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L2 Gain And Passivity Techniques In Nonlinear Control

Today is the era of the Internet of Things (IoT). The recent advances in hardware and information technology have accelerated the deployment of billions of interconnected, smart and adaptive devices in critical infrastructures like health, transportation, environmental control, and home automation. Transferring data over a network without requiring any kind of human-to-computer or human-to-human interaction, brings reliability and convenience to consumers, but also opens a new world of opportunity for intruders, and introduces a whole set of unique and complicated questions to the field of Digital Forensics. Although IoT data could be a rich source of evidence, forensics professionals cope with diverse problems, starting from the huge variety of IoT devices and non-standard formats, to the multi-tenant cloud infrastructure and the resulting multi-jurisdictional litigations. A further challenge is the end-to-end encryption which represents a trade-off between users’ right to privacy and the success of the forensics investigation. Due to its volatile nature, digital evidence has to be acquired and analyzed using validated tools and techniques that ensure the maintenance of the Chain of Custody. Therefore, the purpose of this paper is to identify and discuss the main issues involved in the complex process of IoT-based investigations, particularly all legal, privacy and cloud security challenges. Furthermore, this work provides an overview of the past and current theoretical models in the digital forensics science. Special attention is paid to frameworks that aim to extract data in a privacy-preserving manner or secure the evidence integrity using decentralized blockchain-based solutions. In addition, the present paper addresses the ongoing Forensics-as-a-Service (FaaS) paradigm, as well as some promising cross-cutting data reduction and forensics intelligence techniques. Finally, several other research trends and open issues are presented, with emphasis on the need for proactive Forensics Readiness strategies and generally agreed-upon standards.

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A Survey on the Internet of Things (IoT) Forensics: Challenges, Approaches, and Open Issues

A growing number of affective computing researches recently developed a computer system that can recognize an emotional state of the human user to establish affective human-computer interactions. Various measures have been used to estimate emotional states, including self-report, startle response, behavioral response, autonomic measurement, and neurophysiologic measurement. Among them, inferring emotional states from electroencephalography (EEG) has received considerable attention as EEG could directly reflect emotional states with relatively low costs and simplicity. Yet, EEG-based emotional state estimation requires well-designed computational methods to extract information from complex and noisy multichannel EEG data. In this paper, we review the computational methods that have been developed to deduct EEG indices of emotion, to extract emotion-related features, or to classify EEG signals into one of many emotional states. We also propose using sequential Bayesian inference to estimate the continuous emotional state in real time. We present current challenges for building an EEG-based emotion recognition system and suggest some future directions.

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A Review on the Computational Methods for Emotional State Estimation from the Human EEG

Classification of motor imagery electroencephalograph signals is a fundamental problem in brain–computer interface (BCI) systems. We propose in this paper a classification framework based on long short-term memory (LSTM) networks. To achieve robust classification, a one dimension-aggregate approximation (1d-AX) is employed to extract effective signal representation for LSTM networks. Inspired by classical common spatial pattern, channel weighting technique is further deployed to enhance the effectiveness of the proposed classification framework. Public BCI competition data are used for the evaluation of the proposed feature extraction and classification network, whose performance is also compared with that of the state-of-the-arts approaches based on other deep networks.

LSTM-Based EEG Classification in Motor Imagery Tasks

In this paper the use of a novel feature extraction method oriented to convolutional neural networks (CNN) is discussed in order to solve four-class motor imagery classification problem. Analysis of viable CNN architectures and their influence on the obtained accuracy for the given task is argued. Furthermore, selection of optimal feature map image dimension, filter sizes and other CNN parameters used for network training is investigated. Methods for generating 2D feature maps from 1D feature vectors are presented for commonly used feature types. Initial results show that CNN can achieve high classification accuracy of 68% for the four-class motor imagery problem with less complex feature extraction techniques. It is shown that optimal accuracy highly depends on feature map dimensions, filter sizes, epoch count and other tunable factors, therefore various fine-tuning techniques must be employed. Experiments show that simple FFT energy map generation techniques are enough to reach the state of the art classification accuracy for common CNN feature map sizes. This work also confirms that CNNs are able to learn a descriptive set of information needed for optimal electroencephalogram (EEG) signal classification.DOI: http://dx.doi.org/10.5755/j01.itc.46.2.17528

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Application of Convolutional Neural Networks to Four-Class Motor Imagery Classification Problem

The treatment of phosphogypsum with zeolite to use it in binding material

Brain -- Computer interface (BCI) systems require intensive signal processing in order to form control signals for electronic devices The majority of BCI systems work by reading and interpreting cortically evoked electro-potentials across the scalp via an electro-encephalogram (EEG) An important factor affecting the efficiency of BCI is the number of EEG features To reduce the number of features is an important way to improve the speed In this paper, we consider application of discrete cosine transform (DCT) on EEG signals DCT takes correlated input data and concentrates its energy in just first few transform coefficients This method is used as a feature extraction step and allows data size reduction without losing important information For classification we are using artificial neural networks with different number of hidden neurons and training functions We conclude that the method can be successfully used for the feature extraction and dataset reduction

EEG Dataset Reduction and Feature Extraction Using Discrete Cosine Transform

The increased popularity of brain-computer interfaces (BCIs) has created a new demand for miniaturized and low-cost electroencephalogram (EEG) acquisition devices for entertainment, rehabilitation, and scientific needs. The lack of scientific analysis for such system design, modularity, and unified validation tends to suppress progress in this field and limit supply for new low-cost device availability. To eliminate this problem, this paper presents the design and evaluation of a compact, modular, battery powered, conventional EEG signal acquisition board based on an ADS1298 analog front-end chip. The introduction of this novel, vertically stackable board allows the EEG scaling problem to be solved by effectively reconfiguring hardware for small or more demanding applications. The ability to capture 16 to 64 EEG channels at sample rates from 250 Hz to 1000 Hz and to transfer raw EEG signal over a Bluetooth or Wi-Fi interface was implemented. Furthermore, simple but effective assessment techniques were used for system evaluation. While conducted tests confirm the validity of the system against official datasheet specifications and for real-world applications, the proposed quality verification methods can be further employed for analyzing other similar EEG devices in the future. With 6.59 microvolts peak-to-peak input referred noise and a −97 dB common mode rejection ratio in 0–70 Hz band, the proposed design can be qualified as a low-cost precision cEEG research device.

Development of a Modular Board for EEG Signal Acquisition.

Electroencephalography (EEG) is widely used in clinical diagnosis, monitoring
 and Brain - Computer Interface systems. Usually EEG signals are recorded with
 several electrodes and transmitted through a communication channel for
 further processing. In order to decrease communication bandwidth and
 transmission time in portable or low cost devices, data compression is
 required. In this paper we consider the use of fast Discrete Cosine Transform
 (DCT) algorithms for lossy EEG data compression. Using this approach, the
 signal is partitioned into a set of 8 samples and each set is
 DCT-transformed. The least-significant transform coefficients are removed
 before transmission and are filled with zeros before an inverse transform. We
 conclude that this method can be used in real-time embedded systems, where
 low computational complexity and high speed is required.

/pdf/fast-dct-algorithms-for-eeg-data-compression-in-embedded-41sd7m22j7.pdf

Vacius Jusas

Papers

Application of Convolutional Neural Networks to Four-Class Motor Imagery Classification Problem

The treatment of phosphogypsum with zeolite to use it in binding material

EEG Dataset Reduction and Feature Extraction Using Discrete Cosine Transform

Development of a Modular Board for EEG Signal Acquisition.

Fast DCT algorithms for EEG data compression in embedded systems