Implantable Medical Devices; Networking Security Survey

Abstract The industry of implantable medical devices (IMDs) is constantly evolving, which is dictated by the pressing need to comprehensively address new challenges in the healthcare field. Accordingly, IMDs are becoming more and more sophisticated. Not long ago, the range of IMDs' technical capacities was expanded, making it possible to establish Internet connection in case of necessity and/or emergency situation for the patient. At the same time, while the web connectivity of today's implantable devices is rather advanced, the issue of equipping the IMDs with sufficiently strong security system remains unresolved. In fact, IMDs have relatively weak security mechanisms which render them vulnerable to cyber-attacks that compromise the quality of IMDs' functionalities. This study revolves around the security deficiencies inherent to three types of sensor-based medical devices; biosensors, insulin pump systems and implantable cardioverter defibrillators. Manufacturers of these devices should take into consideration that security and effectiveness of the functionality of implants is highly dependent on the design. In this paper, we present a comprehensive study of IMDs' architecture and specifically investigate their vulnerabilities at networking interface

Analysis of stator faults in induction machines using growing curvilinear component analysis

Fault detection of shorted turns in the stator windings of Induction Motors (IMs) is possible in a variety of ways. As current sensors are usually installed together with the IMs for control and protection purposes, using stator current for fault detection has become a common practice nowadays, as it is much cheaper than installing additional sensors. In this study, stator currents from the healthy and faulty IMs are obtained and analysed via MATLAB® software. The current signatures from healthy and faulty IMs are conditioned using the inbuilt DSP module of the dSPACE prior to analysis using AI techniques. This paper presents a Growing Curvilinear Component Analysis (GCCA) neural network which is able to correctly identify anomalies in the IM and follow the evolution of the stator fault using its current signature, making on-line early fault detection possible

Artificial Neural Networks for Motion Emulation in Virtual Environments

Simulation of natural human movement has proven to be a challenging problem, difficult to be solved by more or less traditional bio-inspired strategies. In opposition to several existing solutions, mainly based upon deterministic algorithms, a data-driven approach is presented herewith, which is able to grasp not only the natural essence of human movements, but also their intrinsic variability, the latter being a necessary feature for many ergonomic applications. For these purposes a recurrent Artificial Neural Network with some novel features (recurrent RPROP, state neurons, weighted cost function) has been adopted and combined with an original pre-processing step on experimental data, resulting in a new hybrid approach for data aggregation. Encouraging results on human hand reaching movements are also presented

Innovative mini ultralight radiosondes to track Lagrangian turbulence fluctuations within warm clouds: electronic design

Characterization of cloud properties remains a challenging task for weather forecasting and climate modelling as cloud properties depend on interdependent natural processes at micro and macro scales. Turbulence plays an important role in particle dynamics inside clouds; however, turbulence mechanisms are not yet fully understood partly due to the difficulty of measuring clouds at the smallest scales. To address these knowledge gaps, an experimental method for measuring the influence of fine-scale turbulence in cloud formation in-situ and producing an in-field cloud Lagrangian dataset is being developed by means of innovative ultra-light radioprobes. This paper presents the electronic system design along with the obtained results from laboratory and field experiments regarding these compact (diameter about 30 cm), light-weight (about 20 g), and expendable devices designed to passively float and track small-scale turbulence fluctuations inside warm clouds. The fully customized mini radioprobe board (5 cm x 5 cm) embeds sensors to measure local fluctuations and transmit data to the ground in near real-time. The tests confirm that the newly developed probes perform well providing accurate information about atmospheric variables, referenced in space. The integration of multiple radioprobes allows for a systematic and accurate monitoring of atmospheric turbulence and its impact on cloud formation