The development of gene sequencing technology sparks an explosive growth of gene data. Thus, the storage of gene data has become an important issue. Recently, researchers begin to investigate deep learning-based gene ...
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ISBN:
(纸本)9798350344868;9798350344851
The development of gene sequencing technology sparks an explosive growth of gene data. Thus, the storage of gene data has become an important issue. Recently, researchers begin to investigate deep learning-based gene datacompression, which outperforms general traditional methods. In this paper, we propose a transformer-based gene compression method named GeneFormer. Specifically, we first introduce a modified transformer encoder with latent array to eliminate the dependency of the nucleotide sequence. Then, we design a multi-level-grouping method to accelerate and improve the compression process. Experimental results on real-world datasets show that our method achieves significantly better compression ratio compared with state-of-the-art method, and the decoding speed is significantly faster than all existing learning-based gene compression methods. We will release our code on github once the paper is accepted.
Monitoring systems produce and transmit large amounts of data. For an efficient transmission, data is often compressed and autoencoders are a widely adopted neural network-based solution. However, this processing step...
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ISBN:
(纸本)9798350383638;9798350383645
Monitoring systems produce and transmit large amounts of data. For an efficient transmission, data is often compressed and autoencoders are a widely adopted neural network-based solution. However, this processing step leads to a loss of information that may negatively impact the performance of downstream tasks, such as anomaly detection. In this work, we propose a loss function for an autoencoder that addresses both compression and anomaly detection. Our key contribution is the inclusion of a regularization term based on information-theoretic quantities that characterize an anomaly detector processing compressed signals. As a result, the proposed approach allows for a better use of the communication channel such that the information preserved by the compressed signal is optimized for both detection and reconstruction, even in scenarios with lightweight compression. We tested the proposed technique with ECG signals affected by synthetic anomalies and the experiments demonstrated an average 17% increase in the probability of detection across three standard detectors. Additionally, we proved that our approach is generalizable to image data.
Artificial Intelligence Generated Content (AIGC) is leading a new technical revolution for the acquisition of digital content and impelling the progress of visual compression towards competitive performance gains and ...
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ISBN:
(纸本)9798350349405;9798350349399
Artificial Intelligence Generated Content (AIGC) is leading a new technical revolution for the acquisition of digital content and impelling the progress of visual compression towards competitive performance gains and diverse functionalities over traditional codecs. This paper provides a thorough review on the recent advances of generative visual compression, illustrating great potentials and promising applications in ultra-low bitrate communication, user-specified reconstruction/filtering, and intelligent machine analysis. In particular, we review the visual datacompression methodologies with deep generative models, and summarize how compact representation and high-quality reconstruction could be actualized via generative techniques. In addition, we generalize related generative compression technologies for machine vision with different-domain analysis. Finally, we discuss the fundamental challenges on generative visual compression techniques and envision their future research directions.
Linear computation coding (LCC) has been developed in [1] as a new framework for the computation of linear functions. LCC significantly reduces the complexity of matrix-vector multiplication [1]. In basic LCC, storage...
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JPEG is a widely used format for images. Most JPEG variants are based upon a block-based DCT transformation followed by quantization and entropy coding. Redundancy at row/column level is explored in [1]. Brunsli [2] a...
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The number of IoT devices is expected to continue its dramatic growth in the coming years and, with it, a growth in the amount of data to be transmitted, processed and stored. compression techniques that support analy...
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ISBN:
(纸本)9781538674628
The number of IoT devices is expected to continue its dramatic growth in the coming years and, with it, a growth in the amount of data to be transmitted, processed and stored. compression techniques that support analytics directly on the compressed data could pave the way for systems to scale efficiently to these growing demands. This paper proposes two novel methods for preprocessing a stream of floating point data to improve the compression capabilities of various IoT data compressors. In particular, these techniques are shown to be helpful with recent compressors that allow for random access and analytics while maintaining good compression. Our techniques improve compression with reductions up to 80% when allowing for at most 1% of recovery error.
The Industrial Internet of Things(IIoT) plays a vital role as a fundamental technology supporting intelligent manufacturing. Existing IIoT platform adopts cloud-edge design, the centralized cloud stores a large amount...
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ISBN:
(纸本)9798350309461
The Industrial Internet of Things(IIoT) plays a vital role as a fundamental technology supporting intelligent manufacturing. Existing IIoT platform adopts cloud-edge design, the centralized cloud stores a large amount of sensitive data, which is vulnerable to attack and faces the risk of single point of failure. As a distributed system, blockchain enables secure and robust IIoT systems. Therefore, this paper builds a blockchain-based IIoT platform to store the data in IIoT into the blockchain and ensure security through a decentralized and distributed consensus mechanism. However, the existing blockchain platforms have low throughput and cannot meet the demand of high concurrent data processing. To solve this problem, this paper designs a dual datacompression optimization scheme, which can effectively reduce the size of the transaction and improve the transaction processing speed. This scheme provides an effective solution for promoting the deep application of blockchain and IIoT.
data-driven optimization is employed to study alternative approaches [1] to the probability estimator of the the Enhanced compression Model (ECM) (which includes additional coding tools on top of the Versatile Video C...
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Smart traffic monitoring at intersections which exploits three-dimensional light detection and ranging (LiDAR) sensor networks is a promising technique for achieving a safe and secure society. One challenge for widely...
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ISBN:
(纸本)9798350304572
Smart traffic monitoring at intersections which exploits three-dimensional light detection and ranging (LiDAR) sensor networks is a promising technique for achieving a safe and secure society. One challenge for widely spreading these systems is to effectively aggregate massive point cloud data generated by multiple LiDAR sensors installed at every corner of intersections with a limited cost and a limited communication bandwidth. To this end, this paper proposes a lightweight point cloud compression method for real-time smart traffic monitoring. The proposed method enables tiny low-cost processors installed at every LiDAR sensor to detect moving parts of a point cloud from point could data in real time. By sending compressed data of moving parts of a point cloud only to edge servers, the communication bandwidth is saved, which helps edge servers to analyze them for preventing traffic accidents in real time. Experimental results using the KoPER intersection dataset show that the average detection rate over 1,200 frames of data is around 95%. The processing time per frame is about 5.4 ms with a commercial edge-oriented processor, which is less than typical frame rates of modern LiDAR sensors. In addition, point could compression ratio of the proposed method is approximately 3.5 times better than that without the moving part detection technique.
Point clouds offer the realistic three-dimensional (3-D) representation of objects or scenes at the expense of high data volume. To compactly represent such data in real-world applications, Video-based Point Cloud Com...
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