Ensemble approach on enhanced compressed noise EEG data signal in wireless body area sensor network

The Wireless Body Area Sensor Network (WBASN) is used for communication among sensor nodes operating on or inside the human body in order to monitor vital body parameters and movements. One of the important applications of WBASN is patients’ healthcare monitoring of chronic diseases such as epileptic seizure. Normally, epileptic seizure data of the electroencephalograph (EEG) is captured and compressed in order to reduce its transmission time. However, at the same time, this contaminates the overall data and lowers classification accuracy. The current work also did not take into consideration that large size of collected EEG data. Consequently, EEG data is a bandwidth intensive. Hence, the main goal of this work is to design a unified compression and classification framework for delivery of EEG data in order to address its large size issue. EEG data is compressed in order to reduce its transmission time. However, at the same time, noise at the receiver side contaminates the overall data and lowers classification accuracy. Another goal is to reconstruct the compressed data and then recognize it. Therefore, a Noise Signal Combination (NSC) technique is proposed for the compression of the transmitted EEG data and enhancement of its classification accuracy at the receiving side in the presence of noise and incomplete data. The proposed framework combines compressive sensing and discrete cosine transform (DCT) in order to reduce the size of transmission data. Moreover, Gaussian noise model of the transmission channel is practically implemented to the framework. At the receiving side, the proposed NSC is designed based on weighted voting using four classification techniques. The accuracy of these techniques namely Artificial Neural Network, Naïve Bayes, k-Nearest Neighbour, and Support Victor Machine classifiers is fed to the proposed NSC. The experimental results showed that the proposed technique exceeds the conventional techniques by achieving the highest accuracy for noiseless and noisy data. Furthermore, the framework performs a significant role in reducing the size of data and classifying both noisy and noiseless data. The key contributions are the unified framework and proposed NSC, which improved accuracy of the noiseless and noisy EGG large data. The results have demonstrated the effectiveness of the proposed framework and provided several credible benefits including simplicity, and accuracy enhancement. Finally, the research improves clinical information about patients who not only suffer from epilepsy, but also neurological disorders, mental or physiological problems

Performance evaluation for compression-accuracy trade-off using compressive sensing for EEG-based epileptic seizure detection in wireless tele-monitoring

Brain is the most important part in the human body controlling muscles and nerves; Electroencephalogram (EEG) signals record brain electric activities.EEG signals capture important information pertinent to different physiological brain states.In this paper, we propose an efficient framework for evaluating the power-accuracy trade-off for EEG-based compressive sensing and classification techniques in the context of epileptic seizure detection in wireless tele-monitoring.The framework incorporates compressive sensing-based energy-efficient compression, and noisy wireless communication channel to study the effect on the application accuracy. Discrete cosine transform (DCT) and compressive sensing are used for EEG signals acquisition and compression.To obtain low-complexity energy-efficient, the best data accuracy with higher compression ratio is sought. A reconstructed algorithm derived from DCT of daubechie’s wavelet 6 is used to decompose the EEG signal at different levels. DCT is combined with the best basis function neural networks for EEG signals classification.Extensive experimental work is conducted, utilizing four classification models.The obtained results show an improvement in classification accuracies and an optimal classification rate of about 95% is achieved when using NN classifier at 85% of CR in the case of no SNR value.The satisfying results demonstrate the effect of efficient compression on maximizing the sensor lifetime without affecting the application’s accuracy

Performance comparison of classification algorithms for EEG-based remote epileptic seizure detection in wireless sensor networks

Identification of epileptic seizure remotely by analyzing the electroencephalography (EEG) signal is very important for scalable sensor-based health systems.Classification is the most important technique for wide-ranging applications to categorize the items according to its features with respect to predefined set of classes.In this paper, we conduct a performance evaluation based on the noiseless and noisy EEG-based epileptic seizure data using various classification algorithms including BayesNet, DecisionTable, IBK, J48/C4.5, and VFI.The reconstructed and noisy EEG data are decomposed with discrete cosine transform into several sub-bands.In addition, some of statistical features are extracted from the wavelet coefficients to represent the whole EEG data inputs into the classifiers.Benchmark on widely used dataset is utilized for automatic epileptic seizure detection including both normal and epileptic EEG datasets.The classification accuracy results confirm that the selected classifiers have greater potentiality to identify the noisy epileptic disorders

3D objects and scenes classification, recognition, segmentation, and reconstruction using 3D point cloud data: A review

Three-dimensional (3D) point cloud analysis has become one of the attractive subjects in realistic imaging and machine visions due to its simplicity, flexibility and powerful capacity of visualization. Actually, the representation of scenes and buildings using 3D shapes and formats leveraged many applications among which automatic driving, scenes and objects reconstruction, etc. Nevertheless, working with this emerging type of data has been a challenging task for objects representation, scenes recognition, segmentation, and reconstruction. In this regard, a significant effort has recently been devoted to developing novel strategies, using different techniques such as deep learning models. To that end, we present in this paper a comprehensive review of existing tasks on 3D point cloud: a well-defined taxonomy of existing techniques is performed based on the nature of the adopted algorithms, application scenarios, and main objectives. Various tasks performed on 3D point could data are investigated, including objects and scenes detection, recognition, segmentation and reconstruction. In addition, we introduce a list of used datasets, we discuss respective evaluation metrics and we compare the performance of existing solutions to better inform the state-of-the-art and identify their limitations and strengths. Lastly, we elaborate on current challenges facing the subject of technology and future trends attracting considerable interest, which could be a starting point for upcoming research studie

Video surveillance using deep transfer learning and deep domain adaptation: Towards better generalization

Recently, developing automated video surveillance systems (VSSs) has become crucial to ensure the security and safety of the population, especially during events involving large crowds, such as sporting events. While artificial intelligence (AI) smooths the path of computers to think like humans, machine learning (ML) and deep learning (DL) pave the way more, even by adding training and learning components. DL algorithms require data labeling and high-performance computers to effectively analyze and understand surveillance data recorded from fixed or mobile cameras installed in indoor or outdoor environments. However, they might not perform as expected, take much time in training, or not have enough input data to generalize well. To that end, deep transfer learning (DTL) and deep domain adaptation (DDA) have recently been proposed as promising solutions to alleviate these issues. Typically, they can (i) ease the training process, (ii) improve the generalizability of ML and DL models, and (iii) overcome data scarcity problems by transferring knowledge from one domain to another or from one task to another. Although the increasing number of articles proposed to develop DTL- and DDA-based VSSs, a thorough review that summarizes and criticizes the state-of-the-art is still missing. To that end, this paper introduces, to the best of the authors' knowledge, the first overview of existing DTL- and DDA-based video surveillance to (i) shed light on their benefits, (ii) discuss their challenges, and (iii) highlight their future perspectives.This research work was made possible by research grant support (QUEX-CENG-SCDL-19/20-1) from Supreme Committee for Delivery and Legacy (SC) in Qatar. The statements made herein are solely the responsibility of the authors. Open Access funding provided by the Qatar National Library.Scopu

Crowd density estimation for crowd management at event entrance

Crowd management is an essential tasks to ensure the safety and smoothness of any events. Using the novel technologies including surveillance cameras, communication technics between security agents, the control of the crowd has become easier. But the sue of these technics still not perfectly effective. This paper presents an approach for managing the crowd at the entrance of event (festival, stadium,..) using surveillance cameras. Using cameras and some panel in each entrance, the crowd density is extracted and illustrated in each panel. So, before reaching any gate , the people can see the available and the not crowded gate to reach the target. The proposed technique help not just in smoothing the motion of the crowd but also minimize the crowdity and abnormal behaviors of the people

Medical IoT: A Comprehensive Survey of Different Encryption and Security Techniques

Recently, there is a revolution in internet of things (IoT) technologies. Research advancements in this field proved to be very useful to automate daily tasks, it quickly reached the medical field resulting in the creation of a new research term called Internet of Medical Things (IoMT). Medical IoT devices have many applications that adds accessibility and reach to the medical field. Such applications vary from remote patient monitoring, remote surgery, and many more health-related tasks. Medical IoT applications require precise readings of biometrics and real time haptic feedback to work as intended without putting any risk on the human life. With all of these IoT medical applications, securing the information becomes a priority. Any un-intentional modification in a biometric reading can prove to be fatal in most scenarios. In this paper, we try to survey the current state-of-the-art encryption techniques that provide different solutions with varying levels of security

From segmentarity to opacity On Gellner and Bourdieu, or why Algerian politics have eluded theoretical analysis and vice versa

Includes bibliographical references. Also available via the Internet, and in SpanishAvailable from British Library Document Supply Centre- DSC:3487. 3841(no 19) / BLDSC - British Library Document Supply CentreSIGLEGBUnited Kingdo

Secure Transmission of IoT mHealth Patient Monitoring Data from Remote Areas Using DTN

In remote rural areas without continuous Internet connectivity, it is hard to envisage the use of mHealth applications for remote patient monitoring. In such areas, patients need to travel long distances to reach the nearest health center. In this article, we propose an approach that solves this problem by transmitting mHealth monitoring data, collected using IoT sensors, using DTN. Thus, buses or other vehicles acting as data mules transmit the mHealth data from remote rural areas to a medical center or hospital in the nearest urban area. The proposed approach includes methods to preserve the security of the data through encryption and secure key exchange, and to authenticate the patients through appropriate hashing of selected information. It allows preserving the privacy of the patients, and it takes into account the intermittent nature of the network by adding redundancy to avoid data loss. 1986-2012 IEEE.This work was made possible by NPRP grant #10- 1205-160012 from the Qatar National Research Fund (a member of Qatar Foundation).Scopus2-s2.0-8508255309

Accelerated IoT Anti-Jamming: A Game Theoretic Power Allocation Strategy

A jamming combating power allocation strategy is proposed to secure the data communication in IoT networks. The proposed strategy aims to minimize the worst case jamming effect on the intended transmission under multi channel fading and total power constraints by modelling the problem as a Colonel Blotto game Nash Equibrium (NE). Both Logistic Regression as well as a specifically designed algorithm are used to iteratively and rapidly obtain the equilibrium strategy. The conducted theoretical derivations and Monte Carlo simulations confirm that the proposed approach can secure the IoT network with a limited amount of power and with a number of iterations that is much reduced compared to state-of-the-art techniques