Spike camera is a kind of neuromorphic camera that records dynamic scenes by firing a stream of binary spikes with extremely high temporal resolution. It demonstrates great potential for vision tasks in high-speed scenarios. One limitation in its current implementation is the relatively low spatial resolution. This paper develops a network called Spk2SRImgNet to super-resolve high resolution images from low resolution spike stream. However, fluctuations in spike stream hinder the performance of spike camera super resolution. To address this issue, we propose a motion aligned collaborative filtering (MACF) module, which is motivated by key ideas in classic image restoration schemes to mitigate fluctuations in spike data. MACF leverages the temporal similarity of spike stream to acquire similar features from neighboring moments via motion alignment. To separate disturbances from features, MACF filters these similar features jointly in transform domain to exploit representation sparsity, and generates refinement features that will be used to update initial fluctuated features. Specifically, MACF designs an inverse motion alignment operation to map these refinement features back to their original positions. The initial features are aggregated with the repositioned refinement features to enhance reliability. Experimental results demonstrate that the proposed method achieves state-of-the-art performance compared with existing methods.

As a bio-inspired vision sensor, spike camera records light intensity by accumulating photons and firing a spike once a preset threshold is reached. For high-light regions, the accumulated photons may reach the threshold multiple times within a readout interval, while only one spike can be stored and read out, resulting in incorrect intensity representation and a limited dynamic range. Multi-level (ML) spike camera enhances the dynamic range by introducing a spike-firing counter (SFC) to count spikes within each readout interval for each pixel, and uses different spike symbols to represent the arrival of different amounts of photons. However, when the light intensity becomes even higher, each pixel requires an SFC with a higher bit depth, causing great cost to the manufacturing process. To address these issues, we propose time-encoding (TE) spike camera, which transforms the counting of spikes to recording of the time at which a specific number of spikes (i.e., an overflow) is reached. To encode time information with as few bits as possible, instead of directly utilising a timer, we leverage a periodic timing signal with a higher frequency than the readout signal. Then the recording of overflow moment can be transformed into recording the number of accumulated timing signal cycles until the overflow occurs. Additionally, we propose an image reconstruction scheme for TE spike camera, which leverages the multi-scale gradient features of spike data. This scheme includes a similarity-based pyramid alignment module to align spike streams across the temporal domain and a light intensity-based refinement module, which utilises the guidance of light intensity to fuse spatial features of the spike data. Experimental results demonstrate that TE spike camera effectively improves the dynamic range of spike camera.

Spike camera is a neuromorphic sensor that can capture high-speed dynamic scenes by firing a continuous stream of binary spikes with extremely high temporal resolution, essentially forming a dense sampling in the temporal dimension. Due to the relative motion between camera and scene, each pixel is actually sampling at a large number of different spatial positions on the object in a short period. Converting this dense sampling from temporal dimension to spatial domain, high resolution images can be reconstructed from the spike stream. However, spike fluctuations and large motion in high-speed scenes pose great challenges for this task, especially for intensity information extraction and temporal alignment. In this paper, we propose a spike camera super resolution network to address these issues. Considering the local temporal correlation of spike stream and correlation consistency within a local region, we introduce a representation module that performs region-adaptive temporal filtering on spikes to mitigate fluctuations and extract stable intensity information from binary data. Additionally, we develop a module for multi-frame feature alignment, leveraging the long-term temporal information of spike stream. To handle large motions, we propagate the motion information from neighboring moment to current feature alignment module, which provides a prior that helps to narrow the search range for current motion offset, improving the accuracy of temporal alignment. Experimental results demonstrate that the proposed network achieves state-of-the-art performance on synthetic and real-captured spike data.

Spike camera is a neuromorphic camera that recurrently accumulates photons and fires spikes to record the incident light intensity at very high temporal resolution, making it particularly suitable for recording high dynamic scenes. This paper addresses the problem of image reconstruction for spike camera. Due to the Poisson effect of photon arrival and the quantization effect of spike readout, the spike interval calculated from a single spike cycle cannot reflect the light intensity accurately. Firstly, this paper analyzes the error of spike interval estimation under static light intensity. Then, this paper focuses on the temporal correlation of continuous spikes under dynamic light intensity. Specifically, it considers 3D space time similarity to weighted average multiple continuous spike intervals for the intensity estimation at a certain pixel. Experimental results demonstrate that the proposed method achieves better performance in both objective and subjective aspects compared with previous reconstruction methods.

A Hybrid Model Predictive Current Control(H-MPC) for Permanent-Magnet Synchronous Motor(PMSM) is proposed in this paper. It incorporates the advantages of feedback Enhanced Model Predictive Control (E-MPC) and Multi-Step MPC based on Linear Extended State Observer (LESO-MPC), which results in rapid dynamic performance and a low steady-state error. E-MPC is utilized for dynamic processes to improve dynamic performance. LESO-MPC is utilized for steady-state processes to reduce steady-state errors and improve robustness. In addition, an adaptive switching method based on a sliding window is proposed to maintain a smooth transition between LESO-MPC and E-MPC. Simulation results verify the effectiveness of the H-MPC over the typical MPC.

Recurrent neural networks (RNNs) have been widely used for hyperspectral image (HSI) classification via sequence modeling. However, most of the RNN methods focus on modeling long-range dependencies along the spectral direction, without fully exploring multi-directional dependencies in the joint spectral-spatial domain. To tackle this issue, we propose MSLAN, a two-branch multi-directional spectral-spatial long short-term memory (LSTM) attention network, for HSI classification. In particular, we employ LSTMs to extract six-directional spatial-spectral features which simultaneously capture the spectral-spatial dependencies along different directions. We then design an attention-based feature fuse module to integrate these directional features, followed by a fully connected layer with cross-entropy loss for classification. Additionally, we incorporate an auxiliary branch into our model to enhance the generalization capability. In this branch, random spatial shuffle and a cosine loss are explored for feature consistency learning by taking into account the varying spatial distributions. The resulting two branch networks, sharing the same network structure and weights, are incorporated into a unified deep learning architecture for training. Experiments show the superiority of MSLAN to the state-of-the-art methods for HSI classification with limited training samples.

Local binary pattern (LBP), as one of the most representative texture operators, has attracted much attention in computer vision and pattern recognition. Many LBP variants were developed in the literature. However, most of them were designed for gray images and their performance remains to be improved for color images. In this paper, we propose a novel color image descriptor named Holistic and Hierarchical Order-Encoding Patterns (H2OEP) for texture classification. In H2OEP, the holistic order-encoding pattern compactly encodes color order variation tendencies for each pixel in color space. The hierarchical order-encoding pattern leverages min ordering, median ordering and max ordering to encode local neighboring relationships across different color channels. Finally, the generated order-encoding patterns are aggregated via central pixel encoding to build 3D joint histograms for image representation. Experiments on four benchmark texture databases demonstrate the effectiveness of the proposed descriptor for color texture classification.

Local binary pattern (LBP) is sensitive to inverse grayscale changes. Several methods address this problem by mapping each LBP code and its complement to the minimum one. However, without distinguishing LBP codes and their complements, these methods show limited discriminative power. In this paper, we introduce a histogram sorting method to preserve the distribution information of LBP codes and their complements. Based on this method, we propose first- and second-order sorted LBP (SLBP) features which are robust to inverse grayscale changes and image rotation. The proposed method focuses on encoding difference-sign information and it can be generalized to embed other difference-magnitude features to obtain complementary representations. Experiments demonstrate the effectiveness of our method for texture classification under (linear or nonlinear) grayscale-inversion and rotation changes.

During transient process, it may take plenty of steps for PMSM to reach the reference torque and flux linkage. Multistep model predictive torque and flux control (MPTC) can select the best sequence of voltage vectors from the initial state to the reference state to achieve the fastest torque response. However, the computation burden is a significant challenge for real-time implementation. This paper proposes a novel fast-response MPTC strategy for PMSM drives. The reference torque is converted to flux linkage in d-axis and q-axis to eliminate the weighting factor in cost function. During the transient process, Pontryagin's maximum principle is used to design minimum-time flux linkage trajectories from the initial state to the reference state. In each control period, along the reference flux linkage trajectories, one-step MPTC is used to select the best voltage vector. The computational burden and dynamic performance of the proposed method are verified by experiments.