Analysis of the mean squared derivative cost function

In this paper, we investigate the mean squared derivative cost functions that arise in various applications such as in motor control, biometrics and optimal transport theory. We provide qualitative properties, explicit analytical formulas and computational algorithms for the cost functions. We also perform numerical simulations to illustrate the analytical results. In addition, as a by-product of our analysis, we obtain an explicit formula for the inverse of a Wronskian matrix that is of independent interest in linear algebra and differential equations theory.Comment: 28 page

Data Driven Techniques for Modeling Coupled Dynamics in Transient Processes

We study the problem of modeling coupled dynamics in transient processes that happen in a network. The problem is considered at two levels. At the node level, the coupling between underlying sub-processes of a node in a network is considered. At the network level, the direct influence among the nodes is considered. After the model is constructed, we develop a network-based approach for change detection in high dimension transient processes. The overall contribution of our work is a more accurate model to describe the underlying transient dynamics either for each individual node or for the whole network and a new statistic for change detection in multi-dimensional time series. Specifically, at the node level, we developed a model to represent the coupled dynamics between the two processes. We provide closed form formulas on the conditions for the existence of periodic trajectory and the stability of solutions. Numerical studies suggest that our model can capture the nonlinear characteristics of empirical data while reducing computation time by about 25% on average, compared to a benchmark modeling approach. In the last two problems, we provide a closed form formula for the bound in the sparse regression formulation, which helps to reduce the effort of trial and error to find an appropriate bound. Compared to other benchmark methods in inferring network structure from time series, our method reduces inference error by up to 5 orders of magnitudes and maintain better sparsity. We also develop a new method to infer dynamic network structure from a single time series. This method is the basis for introducing a new spectral graph statistic for change detection. This statistic can detect changes in simulation scenario with modified area under curve (mAUC) of 0.96. When applying to the problem of detecting seizure from EEG signal, our statistic can capture the physiology of the process while maintaining a detection rate of 40% by itself. Therefore, it can serve as an effective feature to detect change and can be added to the current set of features for detecting seizures from EEG signal

Improving the Structure of a Signal Used for Real-Time Calibrating of the Receiving Channels of Digital Transceiver Modules in Digital Phased Antenna Arrays

Introduction. Modern digital phased array antenna (DPAA) systems incorporate a large number of identical transceiver modules (TMs). These modules require real-time calibration with a high level of accuracy. In a previous work, we proposed a real-time calibration method for all receiver channels, which is based on the use of a calibration signal (CalSig) of the same frequency spectrum as the reflected signal and modulated in phase and amplitude by BPSK and OOK codes, respectively. This method was found to have a number of advantages over conventional approaches. However, the use of the same CalSig sample for all receiving channels increases the noise power gain at the output of a digital beam-forming unit (DBU). To overcome this limitation, we set out to improve the structure of CalSigs by making them pseudo-orthogonal. As a result, the noise power gain at the DBU output can be significantly reduced compared to that obtained in our previous work.Aim. To propose an improved design of a controlled amplitude modulation code OOK generator, which allows creation of pseudo-orthogonal CalSigs. As a result, the noise power gain at the output will increase insignificantly, thus having no negative effect on the quality of digital beam forming, signal processing and calibration.Materials and methods. Theory of system engineering and technology; theory of digital signal processing; system analysis; mathematical modeling.Results. An improved CalSig for calibrating the receiving channels of TMs was obtained. A structural diagram allowing the formation of pseudo-orthogonal CalSigs was synthesized.Conclusions. We proposed a new approach to improving the structure of signals used for real-time calibrating the DPAA receiving channels. A structural diagram of an amplitude-modulated OOK code generator for pseudo-orthogonal CalSigs was developed.Introduction. Modern digital phased array antenna (DPAA) systems incorporate a large number of identical transceiver modules (TMs). These modules require real-time calibration with a high level of accuracy. In a previous work, we proposed a real-time calibration method for all receiver channels, which is based on the use of a calibration signal (CalSig) of the same frequency spectrum as the reflected signal and modulated in phase and amplitude by BPSK and OOK codes, respectively. This method was found to have a number of advantages over conventional approaches. However, the use of the same CalSig sample for all receiving channels increases the noise power gain at the output of a digital beam-forming unit (DBU). To overcome this limitation, we set out to improve the structure of CalSigs by making them pseudo-orthogonal. As a result, the noise power gain at the DBU output can be significantly reduced compared to that obtained in our previous work.Aim. To propose an improved design of a controlled amplitude modulation code OOK generator, which allows creation of pseudo-orthogonal CalSigs. As a result, the noise power gain at the output will increase insignificantly, thus having no negative effect on the quality of digital beam forming, signal processing and calibration.Materials and methods. Theory of system engineering and technology; theory of digital signal processing; system analysis; mathematical modeling.Results. An improved CalSig for calibrating the receiving channels of TMs was obtained. A structural diagram allowing the formation of pseudo-orthogonal CalSigs was synthesized.Conclusions. We proposed a new approach to improving the structure of signals used for real-time calibrating the DPAA receiving channels. A structural diagram of an amplitude-modulated OOK code generator for pseudo-orthogonal CalSigs was developed

Hybrid Time-Power Switching Protocol of Energy Harvesting Bidirectional Relaying Network: Throughput and Ergodic Capacity Analysis

In this paper, we investigate system performance in term of throughput and ergodic capacity of the hybrid time-power switching protocol of energy harvesting bidirectional relaying network. In the first stage, the analytical expression of the system throughput and ergodic capacity of the model system is proposed and derived. In this analysis, both delay-limited and delay-tolerant transmission modes are presented and considered. After that, the effect of various system parameters on the proposed system is investigated and demonstrated by Monte-Carlo simulation. Finally, the results show that the analytical mathematical and simulated results match for all possible parameter values for both schemes

Energy harvesting over Rician fading channel: A performance analysis for half-duplex bidirectional sensor networks under hardware impairments

In this paper, a rigorous analysis of the performance of time-switching energy harvesting strategy that is applied for a half-duplex bidirectional wireless sensor network with intermediate relay over a Rician fading channel is presented to provide the exact-form expressions of the outage probability, achievable throughput and the symbol-error-rate (SER) of the system under the hardware impairment condition. Using the proposed probabilistic models for wireless channels between mobile nodes as well as for the hardware noises, we derive the outage probability of the system, and then the throughput and SER can be obtained as a result. Both exact analysis and asymptotic analysis at high signal-power-to-noise-ratio regime are provided. Monte Carlo simulation is also conducted to verify the analysis. This work confirms the effectiveness of energy harvesting applied in wireless sensor networks over a Rician fading channel, and can provide an insightful understanding about the effect of various parameters on the system performance.Web of Science186art. no. 1781

Clean Energy and Sustainable Development lab activity report, 2014-09-31 to 2015-12-31

The Clean Energy and Sustainable Development laboratory – CleanED – was established in December 2014 with support from USTH and French Embassy in Hanoi. In September 2015, CleanED lab counted five researchers from France and Vietnam, five doctoral fellows and two internationally qualified staff. This international and interdisciplinary research team gets the mission to contribute to the green growth of the energy sector in Vietnam. Its expertise ranges from engineering to public policy on:Natural resources characterization and managementBiomass and waste to energy conversion process technologiesEnergy systems optimization from smart grid to national plan

Improving User Interface and User Experience of MathSpring Intelligent Tutoring System for Students

MathSpring is an intelligent tutoring system that assists students in studying mathematics. Given the complex interactions between students and MathSpring system together with suggestions from students, we believe that its user interface and user experience have room for improvements. To learn the students’ experience and determine our improvement strategy, we quantitatively analyze the students’ suggestions and interview a teacher who uses the system for his class. We then use the results of our analysis and design principles to devise a new design for MathSpring. Lastly, we conduct a user study to evaluate the new design. The results of this study demonstrate that our new design has succeeded at improving the user experience to some extent

Étude du suivi volumétrique et hémodynamique des dissections de l'aorte thoracique : évaluation pronostique

Objectif : Analyser les remaniements morphologiques aortiques afin de comparer l'efficacité du traitement par stent-graft (SG) à celle du traitement médical (TM) chez des patients présentant une dissection aortique de type B et évaluer la morphologie de l'aorte chez les patients atteints une dissection aortique de type A opérée Étudier l'intérêt des mesures volumétriques par rapport aux mesures des diamètres déjà établies. Patients et méthode : Étude rétrospective de 77 patients (TM : 34, SG : 43) suivis pendant 43 mois, et 36 patients dissection type A suivis pendant 38 mois. Mesures des diamètres et volumes du vrai chenal (VC) du faux chenal (FC), de sa composante circulante et thrombosée. Résultats : Efficacité du SG qui a présenté une ré-expansion de 76 % du VC à long terme, contre 27 % pour le TM. La composante thrombosée du FC est estimée à 87 % pour le SG au dernier suivi, contre 60 % pour le TM, permettant une stabilisation plus important du FC par le SG. Les volumes de l'aorte descendante sont augmentés 29% VC et 77% FC. La mesure des volumes s'est avérée plus precise et senssible que celle des diamètres, et peut éviter une sur ou sous estimation d'une variation aortique, lourde de conséquence pour le patient. Conclusion : Notre étude a permis de montrer des remaniements positifs plus importants chez les patients SG, comparativement au TM. Les volumes aortiques descendants et abdominaux ont continué à augmenter (dissection de type A) après chirurgie. La technique de mesures des volumes est un outil rigoureux pouvant par la suite jouer un rôle dans la surveillance des patients et leur évaluation pronostique.Purpose: To analyze type B aortic dissection morphological remodeling and to compare thoracic endovascular aortic repair (TEVAR) efficacy compared to medical treatment (MT) and to assess the morphology of the aorta in patients with aortic dissection type A after surgery. To study volumetric measurements and its benefits face to well known diameter measurements. Patients and methods: Retrospective study of 77 patients (MT: 34, TEVAR: 43) with a 43 months follow up and 36 patients with type A aortic dissection with 38 months follow up. True lumen (TL) and false lumen (FL) diameter and volume measurements, particularly of the FL thrombosed and enhancing parts. Results: TEVAR permitted a 76% TL re-expansion during the whole follow up compared to 27% for MT. The FL thrombosed part was 87% at last CT, and 60% for MT, allowing a better FL stabilization for TEVAR. The volume of aorta descending (aortic dissection type A) increase 29% of TL and 77% of FL. Volumes measurement proved to be a more precise tool than diameters measurement which can lead to risky life threatening over or under evaluation. Conclusion : Volumetric method has demonstrated the efficacy of stent graft treatment compared to medical treatment for type B aortic dissection in term of aortic remodeling. The volumes of descending aorta and abdominal aorta of aortic dissection type A continue increase after surgery. Volume analysis is an accurate and reproducible method and could later lead to prognostic factors. This study is the first step for a hemodynamic study by CFD with CT scanners dynamic

Data Driven Techniques for Modeling Coupled Dynamics in Transient Processes

We study the problem of modeling coupled dynamics in transient processes that happen in a network. The problem is considered at two levels. At the node level, the coupling between underlying sub-processes of a node in a network is considered. At the network level, the direct influence among the nodes is considered. After the model is constructed, we develop a network-based approach for change detection in high dimension transient processes. The overall contribution of our work is a more accurate model to describe the underlying transient dynamics either for each individual node or for the whole network and a new statistic for change detection in multi-dimensional time series. Specifically, at the node level, we developed a model to represent the coupled dynamics between the two processes. We provide closed form formulas on the conditions for the existence of periodic trajectory and the stability of solutions. Numerical studies suggest that our model can capture the nonlinear characteristics of empirical data while reducing computation time by about 25% on average, compared to a benchmark modeling approach. In the last two problems, we provide a closed form formula for the bound in the sparse regression formulation, which helps to reduce the effort of trial and error to find an appropriate bound. Compared to other benchmark methods in inferring network structure from time series, our method reduces inference error by up to 5 orders of magnitudes and maintain better sparsity. We also develop a new method to infer dynamic network structure from a single time series. This method is the basis for introducing a new spectral graph statistic for change detection. This statistic can detect changes in simulation scenario with modified area under curve (mAUC) of 0.96. When applying to the problem of detecting seizure from EEG signal, our statistic can capture the physiology of the process while maintaining a detection rate of 40% by itself. Therefore, it can serve as an effective feature to detect change and can be added to the current set of features for detecting seizures from EEG signal