Molecular communication (MC) enables information transfer through molecules at the nano-scale. This paper presents new and optimized source coding (data compression) methods for MC. In a recent paper, prefix source co...
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Molecular communication (MC) enables information transfer through molecules at the nano-scale. This paper presents new and optimized source coding (data compression) methods for MC. In a recent paper, prefix source coding was introduced into the field, through an MC-adapted version of the Huffman coding. We first show that while MC-adapted Huffman coding improves symbol error rate (SER), it does not always produce an optimal prefix codebook in terms of coding length and power. To address this, we propose optimal molecular prefix coding (MoPC). The major result of this paper is the Molecular arithmetic coding (MoAC), which we derive based on an existing general construction principle for constrained arithmetic channel coding, equipping it with error correction and data compression capabilities for any finite source alphabet. We theoretically and practically show the superiority of MoAC to SAC, our another adaptation of arithmetic source coding to MC. However, MoAC's unique decodability is limited by bit precision. Accordingly, a uniquely-decodable new coding scheme named Molecular arithmetic with Prefix coding (MoAPC) is introduced. On two nucleotide alphabets, we show that MoAPC has a better compression performance than optimized MoPC. MC simulation results demonstrate the effectiveness of the proposed methods.
Lossless image compression techniques play a crucial role in preserving image quality while reducing storage space and transmission bandwidth. This paper proposes a novel hybrid integrated method for lossless image co...
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Lossless image compression techniques play a crucial role in preserving image quality while reducing storage space and transmission bandwidth. This paper proposes a novel hybrid integrated method for lossless image compression by combining Contrast Limited Adaptive Histogram Equalization (CLAHE), two-channel encoding, and adaptive arithmetic coding to achieve highly efficient compression without any loss of image information. The first step of the proposed approach involves applying Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance the local contrast of the image. This pre-processing step aids in reducing the entropy and increasing the redundancy in the image, creating a more favourable environment for subsequent compression algorithms. Next, the image is divided into two channels: one channel focuses on encoding essential structural information, while the other channel handles the finer details. This segregation leverages the inherent properties of images to improve compression efficiency. To achieve further compression gains, an adaptive arithmetic coding algorithm for encoding the data in each channel is utilized. Adaptive arithmetic coding adapts its probability model during the encoding process, leading to improved compression performance compared to traditional static coding methods. The proposed method offers significant potential in various applications, it is especially crucial in medical imaging, where large volumes of high-resolution images are generated during procedures such as MRI, CT scans, or digital pathology, transmitting high-quality images in resource-constrained environments, and facilitating image processing tasks requiring precise data preservation. CLAHE can be a valuable tool in medical imaging to enhance essential diagnostic information in medical images before compression. By improving contrast and visibility of structures, CLAHE may aid in achieving better compression efficiency and reduce the risk of introducing compres
With the widespread proliferation of mobile devices and the rapid development of mobile network technologies, the issue of network security has become increasingly critical, especially in the field of network anomaly ...
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ISBN:
(纸本)9798350361261;9798350361278
With the widespread proliferation of mobile devices and the rapid development of mobile network technologies, the issue of network security has become increasingly critical, especially in the field of network anomaly detection. Traditional anomaly detection methods, such as rule-based and feature-matching techniques, often struggle to effectively cope with increasingly complex network threats. To address this issue, we have incorporated machine learning and deep learning technologies, particularly a strategy of integrating representative tags into word vectors through arithmetic coding. This method, inspired by position encoding, directly enhances the expressive power of the original word vectors and improves the model's precision in recognizing anomalous behaviors in industrial IoT traffic data, such as ransomware, crypto-mining malware, and DDoS Trojans. This encoding strategy uses shorter codes for frequently occurring words and longer codes for less common words, which not only makes the encoding of information more efficient but also helps the model learn and adapt to the data's characteristics more quickly. Our experiments have demonstrated that this approach significantly improves fitting efficiency and detection accuracy compared to traditional models without such encoding. This indicates that combining machine learning technologies with innovative data processing methods can significantly optimize the performance of network anomaly detection.
Convolutional neural networks (CNNs) have gained a huge attention for real-world artificial intelligence (AI) applications such as image classification and object detection. On the other hand, for better accuracy, the...
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Convolutional neural networks (CNNs) have gained a huge attention for real-world artificial intelligence (AI) applications such as image classification and object detection. On the other hand, for better accuracy, the size of the CNNs' parameters (weights) has been increasing, which in turn makes it difficult to enable on-device CNN inferences in resource-constrained edge devices. Though weight pruning and 5-bit quantization methods have shown promising results, it is still challenging to deploy large CNN models in edge devices. In this paper, we propose an encoding and hardware-based decoding technique which can be applied to 5-bit quantized weight data for on-device CNN inferences in resource-constrained edge devices. Given 5-bit quantized weight data, we employ arithmetic coding with range scaling for loss-less weight compression, which is performed offline. When executing on-device inferences with underlying CNN accelerators, our hardware decoder enables a fast in-situ weight decompression with small latency overhead. According to our evaluation results with five widely used CNN models, our arithmetic coding-based encoding method applied to 5-bit quantized weights shows a better compression ratio by 9.6x while also reducing the memory data transfer energy consumption by 89.2%, on average, as compared to the case of uncompressed 32-bit floating-point weights. When applying our technique to pruned weights, we obtain better compression ratios by 57.5x-112.2x while reducing energy consumption by 98.3%-99.1% as compared to the case of 32-bit floating-point weights. In addition, by pipelining the weight decoding and transfer with the CNN execution, the latency overhead of our weight decoding with 16 decoding unit (DU) hardware is only 0.16%-5.48% and 0.16%-0.91% for non-pruned and pruned weights, respectively. Moreover, our proposed technique with 4-DU decoder hardware reduces system-level energy consumption by 1.1%-9.3%.
This paper develops an arithmetic coding algorithm based on delta recurrent neural network for edge computing devices called DRAC. Our algorithm is implemented on a Xilinx Zynq 7000 Soc board. We evaluate DRAC with fo...
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This paper develops an arithmetic coding algorithm based on delta recurrent neural network for edge computing devices called DRAC. Our algorithm is implemented on a Xilinx Zynq 7000 Soc board. We evaluate DRAC with four datasets and compare it with the state-of-the-art compressor DeepZip. The experimental results show that DRAC outperforms DeepZip and achieves 5X speedup ratio and 20X power consumption saving.
Dynamic meshes reasonably represent time-varying 3D objects, but compression is required due to the large amount of data. One compression framework decomposes a dynamic mesh into a base mesh and displacements by using...
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ISBN:
(纸本)9781728198354
Dynamic meshes reasonably represent time-varying 3D objects, but compression is required due to the large amount of data. One compression framework decomposes a dynamic mesh into a base mesh and displacements by using decimation and subdivision. The displacements are converted to coefficients by wavelet transforms, quantized, and compressed by video codec, which is well disseminated. However, the abundance of tools in video codec is too complex for uncorrelated displacements. In this paper, we propose hierarchical arithmetic coding, dividing the coefficient levels into blocks and smaller subblocks. When all levels are zero in a block/subblock, a flag is coded instead of the levels. The experimental results show that the coding complexity was significantly reduced while the coding efficiency was maintained.
Software-based fault tolerance enables the usage of standard hardware in safety-critical applications. arithmetic coding is a promising approach for fault tolerance and is already used in the area of production system...
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ISBN:
(纸本)9781665400497
Software-based fault tolerance enables the usage of standard hardware in safety-critical applications. arithmetic coding is a promising approach for fault tolerance and is already used in the area of production systems. To enable the usage of complex safety functions, e.g. in human-robot collaboration, basic mathematical functions must be supported by the software-based fault-tolerance approach. Thereby, the basic mathematical functions require the full support of floating point values. Therefore, in this paper, we present a methodology to apply arithmetic coding on all types of floating-point functions including the basic mathematical functions. For each type, an example of implementation is shown. To validate our approach, we use the inverse kinematic of a cable robot as an example algorithm where the application of arithmetic coding is done automatically with a source-to-source transformation approach. Fault injection experiments validate the effectiveness of the presented approach.
To enable the usage of standard hardware in safety-critical applications for production systems, new approaches for hardware fault tolerance are required. These approaches must be implemented on software level. As sho...
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ISBN:
(纸本)9781728175348
To enable the usage of standard hardware in safety-critical applications for production systems, new approaches for hardware fault tolerance are required. These approaches must be implemented on software level. As shown in the literature, arithmetic coding is a promising approach, but only supports integer calculations. For complex safety functions, e.g. in robotics, fast floating-point calculations are needed. Therefore, this paper presents a method for direct arithmetic encoding of floating-point calculations with low-performance impact. Moreover, a detailed residual error estimation is given.
Safety-critical systems are becoming more complex with use cases like autonomous driving or human-robot collaboration. Therefore, the performance impact of software-based fault-tolerance methods is challenging. Using ...
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ISBN:
(数字)9783031148354
ISBN:
(纸本)9783031148354;9783031148347
Safety-critical systems are becoming more complex with use cases like autonomous driving or human-robot collaboration. Therefore, the performance impact of software-based fault-tolerance methods is challenging. Using software-based fault tolerance is an attractive approach because commercial off-the-shelf hardware can be used. One possibility to implement software-based fault tolerance are arithmetic codes, already used in safety-critical products. Recently, AN codes have received particular attention;however, they have a significant performance impact in complex safety applications that require 64-bit wide integer calculations. Therefore, we comprehensively analyze different arithmetic codes in this work to identify the best suitable 64-bit integer support. We identify the ones' complement as the best matching encoding strategy through new code metrics, fault simulations, and performance analysis. We validate our results by applying ones' complement coding to a sample algorithm. Performance measurements and fault injection simulation confirm our results.
Entropy coding is a fundamental technology in video coding that removes statistical redundancy among syntax elements. In high efficiency video coding (HEVC), context-adaptive binary arithmetic coding (CABAC) is adopte...
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Entropy coding is a fundamental technology in video coding that removes statistical redundancy among syntax elements. In high efficiency video coding (HEVC), context-adaptive binary arithmetic coding (CABAC) is adopted as the primary entropy coding method. The CABAC consists of three steps: binarization, context modeling, and binary arithmetic coding. As the binarization processes and context models are both manually designed in CABAC, the probability of the syntax elements may not be estimated accurately, which restricts the coding efficiency of CABAC. To address the problem, we propose a convolutional neural network-based arithmetic coding (CNNAC) method and apply it to compress the syntax elements of the intra-predicted residues in HEVC. Instead of manually designing the binarization processes and context models, we propose directly estimating the probability distribution of the syntax elements with a convolutional neural network (CNN), as CNNs can adaptively build complex relationships between inputs and outputs by training with a lot of data. Then, the values of the syntax elements, together with their estimated probability distributions, are fed into a multi-level arithmetic codec to perform entropy coding. In this paper, we have utilized the CNNAC to code the syntax elements of the DC coefficient;the lowest frequency AC coefficient;the second, third, fourth, and fifth lowest frequency AC coefficients;and the position of the last non-zero coefficient in the HEVC intra-predicted residues. The experimental results show that our proposed method achieves up to 6.7% BD-rate reduction and an average of 4.7% BD-rate reduction compared to the HEVC anchor under all intra (AI) configuration.
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