Evaluation of data representation techniques for vibration based road surface condition classification.

The accurate classification of road surface conditions plays a vital role in ensuring road safety and effective maintenance. Vibration-based techniques have shown promise in this domain, leveraging the unique vibration signatures generated by vehicles to identify different road conditions. In this study, we focus on utilizing vehicle-mounted vibration sensors to collect road surface vibrations and comparing various data representation techniques for classifying road surface conditions into four classes: normal road surface, potholes, bad road surface, and speedbumps. Our experimental results reveal that the combination of multiple data representation techniques results in higher performance, with an average accuracy of 93.4%. This suggests that the integration of deep neural networks and signal processing techniques can produce a high-level representation better suited for challenging multivariate time series classification issues.

Intelligent system for human activity recognition in IoT environment.

In recent years, the adoption of machine learning has grown steadily in different fields affecting the day-to-day decisions of individuals. This paper presents an intelligent system for recognizing human's daily activities in a complex IoT environment. An enhanced model of capsule neural network called 1D-HARCapsNe is proposed. This proposed model consists of convolution layer, primary capsule layer, activity capsules flat layer and output layer. It is validated using WISDM dataset collected via smart devices and normalized using the random-SMOTE algorithm to handle the imbalanced behavior of the dataset. The experimental results indicate the potential and strengths of the proposed 1D-HARCapsNet that achieved enhanced performance with an accuracy of 98.67%, precision of 98.66%, recall of 98.67%, and F1-measure of 0.987 which shows major performance enhancement compared to the Conventional CapsNet (accuracy 90.11%, precision 91.88%, recall 89.94%, and F1-measure 0.93).

Multimodal analysis of electroencephalographic and electrooculographic signals.

Electrooculography (EOG) is a method to concurrently obtain electrophysiological signals accompanying an Electroencephalography (EEG), where both methods have a common cerebral pattern and imply a similar medical significance. The most common electrophysiological signal source is EOG that contaminated the EEG signal and thereby decreases the accuracy of measurement and the predicated signal strength. In this study, we introduce a method to improve the correction efficiency for EOG artifacts (EOAs) on raw EEG recordings: We retrieve cerebral information from three EEG signals with high system performance and accuracy by applying feature engineering and a novel machine-learning (ML) procedure. To this end, we use two adaptive algorithms for signal decomposition to remove EOAs from multichannel EEG signals: empirical mode decomposition (EMD) and complete ensemble empirical mode decomposition (CEEMD), both using the Hilbert-Huang transform. First, the signal components are decomposed into multiple intrinsic mode functions. Next, statistical feature extraction and dimension reduction using principal component analysis are employed to select optimal feature sets for the ML procedure that is based on classification and regression models. The proposed CEEMD algorithm enhances the accuracy compared to the EMD algorithm and considerably improves the multi-sensory classification of EEG signals. Models of three different categories are applied, and the classification is based on a K-nearest neighbor (k-NN) algorithm, a decision tree (DT) algorithm, and a support vector machine (SVM) algorithm with accuracies of 94% for K-NN, 75% for DT, and 69% for SVM. For each classification model, a regression learner is used to assist as an evidence rule for the proposed artificial system and to influence the learning process from classification and regression models. The regression learning algorithms applied include algorithms based on an ensemble of trees (ET), a DT, and a SVM. We find that the ET-based regression model exhibits a determination coefficient R2 = 1.00 outperforming the other two approaches with R2 = 0.80 for DT and R2 = 0.76 for SVM.

Wavelet packets-based blind watermarking for medical image management.

The last decade has witnessed an explosive use of medical images and Electronics Patient Record (EPR) in the healthcare sector for facilitating the sharing of patient information and exchange between networked hospitals and healthcare centers. To guarantee the security, authenticity and management of medical images and information through storage and distribution, the watermarking techniques are growing to protect the medical healthcare information. This paper presents a technique for embedding the EPR information in the medical image to save storage space and transmission overheads and to guarantee security of the shared data. In this paper a new method for protecting the patient information in which the information is embedded as a watermark in the discrete wavelet packet transform (DWPT) of the medical image using the hospital logo as a reference image. The patient information is coded by an error correcting code (ECC), BCH code, to enhance the robustness of the proposed method. The scheme is blind so that the EPR can be extracted from the medical image without the need of the original image. Therefore, this proposed technique is useful in telemedicine applications. Performance of the proposed method was tested using four modalities of medical images; MRA, MRI, Radiological, and CT. Experimental results showed no visible difference between the watermarked and the original image. Moreover, the proposed watermarking method is robust against a wide range of attacks such as JPEG coding, Gaussian noise addition, histogram equalization, gamma correction, contrast adjustment, and sharpen filter and rotation.

Best parameters selection for wavelet packet-based compression of magnetic resonance images.

Transmission of compressed medical images is becoming a vital tool in telemedicine. Thus new methods are needed for efficient image compression. This study discovers the best design parameters for a data compression scheme applied to digital magnetic resonance (MR) images. The proposed technique aims at reducing the transmission cost while preserving the diagnostic information. By selecting the wavelet packet's filters, decomposition level, and subbands that are better adapted to the frequency characteristics of the image, one may achieve better image representation in the sense of lower entropy or minimal distortion. Experimental results show that the selection of the best parameters has a dramatic effect on the data compression rate of MR images. In all cases, decomposition at three or four levels with the Coiflet 5 wavelet (Coif 5) results in better compression performance than the other wavelets. Image resolution is found to have a remarkable effect on the compression rate.