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Capacity degradation monitoring of lithium batteries is necessary to ensure the reliability and safety of electric vehicles. However, capacity of cell is related to its complex internal physicochemical reactions and thermal effects and cannot be measured directly. A data-driven remaining capacity estimation approach for lithium-ion batteries based on charging health feature extraction is presented in this work. The proposed method utilizes rational analysis and principal component analysis to extract and optimize health features of charging stage which adapt to various working conditions of battery. The remaining capacity estimation is realized by relevance vector machine and validations of different working conditions are made with six battery data sets provided by NASA Prognostics Center of Excellence. The results show high efficiency and robustness of the proposed method.

As existing low-light color imaging suffers from the unrealistic color representation or blurry texture with a single camera setup, we are motivated to devise a dual camera system using a high spatial resolution (HSR) monochrome camera and another low spatial resolution (LSR) color camera for synthesizing the high-quality color image under low-light illumination conditions. The key problem is how to efficiently learn and fuse cross-camera information for improved presentation in such heterogeneous setup with domain gaps (e.g., color vs. monochrome, HSR vs. LSR). We have divided the end-to-end pipeline into three consecutive modularized sub-tasks, including the reference-based exposure compensation (RefEC), reference-based colorization (RefColor) and reference-based super-resolution (RefSR), to alleviate domain gaps and capture inter-camera dynamics between hybrid inputs. In each step, we leverage the powerful deep neural network (DNN) to respectively transfer and enhance the illuminative, spectral and spatial granularity in a data-driven way. Each module is first trained separately, and then jointly fine-tuned for robust and reliable performance. Experimental results have shown that our work provides the leading performance in synthetic content from popular test datasets when compared to existing algorithms, and offers appealing color reconstruction using real captured scenes from an industrial monochrome and a smartphone RGB cameras, in low-light color imaging application.

High dynamic range (HDR) imaging is widely used in consumer photography, computer game rendering, autonomous driving, and surveillance systems. Reconstructing ghosting-free HDR images of dynamic scenes from a set of multi-exposure images is a challenging task, especially with large object motion, disparity, and occlusions, leading to visible artifacts using existing methods. In this paper, we propose a Pyramidal Alignment and Masked merging network (PAMnet) that learns to synthesize HDR images from input low dynamic range (LDR) images in an end-to-end manner. Instead of aligning under/overexposed images to the reference view directly in pixel-domain, we apply deformable convolutions across multiscale features for pyramidal alignment. Aligned features offer more flexibility to refine the inevitable misalignment for subsequent merging network without reconstructing the aligned image explicitly. To make full use of aligned features, we use dilated dense residual blocks with squeeze-and-excitation (SE) attention. Such attention mechanism effectively helps to remove redundant information and suppress misaligned features. Additional mask-based weighting is further employed to refine the HDR reconstruction, which offers better image quality and sharp local details. Experiments demonstrate that PAMnet can produce ghosting-free HDR results in the presence of large disparity and motion. We present extensive comparative studies using several popular datasets to demonstrate superior quality compared to the state-of-the-art algorithms.

For the inaccurate data predicting problem of the actual attainable maximum charge and discharge power of the lithium-ion battery in practice for unmanned vehicles in the process of running, a combined constraint intelligence algorithm is proposed based on the result of SOC, the battery voltage and the battery current. In view of the hysteresis characteristics of lithium-ion battery presented in the charge and discharge process, a second-order RC hysteresis model was proposed in this paper. In addition, we used the cubature Kalman filter algorithm to estimate the state of charge (SOC) of the battery based on this model, which reduced both the model error and algorithmic error in the SOC estimation significantly. It is also of great importance to accurately predict the state of power (SOP). Then the result of SOC, the battery voltage and the battery current were settled as constraints to predict the actual attainable maximum charge and discharge power of the lithium-ion battery in practice. Compared with the pulse discharge/charge test method and he hybrid pulse power characterization (HPPC) method, the combined constraint intelligence algorithm proposed in this paper is of high accuracy in the process of predicting the battery power.

Immersive video applications grow faster for users to freely navigate within a virtualized 3D environment for entertainment, productivity, training, etc. Fundamentally, such system can be facilitated by an interactive Gigapixel Video Streaming (iGVS) platform from array camera capturing to end user interaction. This interactive system demands a large amount of network bandwidth to sustain the reliable service provisioning, hindering its massive market adoption. Thus, we propose to segment the gigapixel scene into non-overlapped spatial tiles. Each tile only covers a sub-region of the entire scene. One or more tiles will be used to represent an instantaneous viewport interested by a specific user. Tiles are then encoded at a variety of quality scales using various combinations of spatial, temporal and amplitude resolutions (STAR), which are typically encapsulated into temporally-aligned tile video chunks (or simply chunks). Chunks at different quality level can be processed in parallel for real-time purpose. With such setup, diverse chunk combinations can be simultaneously accessed by heterogeneous user per its request, and viewport-adaptation based content navigation in an immersive space can be also realized by adapting multiscale chunks properly, under the bandwidth constraints. A serial computational vision models measuring the perceptual quality of viewport video in terms of its quality scales, adaptation factors, as well as the peripheral vision thresholds, are devised to prepare and guide the chunk adaptation for the best perceptual quality index. Furthermore, in response to the time-varying network, a deep reinforcement learning (DRL) based adaptive real-time streaming (ARS) scheme is developed, by learning the future decision from the historical network states, to maximize the overall quality of experience (QoE) in a practical Internet-based streaming scenario. Our experiments have revealed that averaged QoE can be improved by about 60%, and its standard deviation can be also reduced by ≈ 30%, in comparison to the popular Google congestion control algorithm widely adopted in existing system for adaptive streaming, demonstrating the efficiency of our multiscale accelerated iGVS for immersive video application.