LZCode based compression method for effectively using the memory of embedded devices

Lee, Hyunchul; Kim, Kangseok; Choi, Okkyung; Yeh, Hongjin

doi:10.1002/dac.2394

Cited by 3 publications

(2 citation statements)

References 11 publications

(10 reference statements)

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“…In order to accomplish the aims mentioned, there are many traditional source coding approaches (e.g., Huffman code [14] , L-Z code [15] , etc. ), which bases on a statistical structure depending on the raw data.…”

Section: Introduction mentioning

confidence: 99%

Novel encoder for ambient data compression applied to microcontrollers in agricultural robots

Wang

et al. 2022

International Journal of Agricultural and Biological Engineering

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Agricultural robots are flexible to obtain ambient information across large areas of farmland. However, it needs to face two major challenges: data compression and filtering noise. To address these challenges, an encoder for ambient data compression, named Tiny-Encoder, was presented to compress and filter raw ambient information, which can be applied to agricultural robots. Tiny-Encoder is based on the operation of convolutions and pooling, and it has a small number of layers and filters. With the aim of evaluating the performance of Tiny-Encoder, different three types of ambient information (including temperature, humidity, and light) were selected to show the performance of compressing raw data and filtering noise. In the task of compressing raw data, Tiny-Encoder obtained higher accuracy (less than the maximum error of sensors ±0.5°C or ±3.5% RH) and more appropriate size (the largest size is 205 KB) than the other two auto-encoders based convolutional operations with different compressed features (including 20, 60, and 200 features). As for filtering noise, Tiny-Encoder has comparable performance with three conventional filtering approaches (including median filtering, Gaussian filtering, and Savitzky-Golay filtering). With large kernel size (i.e., 5), Tiny-Encoder has the best performance among these four filtering approaches: the coefficients of variation with the large kernel (i.e., 5) were 8.6189% (temperature), 10.2684% (humidity), 57.3576% (light), respectively. Overall, Tiny-Encoder can be used for ambient information compression applied to microcontrollers in agricultural information acquisition robots.

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Section: Introduction mentioning

confidence: 99%

Novel encoder for ambient data compression applied to microcontrollers in agricultural robots

Wang

et al. 2022

International Journal of Agricultural and Biological Engineering

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“…When the erase operation executes, to erase a target page, other pages that are in the same block with the target page are moved to another block. Then, it erases the target page's block [10]. For this reason, the erase operation incurs larger overhead than read and write operations [11].…”

Section: Introductionmentioning

confidence: 99%

The uFLIP benchmark revisited for evaluating SSDs

Kang

Cha

et al. 2015

Int J Communication

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SUMMARYSolid-state drives (SSDs) provide higher bandwidth and lower access latency than hard disk drives (HDDs) and thus have been rapidly replacing HDDs. SSD manufacturers, which produce different types of SSDs, do not open their SSD architecture and control firmware in detail. To find the best SSD for a given application, it is necessary to compare and analyze the performances of different kinds of SSDs. A benchmark is widely used to analyze the storage devices performance. However, most existing benchmarks are mainly targeted for HDDs. The uFLIP (understanding Flash IO Patterns) is a benchmark proposed to consider flash devices' characteristics. In this paper, we first exploit the uFLIP benchmark to analyze and to evaluate the performances of different types of SSDs. Through extensive experiments, we have identified common SSD characteristics: (1) the input/output (IO) size and queue depth affect the parallelism that is an inherent characteristic in SSDs; (2) the size of SSDs access space considerably affects the SSDs performance. Then, via a series of experiments, we verify whether the uFLIP benchmark could correctly characterize applications by its micro-benchmarks. The results reveal that the performance of the uFLIP micro-benchmarks, produced to have features similar to that of a target application, is quite different from the performance of the target application.

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A Comprehensive Review of Distributed Coding Algorithms for Visual Sensor Network (VSN)

Ebrahim

Chong²

2022

Int. j. commun. netw. inf. secur.

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Since the invention of low cost camera, it has been widely incorporated into the sensor node in Wireless Sensor Network (WSN) to form the Visual Sensor Network (VSN). However, the use of camera is bringing with it a set of new challenges, because all the sensor nodes are powered by batteries. Hence, energy consumption is one of the most critical issues that have to be taken into consideration. In addition to this, the use of batteries has also limited the resources (memory, processor) that can be incorporated into the sensor node. The life time of a VSN decreases quickly as the image is transferred to the destination. One of the solutions to the aforementioned problem is to reduce the data to be transferred in the network by using image compression. In this paper, a comprehensive survey and analysis of distributed coding algorithms that can be used to encode images in VSN is provided. This also includes an overview of these algorithms, together with their advantages and deficiencies when implemented in VSN. These algorithms are then compared at the end to determine the algorithm that is more suitable for VSN.

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LZCode based compression method for effectively using the memory of embedded devices

Cited by 3 publications

References 11 publications

Novel encoder for ambient data compression applied to microcontrollers in agricultural robots

Novel encoder for ambient data compression applied to microcontrollers in agricultural robots

The uFLIP benchmark revisited for evaluating SSDs

A Comprehensive Review of Distributed Coding Algorithms for Visual Sensor Network (VSN)

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