Predicting and Visualising City Noise Levels to Support Tinnitus Sufferers

On a daily basis, urban residents are unconsciously exposed to hazardous noise levels. This has a detrimental effect on the ear-drum, with symptoms often not apparent till later in life. The impact of harmful noises levels has a damaging impact on wellbeing. It is estimated that 10 million people suffer from damaged hearing in the UK alone, with 6.4million of retirement age or above. With this number expected to increase significantly by 2031, the demand and cost for healthcare providers is expected to intensify. Tinnitus affects about 10 percent of the UK population, with the condition ranging from mild to severe. The effects can have psychological impact on the patient. Often communication becomes difficult, and the sufferer may also be unable to use a hearing aid due to buzzing, ringing or monotonous sounds in the ear. Action on Hearing Loss states that sufferers of hearing related illnesses are more likely to withdraw from social activities. Tinnitus sufferers are known to avoid noisy environments and busy urban areas, as exposure to excessive noise levels exacerbates the symptoms. In this paper, an approach for evaluating and predicting urban noise levels is put forward. The system performs a data classification process to identify and predict harmful noise areas at diverse periods. The goal is to provide Tinnitus sufferers with a real-time tool, which can be used as a guide to find quieter routes to work; identify harmful areas to avoid or predict when noise levels on certain roads will be dangerous to the ear-drum. Our system also performs a visualisation function, which overlays real-time noise levels onto an interactive 3D map. Keywords: Hazardous Noise Levels, Data Classification, Tinnitus, Visualisation, Hearing Loss, Prediction, Real-Tim

Guest Editorial Special Issue on: Big Data Analytics in Intelligent Systems

The amount of information that is being created, every day, is quickly growing. As such, it is now more common than ever to deal with extremely large datasets. As systems develop and become more intelligent and adaptive, analysing their behaviour is a challenge. The heterogeneity, volume and speed of data generation are increasing rapidly. This is further exacerbated by the use of wireless networks, sensors, smartphones and the Internet. Such systems are capable of generating a phenomenal amount of information and the need to analyse their behaviour, to detect security anomalies or predict future demands for example, is becoming harder. Furthermore, securing such systems is a challenge. As threats evolve, so should security measures develop and adopt increasingly intelligent security techniques. Adaptive systems must be employed and existing methods built upon to provide well-structured defence in depth. Despite the clear need to develop effective protection methods, the task is a difficult one, as there are significant weaknesses in the existing security currently in place. Consequently, this special issue of the Journal of Computer Sciences and Applications discusses big data analytics in intelligent systems. The specific topics of discussion include the Internet of Things, Web Services, Cloud Computing, Security and Interconnected Systems

Behaviour analysis techniques for supporting critical infrastructure security

Protecting critical infrastructures from cyber-threats in an increasingly digital age is a matter of growing urgency for governments and private industries across the globe. In a climate where cyber-security is an uncertainty, fresh and adaptive solutions to existing computer security approaches are a must. In this paper, we present our approach to supporting critical infrastructure security. The use of our critical infrastructure simulation, developed using Siemens Tecnomatix Plant Simulator and the programming language SimTalk, is used to construct realistic data from a simulated nuclear power plant. The data collected from the simulation, when both functioning as normal and during a cyber-attack scenario, is done through the use of an observer pattern. By extracting features from the data collected, threats to the system are identified by modelling system behaviour and identifying changes in patterns of activity by using three data classification techniques

Estudo comparativo da implementação coprocessada em sistemas em chip do algoritmo de treinamento do classificador LDA aplicado em interfaces cérebro-máquina

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Faculdade UnB Gama, 2018.As Interfaces Cérebro-Máquina (BCI, do inglês Brain Computer Interfaces são sistemas capazes de realizar uma comunicação entre o cérebro e dispositivos eletrônicos. Cada vez mais estudadas no âmbito científico as BCIs já apresentam um grande número de aplicações. Um dos principais procedimentos para implementação de uma BCI é a classificação dos sinais gerados pelo cérebro, pois é após a classificação que os processos de comandos são executados. Existem vários algoritmos que realizam este tipo de classificação, um deles é o classificador Linear Discriminant Analysis (LDA). Em 2010 o cientista francês Fabien Lotte publicou um trabalho no qual realiza a implementação deste classificador, obtendo como melhor resultado de acurácia 96,43% na classificação de sinais de imagética motora, fornecidos pela competição BCI Competition III. Um dos pontos importantes e de maior necessidade de processamento para implementação deste classificador é processo de treinamento, nos quais são obtidos os hiperplanos capazes de separar as classes dos sinais em estudo. Um dos sistemas capazes acelerar algoritmos que realizam este tipo de cálculo são os SoCs que contêm FPGA, nos quais são explorados os paralelismos de processos. Sendo assim, neste trabalho é apresentado um estudo da implementação em cálculos de ponto flutante do algoritmo de treinamento do classificador LDA em um sistema coprocessado hardware-software utilizando o Sistema em Chip (SoC, do inglês System on Chip) Zynq-7000 (composto por um processador ARM Cortex A9 dual core e um FPGA Artix-7 ). Esta implementação é comparada com a implementação em Matlab desenvolvida por Fabien Lotte e a implementação em um sistema embarcado utilizando Linguagem de programação C. Os resultados mostraram que o algoritmo implementado em linguagem C apresentou melhor desempenho computacional da ordem de 93 vezes mais rápido que o algoritmo executado em Matlab. Já o sistema coprocessado apresenta um melhor desempenho em funções de cálculo devido ao seu paralelismo. Entretanto a latência do barramento de comunicação do sistema em hardware com o sistema em software é um limitante do seu desempenho, apresentando velocidade de processamento de aproximadamente 8 vezes mais rápido que a implementação em Matlab. Além disso, as implementações em linguagem C e sistema coprocessado apresentaram um consumo energético de aproximadamente 7 vezes menor que a implementação em um computador tradicional.The Brain Computer Interface (BCI) are systems capable of making a communication between the brain and electronical devices. As they are scientifically studied more and more, BCIs already present a big number of applications. One of the main principles of implementation of a BCI is the classification of the signals generated by the brain and starting from the classification that the processes of commands are executed. There are numerous algorithms that perform this type of classification, one of them is the Linear Discriminant Analysis classifier (LDA). In 2010 the French scientist Fabien Lotte published a work in which realizes the implementation of this classifier, obtaining as best result of accuracy 96.43% in the classification of signals of motor imagery provided by the BCI Competition III. One of the important points and the greater processing need to implement this classifier is a process of training, in which the hyperplanes capable of separating classes from the signals in study are obtained. These hyperplanes are obtained through matrix calculations. One of the systems able to accelerate algorithms that perform this type of calculation are System on Chip (SoC) that contain FPGA, in which the parallelism of processes is explored. Therefore, in this work it is presented a study of the implementation in floatingpoint calculations of the algorithm of training of the LDA classifier in a hardware-software co-processed system using the Zynq-7000 SoC system (consisting of an ARM Cortex A9 dual core processor and a FPGA Artix-7). In which it compares with implementations in Matlab developed by Fabien Lotte and the implementation of a embedded system using C programming language. The results showed that the algorithm implemented in C language presented better computational performance of the order of 93 times faster than the algorithm executed in Matlab. The co-processed system performs better in computing functions because of its parallelism. However, the system communication bus latency in hardware with the software system is a limitation of its performance, presenting speed approximately 8 times faster than the Matlab implementation. In addition, implementations in C and co-processed Language presented a energy consumption approximately 7 times lower than the traditional computer

Study of Electroencephalographic Signal Processing and Classification Techniques towards the use of Brain-Computer Interfaces in Virtual Reality Applications

A Brain-Computer Interface (BCI) is a communication system which enables its users to send commands to a computer by using brain activity only, this brain activity being measured, generally by ElectroEncephaloGraphy (EEG), and processed by the system. In the first part of this thesis, dedicated to EEG signal processing and classification techniques, we aimed at designing interpretable and more efficient BCI. To this end, we first proposed FuRIA, a feature extraction algorithm based on inverse solutions. This algorithm can automatically identify relevant brain regions and frequency bands for classifying mental states. We also proposed and studied the use of Fuzzy Inference Systems (FIS) for classification. Our evaluations showed that FuRIA and FIS could reach state-of-the-art results in terms of classification performances. Moreover, we proposed an algorithm that uses both of them in order to design a fully interpretable BCI system. Finally, we proposed to consider self-paced BCI design as a pattern rejection problem. Our study introduced novel techniques and identified the most appropriate classifiers and rejection techniques for self-paced BCI design. In the second part of this thesis, we focused on designing virtual reality (VR) applications controlled by a BCI. First, we studied the performances and preferences of participants who interacted with an entertaining VR application, thanks to a self-paced BCI. Our results stressed the need to use subject-specific BCI as well as the importance of the visual feedback. Then, we developed a VR application which enables a user to explore a virtual museum by using thoughts only. In order to do so, we designed a self-paced BCI and proposed an interaction technique which enables the user to send high-level commands. Our first evaluation suggested that a user could explore the museum faster with this interaction technique than with current techniques.Une Interface Cerveau-Ordinateur (ICO) est un système de communication qui permet à ses utilisateurs d'envoyer des commandes à un ordinateur via leur activité cérébrale, cette activité étant mesurée, généralement par ÉlectroEncéphaloGraphie (EEG), et traitée par le système. Dans la première partie de cette thèse, dédiée au traitement et à la classification des signaux EEG, nous avons cherché à concevoir des ICOs interprétables et plus efficaces. Pour ce faire, nous avons tout d'abord proposé FuRIA, un algorithme d'extraction de caractéris- tiques utilisant les solutions inverses. Nous avons également proposé et étudié l'utilisation des Systèmes d'Inférences Flous (SIF) pour la classification. Nos évaluations ont montré que FuRIA et les SIF pouvaient obtenir de très bonnes performances de classification. De plus, nous avons proposé une méthode utilisant ces deux algorithmes afin de concevoir une ICO complétement interprétable. Enfin, nous avons proposé de considérer la conception d'ICOs asynchrones comme un problème de rejet de motifs. Notre étude a introduit de nouvelles techniques et a permis d'identifier les classifieurs et les techniques de rejet les plus appropriés pour ce problème. Dans la deuxième partie de cette thèse, nous avons cherché à concevoir des applications de Réalité Virtuelle (RV) controlées par une ICO. Nous avons tout d'abord étudié les performances et les préférences de participants qui interagissaient avec une application ludique de RV à l'aide d'une ICO asynchrone. Nos résultats ont mis en évidence le besoin d'utiliser des ICO adaptées à l'utilisateur ainsi que l'importance du retour visuel. Enfin, nous avons développé une application de RV permettant à un utilisateur d'explorer un musée virtuel par la pensée. Dans ce but, nous avons conçu une ICO asynchrone et proposé une nouvelle technique d'interaction permettant à l'utilisateur d'envoyer des commandes de haut niveau. Une première évaluation semble montrer que l'utilisateur peut explorer le musée plus rapidement avec cette technique qu'avec les techniques actuelles

Study of electroencephalographic signal processing and classification techniques towards the use of brain-computer interfaces in virtual reality applications

Une Interface Cerveau-Ordinateur (ICO) est un système permettant d envoyer des commandes à un ordinateur via l activité cérébrale, généralement mesurée par ElectroEncéphaloGraphie (EEG). Dans la première partie de cette thèse, nous avons proposé l algorithme FuRIA s appuyant sur les solutions inverses, ainsi que l utilisation de systèmes d inférence flous pour traiter et classifier les signaux EEG dans les ICO. Nos résultats suggèrent que ces méthodes permettent d obtenir de très bonnes performances ainsi que de concevoir une ICO complètement interprétable. Nous avons également proposé des méthodes pour améliorer les performances des ICO asynchrones. Dans la deuxième partie de cette thèse, nous avons développé et évalué une application ludique de réalité virtuelle contrôlée par une ICO. Les résultats ont notamment mis en avant l importance du retour visuel. Enfin, nous avons proposé une application permettant à un utilisateur de visiter un musée virtuel par la pensée de manière efficace.A Brain-Computer Interface is a system which enables a user to send commands to a computer using only brain activity, this activity being generally measured by ElectroEncephaloGraphy (EEG). In the first part of this thesis, we have proposed the FuRIA algorithm which is based on inverse solutions, as well as the use of fuzzy inference systems to process and classify EEG signals. Our results suggest that these methods can lead to very good performances and can be used to design an interpretable BCI. We have also proposed methods to increase the performances of current self-paced BCI. In the second part of this thesis, we have developed and evaluated an entertaining virtual reality application controlled by a BCI. Our results have notably stressed the importance of the visual feedback. Finally, we have proposed an application that enables a user to explore, in an efficient manner, a virtual museum by thoughts.RENNES-INSA (352382210) / SudocRENNES-INRIA Rennes Irisa (352382340) / SudocSudocFranceF

A brain-computer interface for navigation in virtual reality

L'interface cerveau-ordinateur (ICO) décode les signaux électriques du cerveau requise par l’électroencéphalographie et transforme ces signaux en commande pour contrôler un appareil ou un logiciel. Un nombre limité de tâches mentales ont été détectés et classifier par différents groupes de recherche. D’autres types de contrôle, par exemple l’exécution d'un mouvement du pied, réel ou imaginaire, peut modifier les ondes cérébrales du cortex moteur. Nous avons utilisé un ICO pour déterminer si nous pouvions faire une classification entre la navigation de type marche avant et arrière, en temps réel et en temps différé, en utilisant différentes méthodes. Dix personnes en bonne santé ont participé à l’expérience sur les ICO dans un tunnel virtuel. L’expérience fut a était divisé en deux séances (48 min chaque). Chaque séance comprenait 320 essais. On a demandé au sujets d’imaginer un déplacement avant ou arrière dans le tunnel virtuel de façon aléatoire d’après une commande écrite sur l'écran. Les essais ont été menés avec feedback. Trois électrodes ont été montées sur le scalp, vis-à-vis du cortex moteur. Durant la 1re séance, la classification des deux taches (navigation avant et arrière) a été réalisée par les méthodes de puissance de bande, de représentation temporel-fréquence, des modèles autorégressifs et des rapports d’asymétrie du rythme β avec classificateurs d’analyse discriminante linéaire et SVM. Les seuils ont été calculés en temps différé pour former des signaux de contrôle qui ont été utilisés en temps réel durant la 2e séance afin d’initier, par les ondes cérébrales de l'utilisateur, le déplacement du tunnel virtuel dans le sens demandé. Après 96 min d'entrainement, la méthode « online biofeedback » de la puissance de bande a atteint une précision de classification moyenne de 76 %, et la classification en temps différé avec les rapports d’asymétrie et puissance de bande, a atteint une précision de classification d’environ 80 %.A Brain-Computer Interface (BCI) decodes the brain signals representing a desire to do something, and transforms those signals into a control command. However, only a limited number of mental tasks have been previously detected and classified. Performing a real or imaginary navigation movement can similarly change the brainwaves over the motor cortex. We used an ERS-BCI to see if we can classify between movements in forward and backward direction offline and then online using different methods. Ten healthy people participated in BCI experiments comprised two-sessions (48 min each) in a virtual environment tunnel. Each session consisted of 320 trials where subjects were asked to imagine themselves moving in the tunnel in a forward or backward motion after a randomly presented (forward versus backward) command on the screen. Three EEG electrodes were mounted bilaterally on the scalp over the motor cortex. Trials were conducted with feedback. In session 1, Band Power method, Time-frequency representation, Autoregressive models and asymmetry ratio were used in the β rhythm range with a Linear-Discriminant-analysis classifier and a Support Vector Machine classifier to discriminate between the two mental tasks. Thresholds for both tasks were computed offline and then used to form control signals that were used online in session 2 to trigger the virtual tunnel to move in the direction requested by the user's brain signals. After 96 min of training, the online band-power biofeedback training achieved an average classification precision of 76 %, whereas the offline classification with asymmetrical ratio and band-power achieved an average classification precision of 80%

Critical Infrastructure Automated Immuno-Response System (CIAIRS)

Critical Infrastructures play a central role in the world around us and are the backbone of everyday life. Their service provision has become more widespread, to the point where it is now practically ubiquitous in many societies. Critical Infrastructure assets contribute to the economy and society as a whole. Their impact on the security, economy and health sector are extremely vital. Critical Infrastructures now possess levels of automation that require the integration of, often, mutually incompatible technologies. Their increasing complexity has led to the creation of direct and indirect interdependent connections amongst the infrastructure groupings. In addition, the data generated is vast as the intricate level of interdependency between infrastructures has grown. Since Critical Infrastructures are the backbone of everyday life, their protection from cyber-threats is an increasingly pressing issue for governments and private industries. Any failures, caused by cyber-attacks, have the ability to spread through interconnected systems and are a challenge to detect; especially as the Internet is now heavily reliant on Critical Infrastructures. This has led to different security threats facing interconnected security systems. Understanding the complexity of Critical Infrastructure interdependencies, how to take advantage of it in order to minimize the cascading problem, enables the prediction of potential problems before they happen. Therefore, this work firstly discusses the interdependency challenges facing Critical Infrastructures; and how it can be used to create a support network against cyber-attacks. In much, the same way as the human immune system is able to respond to intrusion. Next, the development of a distributed support system is presented. The system employs behaviour analysis techniques to support interconnected infrastructures and distribute security advice throughout a distributed system of systems. The approach put forward is tested through a statistical analysis methodology, in order to investigate the cascading failure effect whilst taking into account the independent variables. Moreover, our proposed system is able to detect cyber-attacks and share the knowledge with interconnected partners to create an immune system network. The development of the ‘Critical Infrastructure Auto-Immune Response System’ (CIAIRS) is presented with a detailed discussion on the main segments that comprise the framework and illustrates the functioning of the system. A semi-structured interview helped to demonstrate our approach by using a realistic simulation to construct data and evaluate the system output

Diseño de un sistema inteligente que permita la detección del estado etílico del conductor de una motocicleta, mediante el procesamiento y registro de señales neurológicas (EEG)

Diseñar un sistema inteligente que permita la detección del estado etílico del conductor de una motocicleta, mediante el procesamiento y registro de señales neurológicas (EEG) para la prevención de accidentes tránsito en el EcuadorLa presente investigación tiene como objetivo principal desarrollar un sistema inteligente que permita la detección del estado de alcoholemia para la prevención de accidentes de tránsito en motocicletas. El desarrollo del sistema tiene un enfoque en la utilización de dispositivos tecnológicos y herramientas de Deep learning. El marco referencial dentro del presente estudio con relación a accidentes de tránsito de motocicletas en el Ecuador, es comprendido por el periodo 2008-2022. El desarrollo del sistema fue realizado bajo el modelo iterativo que consiste en las siguientes etapas: diseño del sistema, recolección de datos, procesamiento de datos, pruebas de funcionamiento e implementación del sistema. El enfoque en la recolección de señales encefalográficas se basa en el planteamiento de escenarios en donde a los sujetos puestos a prueba se les suministra determinadas cantidades de alcohol para determinar sus afectaciones en el sistema nervioso, y así se establece la base de datos para el entrenamiento del algoritmo de Deep Learning para la clasificación del estado de alcoholemia. Los resultados obtenidos sugieren que desde cantidades pequeñas consumidas de alcohol provocan anomalías en nuestro sistema nervioso. Las señales que están dentro de la banda Gamma, son las más susceptibles a la inhibición de las funciones cerebrales provocadas por la ingesta de alcohol. Concluyendo que los valores de atención y tiempos de reacción se reducen a medida que se consume mayor cantidad de alcohol.Ingenierí