the camera in the 3-DOF rotation estimation, which yields a less than 4.8% relative error and a 21.6% decrease in power consumption. Experimental results show that the best approach to prosthesis pose estimation is a combination of monocular vision-based translation estimation and IMU-based rotation estimation, and we have verified the feasibility and validity of this system in prosthesis pose estimation.
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