Perceiving Unknown in Dark from Perspective of Cell Vibration

Low light very likely leads to the degradation of image quality and even causes visual tasks' failure. Existing image enhancement technologies are prone to over-enhancement or color distortion, and their adaptability is fairly limited. In order to deal with these problems, we utilise the mechanism of biological cell vibration to interpret the formation of color images. In particular, we here propose a simple yet effective cell vibration energy (CVE) mapping method for image enhancement. Based on a hypothetical color-formation mechanism, our proposed method first uses cell vibration and photoreceptor correction to determine the photon flow energy for each color channel, and then reconstructs the color image with the maximum energy constraint of the visual system. Photoreceptor cells can adaptively adjust the feedback from the light intensity of the perceived environment. Based on this understanding, we here propose a new Gamma auto-adjustment method to modify Gamma values according to individual images. Finally, a fusion method, combining CVE and Gamma auto-adjustment (CVE-G), is proposed to reconstruct the color image under the constraint of lightness. Experimental results show that the proposed algorithm is superior to six state of the art methods in avoiding over-enhancement and color distortion, restoring the textures of dark areas and reproducing natural colors. The source code will be released at https://github.com/leixiaozhou/CVE-G-Resource-Base.Comment: 13 pages, 17 figure

Low-light Image Enhancement Using Cell Vibration Model

 Low light very likely leads to the degradation of an image's quality and even causes visual task failures. Existing image enhancement technologies are prone to overenhancement, color distortion or time consumption, and their adaptability is fairly limited. Therefore, we propose a new single low-light image lightness enhancement method. First, an energy model is presented based on the analysis of membrane vibrations induced by photon stimulations. Then, based on the unique mathematical properties of the energy model and combined with the gamma correction model, a new global lightness enhancement model is proposed. Furthermore, a special relationship between image lightness and gamma intensity is found. Finally, a local fusion strategy, including segmentation, filtering and fusion, is proposed to optimize the local details of the global lightness enhancement images. Experimental results show that the proposed algorithm is superior to nine state-of-the-art methods in avoiding color distortion, restoring the textures of dark areas, reproducing natural colors and reducing time cost. The image source and code will be released at https://github.com/leixiaozhou/CDEFmethod. </p

Banyan Tree Growth Optimization and Application

In the era of big data, the number of science and technology service resources has proliferated, and the integration and clustering of science and technology documents become a challenging issue. This paper proposes a novel meta-heuristic algorithm, banyan tree growth optimization (BTGO), for resource clustering of science and technology services. The proposed algorithm is inspired by the growth process of banyan tree, which periodically uses three operators including rooting, multi-trunk, and adjustment to search the solution space globally according to the growth conditions of different stages. To evaluate the performance of BTGO, 29 CEC17 benchmark functions were first utilized to examine its effectiveness. Moreover, a clustering study on UCI datasets is then presented, which compares the suggested algorithm with seven advanced metaheuristic optimization algorithms. The results of numerical experiments and standard datasets demonstrate the effectiveness and efficiency of BTGO. In clustering optimization problems, BTGO can not only finding the optimal solution efficiently, but also improving the clustering accuracy and NMI significantly. Our method was successfully applied to solve the science and technology text clustering problem and validated on the Hainan Science and Technology Service Experimental Platform.</p

Secure Control of Networked Inverted Pendulum Visual Servo System with Adverse Effects of Image Computation

When visual image information is transmitted viacommunication networks, it easily suffers from image attacks,leading to system performance degradation or even crash. Thispaper investigates secure control of networked inverted pendulumvisual servo system (NIPVSS) with adverse effects of image com-putation. Firstly, the image security limitation of the traditionalNIPVSS is revealed, where its stability will be destroyed byeavesdropping-based image attacks. Then, a new NIPVSS withthe fast scaled-selective image encryption (F2SIE) algorithm isproposed, which not only meets the real-time requirement byreducing the computational complexity, but also improve thesecurity by reducing the probability of valuable information beingcompromised by eavesdropping-based image attacks. Secondly,adverse effects of the F2SIE algorithm and image attacks areanalysed, which will produce extra computational delay anderrors. Then, a closed-loop uncertain time-delay model of thenew NIPVSS is established, and a robust controller is designedto guarantee system asymptotic stability. Finally, experimentalresults of the new NIPVSS demonstrate the feasibility andeffectiveness of the proposed method.</p

Application research of view high speed detection algorithm of small field based on sparse features

Aiming at the problem that the features of small field of view detection in the high-speed pipeline are difficult to mine and the millisecond cycle is fast, this paper takes the cigarette filter bead streamline detection as an example, and proposes a small field of view high-speed detection algorithm based on sparse features. Firstly, by adjusting the light source, a 'light spot' feature with strong robustness is designed. Secondly, the sparse representation and dictionary learning are used to obtain the projection histogram features of the light spot. To overcome the interference of unstructured backgrounds, the algorithm is combined with Markov-Bayesian reasoning to reduce the spot detection rate, and finally realize the high-speed accurate recognition of the bead in low contrast. The view high speed detection algorithm of small field based on sparse features was verified on the simulation and experimental platform. The conclusions show that the extracted light spot features can overcome the interference of the color, size and low contrast of the bead, and maintain the stability of the feature. The fused Markov-Bayesian sparse representation algorithm can improve the recognition accuracy of the spot. The method can achieve 3 000 tests per minute, and the detection accuracy can reach 99.5%

Sliding mode variable structure control for inverted pendulum visual servo systems

This paper investigates the Sliding Mode Variable Structure Control (SMVSC) for a class of typical Cyber-Physical Systems (CPSs), Inverted Pendulum Visual Servo Systems (IPVSSs). In the current CPSs SMVSC studies, the visual servo is rarely involved and there is a lack of the corresponding verification platform. In this paper, we propose an SMVSC algorithm for the self-developed IPVSS to encounter the time-varying Image Processing Computation Delay (IPCD) caused by visual servo. Within this framework, a linear sliding surface is constructed based on the Ackermann pole assignment method and an SMVSC law is given. In addition, the relationship between the IPCD and the stability of sliding motion on the specified linear sliding surface is quantified. Furthermore, we prove that the designed SMVSC law can guarantee the system state to reach the linear sliding surface in a finite time and remain stable for all subsequent time. Finally, the simulation and real-time control experiments verify the feasibility and effectiveness of the proposed method

Computational intelligence, networked systems and their applications

The 2014 International Conference on Life System Modeling and Simulation (LSMS 2014) and 2014 International Conference on Intelligent Computing for Sustainable Energy and Environment (ICSEE 2014), which were held during September 20–23, in Shanghai, China, aimed to bring together international researchers and practitioners in the field of life system modeling and simulation as well as intelligent computing theory and methodology with applications to sustainable energy and environment. These events built on the success of previous LSMS conferences held in Shanghai and Wuxi in 2004, 2007, and 2010, and ICSEE conferences held in Wuxi and Shanghai in 2010 and 2012, and are based on large-scale RCUK/NSFC jointly funded UK–China collaboration projects on energy

Co-Design Secure Control Based on Image Attack Detection and Data Compensation for Networked Visual Control Systems

The incomplete and untrue data caused by cyberattacks (e.g., image information leakage and tampering) willaffect control performance and even lead to system instability.To address this problem, a novel co-design secure control methodbased on image attack detection and data compensation fornetworked visual control systems (NVCSs) is proposed. Firstly,the existing problems of NVCSs under image attacks are an-alyzed, and a co-design secure control method including imageencryption, watermarking-based attack detection and online datacompensation is presented. Then, a detector based on double-layer detection mechanism of timeout and digital watermarkingis designed for real-time, integrity and authenticity discriminationof the image. Furthermore, according to the detection results, anonline compensation scheme based on cubic spline interpolationand post-prediction update is proposed to reduce the effect ofcumulative errors and improve control performance. Finally,the online compensation scheme is optimized by consideringthe characters of networked inverted pendulum visual controlsystems, and experimental results demonstrate the feasibility andeffectiveness of the proposed detection and control method.</p

Co-Design Secure Control Based on Image Attack Detection and Data Compensation for Networked Visual Control Systems

The incomplete and untrue data caused by cyberattacks (e.g., image information leakage and tampering) willaffect control performance and even lead to system instability.To address this problem, a novel co-design secure control methodbased on image attack detection and data compensation fornetworked visual control systems (NVCSs) is proposed. Firstly,the existing problems of NVCSs under image attacks are an-alyzed, and a co-design secure control method including imageencryption, watermarking-based attack detection and online datacompensation is presented. Then, a detector based on double-layer detection mechanism of timeout and digital watermarkingis designed for real-time, integrity and authenticity discriminationof the image. Furthermore, according to the detection results, anonline compensation scheme based on cubic spline interpolationand post-prediction update is proposed to reduce the effect ofcumulative errors and improve control performance. Finally,the online compensation scheme is optimized by consideringthe characters of networked inverted pendulum visual controlsystems, and experimental results demonstrate the feasibility andeffectiveness of the proposed detection and control method.</p

Weighted multi-error information entropy based you only look once network for underwater object detection

Underwater object detection is considered as one of the most challenging issues in computer vision. In this paper, a weighted multi-error information entropy based YOLO (You Only Look Once) network is proposed to address underwater illumination noise affecting the detection accuracy. First, underwater illumination is essentially structural and non-uniform, and it is modeled as an independent and piecewise identical distribution, which is a generic noise model to describe the complex underwater illuminating environment and accommodates the traditional Gaussian distribution as a special case. Second, assisted by the proposed illumination noise model, a minimum weighted error entropy criterion, which is an information-theoretic learning method, is introduced into the loss function of YOLO network, and then the network parameters are trained and optimized to improve the detection performance. Furthermore, a multi-error processing strategy is simultaneously used to handle vector errors during information back-propagation in order to accelerate convergence. Experiments on underwater object detection datasets including URPC2018, URPC2019 and Enhanced dataset, show the proposed weighted multi-error information entropy based YOLOv8 network gets mean average precision (MAP) of 88.7%, 91.8% and 96.7% respectively, and average frames per second (FPS) of 116.6. These two evaluation metrics are better than the baseline YOLOv8 and the existing advanced non-YOLO approaches by at least 5.2% and 5.3% respectively. The results verify the effectiveness and superiority of the proposed network for underwater object detection in complex underwater environment.</p