Fovea, located in the centre of the retina, is specialized for high-acuity vision. Mimicking the sampling mechanism of the fovea, a retina-inspired camera, named spiking camera, is developed to record the external information with a sampling rate of 40,000 Hz, and outputs asynchronous binary spike streams. Although the temporal resolution of visual information is improved, how to reconstruct the scenes is still a challenging problem. In this paper, we present a novel high-speed image reconstruction model through the short-term plasticity (STP) mechanism of the brain. We derive the relationship between postsynaptic potential regulated by STP and the firing frequency of each pixel. By setting up the STP model at each pixel of the spiking camera, we can infer the scene radiance with the temporal regularity of the spike stream. Moreover, we show that STP can be used to distinguish the static and motion areas and further enhance the reconstruction results. The experimental results show that our methods achieve state-of-the-art performance in both image quality and computing time.
Underwater wireless optical communication has attracted widespread attention due to its advantages of high bandwidth and low delay. Seawater environment contains different substances, which will affect the received intensity and time delay of communication. This paper proposes an adaptive Monte-Carlo method to analyze the impact of zooplankton on the received intensity, receiving time, spatial distribution of energy, transmission distance and misalignment. According to the simulation results, when seawater contains more zooplankton, the received intensity is weaker, the receiving time is longer, and the energy is more dispersed.
Spiking Neural Networks (SNNs) have attracted enormous research interest due to temporal information processing capability, low power consumption, and high biological plausibility. However, the formulation of efficient and high-performance learning algorithms for SNNs is still challenging. Most existing learning methods learn weights only, and require manual tuning of the membrane-related parameters that determine the dynamics of a single spiking neuron. These parameters are typically chosen to be the same for all neurons, which limits the diversity of neurons and thus the expressiveness of the resulting SNNs. In this paper, we take inspiration from the observation that membrane-related parameters are different across brain regions, and propose a training algorithm that is capable of learning not only the synaptic weights but also the membrane time constants of SNNs. We show that incorporating learnable membrane time constants can make the network less sensitive to initial values and can speed up learning. In addition, we reevaluate the pooling methods in SNNs and find that max-pooling will not lead to significant information loss and have the advantage of low computation cost and binary compatibility. We evaluate the proposed method for image classification tasks on both traditional static MNIST, Fashion-MNIST, CIFAR-10 datasets, and neuromorphic N-MNIST, CIFAR10-DVS, DVS128 Gesture datasets. The experiment results show that the proposed method outperforms the state-of-the-art accuracy on nearly all datasets, using fewer time-steps. Our codes are available at https://github.com/fangwei123456/Parametric-Leaky-Integrate-and-Fire-Spiking-Neuron.
Spiking Neural Networks (SNNs), as bio-inspired energy-efficient neural networks, have attracted great attentions from researchers and industry. The most efficient way to train deep SNNs is through ANN-SNN conversion. However, the conversion usually suffers from accuracy loss and long inference time, which impede the practical application of SNN. In this paper, we theoretically analyze ANN-SNN conversion and derive sufficient conditions of the optimal conversion. To better correlate ANN-SNN and get greater accuracy, we propose Rate Norm Layer to replace the ReLU activation function in source ANN training, enabling direct conversion from a trained ANN to an SNN. Moreover, we propose an optimal fit curve to quantify the fit between the activation value of source ANN and the actual firing rate of target SNN. We show that the inference time can be reduced by optimizing the upper bound of the fit curve in the revised ANN to achieve fast inference. Our theory can explain the existing work on fast reasoning and get better results. The experimental results show that the proposed method achieves near loss-less conversion with VGG-16, PreActResNet-18, and deeper structures. Moreover, it can reach 8.6× faster reasoning performance under 0.265× energy consumption of the typical method. The code is available at https://github.com/DingJianhao/OptSNNConvertion-RNL-RIL.
A gradient metasurface wall structure operating in Ka-band is proposed. To reduce mutual coupling, side metallic walls with a designed metasurface arrays are incorporated into both sides of the unit antenna in the antenna array. These walls can also improve the performance of antenna based on the anomalous reflection. Simulation and experimental results show that the coupling coefficient reduces from -30 dB to -40 dB at the operating frequency. The 3dB beam width of the antenna is broadened from 35° to 38° in H plane and from 23.4° to 31.5° in E plane, due to the characteristic of controllable reflection direction. Because of the freedom in controlling the reflected waves and the high isolation, this gradient metasurface wall structure is of important application values in Millimeter-wave imaging system and MIMO system.
Spiking Neural Networks (SNNs) have been attached great importance due to their biological plausibility and high energy-efficiency on neuromorphic chips. As these chips are usually resource-constrained, the compression of SNNs is thus crucial along the road of practical use of SNNs. Most existing methods directly apply pruning approaches in artificial neural networks (ANNs) to SNNs, which ignore the difference between ANNs and SNNs, thus limiting the performance of the pruned SNNs. Besides, these methods are only suitable for shallow SNNs. In this paper, inspired by synaptogenesis and synapse elimination in the neural system, we propose gradient rewiring (Grad R), a joint learning algorithm of connectivity and weight for SNNs, that enables us to seamlessly optimize network structure without retraining. Our key innovation is to redefine the gradient to a new synaptic parameter, allowing better exploration of network structures by taking full advantage of the competition between pruning and regrowth of connections. The experimental results show that the proposed method achieves minimal loss of SNNs' performance on MNIST and CIFAR-10 datasets so far. Moreover, it reaches a ~3.5% accuracy loss under unprecedented 0.73% connectivity, which reveals remarkable structure refining capability in SNNs. Our work suggests that there exists extremely high redundancy in deep SNNs. Our codes are available at https://github.com/Yanqi-Chen/Gradient-Rewiring.
Recently, a novel retina-inspired camera, namely spike camera, has shown great potential for recording high-speed dynamic scenes. Unlike the conventional digital cameras that compact the visual information within the exposure interval into a single snapshot, the spike camera continuously outputs binary spike streams to record the dynamic scenes, yielding a very high temporal resolution. Most of the existing reconstruction methods for spike camera focus on reconstructing images with the same resolution as spike camera. However, as a trade-off of high temporal resolution, the spatial resolution of spike camera is limited, resulting in inferior details of the reconstruction. To address this issue, we develop a spike camera super-resolution framework, aiming to super resolve high-resolution intensity images from the low-resolution binary spike streams. Due to the relative motion between the camera and the objects to capture, the spikes fired by the same sensor pixel no longer describes the same points in the external scene. In this paper, we properly exploit the relative motion and derive the relationship between light intensity and each spike, so as to recover the external scene with both high temporal and high spatial resolution. Experimental results demonstrate that the proposed method can reconstruct pleasant high-resolution images from low-resolution spike streams.
In passive millimeter wave radiation images, reflection will affect the analysis of the target and scene. Based on the theoretical model of reflection, this paper analyzes the change of brightness temperature(TB) in the target region and the reflected region under specific scenes. A target detection method based on the change of TB is proposed to distinguish the target and its reflection region and obtain the target outline. The effectiveness of the method is verified by simulation and imaging experiments.