Framework for Simulation of Heterogeneous MpSoC for Design Space Exploration

Due to the ever-growing requirements in high performance data computation, multiprocessor systems have been proposed to solve the bottlenecks in uniprocessor systems. Developing efficient multiprocessor systems requires effective exploration of design choices like application scheduling, mapping, and architecture design. Also, fault tolerance in multiprocessors needs to be addressed. With the advent of nanometer-process technology for chip manufacturing, realization of multiprocessors on SoC (MpSoC) is an active field of research. Developing efficient low power, fault-tolerant task scheduling, and mapping techniques for MpSoCs require optimized algorithms that consider the various scenarios inherent in multiprocessor environments. Therefore there exists a need to develop a simulation framework to explore and evaluate new algorithms on multiprocessor systems. This work proposes a modular framework for the exploration and evaluation of various design algorithms for MpSoC system. This work also proposes new multiprocessor task scheduling and mapping algorithms for MpSoCs. These algorithms are evaluated using the developed simulation framework. The paper also proposes a dynamic fault-tolerant (FT) scheduling and mapping algorithm for robust application processing. The proposed algorithms consider optimizing the power as one of the design constraints. The framework for a heterogeneous multiprocessor simulation was developed using SystemC/C++ language. Various design variations were implemented and evaluated using standard task graphs. Performance evaluation metrics are evaluated and discussed for various design scenarios

A Fast and Simple Algorithm for Computing M Shortest Paths in Stage Graph

We consider the problem of computing m shortest paths between a source node s and a target node t in a stage graph. Polynomial time algorithms known to solve this problem use complicated data structures. This paper proposes a very simple algorithm for computing all m shortest paths in a stage graph efficiently. The proposed algorithm does not use any complicated data structure and can be implemented in a straightforward way by using only array data structure. This problem appears as a sub-problem for planning risk reduced multiple k-legged trajectories for aerial vehicles

A Review on the Usage of Machine Learning Methods in Gait Analysis and Possibility of a Portable Gait Analysis Device

Gait analysis is a valuable tool for evaluating and monitoring an individual\u27s walking pattern, which is used to recognize movement-related irregularities. Lately, machine learning methods have been introduced in the processing of the gait analysis data to help monitor and analyze the data. Given the increased interest in the area, this paper will focus on two parts: one is analyzing and reviewing the latest Machine learning Methods and sensors used, and the second is the possibility of a portable device capable of measuring and processing an individual\u27s gait. The analysis of the Machine learning models and sensors papers illustrated that several algorithms and methods used had shown a possibility in helping to identify and monitor neurodegenerative disease, which is an excellent area for further reserach. Additionally, the second part of the study showed that a portable device capable of measuring and processing an individual\u27s gait is possible and would be capable of data processing onsite. However, that device would have a disadvantage over the conventional gait analysis.https://digitalscholarship.unlv.edu/durep_posters/1030/thumbnail.jp

Implementation of Large Neural Networks Using Decomposition

The article presents methods of dealing with huge data in the domain of neural networks. The decomposition of neural networks is introduced and its efficiency is proved by the authors’ experiments. The examinations of the effectiveness of argument reduction in the above filed, are presented. Authors indicate, that decomposition is capable of reducing the size and the complexity of the learned data, and thus it makes the learning process faster or, while dealing with large data, possible. According to the authors experiments, in some cases, argument reduction, makes the learning process harder

Crochet: Engaging Secondary School Girls in Art for STEAM’s Sake

Recent STEAM programs have made accomplishments in recruiting K-12 girl students to participate in STEAM activities. Educational researchers have called for studies of how STEM programs engage girls. However, little research has embedded STEM education with girl education such as their emotional needs, identity, and self-expression. This study examined how crochet that was embedded in a STEM summer camp impacted their sense of belonging, creativity, well-being, and STEAM learning. For this qualitative study, surveys were conducted with 37 student participants and Discord was used as part of the data sources. Findings indicated that crocheting enhanced students’ sense of belonging, creativity, well-being, as well as STEM learning. This study contributes to the STEM learning program design for girls in secondary schools with two closely related theories: constructivist learning environment theory and sense of belonging theory. This study added new knowledge to the research of crochet in girl education and STEM program design

A Fast and Simple Algorithm for Computing M-shortest Paths in State Graph

We consider the problem of computing m shortest paths between a source node s and a target node t in a stage graph. Polynomial time algorithms known to solve this problem use complicated data structures. This paper proposes a very simple algorithm for computing all m shortest paths in a stage graph efficiently. The proposed algorithm does not use any complicated data structure and can be implemented in a straightforward way by using only array data structure. This problem appears as a sub-problem for planning risk reduced multiple k-legged trajectories for aerial vehicles

Finite-time Sliding Mode and Super-twisting Control of Fighter Aircraft

The development of two nonlinear robust higher-order flight control systems for roll-coupled maneuvers of fighter aircraft with uncertain parameters is discussed in this article. The objective is to independently control the output variables (roll angle, pitch angle and sideslip angle) using aileron, elevator and rudder control surfaces. For a nominal model of aircraft, first a finite time stabilizing (FTS) control law, based on the notion of geometric homogeneity, is designed. Then for robust control in the presence of parameter uncertainties, (i) a discontinuous sliding mode (DSM) control law and (ii) a super-twisting (STW) continuous control law is designed. It is shown that in the composite closed-loop system consisting of either (a) the FTS and DSM control laws or (b) the FTS and STW control systems, the output trajectory tracking error and its first-order derivative converge to the origin in finite time. Digital simulation results for a swept-wing fighter aircraft, including the two composite control systems, are obtained. These results show that each of the designed flight controllers accomplishes precise simultaneous large longitudinal and lateral maneuvers, despite uncertainties in the aerodynamic and inertia parameters, turbulence, and partial loss of control surface effectiveness

On the Lubensky-Nelson model of polymer translocation through nanopores

We revisit the one-dimensional stochastic model of Lubensky and Nelson [Biophys. J 77, 1824 (1999)] for the electrically driven translocation of polynucleotides through alpha-hemolysin pores. We show that the model correctly describes two further important properties of the experimentally observed translocation time distributions, namely their spread (width) and their exponential decay. The resulting overall agreement between theoretical and experimental translocation time distributions is thus very good