An Agricultural Spraying Robot Based on the Machine Vision

Accurate target spraying is a key technology in modern and intelligent agriculture. For solving the problems of pesticide waste and poisoning in the spraying process, a spraying robot based on binocular machine vision was proposed in this paper. A digital signal processor was used to identify and locate tomatoes as well as to control the nozzle spray. A stereoscopic vision model was established, and color normalization, 2G-R-B, was adopted to implement background segmentation between plants and soil. As for the tomatoes and plants, depth information and circularity depended on the nozzle’s target, and the plant shape area determined the amount of pesticide. Experiments shown that the recognition rate of this spraying robot was up to 92.5% for tomatoes

Efficient Spiking Transformer Enabled By Partial Information

Spiking neural networks (SNNs) have received substantial attention in recent years due to their sparse and asynchronous communication nature, and thus can be deployed in neuromorphic hardware and achieve extremely high energy efficiency. However, SNNs currently can hardly realize a comparable performance to that of artificial neural networks (ANNs) because their limited scalability does not allow for large-scale networks. Especially for Transformer, as a model of ANNs that has accomplished remarkable performance in various machine learning tasks, its implementation in SNNs by conventional methods requires a large number of neurons, notably in the self-attention module. Inspired by the mechanisms in the nervous system, we propose an efficient spiking Transformer (EST) framework enabled by partial information to address the above problem. In this model, we not only implemented the self-attention module with a reasonable number of neurons, but also introduced partial-information self-attention (PSA), which utilizes only partial input signals, further reducing computational resources compared to conventional methods. The experimental results show that our EST can outperform the state-of-the-art SNN model in terms of accuracy and the number of time steps on both Cifar-10/100 and ImageNet datasets. In particular, the proposed EST model achieves 78.48% top-1 accuracy on the ImageNet dataset with only 16 time steps. In addition, our proposed PSA reduces flops by 49.8% with negligible performance loss compared to a self-attention module with full information

On the smoothness of Orlicz sequence spaces equipped with Orlicz norm

We give a criterion of smoothness of Orlicz sequence spaces with Orlicz norm

Cross-Camera Human Motion Transfer by Time Series Analysis

Along with advances in optical sensors is the increasingly common practice of building an imaging system with heterogeneous cameras. While high-resolution (HR) video acquisition and analysis benefit from hybrid sensors, the intrinsic characteristics of multiple cameras lead to a challenging motion transfer problem. In this paper, we propose an algorithm using time series analysis for motion transfer among multiple cameras. Specifically, we first identify seasonality in the motion data, and then build an additive time series model to extract patterns that could be transferred across different cameras. Our approach has a complete and clear mathematical formulation, and the algorithm is also efficient and interpretable. Through the experiment on real-world data, we demonstrate the effectiveness of our method. Furthermore, our motion transfer algorithm could combine with and facilitate downstream tasks, e.g., enhancing pose estimation on low-resolution (LR) videos with inherent patterns extracted from HR ones.Comment: 10 pages, 9 figure

A fully hardware-based memristive multilayer neural network

Memristive crossbar arrays promise substantial improvements in computing throughput and power efficiency through in-memory analog computing. Previous machine learning demonstrations with memristive arrays, however, relied on software or digital processors to implement some critical functionalities, leading to frequent analog/digital conversions and more complicated hardware that compromises the energy efficiency and computing parallelism. Here, we show that, by implementing the activation function of a neural network in analog hardware, analog signals can be transmitted to the next layer without unnecessary digital conversion, communication, and processing. We have designed and built compact rectified linear units, with which we constructed a two-layer perceptron using memristive crossbar arrays, and demonstrated a recognition accuracy of 93.63% for the Modified National Institute of Standard and Technology (MNIST) handwritten digits dataset. The fully hardware-based neural network reduces both the data shuttling and conversion, capable of delivering much higher computing throughput and power efficiency