Toward Cognitive Navigation: Design and Implementation of a Biologically Inspired Head Direction Cell Network

E2VIDX: improved bridge between conventional vision and bionic vision

Common RGBD, CMOS, and CCD-based cameras produce motion blur and incorrect exposure under high-speed and improper lighting conditions. According to the bionic principle, the event camera developed has the advantages of low delay, high dynamic range, and no motion blur. However, due to its unique data representation, it encounters significant obstacles in practical applications. The image reconstruction algorithm based on an event camera solves the problem by converting a series of "events" into common frames to apply existing vision algorithms. Due to the rapid development of neural networks, this field has made significant breakthroughs in past few years. Based on the most popular Events-to-Video (E2VID) method, this study designs a new network called E2VIDX. The proposed network includes group convolution and sub-pixel convolution, which not only achieves better feature fusion but also the network model size is reduced by 25%. Futhermore, we propose a new loss function. The loss function is divided into two parts, first part calculates the high level features and the second part calculates the low level features of the reconstructed image. The experimental results clearly outperform against the state-of-the-art method. Compared with the original method, Structural Similarity (SSIM) increases by 1.3%, Learned Perceptual Image Patch Similarity (LPIPS) decreases by 1.7%, Mean Squared Error (MSE) decreases by 2.5%, and it runs faster on GPU and CPU. Additionally, we evaluate the results of E2VIDX with application to image classification, object detection, and instance segmentation. The experiments show that conversions using our method can help event cameras directly apply existing vision algorithms in most scenarios.

A Navigation Path Search and Optimization Method for Mobile Robots Based on the Rat Brain's Cognitive Mechanism

Rats possess exceptional navigational abilities, allowing them to adaptively adjust their navigation paths based on the environmental structure. This remarkable ability is attributed to the interactions and regulatory mechanisms among various spatial cells within the rat's brain. Based on these, this paper proposes a navigation path search and optimization method for mobile robots based on the rat brain's cognitive mechanism. The aim is to enhance the navigation efficiency of mobile robots. The mechanism of this method is based on developing a navigation habit. Firstly, the robot explores the environment to search for the navigation goal. Then, with the assistance of boundary vector cells, the greedy strategy is used to guide the robot in generating a locally optimal path. Once the navigation path is generated, a dynamic self-organizing model based on the hippocampal CA1 place cells is constructed to further optimize the navigation path. To validate the effectiveness of the method, this paper designs several 2D simulation experiments and 3D robot simulation experiments, and compares the proposed method with various algorithms. The experimental results demonstrate that the proposed method not only surpasses other algorithms in terms of path planning efficiency but also yields the shortest navigation path. Moreover, the method exhibits good adaptability to dynamic navigation tasks.

American Sign Language Translation Using Wearable Inertial and Electromyography Sensors for Tracking Hand Movements and Facial Expressions

A sign language translation system can break the communication barrier between hearing-impaired people and others. In this paper, a novel American sign language (ASL) translation method based on wearable sensors was proposed. We leveraged inertial sensors to capture signs and surface electromyography (EMG) sensors to detect facial expressions. We applied a convolutional neural network (CNN) to extract features from input signals. Then, long short-term memory (LSTM) and transformer models were exploited to achieve end-to-end translation from input signals to text sentences. We evaluated two models on 40 ASL sentences strictly following the rules of grammar. Word error rate (WER) and sentence error rate (SER) are utilized as the evaluation standard. The LSTM model can translate sentences in the testing dataset with a 7.74% WER and 9.17% SER. The transformer model performs much better by achieving a 4.22% WER and 4.72% SER. The encouraging results indicate that both models are suitable for sign language translation with high accuracy. With complete motion capture sensors and facial expression recognition methods, the sign language translation system has the potential to recognize more sentences.