Embedded Artificial Intelligence for Tactile Sensing

Electronic tactile sensing becomes an active research field whether for prosthetic applications, robotics, virtual reality or post stroke patients rehabilitation. To achieve such sensing, an array of sensors is used to retrieve human-skin like information, which is called Electronic skin (E-skin). Humans through their skins, are able to collect different types of information e.g. pressure, temperature, texture, etc. which are then passed to the nervous system, and finally to the brain in order to extract high level information from these sensory data. In order to make E-skin capable of such task, data acquired from E-skin should be filtered, processed, and then conveyed to the user (or robot). Processing these sensory information, should occur in real-time, taking in consideration the power limitation in such applications, especially prosthetic applications. The power consumption itself is related to different factors, one factor is the complexity of the algorithm e.g. number of FLOPs, and another is the memory consumption. In this thesis, I will focus on the processing of real tactile information, by 1)exploring different algorithms and methods for tactile data classification, 2)data organization and preprocessing of such tactile data and 3)hardware implementation. More precisely the focus will be on deep learning algorithms for tactile data processing mainly CNNs and RNNs, with energy-efficient embedded implementations. The proposed solution has proved less memory, FLOPs, and latency compared to the state of art (including tensorial SVM), applied to real tactile sensors data. Keywords: E-skin, tactile data processing, deep learning, CNN, RNN, LSTM, GRU, embedded, energy-efficient algorithms, edge computing, artificial intelligence

Understanding Economic Cycle Effects on Top Executive Compensation: An Analysis Applied to American NYSE Listed Companies

Rapport de recherche présenté à la Faculté des arts et des sciences en vue de l'obtention du grade de Maîtrise en sciences économiques

The Nature Of Scientific Explanation (NOSE): Using a philosophically guided framework to examine the nature and quality of scientific explanations constructed by freshman college students, science teachers, and practicing scientists

Issues regarding scientific explanation have been of interest to philosophers from Pre-Socratic times. The notion of scientific explanation is of interest not only to philosophers, but also to science educators as is clearly evident in the emphasis given to K-12 students’ construction of explanations in current national science education reform efforts – the Next Generation Science Standards NGSS (NGSS Lead States, 2013). Nonetheless, there is a dearth of research on conceptualizing explanation in science education. Scientific explanation seems to be ill-defined (or left undefined) among researchers, science teachers and, in turn, students (Braaten & Windschitl, 2010, p. 639). Guided by philosophical models of and approaches to explanation, this study proposed a framework – the Nature of Scientific Explanation (NOSE) – for assessing the type, nature and quality of scientific explanations. Furthermore, to establish the validity and usefulness of the NOSE framework, the study aimed to (a) examine college freshman science students’, secondary science teachers’, and practicing scientists’ explanations, (b) elucidate their perceptions of explanations and how they compare to the formal analytical NOSE framework and (c) characterize the nature of the criteria that participant students, teachers, and scientists deploy when assessing the “validity” of explanations. The following research questions guided the study: (1) How do college freshmen science students’, secondary science teachers’ and practicing scientists’ explanations fare when assessed using the NOSE framework? In other words, what is the nature (structural elements) and quality of participants’ scientific explanations when analyzed using the NOSE framework? (2) How do college freshmen science students’, secondary science teachers’ and practicing scientists’ explanations of scientific phenomena compare and contrast when analyzed using the NOSE framework? (3) What criteria do college freshmen science students, secondary science teachers, and practicing scientists use in judging the quality of scientific explanations? How are these criteria consistent among and/or different across the three groups? (4) To what extent are freshmen science students’, secondary science teachers’ and practicing scientists’ views of the quality of scientific explanations aligned with those of NOSE framework? The study was exploratory in nature. In-depth, semi structured interviews served as the main instrument of data collection. In two separate interviews, participants first constructed explanations of everyday scientific phenomena and then provided feedback on the explanations constructed by other participants. Participants comprised three groups from a large, Midwestern University and neighboring communities: freshman college students, secondary science teachers, and practicing scientists. Each group comprised 10 participants (50% male, 50% female). The study was conducted in two phases. First, during semi-structured individual interviews all participants generated explanations of various scientific phenomena. Interview transcripts were used to generate an explanation map for each participant following procedures of the NOSE framework developed in this study. During the second phase of the study, participants in each group assessed and provided feedback on the explanations generated during the first phase by other participants. The assignment of explanations to be examined was randomized and ensured that each participant assessed all four scenarios. This examination took place in the context of a second, semi-structured interview. All interviews were audiotaped and transcribed verbatim for analysis. Data analysis comprised three phases. The first involved (a) the construction of explanation maps from participant transcripts; (b) analysis of maps and corresponding transcripts for emerging participant criteria; (c) using the NOSE framework to generate a profile of participants’ types and quality of explanations articulated during the first interview; (d) the explanation maps for each group of participants (students, science teachers, and scientists) were examined to generate a full descriptive account or profile of these maps. This analysis resulted in three profiles, one each for the group of participants; and (e) finally the profiles were compared and contrasted to make assertions regarding ways in which students, teachers, and scientists’ explanations were similar or different from NOSE framework analysis. The second phase focused on analyzing transcripts generated during the second interview to characterize participants’ perceptions of the nature of explanations, and derive the criteria deployed by members of the three groups to judge the “validity” or “goodness” of explanations. This resulted in individual profiles as to perceptions of the nature of explanations and criteria used to judge explanations. Profiles within each group of participants were analyzed for general patterns to generate a common set of criteria that each group used in their assessment, when applicable. These common sets were then compared and contrasted across the three groups. The third phase of data analysis focused on comparing and contrasting the sets of criteria derived from the second phase with those NOSE framework. Analysis in this third phase was more conceptual in nature and focused on how the three groups of participants fared in terms of explanation when their explanations were analyzed using NOSE framework. In general, major findings showed that, when analyzed using NOSE framework, participant scientists did significantly “better” than teachers and students. What is more, most participants across all three groups judged as “best” or “complete” or “good” the explanations made by participant scientists, even though group memberships of the explainers were held anonymous. In addition, scientists had more adequate scientific explanations, from a NOSE perspective, in the sense of providing more relevant and accurate structural elements. Analysis showed that participant explanation maps demonstrated similarities and differences across the three groups. Mainly, scientists’ explanations included more pieces of knowledge and lawlike statements, which were relevant and accurate and/or based on prior content knowledge compared to students’ and teachers’ explanations. Participants’ perceptions of explanations differed significantly. Students tended to think of explanation as a “true” answer to a why-question based on observations. However, teachers and scientists tended to perceive explanation as a testable and verifiable tool that provides understanding. More important were the criteria that participants used to assess explanations. Context-dependence and learner-dependence turned out to be two of the most important aspects of explanations considered by participants. In conclusion, the present study highlights the need articulated by many researchers in science education to understand additional aspects specific to scientific explanation. The study highlighted the importance of not only the structural elements that make up a scientific explanation, but also the connectedness of these elements within the context of teaching and learning. The present findings provide an initial framework for judging the validity of students’ and science teachers’ scientific explanations

Sampled Fiber Gratings for High-Resolution and high-Speed Photonic Signal Processing

A novel sampled grating for high-resolution, highspeed signal processing is presented. Simulation based on Sinc2 sampled and rational sampled fiber grating modeling show that a large number of sub-ps time delay steps are attainable, corresponding to a sampling frequency in excess of 1THz. Design method is described for deriving sampling functions that meet specific true-time-delay profile requirements

Polymer chain distribution reorganization for improving the power conversion efficiency of polymer solar cells

We investigate the influence of the post solvent evaporation time delay on the performance of polymer solar cell (PSC) devices employing a bulk heterojunction photoactive polymer layer of regioregular poly(3-hexylthiophene) as electron donor and polymer [6,6]-thienylC61 butyric acid methyl ester as an electron acceptor. Characterization of the fabricated solar cell devices clearly demonstrates balanced transport of electrons and holes in the blend and confirms increased surface roughness and crystallinity of the films when post solvent evaporation time delay is optimised. An optimum device performance is obtained with 72 hours of post solvent evaporation time delay, achieving a power conversion efficiency of 4.1%, which is 0.9% higher than similar devices made without enough time for polymer-chaindistribution reorganization

Optical Magnetometer Employing Adaptive Noise Cancellation for Unshielded Magnetocardiography

This paper demonstrates the concept of an optical magnetometer for magnetocardiography. The magnetometer employs a standard Least-Mean-Squares (LMS) algorithm for heart magnetic field measurement within unshielded environment. Experimental results show that the algorithm can extract a weak heart signal from a much-stronger magnetic noise and detect the P, QRS, and T heart features and completely suppress the common power line noise component at 50 Hz

High sensitivity optically pumped quantum magnetometer

Quantum magnetometers based on optical pumping can achieve sensitivity as high as what SQUID-based devices can attain. In this paper, we discuss the principle of operation and the optimal design of an optically pumped quantum magnetometer. The ultimate intrinsic sensitivity is calculated showing that optimal performance of the magnetometer is attained with an optical pump power of 20 W and an operation temperature of 48°C. Results show that the ultimate intrinsic sensitivity of the quantum magnetometer that can be achieved is 327 fT/Hz1/2 over a bandwidth of 26 Hz and that this sensitivity drops to 130 pT/Hz1/2 in the presence of environmental noise. The quantum magnetometer is shown to be capable of detecting a sinusoidal magnetic field of amplitude as low as 15 pT oscillating at 25 Hz