A Modified KNN Algorithm for High-Performance Computing on FPGA of Real-Time m-QAM Demodulators

Márquez-Viloria, David; Castano-Londono, Luis; Guerrero-González, Neil

doi:10.3390/electronics10050627

Cited by 9 publications

(11 citation statements)

References 28 publications

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“…In general, this is implemented offline, and not including any real application. Thus proposed models should involve the algorithm being applied in real time [34] [35]. With experimental datasets, KNN searches for k samples in the closest samples.…”

Section: ) Knn Algorithmmentioning

confidence: 99%

A new enhancement of the k-NN algorithm by Using an optimization technique

Altayef

Anayi

Packianather

2022

2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE)

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Of a number of ML (Machine Learning) algorithms, k-nearest neighbour (KNN) is among the most common for data classification research, and classifying diseases and faults, which is essential due to frequent alterations in the training dataset, in which it would be expensive using most methods to construct a different classifier every time this happens. Therefore, KNN can be used effectively as it does not require a residual classifier to be constructed in advance. KNN offers ease of use and can be applied across a broad variation spectrum. Here, a novel KNN classification approach is put forward using the Bayesian Optimization Algorithm (BOA) for optimisation. This paper seeks to make classification more accurate and suggest alterations of nearest neighbour K value to use information about dataset structure and the similarity measure of distance. The findings of experimental work based on the University of California Irvine (UCI) repository datasets in general shows improved performance of classifiers compared with conventional KNN and give greater reliability without a significant time cost to speed.

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Section: ) Knn Algorithmmentioning

confidence: 99%

A new enhancement of the k-NN algorithm by Using an optimization technique

Altayef

Anayi

Packianather

2022

2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE)

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“…Another possibility to codify the models is using high-level synthesis (HLS) languages such as C, C++, or System C [30,31]. HLS can be used to design FPGA circuits, where hardware implementations can be easily described and replaced in the target device using shorter and more abstract structures instead of using verbose and extremely detailed HDL structures [32,33].…”

Section: Hil Modelmentioning

confidence: 99%

Comparison of Different Design Alternatives for Hardware-in-the-Loop of Power Converters

et al. 2021

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This paper aims to compare different design alternatives of hardware-in-the-loop (HIL) for emulating power converters in Field Programmable Gate Arrays (FPGAs). It proposes various numerical formats (fixed and floating-point) and different approaches (pure VHSIC Hardware Description Language (VHDL), Intellectual Properties (IPs), automated MATLAB HDL code, and High-Level Synthesis (HLS)) to design power converters. Although the proposed models are simple power electronics HIL systems, the idea can be extended to any HIL system. This study compares the design effort of different coding methods and numerical formats considering possible synthesis tools (Precision and Vivado), and it comprises an analytical discussion in terms of area and speed. The different models are synthesized as ad-hoc modules in general-purpose FPGAs, but also using the NI myRIO device as an example of a commercial tool capable of implementing HIL models. The comparison confirms that the optimum design alternative must be chosen based on the application (complexity, frequency, etc.) and designers’ constraints, such as available area, coding expertise, and design effort.

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“…The K-Nearest neighbor (KNN) algorithm is a supervised technique that is often utilized in a wide range of situations due to its efficiency and simplicity. The most modern development highlights KNN's potential for reducing distortion in a dataset (60)(61)(62)(63). Big data analysis with a KNN classifier demands powerful computing resources.…”

Section: K-nearest Neighbor (Knn)mentioning

confidence: 99%

Application of KNN-based isometric mapping and fuzzy c-means algorithm to predict short-term rockburst risk in deep underground projects

Kamran

Ullah

Ahmad

et al. 2022

Front. Public Health

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The rockburst phenomenon is the major source of the high number of casualties and fatalities during the construction of deep underground projects. Rockburst poses a severe hazard to the safety of employees and equipment in subsurface mining operations. It is a hot topic in recent years to examine and overcome rockburst risks for the safe installation of deep urban engineering designs. Therefore, for a cost-effective and safe underground environment, it is crucial to determine and predict rockburst intensity prior to its occurrence. A novel model is presented in this study that combines unsupervised and supervised machine learning approaches in order to predict rockburst risk. The database for this study was built using authentic microseismic monitoring occurrences from the Jinping-II hydropower project in China, which consists of 93 short-term rockburst occurrences with six influential features. The prediction process was succeeded in three steps. Firstly, the original rockburst database's magnification was reduced using a state-of-the-art method called isometric mapping (ISOMAP) algorithm. Secondly, the dataset acquired from ISOMAP was categorized using the fuzzy c-means algorithm (FCM) to reduce the minor spectral heterogeneity impact in homogenous areas. Thirdly, K-Nearest neighbor (KNN) was employed to anticipate different levels of short-term rockburst datasets. The KNN's classification performance was examined using several performance metrics. The proposed model correctly classified about 96% of the rockbursts events in the testing datasets. Hence, the suggested model is a realistic and effective tool for evaluating rockburst intensity. Therefore, the proposed model can be employed to forecast the rockburst risk in the early stages of underground projects that will help to minimize casualties from rockburst.

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A Modified KNN Algorithm for High-Performance Computing on FPGA of Real-Time m-QAM Demodulators

Cited by 9 publications

References 28 publications

A new enhancement of the k-NN algorithm by Using an optimization technique

A new enhancement of the k-NN algorithm by Using an optimization technique

Comparison of Different Design Alternatives for Hardware-in-the-Loop of Power Converters

Application of KNN-based isometric mapping and fuzzy c-means algorithm to predict short-term rockburst risk in deep underground projects

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