A new power MEMS component with variable capacitance

Autonomous devices such as wireless sensors and sensor networks need a long battery lifetime in a small volume. Incorporating micro-power generators based on ambient energy increases the lifetime of these systems while reducing the volume. This paper describes a new approach to the conversion of mechanical energy, available in vibrations, to electrical energy. The conversion principle is based on charge transportation between two parallel capacitors. An electret is used to polarize the device. A large-signal model was developed, allowing simulations of the behavior of the generator. A small-signal model was then derived in order to quantify the output power as a function of the design parameters. These models show the possibility of generating up to 40 muW with a device of 10 mm 2. A layout was made based on a standard SOI-technology, available in an MPW. With this design a power of 1 muW at 1020 Hz is expected

Imaging markers of disability in aquaporin-4 immunoglobulin G seropositive neuromyelitis optica: a graph theory study

Neuromyelitis optica spectrum disorders lack imaging biomarkers associated with disease course and supporting prognosis. This complex and heterogeneous set of disorders affects many regions of the central nervous system, including the spinal cord and visual pathway. Here, we use graph theory-based multimodal network analysis to investigate hypothesis-free mixed networks and associations between clinical disease with neuroimaging markers in 40 aquaporin-4-immunoglobulin G antibody seropositive patients (age = 48.16 ± 14.3 years, female:male = 36:4) and 31 healthy controls (age = 45.92 ± 13.3 years, female:male = 24:7). Magnetic resonance imaging measures included total brain and deep grey matter volumes, cortical thickness and spinal cord atrophy. Optical coherence tomography measures of the retina and clinical measures comprised of clinical attack types and expanded disability status scale were also utilized. For multimodal network analysis, all measures were introduced as nodes and tested for directed connectivity from clinical attack types and disease duration to systematic imaging and clinical disability measures. Analysis of variance, with group interactions, gave weights and significance for each nodal association (hyperedges). Connectivity matrices from 80% and 95% F-distribution networks were analyzed and revealed the number of combined attack types and disease duration as the most connected nodes, directly affecting changes in several regions of the central nervous system. Subsequent multivariable regression models, including interaction effects with clinical parameters, identified associations between decreased nucleus accumbens (β = −0.85, P = 0.021) and caudate nucleus (β = −0.61, P = 0.011) volumes with higher combined attack type count and longer disease duration, respectively. We also confirmed previously reported associations between spinal cord atrophy with increased number of clinical myelitis attacks. Age was the most important factor associated with normalized brain volume, pallidum volume, cortical thickness and the expanded disability status scale score. The identified imaging biomarker candidates warrant further investigation in larger-scale studies. Graph theory-based multimodal networks allow for connectivity and interaction analysis, where this method may be applied in other complex heterogeneous disease investigations with different outcome measures

Investigation of genetic algorithms in optical network design.

The recent advances in light wave communication technology over the past several years enabled us to share enormous optical bandwidth among users in local, metropolitan and wide-area networks. But with the increase in number of users utilizing the network it leads to congestion of network. Congestion is a major issue while evaluating the performance of a network. The lower the congestion in a network, the less is the cost of the hardware (optical & electronic). The problem we are studying is that of designing an optimum ordering of nodes if we are using a logical de Brujin topology. To determine this, we will use the Genetic Algorithm approach. Our approach involves the use of a new cross over strategy (sub-graph cross over) to solve the problem of designing large networks. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2001 .T33. Source: Masters Abstracts International, Volume: 40-06, page: 1555. Adviser: Subir Bandyopadhyay. Thesis (M.Sc.)--University of Windsor (Canada), 2001

Computer Aided Verification

This open access two-volume set LNCS 13371 and 13372 constitutes the refereed proceedings of the 34rd International Conference on Computer Aided Verification, CAV 2022, which was held in Haifa, Israel, in August 2022. The 40 full papers presented together with 9 tool papers and 2 case studies were carefully reviewed and selected from 209 submissions. The papers were organized in the following topical sections: Part I: Invited papers; formal methods for probabilistic programs; formal methods for neural networks; software Verification and model checking; hyperproperties and security; formal methods for hardware, cyber-physical, and hybrid systems. Part II: Probabilistic techniques; automata and logic; deductive verification and decision procedures; machine learning; synthesis and concurrency. This is an open access book