Comparison of file sanitization techniques in usb based on average file entropy valves

Nowadays, the technology has become so advanced that many electronic gadgets are in every household today. The fast growth of technology today gives the ability for digital devices like smartphones and laptops to have a huge size of storage which is letting people to keep many of their infonnation like contact lists, photos, videos and even personal infonnation. When these infonnation are not useful anymore, users will delete them. However, the growth of technology also letting people to recover back data that has been deleted. In this case, users do not realise that their deleted data can be recovered and then used by unauthorized user. The data deleted is invisible but not gone. This is where file sanitization plays it role. File sanitization is the process of deleting the memory of the content and over write it with a different characters. In this research, the methods chosen to sanitize file are Write Zero, Write Zero Randomly and Write Zero Alternately. All of the techniques will overwrite data with zero. The best technique is chosen based on the comparison of average entropy value of the files after they have been overwritten. Write Zero is the only technique that is provided by many software like WipeFile and BitKiller. There is no software that provide Write Zero Randomly technique except for sanitizing disk using dd. As for that, Write Zero Randomly and proposed technique, Write Zero Alternately are developed using C programming language in Dev-C++. In this research, sanitization with Write Zero has the lowest average entropy value for text document (TXT), Microsoft Word (DOCX) and image (JPG) with 100% of data in the files undergone this technique have been zero-filled compared to Write Zero Randomly and Write Zero Alternately. Next, Write Zero Alternately is more efficient in tenns of average entropy by 4.64 bpB to its closest competitor which is Write Zero Randomly with 5.02 bpB. This shows that Write Zero is the best sanitization method. These file sanitization techniques are important to keep the confidentiality against unauthorized user

Neuroimaging of Human Balance Control: A Systematic Review

This review examined 83 articles using neuroimaging modalities to investigate the neural correlates underlying static and dynamic human balance control, with aims to support future mobile neuroimaging research in the balance control domain. Furthermore, this review analyzed the mobility of the neuroimaging hardware and research paradigms as well as the analytical methodology to identify and remove movement artifact in the acquired brain signal. We found that the majority of static balance control tasks utilized mechanical perturbations to invoke feet-in-place responses (27 out of 38 studies), while cognitive dual-task conditions were commonly used to challenge balance in dynamic balance control tasks (20 out of 32 studies). While frequency analysis and event related potential characteristics supported enhanced brain activation during static balance control, that in dynamic balance control studies was supported by spatial and frequency analysis. Twenty-three of the 50 studies utilizing EEG utilized independent component analysis to remove movement artifacts from the acquired brain signals. Lastly, only eight studies used truly mobile neuroimaging hardware systems. This review provides evidence to support an increase in brain activation in balance control tasks, regardless of mechanical, cognitive, or sensory challenges. Furthermore, the current body of literature demonstrates the use of advanced signal processing methodologies to analyze brain activity during movement. However, the static nature of neuroimaging hardware and conventional balance control paradigms prevent full mobility and limit our knowledge of neural mechanisms underlying balance control

Gait rehabilitation monitor

This work presents a simple wearable, non-intrusive affordable mobile framework that allows remote patient monitoring during gait rehabilitation, by doctors and physiotherapists. The system includes a set of 2 Shimmer3 9DoF Inertial Measurement Units (IMUs), Bluetooth compatible from Shimmer, an Android smartphone for collecting and primary processing of data and persistence in a local database. Low computational load algorithms based on Euler angles and accelerometer, gyroscope and magnetometer signals were developed and used for the classification and identification of several gait disturbances. These algorithms include the alignment of IMUs sensors data by means of a common temporal reference as well as heel strike and stride detection algorithms to help segmentation of the remotely collected signals by the System app to identify gait strides and extract relevant features to feed, train and test a classifier to predict gait abnormalities in gait sessions. A set of drivers from Shimmer manufacturer is used to make the connection between the app and the set of IMUs using Bluetooth. The developed app allows users to collect data and train a classification model for identifying abnormal and normal gait types. The system provides a REST API available in a backend server along with Java and Python libraries and a PostgreSQL database. The machine-learning type is Supervised using Extremely Randomized Trees method. Frequency, time and time-frequency domain features were extracted from the collected and processed signals to train the classifier. To test the framework a set of gait abnormalities and normal gait were used to train a model and test the classifier.Este trabalho apresenta uma estrutura móvel acessível, simples e não intrusiva, que permite a monitorização e a assistência remota de pacientes durante a reabilitação da marcha, por médicos e fisioterapeutas que monitorizam a reabilitação da marcha do paciente. O sistema inclui um conjunto de 2 IMUs (Inertial Mesaurement Units) Shimmer3 da marca Shimmer, compatíveís com Bluetooth, um smartphone Android para recolha, e pré-processamento de dados e armazenamento numa base de dados local. Algoritmos de baixa carga computacional baseados em ângulos Euler e sinais de acelerómetros, giroscópios e magnetómetros foram desenvolvidos e utilizados para a classificação e identificação de diversas perturbações da marcha. Estes algoritmos incluem o alinhamento e sincronização dos dados dos sensores IMUs usando uma referência temporal comum, além de algoritmos de detecção de passos e strides para auxiliar a segmentação dos sinais recolhidos remotamente pelaappdestaframeworke identificar os passos da marcha extraindo as características relevantes para treinar e testar um classificador que faça a predição de deficiências na marcha durante as sessões de monitorização. Um conjunto de drivers do fabricante Shimmer é usado para fazer a conexão entre a app e o conjunto de IMUs através de Bluetooth. A app desenvolvida permite aos utilizadores recolher dados e treinar um modelo de classificação para identificar os tipos de marcha normais e patológicos. O sistema fornece uma REST API disponível num servidor backend recorrendo a bibliotecas Java e Python e a uma base de dados PostgreSQL. O tipo de machine-learning é Supervisionado usando Extremely Randomized Trees. Features no domínio do tempo, da frequência e do tempo-frequência foram extraídas dos sinais recolhidos e processados para treinar o classificador. Para testar a estrutura, um conjunto de marchas patológicas e normais foram utilizadas para treinar um modelo e testar o classificador

Characterization of errors and noises in MEMS inertial sensors using Allan variance method

This thesis work has addressed the problems of characterizing and identifying the noises inherent to inertial sensors as gyros and accelerometers, which are embedded in inertial navigation systems, with the purpose of estimating the errors on the obtained position. The analysis of the Allan Variance method (AVAR) to characterize and identify the noises related to these sensors, has been done. The practical implementation of the AVAR method for the noises characterization has been performed over an experimental setup using the IMU 3DM-GX3 -25 data and the Matlab environment. From the AVAR plots it was possible to identify the Angle Random Walk and the Bias Instability in the gyros, and the Velocity Random Walk and Bias Instability in the accelerometers. A denoising process was also performed by using the Discrete Wavelet Transforms and the Median Filter. After the filtering the AVAR plots showed that the ARW was almost removed or attenuated using Wavelets, but not good results were obtained with the Median Filter

Fall Prevention Using Linear and Nonlinear Analyses and Perturbation Training Intervention

abstract: Injuries and death associated with fall incidences pose a significant burden to society, both in terms of human suffering and economic losses. The main aim of this dissertation is to study approaches that can reduce the risk of falls. One major subset of falls is falls due to neurodegenerative disorders such as Parkinson’s disease (PD). Freezing of gait (FOG) is a major cause of falls in this population. Therefore, a new FOG detection method using wavelet transform technique employing optimal sampling window size, update time, and sensor placements for identification of FOG events is created and validated in this dissertation. Another approach to reduce the risk of falls in PD patients is to correctly diagnose PD motor subtypes. PD can be further divided into two subtypes based on clinical features: tremor dominant (TD), and postural instability and gait difficulty (PIGD). PIGD subtype can place PD patients at a higher risk for falls compared to TD patients and, they have worse postural control in comparison to TD patients. Accordingly, correctly diagnosing subtypes can help caregivers to initiate early amenable interventions to reduce the risk of falls in PIGD patients. As such, a method using the standing center-of-pressure time series data has been developed to identify PD motor subtypes in this dissertation. Finally, an intervention method to improve dynamic stability was tested and validated. Unexpected perturbation-based training (PBT) is an intervention method which has shown promising results in regard to improving balance and reducing falls. Although PBT has shown promising results, the efficacy of such interventions is not well understood and evaluated. In other words, there is paucity of data revealing the effects of PBT on improving dynamic stability of walking and flexible gait adaptability. Therefore, the effects of three types of perturbation methods on improving dynamics stability was assessed. Treadmill delivered translational perturbations training improved dynamic stability, and adaptability of locomotor system in resisting perturbations while walking.Dissertation/ThesisDoctoral Dissertation Biomedical Engineering 201