The model of membrane computing, also known under the name of P systems, is a bio-inspired large-scale parallel computing paradigm having a good potential for the design of massively parallel algorithms. For its implementation it is very natural to choose hardware platforms that have important inherent parallelism, such as field-programmable gate arrays (FPGAs) or compute unified device architecture (CUDA)-enabled graphic processing units (GPUs). This article performs an overview of all existing approaches of hardware implementation in the area of P systems. The quantitative and qualitative attributes of FPGA-based implementations and CUDA-enabled GPU-based simulations are compared to evaluate the two methodologies.

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An Overview of Hardware Implementation of Membrane Computing Models

Molecular computing and bioinformatics are two important interdisciplinary sciences that study molecules and computers. Molecular computing is a branch of computing that uses DNA, biochemistry, and molecular biology hardware, instead of traditional silicon-based computer technologies. Research and development in this area concerns theory, experiments, and applications of molecular computing. The core advantage of molecular computing is its potential to pack vastly more circuitry onto a microchip than silicon will ever be capable of—and to do it cheaply. Molecules are only a few nanometers in size, making it possible to manufacture chips that contain billions—even trillions—of switches and components. To develop molecular computers, computer scientists must draw on expertise in subjects not usually associated with their field, including organic chemistry, molecular biology, bioengineering, and smart materials. Bioinformatics works on the contrary; bioinformatics researchers develop novel algorithms or software tools for computing or predicting the molecular structure or function. Molecular computing and bioinformatics pay attention to the same object, and have close relationships, but work toward different orientations.

/pdf/molecular-computing-and-bioinformatics-2211jjaclx.pdf

Molecular Computing and Bioinformatics

As a flexible passive 3D sensing means, unsupervised learning of depth from monocular videos is becoming an important research topic. It utilizes the photometric errors between the target view and the synthesized views from its adjacent source views as the loss instead of the difference from the ground truth. Occlusion and scene dynamics in real-world scenes still adversely affect the learning, despite significant progress made recently. In this paper, we show that deliberately manipulating photometric errors can efficiently deal with these difficulties better. We first propose an outlier masking technique that considers the occluded or dynamic pixels as statistical outliers in the photometric error map. With the outlier masking, the network learns the depth of objects that move in the opposite direction to the camera more accurately. To the best of our knowledge, such cases have not been seriously considered in the previous works, even though they pose a high risk in applications like autonomous driving. We also propose an efficient weighted multi-scale scheme to reduce the artifacts in the predicted depth maps. Extensive experiments on the KITTI dataset and additional experiments on the Cityscapes dataset have verified the proposed approach's effectiveness on depth or ego-motion estimation. Furthermore, for the first time, we evaluate the predicted depth on the regions of dynamic objects and static background separately for both supervised and unsupervised methods. The evaluation further verifies the effectiveness of our proposed technical approach and provides some interesting observations that might inspire future research in this direction.

Unsupervised Monocular Depth Perception: Focusing on Moving Objects

Asynchronous spiking neural P systems with local synchronization of rules

The existing edge detection methods can often encounter several common shortcomings: discontinuous edges, weak edges, easy to be affected by noise, and difficulty in setting gradient thresholds. To address these shortcomings, this paper proposes a neural-like computing model, called dynamic threshold neural P systems with orientation, termed as ODTNP systems. In addition to the spiking and dynamic threshold mechanisms, ODTNP systems also integrate the gradient magnitude and gradient direction information. The combination of these mechanisms is beneficial to ODTNP systems to achieve edge detection. Based on ODTNP systems, a novel edge detector is developed. The proposed ODTNP-based edge detector is evaluated on the benchmark Berkeley segmentation data set (BSDS500) and is compared with five baseline edge detection methods and four state-of-the-art deep-learning-based edge detection methods. Experimental results demonstrate the availability and effectiveness of the proposed edge detector. 

A novel edge detection method based on dynamic threshold neural P systems with orientation

Accurate detection of independent moving objects is the key to ensure the safety of driverless vehicles during travelling. In this study, a moving object detection method based on a vehicle-mounted binocular camera is proposed. Firstly, a feature matching points selection strategy is designed for high accuracy camera ego-motion estimation. Then, the residual optical flow filed generated only by moving objects is estimated by taking into account the camera ego-motion and global mixed optical flow. A dynamic threshold segmentation method based on disparity is proposed to separate the moving regions from the residual optical flow field. Finally, 3D and 2D information are combined for extracting each single moving object from moving regions. The innovation of the proposed method is that it can detect any type and any size of moving object theoretically by using the dense motion information of the images. By taking the data in KITTI database as the test samples, the detection precision of the moving objects using the proposed algorithm can reach up to 91% without considering tracking strategy.

Independent Moving Object Detection Based on a Vehicle Mounted Binocular Camera

Image edge detection is a fundamental problem in image processing and computer vision, particularly in the area of feature extraction. However, the time complexity increases squarely with the increase of image resolution in conventional serial computing mode. This results in being unbearably time consuming when dealing with a large amount of image data. In this paper, a novel resolution free parallel implementation algorithm for gradient based edge detection, namely EDENP, is proposed. The key point of our method is the introduction of an enzymatic numerical P system (ENPS) to design the parallel computing algorithm for image processing for the first time. The proposed algorithm is based on a cell-like P system with a nested membrane structure containing four membranes. The start and stop of the system is controlled by the variables in the skin membrane. The calculation of edge detection is performed in the inner three membranes in a parallel way. The performance and efficiency of this algorithm are evaluated on the CUDA platform. The main advantage of EDENP is that the time complexity of O ( 1 ) can be achieved regardless of image resolution theoretically.

https://www.mdpi.com/1420-3049/24/7/1235/pdf

A Resolution-Free Parallel Algorithm for Image Edge Detection within the Framework of Enzymatic Numerical P Systems.

https://www.techscience.com/CMES/v129n1/44192/pdf

Adaptive Object Tracking Discriminate Model for Multi-Camera Panorama Surveillance in Airport Apron

A Szilard language is a well-known tool in formal language theory to express the derivation process in a grammar system or grammar. Matrix InsDel (insertion–deletion) system is a well-known variant of InsDel system, where the idea of matrix control is combined with InsDel systems. The size of a matrix InsDel system is represented by a septuple of integers $$(k; p, m, m^{'};$$
 $$q, n, n^{'})$$
 , where k represents maximum number of rules in a matrix, i.e., size of a matrix. The parameters p, m and $$m^{'}$$
 represent the maximal length of the strings inserted, the maximal length of the left context and the maximal length of the right context of the insertion rules, respectively. The parameters $$q, n, n^{'}$$
 represent the same for deletion rules. In this paper, we investigate the Szilard languages of matrix InsDel systems with matrices of size 2. We give examples of regular, context-free and context-sensitive languages which cannot be the Szilard language of any matrix InsDel system. We show that any regular language can be represented as a homomorphic image of Szilard language of matrix InsDel system of size (2; 2, 0, 0; 1, 0, 0). Any linear language, meta-linear language and rational (or regular) closure of linear language can be obtained as the homomorphic image of Szilard language of matrix InsDel systems of size (2; 1, 1, 0; 1, 1, 0), and (2; 1, 0, 1; 1, 0, 1). Moreover, any recursively enumerable language can be obtained as the homomorphic image of the Szilard language of matrix InsDel systems of size (2; 1, 1, 0; 1, 1, 1), (2; 1, 0, 1; 1, 1, 1), (2; 1, 1, 1; 1, 1, 0), (2; 1, 1, 1; 1, 0, 1), (2; 1, 1, 0; 2, 0, 0), (2; 1, 0, 1; 2, 0, 0), (2; 2, 0, 0; 1, 1, 0),  and (2; 2, 0, 0; 1, 0, 1).

Dequan Guo

Papers

Independent Moving Object Detection Based on a Vehicle Mounted Binocular Camera

A Resolution-Free Parallel Algorithm for Image Edge Detection within the Framework of Enzymatic Numerical P Systems.

Adaptive Object Tracking Discriminate Model for Multi-Camera Panorama Surveillance in Airport Apron

On homomorphic images of the Szilard languages of matrix insertion–deletion systems with matrices of size 2