46 research outputs found
Malicious UAV detection using integrated audio and visual features for public safety applications
RÉSUMÉ: Unmanned aerial vehicles (UAVs) have become popular in surveillance, security, and remote monitoring. However, they also pose serious security threats to public privacy. The timely detection of a malicious drone is currently an open research issue for security provisioning companies. Recently, the problem has been addressed by a plethora of schemes. However, each plan has a limitation, such as extreme weather conditions and huge dataset requirements. In this paper, we propose a novel framework consisting of the hybrid handcrafted and deep feature to detect and localize malicious drones from their sound and image information. The respective datasets include sounds and occluded images of birds, airplanes, and thunderstorms, with variations in resolution and illumination. Various kernels of the support vector machine (SVM) are applied to classify the features. Experimental results validate the improved performance of the proposed scheme compared to other related methods
Thai Voice-Controlled Analysis for Car Parking Assistance in System-on-Chip Architecture
This paper introduces an analysis of Thai speech recognition for controlled car parking assist in the system-on-chip architecture. The objective is to investigate the male and female voice command signals, including Thai and English words, issued by the native Thai users. Hardware and software co-design by the Xilinx VIVADO are designed on an ARM multicore processor and a reconfigurable system on a ZYBO board. The experiments for Thai and English word recognition are conducted by using the Mel-frequency cepstral coefficient approach and presented in the form of spectrograms. The comparison of a voice command via Bluetooth and a reference command stored on an SD card and the ZYBO embedded board on a miniature electric vehicle is verified with the Pearson’s correlation coefficient (PCC). The experimental results show the accuracies of the received Thai/English, male/female, and indoor/outdoor voice commands as compared with the reference voice commands in the noisy surroundings. Hence, our system can support Thai/English and male/female voice commands to perform a set of actions for maneuvering a car by the PCC
NRC-Net: Automated noise robust cardio net for detecting valvular cardiac diseases using optimum transformation method with heart sound signals
Cardiovascular diseases (CVDs) can be effectively treated when detected
early, reducing mortality rates significantly. Traditionally, phonocardiogram
(PCG) signals have been utilized for detecting cardiovascular disease due to
their cost-effectiveness and simplicity. Nevertheless, various environmental
and physiological noises frequently affect the PCG signals, compromising their
essential distinctive characteristics. The prevalence of this issue in
overcrowded and resource-constrained hospitals can compromise the accuracy of
medical diagnoses. Therefore, this study aims to discover the optimal
transformation method for detecting CVDs using noisy heart sound signals and
propose a noise robust network to improve the CVDs classification
performance.For the identification of the optimal transformation method for
noisy heart sound data mel-frequency cepstral coefficients (MFCCs), short-time
Fourier transform (STFT), constant-Q nonstationary Gabor transform (CQT) and
continuous wavelet transform (CWT) has been used with VGG16. Furthermore, we
propose a novel convolutional recurrent neural network (CRNN) architecture
called noise robust cardio net (NRC-Net), which is a lightweight model to
classify mitral regurgitation, aortic stenosis, mitral stenosis, mitral valve
prolapse, and normal heart sounds using PCG signals contaminated with
respiratory and random noises. An attention block is included to extract
important temporal and spatial features from the noisy corrupted heart
sound.The results of this study indicate that,CWT is the optimal transformation
method for noisy heart sound signals. When evaluated on the GitHub heart sound
dataset, CWT demonstrates an accuracy of 95.69% for VGG16, which is 1.95%
better than the second-best CQT transformation technique. Moreover, our
proposed NRC-Net with CWT obtained an accuracy of 97.4%, which is 1.71% higher
than the VGG16
Accurate Reader Identification for the Arabic Holy Quran Recitations Based on an Enhanced VQ Algorithm
The Speaker identification process is not a new trend; however, for the Arabic Holy Quran recitation, there are still quite improvements that can make this process more accurate and reliable. This paper collected the input data from 14 native Arabic reciters, consisting of “Surah Al-Kawthar” speech signals from the Holy Quran. Moreover, this paper discusses the accuracy rates for 8 and 16 features. Indeed, a modified Vector Quantization (VQ) technique will be presented, in addition to realistically matching the centroids of the various codebooks and measuring systems’ effectiveness. Note that the VQ technique will be utilized to generate the codebooks by clustering these features into a finite number of centroids. The proposed system’s software was built and executed using MATLAB®. The proposed system’s total accuracy rate was 97.92% and 98.51% for 8 and 16 centroids codebooks, respectively. However, this study discussed two validation tactics to ensure that the outcomes are reliable and can be reproduced. Hence, the K-mean clustering algorithm has been used to validate the obtained results and discuss the outcomes of this study. Finally, it has been found that the improved VQ method gives a better result than the K-means method
A Comprehensive Survey on Rare Event Prediction
Rare event prediction involves identifying and forecasting events with a low
probability using machine learning and data analysis. Due to the imbalanced
data distributions, where the frequency of common events vastly outweighs that
of rare events, it requires using specialized methods within each step of the
machine learning pipeline, i.e., from data processing to algorithms to
evaluation protocols. Predicting the occurrences of rare events is important
for real-world applications, such as Industry 4.0, and is an active research
area in statistical and machine learning. This paper comprehensively reviews
the current approaches for rare event prediction along four dimensions: rare
event data, data processing, algorithmic approaches, and evaluation approaches.
Specifically, we consider 73 datasets from different modalities (i.e.,
numerical, image, text, and audio), four major categories of data processing,
five major algorithmic groupings, and two broader evaluation approaches. This
paper aims to identify gaps in the current literature and highlight the
challenges of predicting rare events. It also suggests potential research
directions, which can help guide practitioners and researchers.Comment: 44 page