An asynchronous updating Boolean network is employed to simulate and analyze the gene expression of a particular tissue or species, revealing the life activity process from a system perspective to reveal the disease mechanism and treat the disease. Therefore, to ensure the safe transmission of the asynchronous updating Boolean network in the network, we designed an asynchronous updating Boolean network encryption algorithm based on chaos (ABNEA). First, a novel 2D chaotic system (2D-FPSM) is designed. This system has better performance than the classical 2D chaotic system. It is very suitable for cryptographic systems to generate key streams. Second, an encoding rule is designed to convert the asynchronous updating Boolean network to a Boolean matrix and propagate it on the network as an image. The receiver and sender jointly save the encoding rule. Last, to protect the safe propagation of the Boolean network matrix on the network, the method of synchronous scrambling-diffusion is adapted to encrypt the Boolean network matrix based on the 2D-FPSM. Simulation experiments and security analysis show that the average correlation of adjacent pixels of ciphertext are 0.0010, -0.0010, -0.0020, and the average information entropy is 7.9984. The ABNEA can complete the encryption tasks of asynchronously updating Boolean networks and exhibits good security characteristics.

Block RAMs (BRAMs) play an important role in modern heterogenous FPGAs, hence how to test them comprehensively and effectively becomes a major concern. On-chip Partial Bitstream Relocation (PBR) technique based on FPGA Dynamic Partial Reconfiguration (DPR) can decrease the time spent on configuring modules in FPGA while reducing the memory resources overhead for storing partial bitstreams of the reconfigurable modules. The previous PBR technique is difficult to be combined with BRAM test directly, because they are somehow tedious, unsuitable for large-scale design or limited to specific devices. Besides, the problem exists for BRAM testing is that fault model is still incomplete and testing algorithms need to be improved to achieve higher fault coverage. An Effective BRAM test method based on a novel PBR technique is proposed in this paper. Our test method establishes a complete fault model for BRAM and improves the testing algorithms for faults in BRAM ECC circuits and intra-word coupling faults in SRAM cells. On-board experiments are carried out with Xilinx xc7vx690t device, and 14 BRAM configurations are used to fully test BRAMs. In conjunction with the proposed PBR technique, the number of configurations can be reduced to 10, which leads to a 35.7% time saving.

Quantization has proven effective in high-resolution and large-scale simulations, which benefit from bit-level memory saving. However, identifying a quantization scheme that meets the requirement of both precision and memory efficiency requires trial and error. In this paper, we propose a novel framework to allow users to obtain a quantization scheme by simply specifying either an error bound or a memory compression rate. Based on the error propagation theory, our method takes advantage of auto-diff to estimate the contributions of each quantization operation to the total error. We formulate the task as a constrained optimization problem, which can be efficiently solved with analytical formulas derived for the linearized objective function. Our workflow extends the Taichi compiler and introduces dithering to improve the precision of quantized simulations. We demonstrate the generality and efficiency of our method via several challenging examples of physics-based simulation, which achieves up to 2.5× memory compression without noticeable degradation of visual quality in the results. Our code and data are available at https://github.com/Hanke98/AutoQantizer.

With the emerging applications such as AI/ML, exploring the FPGA design space for the optimal performance becomes important and also challenging. The popular tool COFFE was built on an academic architecture and cannot be applied directly to modern FPGA chips with GRM (general routing matrix) architecture. In this work, we present our recently developed fully Automated Transistor-level Efficient and Accurate tool, AutoTEA, which features accurate area and delay models, and a fast solution space exploration method for GRM FPGA circuit optimization. The results show that AutoTEA is able to improve a previously manually optimized design (on the tape-out FPGA chip) by 11%.

High-resolution simulations can deliver great visual quality, but they are often limited by available memory, especially on GPUs. We present a compiler for physical simulation that can achieve both high performance and significantly reduced memory costs, by enabling flexible and aggressive quantization. Low-precision ("quantized") numerical data types are used and packed to represent simulation states, leading to reduced memory space and bandwidth consumption. Quantized simulation allows higher resolution simulation with less memory, which is especially attractive on GPUs. Implementing a quantized simulator that has high performance and packs the data tightly for aggressive storage reduction would be extremely labor-intensive and error-prone using a traditional programming language. To make the creation of quantized simulation practical, we have developed a new set of language abstractions and a compilation system. A suite of tailored domain-specific optimizations ensure quantized simulators often run as fast as the full-precision simulators, despite the overhead of encoding-decoding the packed quantized data types. Our programming language and compiler, based on Taichi , allow developers to effortlessly switch between different full-precision and quantized simulators, to explore the full design space of quantization schemes, and ultimately to achieve a good balance between space and precision. The creation of quantized simulation with our system has large benefits in terms of memory consumption and performance, on a variety of hardware, from mobile devices to workstations with high-end GPUs. We can simulate with levels of resolution that were previously only achievable on systems with much more memory, such as multiple GPUs. For example, on a single GPU, we can simulate a Game of Life with 20 billion cells (8× compression per pixel), an Eulerian fluid system with 421 million active voxels (1.6× compression per voxel), and a hybrid Eulerian-Lagrangian elastic object simulation with 235 million particles (1.7× compression per particle). At the same time, quantized simulations create physically plausible results. Our quantization techniques are complementary to existing acceleration approaches of physical simulation: they can be used in combination with these existing approaches, such as sparse data structures, for even higher scalability and performance.

We present a computational design system that assists users to model, optimize, and fabricate quad-robots with soft skins. Our system addresses the challenging task of predicting their physical behavior by fully integrating the multibody dynamics of the mechanical skeleton and the elastic behavior of the soft skin. The developed motion control strategy uses an alternating optimization scheme to avoid expensive full space time-optimization, interleaving space-time optimization for the skeleton, and frame-by-frame optimization for the full dynamics. The output are motor torques to drive the robot to achieve a user prescribed motion trajectory. We also provide a collection of convenient engineering tools and empirical manufacturing guidance to support the fabrication of the designed quad-robot. We validate the feasibility of designs generated with our system through physics simulations and with a physically-fabricated prototype.

The scale effects of the spatial autocorrelation (SA) measurement has been explored for decades. However, the effects of the data aggregation levels and spatial resolution on the SA measurement are often confused. Whether the two types of scale effects are the same is still unclear and requires further investigation. We retrieved the land surface temperature (LST) from Landsat 8 images in 30 capital cities of China. By aggregating the LST images, we observed a decrease in the SA of the LST as the data aggregation level increased; this relationship can be fitted well with a negative logarithmic function. We derived an indicator to measure the scale effects intensity of SA, which was negatively correlated with the spatial complexity of LST. Both aggregating images and the increasing spatial resolution induce weaker SA, but the effect of the former was stronger. The aggregating images negatively affected the SA degree regardless of the spatial resolutions of the original images. The SA degrees of the aggregated images were far below those of the real-life images. This study suggests that the scale effects caused by aggregation levels and spatial resolutions are different, and cautions should be taken when applying relevant conclusions derived from aggregating images.

Focusing on the video text detection, which is challenging and with wide potential applications, a novel stroke width feature is proposed and a system which detects text regions based on multi-scale corner detection is implemented in this paper. In our system, candidate text regions are generated by applying morphologic operation based on corner points detected in different scales, and non-text regions are filtered by combining proposed stroke width feature with some simple geometric properties. Moreover, there is a new multi-instance semi-supervised learning strategy being proposed in this paper considering the unknown contrast parameter in stroke width extraction. Experiments taken on video frames from different kinds of video shots prove that the proposed approach is both efficient and accurate for video text detection.

Dynamic weather conditions, which mainly include rain and snow, make prevailing algorithms for many applications of outdoor video analysis and computer vision lapse. To remove dynamic weather conditions, the authors propose a pixel‐wise framework combining a detection method with a removal approach. Dynamic weather conditions are detected by a strategy‐driven state transition, which integrates static initialisation using K ‐means clustering with dynamic maintenance of Gaussian mixture model. Moreover, a variable time window is presented for removal of rain and snow. Each component of the framework is addressed using detailed descriptions of corresponding algorithms. Experiments demonstrate the effectiveness of the method on detection and removal of dynamic weather conditions.

A background estimation system, which integrates pixel-level features with a region-level one and combines short-term and long-term analysis of videos in outdoor illumination variations, is proposed for accurate foreground detection. Firstly, we discuss autocorrelation-based features for identification of the presence of foreground and outdoor illumination variations in short-term sequences, and propose an adaptive threshold learning approach insensitive to inner-pixel fast illumination variation based on histograms of intensity differences between successive frames. Then, we employ a pixel-wise rapid autoregressive model against gradual illumination change for background estimation in long-term sequence. Finally, we devise a texture measure to eliminate the regional effect of fast illumination variation. The effectiveness of our system is demonstrated using experiments on foreground detection in videos with various illumination changes.

Evaluation of different weather conditions provides a first step support for many different applications of outdoor video analysis and computer vision. In this paper, a simple but effective classification method on visual effects of different weather conditions is proposed. Due to the complex manifestations of weather conditions, we firstly provide a two-stage classification scheme. Then, we extract spatio-temporal and chromatic features to represent different weather situations. Using these features, we develop a classifier based on an experiential decision binary tree associated with C-SVM. The experimental results of classification on our newly-built video dataset indicate the effectiveness of our method.

This paper presents a computer vision system for tracking high-speed non-rigid skaters over a large playing area in short track speeding skating competitions. The outputs of the tracking system are spatio-temporal trajectories of the players which can be further processed and analyzed by sport experts. Given very fast and non-smooth camera motions to capture highly complex and dynamic scenes of skating, tracking amorphous skaters should be a challenging task. We propose a new method of (1) automatically computing the transformation matrices to map each frame of the imagery to the globally consistent model of the rink and (2) incorporating the hierarchical model based on the contextual knowledge and multiple cues into the unscented Kalman filter to improve the tracking performance when occlusion occurs. Experimental results show that the proposed algorithm is very efficient and effective on video recorded live by the authors in the World Short Track Speed Skating Championships.