New Dedicated Measuring Devices for Intra-Palm Propagation Signals

We investigated the use of intra-palm propagation signals as biometrics. The intra-palm propagation signal is an electromagnetic wave that is propagated in the shallow part of the skin of a palm. However, the use of a support vector machine (SVM), which is a powerful machine learning method, does not enable superior verification performance. In this paper, we focus on problems related to measuring devices. The first problem is the contact stability between the electrodes and a palm, the second problem is the variation of the electrode position on a palm, and the third problem is the size of a palm. We develop new measuring devices by considering these problems and perform experiments to evaluate their effects

Performance Evaluation of Intra-Palm Propagation Signals as Biometrics

The use of intra-palm propagation signals as biometrics is proposed. The intra-palm propagation signal is an electromagnetic wave propagated in the palm. In this study, intra-palm propagation signals are measured using dedicated measuring devices and their verification performance based on the Support Vector Machine is evaluated using twenty-one subjects. The equal error rate is approximately 25 %

Effect of Propagation Signal and Path on Verification Performance Using Intra-Body Propagation Signals

Biometrics is the verification or the identification method of users by measuring and analyzing their biometric data, which is only applicable to continuous authentication in a system. In particular, unconsciously presentable biometric modalities are also applicable to an authentication system. As such a biometrics, to use intra-body propagation signals that propagate on a body surface as electromagnetic waves have been proposed. In conventional approaches, verification performance on palms has been evaluated by a white signal as a propagation signal. In this paper, it is reported that the effects of using a synthesized signal by sinusoidal waves with fixed amplitudes and phases instead of the white signal and propagating this signal on other body parts on verification

Unconscious Biometrics for Continuous User Verification

In user management system, continuous or successive (ondemand) authentication is required to prevent identity theft. In particular, biometrics of which data are unconsciously presented to authentication systems is necessary. In this paper, brain waves and intra-palm propagation signals are introduced as biometrics and their verification performances using actually measured data are presented

Programmable photonics : an opportunity for an accessible large-volume PIC ecosystem

We look at the opportunities presented by the new concepts of generic programmable photonic integrated circuits (PIC) to deploy photonics on a larger scale. Programmable PICs consist of waveguide meshes of tunable couplers and phase shifters that can be reconfigured in software to define diverse functions and arbitrary connectivity between the input and output ports. Off-the-shelf programmable PICs can dramatically shorten the development time and deployment costs of new photonic products, as they bypass the design-fabrication cycle of a custom PIC. These chips, which actually consist of an entire technology stack of photonics, electronics packaging and software, can potentially be manufactured cheaper and in larger volumes than application-specific PICs. We look into the technology requirements of these generic programmable PICs and discuss the economy of scale. Finally, we make a qualitative analysis of the possible application spaces where generic programmable PICs can play an enabling role, especially to companies who do not have an in-depth background in PIC technology

A virtual hand assessment system for efficient outcome measures of hand rehabilitation

Previously held under moratorium from 1st December 2016 until 1st December 2021.Hand rehabilitation is an extremely complex and critical process in the medical rehabilitation field. This is mainly due to the high articulation of the hand functionality. Recent research has focused on employing new technologies, such as robotics and system control, in order to improve the precision and efficiency of the standard clinical methods used in hand rehabilitation. However, the designs of these devices were either oriented toward a particular hand injury or heavily dependent on subjective assessment techniques to evaluate the progress. These limitations reduce the efficiency of the hand rehabilitation devices by providing less effective results for restoring the lost functionalities of the dysfunctional hands. In this project, a novel technological solution and efficient hand assessment system is produced that can objectively measure the restoration outcome and, dynamically, evaluate its performance. The proposed system uses a data glove sensorial device to measure the multiple ranges of motion for the hand joints, and a Virtual Reality system to return an illustrative and safe visual assistance environment that can self-adjust with the subject’s performance. The system application implements an original finger performance measurement method for analysing the various hand functionalities. This is achieved by extracting the multiple features of the hand digits’ motions; such as speed, consistency of finger movements and stability during the hold positions. Furthermore, an advanced data glove calibration method was developed and implemented in order to accurately manipulate the virtual hand model and calculate the hand kinematic movements in compliance with the biomechanical structure of the hand. The experimental studies were performed on a controlled group of 10 healthy subjects (25 to 42 years age). The results showed intra-subject reliability between the trials (average of crosscorrelation ρ = 0.7), inter-subject repeatability across the subject’s performance (p > 0.01 for the session with real objects and with few departures in some of the virtual reality sessions). In addition, the finger performance values were found to be very efficient in detecting the multiple elements of the fingers’ performance including the load effect on the forearm. Moreover, the electromyography measurements, in the virtual reality sessions, showed high sensitivity in detecting the tremor effect (the mean power frequency difference on the right Vextensor digitorum muscle is 176 Hz). Also, the finger performance values for the virtual reality sessions have the same average distance as the real life sessions (RSQ =0.07). The system, besides offering an efficient and quantitative evaluation of hand performance, it was proven compatible with different hand rehabilitation techniques where it can outline the primarily affected parts in the hand dysfunction. It also can be easily adjusted to comply with the subject’s specifications and clinical hand assessment procedures to autonomously detect the classification task events and analyse them with high reliability. The developed system is also adaptable with different disciplines’ involvements, other than the hand rehabilitation, such as ergonomic studies, hand robot control, brain-computer interface and various fields involving hand control.Hand rehabilitation is an extremely complex and critical process in the medical rehabilitation field. This is mainly due to the high articulation of the hand functionality. Recent research has focused on employing new technologies, such as robotics and system control, in order to improve the precision and efficiency of the standard clinical methods used in hand rehabilitation. However, the designs of these devices were either oriented toward a particular hand injury or heavily dependent on subjective assessment techniques to evaluate the progress. These limitations reduce the efficiency of the hand rehabilitation devices by providing less effective results for restoring the lost functionalities of the dysfunctional hands. In this project, a novel technological solution and efficient hand assessment system is produced that can objectively measure the restoration outcome and, dynamically, evaluate its performance. The proposed system uses a data glove sensorial device to measure the multiple ranges of motion for the hand joints, and a Virtual Reality system to return an illustrative and safe visual assistance environment that can self-adjust with the subject’s performance. The system application implements an original finger performance measurement method for analysing the various hand functionalities. This is achieved by extracting the multiple features of the hand digits’ motions; such as speed, consistency of finger movements and stability during the hold positions. Furthermore, an advanced data glove calibration method was developed and implemented in order to accurately manipulate the virtual hand model and calculate the hand kinematic movements in compliance with the biomechanical structure of the hand. The experimental studies were performed on a controlled group of 10 healthy subjects (25 to 42 years age). The results showed intra-subject reliability between the trials (average of crosscorrelation ρ = 0.7), inter-subject repeatability across the subject’s performance (p > 0.01 for the session with real objects and with few departures in some of the virtual reality sessions). In addition, the finger performance values were found to be very efficient in detecting the multiple elements of the fingers’ performance including the load effect on the forearm. Moreover, the electromyography measurements, in the virtual reality sessions, showed high sensitivity in detecting the tremor effect (the mean power frequency difference on the right Vextensor digitorum muscle is 176 Hz). Also, the finger performance values for the virtual reality sessions have the same average distance as the real life sessions (RSQ =0.07). The system, besides offering an efficient and quantitative evaluation of hand performance, it was proven compatible with different hand rehabilitation techniques where it can outline the primarily affected parts in the hand dysfunction. It also can be easily adjusted to comply with the subject’s specifications and clinical hand assessment procedures to autonomously detect the classification task events and analyse them with high reliability. The developed system is also adaptable with different disciplines’ involvements, other than the hand rehabilitation, such as ergonomic studies, hand robot control, brain-computer interface and various fields involving hand control

Increasing the robustness of active upper limb prostheses

This thesis is based on my work done at the Institute for Neurorehabilitation Systems at the University Medical Center Goettingen. My work has been partially founded by German Ministry for Education and Research (BMBF) via the Bernstein Focus Neurotechnology (BFNT) Göttingen under grant number 1GQ0810 The local ethics committee approved all studies involving human subjects, and all subjects signed informed consents prior to their participation in the studies. The entire thesis has been originally written by me. Part of the materials used in this thesis have also been published in journals or conferences, where I am the first or corresponding author. All rights for re-use of previously published material were obtained. Reused figures and tables of IEEE publications are marked with © [Year] IEEE. Hereby I declare that I have written this thesis independently and with no other aids and sources than quoted

Produce documents and media information

Lightning data and information were collected from the United States, Germany, France, Brazil, China, and Australia for the dual purposes of compiling a global lightning data base and producing publications on the Marshall Space Flight Center's lightning program. Research covers the history of lightning, the characteristics of a storm, types of lightningdischarges, observations from airplanes and spacecraft, the future fole of planes and spacecraft in lightning studies, lightning detection networks, and the relationships between lightning and rainfall. Descriptions of the Optical Transient Dectector, the Lightning Imaging Sensor, and the Lightning Mapper Sensor are included