The need for continuous size downscaling of silicon transistors is driving the industrial development of strategies to enable further footprint reduction
. The atomic thickness of two-dimensional ...materials allows the potential realization of high-area-efficiency transistor architectures. However, until now, the design of devices composed of two-dimensional materials has mimicked the basic architecture of silicon circuits
. Here, we report a transistor based on a two-dimensional material that can realize photoswitching logic (OR, AND) computing in a single cell. Unlike the conventional transistor working mechanism, the two-dimensional material logic transistor has two surface channels. Furthermore, the material thickness can change the logic behaviour-the architecture can be flexibly expanded to achieve in situ memory such as logic computing and data storage convergence in the same device. These devices are potentially promising candidates for the construction of new chips that can perform computing and storage with high area-efficiency and unique functions.
For the next-generation video coding standard Versatile Video Coding (VVC), several new contributions have been proposed to improve the coding efficiency, especially in the transformation operations. ...This paper proposes a unified <inline-formula> <tex-math notation="LaTeX">32\times 32 </tex-math></inline-formula> block-based transform architecture for the VVC standard that enables 2D Discrete Sine Transform-VII (DST-VII) and Discrete Cosine Transform-VIII (DCT-VIII) of all sizes. It mainly gives three contributions: 1) The N-Dimensional Reduced Adder Graph (RAG-n) algorithm is adopted to design the minimal adder-oriented computational units. 2) The storage of the asymmetric transform units can be realized in the dual-port SRAM-based transpose memory. 3) The pipelined 2D transformations of mixed block sizes are achieved with the throughput rate of 32 samples per cycle. The synthesis results indicate that this architecture can reduce area by up to 73.1% compared with other state-of-the-art works. Moreover, power saving ranging from 4.9% to 9.9% can be achieved. Regarding the transpose memory, at least 21.9% of the area can be saved by using SRAM.
•A novel recognition site, 5-chlorothiophene-2-carbonyl chloride, which was used as an electron donor combined with coumarin to form a PET fluorescence probe for detecting hydrazine.•The combination ...of 5-chlorothiophene-2-carbonyl chloride and coumarin endowed the probe excellent selectivity and sensitivity towards hydrazine.•The probe could operate over a wide pH region (4–10) owing to the strong bonding ability between 5-chlorothiophene-2-carbonyl chloride and coumarin.
Hydrazine (N2H4) has been classified as an environmental contaminant and human carcinogen owing to its high toxicity. Being exposed to high levels of hydrazine can cause irreversible damage to human bodies. Hence, it is significant to explore an effective method to selectively recognize and detect it. In this work, a new “turn-on” fluorescence probe, coumarin-thiophene (C-T), was designed based on PET (photo-induced electron transfer) mechanism by employing 5-chlorothiophene-2-carbonyl chloride as a electron donor and coumarin as a electron acceptor. The flourescence could be recovered due to the interruption of PET by stripping the 5-chlorothiophene-2-carbonyl chloride when treated with hydrazine. The mechanism was further verified through 1H NMR titration and spectral analysis. Additionally, The probe exhibited a high selectivity towards hydrazine over other common ions and amine-containing species with a distinct fluorescence enhancement at 450 nm. The probe could operate over a wide pH region (4–10) with a detection limit as low as 0.0047 μM (1.5 ppb). Furthermore, it could selectively detect hydrazine in different water samples which indicated its potential for practical applications.
Salient ship detection plays an important role in ensuring the safety of maritime transportation and navigation. However, due to the influence of waves, special weather, and illumination on the sea, ...existing saliency methods are still unable to achieve effective ship detection in a complex marine environment. To solve the problem, this paper proposed a novel saliency method based on an attention nested U-Structure (AU2Net). First, to make up for the shortcomings of the U-shaped structure, the pyramid pooling module (PPM) and global guidance paths (GGPs) are designed to guide the restoration of feature information. Then, the attention modules are added to the nested U-shaped structure to further refine the target characteristics. Ultimately, multi-level features and global context features are integrated through the feature aggregation module (FAM) to improve the ability to locate targets. Experiment results demonstrate that the proposed method could have at most 36.75% improvement in F-measure (Favg) compared to the other state-of-the-art methods.
Applications have different preferences for caches, sometimes even within the different running phases. Caches with fixed parameters may compromise the performance of a system. To solve this problem, ...we propose a real-time adaptive reconfigurable cache based on the decision tree algorithm, which can optimize the average memory access time of cache without modifying the cache coherent protocol. By monitoring the application running state, the cache associativity is periodically tuned to the optimal cache associativity, which is determined by the decision tree model. This paper implements the proposed decision tree-based adaptive reconfigurable cache in the GEM5 simulator and designs the key modules using Verilog HDL. The simulation results show that the proposed decision tree-based adaptive reconfigurable cache reduces the average memory access time compared with other adaptive algorithms.
Quadtree with nested multi-type tree (QTMT) partition structure in Versatile Video Coding (VVC) contributes to superior encoding performance compared to the basic quad-tree (QT) structure in High ...Efficiency Video Coding (HEVC). However, the improvement of performance leads to an un-avoidable increase of computational complexity. To achieve a balance between coding efficiency and compression quality, we propose a fast intra partition algorithm based on variance and gradient to solve the rectangular partition problem in VVC. First, further splitting of smooth areas is terminated. Then, QT partition is chosen depending on the gradient features extracted by Sobel operator. Finally, one partition from five possible QTMT partitions is directly chosen by computing the variance of variance of sub-CUs. The theoretical basis of our method is that a homogeneous area tends to be predicted with a larger coding unit (CU), and sub-parts of a split CU are prone to have different textures from each other. To our knowledge, this is the first attempt to apply traditional method to accelerating the rectangular partition problem in VVC intra prediction. Experimental results show that the proposed method can save averagely 53.17% encoding time with only 1.62% BDBR increase and 0.09dB BDPSNR loss compared to anchor VTM4.0.
This letter proposes a novel 1.5-D algorithm for multi-channel electroencephalogram (EEG) compression. The proposed algorithm only needs to perform 1-D Discrete Wavelet Transform (DWT) rather than ...the 2-D version employed by previous works, and thus it results in lower computational complexity and power dissipation. In this algorithm, a new 2-D arranging method that exploits correlations between different sub-bands is developed to concentrate the energy, which causes more efficient compression using No List Set Partitioning in Hierarchical Trees (NLSPIHT) algorithm. Experimental results demonstrate that the proposed algorithm outperforms 2-D NLSPIHT algorithm under the same compression ratio (CR) and it is slightly inferior to 2-D SPIHT algorithm in the near-lossless compression regime, but it can provide a better fidelity with respect to higher CRs.
Convolutional Neural Networks (CNNs) and Transformers have achieved remarkable performance in detection and classification tasks. Nevertheless, their feature extraction cannot consider both local and ...global information, so the detection and classification performance can be further improved. In addition, more and more deep learning networks are designed as more and more complex, and the amount of computation and storage space required is also significantly increased. This paper proposes a combination of CNN and transformer, and designs a local feature enhancement module and global context modeling module to enhance the cascade network. While the local feature enhancement module increases the range of feature extraction, the global context modeling is used to capture the feature maps' global information. To decrease the model complexity, a shared sublayer is designed to realize the sharing of weight parameters between the adjacent convolutional layers or cross convolutional layers, thereby reducing the number of convolutional weight parameters. Moreover, to effectively improve the detection performance of neural networks without increasing network parameters, the optimal transport assignment approach is proposed to resolve the problem of label assignment. The classification loss and regression loss are the summations of the cost between the demander and supplier. The experiment results demonstrate that the proposed Combination of CNN and Transformer with Shared Sublayer (CCTSS) performs better than the state-of-the-art methods in various datasets and applications.
As a kind of marine vehicles, Unmanned Surface Vehicles (USV) are widely used in military and civilian fields because of their low cost, good concealment, strong mobility and high speed. ...High-precision detection of obstacles plays an important role in USV autonomous navigation, which ensures its subsequent path planning. In order to further improve obstacle detection performance, we propose an encoder-decoder architecture named Fusion Refinement Network (FRN). The encoder part with a deeper network structure enables it to extract more rich visual features. In particular, a dilated convolution layer is used in the encoder for obtaining a large range of obstacle features in complex marine environment. The decoder part achieves the multiple path feature fusion. Attention Refinement Modules (ARM) are added to optimize features, and a learnable fusion algorithm called Feature Fusion Module (FFM) is used to fuse visual information. Experimental validation results on three different datasets with real marine images show that FRN is superior to state-of-the-art semantic segmentation networks in performance evaluation. And the MIoU and MPA of the FRN can peak at 97.01% and 98.37% respectively. Moreover, FRN could maintain a high accuracy with only 27.67M parameters, which is much smaller than the latest obstacle detection network (WaSR) for USV.
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