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Stochastic computing is a non‐traditional computing approach that utilizes stochastic bitstreams to represent numerical values. This representation enables the implementation of circuits using basic logic gates, named stochastic circuits. This work proposes a versatile architecture with correlated input bitstreams for biased accumulation. The design offers a notable reduction in the reliance on random number generators, resulting in lower hardware costs and higher computing accuracy when compared with the existing designs. Experimental results show that the application of Gaussian smoothing has a reduction in area by at least 45% and an improvement in peak signal‐to‐noise ratio of at least 30%. [ABSTRACT FROM AUTHOR]

In the Beyond Moore era, QCA (Quantum-dot Cellular Automata) has been widely studied because of its low power consumption and high device concentration. As the core of chip design, EDA technology has become an indispensable step in QCA research. It refers to the use of computer-aided design software to complete large-scale circuit function design, logic synthesis, placement and routing, design rule check and other processes. In the present study, there are still many defects in QCA placement and routing. The main problems it faces are low success rate, too large area and too many cross lines. To address these problems, our team had proposed a hybrid strategy based on the genetic algorithm and the improved A-star algorithm. But the running time of the previously proposed algorithm is not satisfying, besides it lacks self-adaption feature which is important as the problem size varies among different circuits. This paper replaces the genetic algorithm with a simulated annealing algorithm, and introduces a self-adaption strategy to better accommodate different circuit sizes. Experimental results show that the new algorithm combination possesses a higher success rate, lower running time and more intelligent adaption ability. [ABSTRACT FROM AUTHOR]

Many algorithms for image processing do not require particularly high precision, but they rely on complicated arithmetic operations for every pixel in an image. The Bernsen algorithm is a typical local thresholding algorithm for solving the problem of uneven lighting. However, this algorithm requires a significant computing overhead and is extremely sensitive to noise. In this work, two stochastic computing architectures are proposed for implementing the Bernsen algorithm by using, respectively, uncorrelated and correlated input bitstreams. Experimental results show that both designs, especially the one using correlated bitstreams, present high fault tolerance of soft errors and low hardware cost in comparison with its conventional binary implementation. However, SC logic with uncorrelated inputs is not always superior to its corresponding binary circuit in energy consumption, especially the circuit that needs long input bitstreams. That means that a reasonable use of correlation can further optimize the SC circuit design. [ABSTRACT FROM AUTHOR]

In this study, the structural characteristics of the attached microbial community during biofilm formation and the metabolic requirements under anaerobic conditions were evaluated. In particular, 16S rDNA sequencing technology was used to study the structural changes of the attached microbial community during biofilm formation (day 0, 10, 20, and 30) in an aquaculture system, and Biolog microplate technology was used to study metabolic characteristics under anaerobic conditions during biofilm formation. The AWCD (average well colour development), an indicator of carbon metabolism, of attached microorganisms during biofilm formation under anaerobic conditions differed significantly among time points (P < 0.05), and the carbon source utilization capacity was enhanced. Under anaerobic conditions during biofilm formation, the utilization of amines in six major carbon sources was the highest, followed by polymers, carbohydrates, amino acids, and carboxylic acids, and the utilization of phenols was the lowest. Under anaerobic conditions, the utilization rate by the attached microorganisms during biofilm formation was highest for Tween 40, followed by Tween 80, D-xylose, 4-hydroxybenzoic acid, α-D-glucose-1-phosphate, L-phenylalanine, and phenylethylamine. The 16s rDNA sequencing results showed that the dominant phyla of attached microorganisms during biofilm formation were mainly Proteobacteria, Bacteroidetes, Firmicutes, and Cyanobacteria. On the 10th and 20th days of biofilm formation, attached microorganisms were enriched for Rhodobacter of α-Proteobacteria and Janthinobacterium of β-Proteobacteria, which play important roles in biofilm denitrification. However, on the 30th day, enriched bacteria included the Burkholderiales of β-Proteobacteria, the Xanthomonadaceae and Thermomonas of γ- Proteobacteria, which function in cyanide decomposition.

The intelligent monitoring of cutting tools used in the manufacturing industry is steadily becoming more convenient. To accurately predict the state of tools and tool breakages, this study proposes a tool wear prediction technique based on multi-sensor information fusion. First, the vibrational, current, and cutting force signals transmitted during the machining process were collected, and the features were extracted. Next, the Kalman filtering algorithm was used for feature fusion, and a predictive model for tool wear was constructed by combining the ResNet and long short-term memory (LSTM) models (called ResNet-LSTM). Experimental data for thin-walled parts obtained under various machining conditions were utilized to monitor the changes in tool conditions. A comparison between the ResNet and LSTM tool wear prediction models indicated that the proposed ResNet-LSTM model significantly improved the prediction accuracy compared to the individual LSTM and ResNet models. Moreover, ResNet-LSTM exhibited adaptive noise reduction capabilities at the front end of the network for signal feature extraction, thereby enhancing the signal feature extraction capability. The ResNet-LSTM model yielded an average prediction error of 0.0085 mm and a tool wear prediction accuracy of 98.25%. These results validate the feasibility of the tool wear prediction method proposed in this study. [ABSTRACT FROM AUTHOR]

Reinforcement, extinction, generalization and differentiation are all basic principles of Pavlov associative memory. Most memristive neural networks that simulate associative memory only consider reinforcement and extinction, while ignoring differentiation and generalization. In this paper, a memristive circuit of associative memory with generalization and differentiation is proposed to solve the above problem. It implements the functions of learning, forgetting, long-term memory, generalization and differentiation. Learning and forgetting correspond to reinforcement and extinction in associative memory respectively. Spontaneous recovery, in which forgotten reflexes can reappear in the absence of an unconditional stimulus, is also discussed here. Besides, a special differentiation method that takes into account the time delay is designed and demonstrated. The proposed memristive circuit of associative memory provides a reference for the theoretical research and application of artificial neural networks. [ABSTRACT FROM AUTHOR]