Deterministic compressive sensing by chirp codes: a MATLAB® tutorial

Khosravy, Mahdi; Nitta, Naoko; Asharif, Faramarz; Melo, Katia; Duque, Carlos A.

doi:10.1016/b978-0-12-821247-9.00012-3

Cited by 8 publications

(5 citation statements)

References 19 publications

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“…The purpose of this research does not lie in proving the added value of CS, since there is plenty of literature available on that topic [ 2 , 4 , 8 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. However, significant steps forward can still be made in the development process of acquisition systems implementing the proposed embedded CS strategies [ 2 ].…”

Section: Code Generation For Embedded Compressive Sensingmentioning

confidence: 99%

Automated Firmware Generation for Compressive Sensing on Heterogeneous Hardware

Baeyens

Denil

Steckel

et al. 2022

Sensors

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In this paper, a model-based firmware generator is presented towards complex sampling schemes. The framework is capable of automatically generating a fixed-rate Shannon-compliant acquisition scheme, as well as a variable-rate compressive sensing acquisition scheme. The generation starts from a model definition, which consists of two main components, namely an acquisition sequence to implement and the platform on which the sequence should be implemented. This model is then combined with the specifications to be transformed into a functional firmware. When generating firmware for compressive sensing (CS) purposes, the defined acquisition sequence is automatically generated to implement a pseudo-random sampling scheme in agreement with the defined undersampling factor. The evaluation of the generated firmware is done by means of an example use-case, including a proposed strategy for synchronization between CS setups. This research attempts to reduce the development complexity for embedded CS to lower the threshold towards effective usage in the field.

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Section: Code Generation For Embedded Compressive Sensingmentioning

confidence: 99%

Automated Firmware Generation for Compressive Sensing on Heterogeneous Hardware

Baeyens

Denil

Steckel

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Such a data transfer can be done time by time as the network is more free and less transmission is carried out. Also to have less bulk of data but with same content of information, compressive sensing [50][51][52][53][54][55][56][57][58] can be deployed for transferring the data to DACS. However, there should be an alertness to labeling noise in mining process of completing a partially annotated dataset [59].…”

Section: Iot-specific Agriculture Machinery Health Monitoringmentioning

confidence: 99%

Optimum Lightweight AI End Device for Health Monitoring of Agriculture Vehicles

Gupta

2021

Springer Tracts in Nature-Inspired Computing

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“…Potentially, optimization can improve the efficiency of the systems in a wide range of industrial applications like compressive sensing [6][7][8][9][10][11][12][13][14], optical instruments [15], power quality [16][17][18], sentiment mining [19], image adaptation [20,21], sentiment mining [19], data mining [22][23][24], power line communications [25], featuring big data [26], location-based service [27], telecommunications [28][29][30][31][32][33], analysis of texture [34], planning power system [35], public transportation systems [36], faulty condition diagnosis in agriculture machinery [37][38][39], interaction of human and robot [40], medical images [34,41,42], study of human motion [43], noise removal [44,45], smart ambient [46], electrocardiogram processing [47][48]…”

Section: Introductionmentioning

confidence: 99%