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An electronic health (e-health) system, such as a medical cyber-physical system, offers a number of benefits (e.g. inform medical diagnosis). There are, however, a number of considerations in the implementation of the medical cyber-physical system, such as the integrity of medical / healthcare data (e.g. manipulated data can result in misdiagnosis). A number of digital signature schemes have been proposed in recent years to mitigate some of these challenges. However, the security of existing signatures is mostly based on conventional difficult mathematical problems, which are known to be insecure against quantum attacks. In this paper, we propose a certificateless signature scheme, based on NTRU lattice. The latter is based on the difficulty of small integer solutions on the NTRU lattice, and is known to be quantum attack resilience. Security analysis and performance evaluations demonstrate that our proposed scheme achieves significantly reduced communication and computation costs in comparison to two other competing quantum resilience schemes, while being quantum attack resilience. [ABSTRACT FROM AUTHOR]

Due to lack of a central bureaucrat in mobile ad hoc networks, the security of the network becomes serious issue. During malicious attacks, according to the motivation of intruder the severity of the threat varies. It may lead to loss of data, energy or throughput. This paper proposes a lightweight Intruder Node Detection and Isolation Action mechanism (INDIA) using feature extraction, feature optimization and classification techniques. The indirect and direct trust features are extracted from each node and the total trust feature is computed by combining them. The trust features are extracted from each node of MANET and these features are optimized using Particle Swarm Optimization (PSO) algorithm as feature optimization technique. These optimized feature sets are then classified using Neural Networks (NN) classifier which identifies the intruder node. The performance of the proposed methodology is studied in terms of various parameters such as success rate in packet delivery, delay in communication and the amount of energy consumption for identifying and isolating the intruder. [ABSTRACT FROM AUTHOR]

Surgical navigation systems can help doctors maximize the accuracy of surgeries, minimize operation durations, avoid mistakes, and improve the survival chances of patients. The tracking of device is an important component in surgical navigation systems. However, commercial surgical tracking devices are expensive, thus hindering the development of surgical navigation systems, particularly in developing countries. Therefore, an accurate and low-cost near-infrared optical tracking system is presented in this study for the real-time tracking of surgical tools and for measuring and displaying the positions of these tools relative to lesions and other targets inside a patient's body. A relative algorithm for the registration of surgical tools is also proposed in this paper to yield easy, safe, and precise tracking. Experiments are conducted to test the performance of the system. Results show that the mean square errors of the distances between the light-emitting points on the surgical tools are less than 0.3 mm, with the mean square error of distance between the tip and light-emitting points is less than 0.025 mm and that between two adjacent corner points is 0.2714 mm. [ABSTRACT FROM AUTHOR]

New generation of healthcare is represented by wearable health monitoring systems, which provide real-time monitoring of patient's physiological parameters. It is expected that continuous ambulatory monitoring of vital signals will improve treatment of patients and enable proactive personal health management. In this paper, we present the implementation of a multimodal real-time system for epilepsy management. The proposed methodology is based on a data streaming architecture and efficient management of a big flow of physiological parameters. The performance of this architecture is examined for varying spatial resolution of the recorded data. [ABSTRACT FROM AUTHOR]

A Brain Computer Interface (BCI) allows communication for impaired people unable to express their intention with common channels. Electroencephalography (EEG) represents an effective tool to allow the implementation of a BCI. The present paper describes a modular framework for the implementation of the graphic interface for binary BCIs based on the selection of symbols in a table. The proposed system is also designed to reduce the time required for writing text. This is made by including a motivational tool, necessary to improve the quality of the collected signals, and by containing a predictive module based on the frequency of occurrence of letters in a language, and of words in a dictionary. The proposed framework is described in a top-down approach through its modules: signal acquisition, analysis, classification, communication, visualization, and predictive engine. The framework, being modular, can be easily modified to personalize the graphic interface to the needs of the subject who has to use the BCI and it can be integrated with different classification strategies, communication paradigms, and dictionaries/languages. The implementation of a scenario and some experimental results on healthy subjects are also reported and discussed: the modules of the proposed scenario can be used as a starting point for further developments, and application on severely disabled people under the guide of specialized personnel. [ABSTRACT FROM AUTHOR]

With the advances in wearable computing and various wireless technologies, there is an increasing trend to outsource body signals from wireless body area network (WBAN) to outside world including cyber space, healthcare big data clouds, etc. Since the environmental and physiological data collected by multimodal sensors have different importance, the provisioning of quality of service (QoS) for the sensory data in WBAN is a critical issue. This paper proposes multiple level-based QoS design at WBAN media access control layer in terms of user level, data level and time level. In the proposed QoS provisioning scheme, different users have different priorities, various sensory data collected by different sensor nodes have different importance, while data priority for the same sensor node varies over time. The experimental results show that the proposed multi-level based QoS provisioning solution in WBAN yields better performance for meeting QoS requirements of personalized healthcare applications while achieving energy saving. [ABSTRACT FROM AUTHOR]

Medical technology makes an inevitable trend for the elderly population, therefore the intelligent home care is an important direction for science and technology development, in particular, elderly in-home safety management issues become more and more important. In this research, a low of operation algorithm and using the triangular pattern rule are proposed, then can quickly detect fall-down movements of humanoid by the installation of a robot with camera vision at home that will be able to judge the fall-down movements of in-home elderly people in real time. In this paper, it will present a preliminary design and experimental results of fall-down movements from body posture that utilizes image pre-processing and three triangular-mass-central points to extract the characteristics. The result shows that the proposed method would adopt some characteristic value and the accuracy can reach up to 90 % for a single character posture. Furthermore the accuracy can be up to 100 % when a continuous-time sampling criterion and support vector machine (SVM) classifier are used. [ABSTRACT FROM AUTHOR]

This paper presents Google speech API based aid for deaf and blind people. The live streaming speech on the microphone is sent to Google API server which converts the speech signal into text and displaying onto a LCD screen and amplifies the speech via speaker. The aid will use Request procedure protocol to send the encoded Mp3 audio to Google API server where the speech signal is converted into suitable text and sent back to the Raspberry pi using repeated request protocol. This aid is designed to address issue with mild deafness and blind person. This will enable the deaf and blind persons to work effectively at home, office and any public places with ease. The aid works at low latency at good internet connectivity. [ABSTRACT FROM AUTHOR]

Noise is an important factor that degrades the quality of medical images. Impulse noise is a common noise caused by malfunctioning of sensor elements or errors in the transmission of images. In medical images due to presence of white foreground and black background, many pixels have intensities similar to impulse noise and hence the distinction between noisy and regular pixels is difficult. Therefore, it is important to design a method to accurately remove this type of noise. In addition to the accuracy, the complexity of the method is very important in terms of hardware implementation. In this paper a low complexity de-noising method is proposed that distinguishes between noisy and non-noisy pixels and removes the noise by local analysis of the image blocks. All steps are designed to have low hardware complexity. Simulation results show that in the case of magnetic resonance images, the proposed method removes impulse noise with an acceptable accuracy. [ABSTRACT FROM AUTHOR]

Abstract: This paper will present the application potential of the cognitive approach to data interpretation, with special reference to medical areas. The possibilities of using the meaning approach to data description and analysis will be proposed for data analysis tasks in Cloud Computing. The methods of cognitive data management in Cloud Computing are aimed to support the processes of protecting data against unauthorised takeover and they serve to enhance the data management processes. The accomplishment of the proposed tasks will be the definition of algorithms for the execution of meaning data interpretation processes in safe Cloud Computing.Highlights: • We proposed a cognitive methods for data description.• Proposed a techniques for secure data in Cloud Computing.• Application of cognitive approaches for medicine was described. [ABSTRACT FROM AUTHOR]

Blood pressure measurement based on oscillometry is one of the most popular techniques to check a health condition of individual subjects. This paper proposes a support vector using fusion estimator with a bootstrap technique for oscillometric blood pressure (BP) estimation. However, some inherent problems exist with this approach. First, it is not simple to identify the best support vector regression (SVR) estimator, and worthy information might be omitted when selecting one SVR estimator and discarding others. Additionally, our input feature data, acquired from only five BP measurements per subject, represent a very small sample size. This constitutes a critical limitation when utilizing the SVR technique and can cause overfitting or underfitting, depending on the structure of the algorithm. To overcome these challenges, a fusion with an asymptotic approach (based on combining the bootstrap with the SVR technique) is utilized to generate the pseudo features needed to predict the BP values. This ensemble estimator using the SVR technique can learn to effectively mimic the non-linear relations between the input data acquired from the oscillometry and the nurse’s BPs. [ABSTRACT FROM AUTHOR]