A deep learning approach to predict the number of k-barriers for intrusion detection over a circular region using wireless sensor networks
Abstract Wireless Sensor Networks (WSNs) is a promising technology with enormous
applications in almost every walk of life. One of the crucial applications of WSNs is intrusion …
applications in almost every walk of life. One of the crucial applications of WSNs is intrusion …
Algoritma Fungsi Perlatihan pada Machine Learning berbasis ANN untuk Peramalan Fenomena Bencana
Research has been carried out with several training functions using standard
backpropagation methods, One-Step Secant (OSS), and Bayesian regulation. The purpose …
backpropagation methods, One-Step Secant (OSS), and Bayesian regulation. The purpose …
Prediksi Prestasi Mahasiswa Dengan Menggunakan Algoritma Backpropagation
This study aims to overcome the problems in predicting student achievement at the
Polytechnic Business Indonesia Pematangsiantar. To predict student achievement is done …
Polytechnic Business Indonesia Pematangsiantar. To predict student achievement is done …
[HTML][HTML] FPGA-Based CNN for eye detection in an Iris recognition at a distance system
CA Ruiz-Beltrán, A Romero-Garcés… - Electronics, 2023 - mdpi.com
Neural networks are the state-of-the-art solution to image-processing tasks. Some of these
neural networks are relatively simple, but the popular convolutional neural networks (CNNs) …
neural networks are relatively simple, but the popular convolutional neural networks (CNNs) …
[HTML][HTML] Real-time FPGA-based laser absorption spectroscopy using on-chip machine learning for 10 kHz intra-cycle emissions sensing towards adaptive …
KK Schwarm, RM Spearrin - Applications in Energy and Combustion …, 2023 - Elsevier
Fast emissions sensing is needed to enable rapid optimization on-the-fly for increasingly
adaptive engine architectures to improve performance over a wide range of loads and to …
adaptive engine architectures to improve performance over a wide range of loads and to …
[HTML][HTML] Evaluating Wind Speed Forecasting Models: A Comparative Study of CNN, DAN2, Random Forest and XGBOOST in Diverse South African Weather …
The main source of electricity worldwide stems from fossil fuels, contributing to air pollution,
global warming, and associated adverse effects. This study explores wind energy as a …
global warming, and associated adverse effects. This study explores wind energy as a …
[HTML][HTML] Performance analysis of multiple input single layer neural network hardware chip
An artificial neural network (ANN) is a computational system that is designed to replicate and
process the behavior of the human brain using neuron nodes. ANNs are made up of …
process the behavior of the human brain using neuron nodes. ANNs are made up of …
Low-voltage energy efficient neural inference by leveraging fault detection techniques
M Safarpour, TZ Deng, J Massingham… - 2021 IEEE Nordic …, 2021 - ieeexplore.ieee.org
Operating at reduced voltages offers substantial energy efficiency improvement but at the
expense of increasing the probability of computational errors due to hardware faults. In this …
expense of increasing the probability of computational errors due to hardware faults. In this …
[HTML][HTML] FPGA-based reconfigurable convolutional neural network accelerator using sparse and convolutional optimization
KMV Gowda, S Madhavan, S Rinaldi, PB Divakarachari… - Electronics, 2022 - mdpi.com
Nowadays, the data flow architecture is considered as a general solution for the acceleration
of a deep neural network (DNN) because of its higher parallelism. However, the …
of a deep neural network (DNN) because of its higher parallelism. However, the …
A high-level approach for energy efficiency improvement of fpgas by voltage trimming
Chip manufacturers define voltage margins on top of the “best-case” operational voltage of
their chips to ensure reliable functioning in the worst-case settings. The margins guarantee …
their chips to ensure reliable functioning in the worst-case settings. The margins guarantee …