Sensory extensions enhance our awareness by transforming variations in stimuli normally undetectable by human senses into perceivable outputs. Similarly, interactive simulations for learning promote an understanding of abstract phenomena. Combining sensory extension devices with interactive simulations gives users the novel opportunity to connect their sensory experiences in the physical world to computer-simulated concepts. We explore this opportunity by designing a suite of wearable sensory extension devices that interface with a uniquely inclusive PhET Simulation, Ratio and Proportion. In this simulation, two hands can be moved on-screen to various values, representing different mathematical ratios. Users explore changing hand heights to find and maintain ratios through visual and auditory feedback. Our sensory extension devices translate force, distance, sound frequency, and magnetic field strength to quantitative values in order to control individual hands in the computer simulation. This paper describes the design of the devices and our analysis of feedback from 23 high-school aged youth who used our designs to interact with the Ratio and Proportion simulation.

In this paper we show that our Machine Learning (ML) approach, CoMLSim (Composable Machine Learning Simulator), can simulate PDEs on highly-resolved grids with higher accuracy and generalization to out-of-distribution source terms and geometries than traditional ML baselines. Our unique approach combines key principles of traditional PDE solvers with local-learning and low-dimensional manifold techniques to iteratively simulate PDEs on large computational domains. The proposed approach is validated on more than 5 steady-state PDEs across different PDE conditions on highly-resolved grids and comparisons are made with the commercial solver, Ansys Fluent as well as 4 other state-of-the-art ML methods. The numerical experiments show that our approach outperforms ML baselines in terms of 1) accuracy across quantitative metrics and 2) generalization to out-of-distribution conditions as well as domain sizes. Additionally, we provide results for a large number of ablations experiments conducted to highlight components of our approach that strongly influence the results. We conclude that our local-learning and iterative-inferencing approach reduces the challenge of generalization that most ML models face.

E-textiles, which embed circuitry into textile fabrics, blend art and creative expression with engineering, making it a popular choice for STEAM classrooms [6, 12]. Currently, e-textile development relies on tools intended for traditional embedded systems, which utilize printed circuit boards and insulated wires. These tools do not translate well to e-textiles, which utilize fabric and uninsulated conductive thread. This mismatch of tools and materials can lead to an overly complicated development process for novices. In particular, rapid prototyping tools for traditional embedded systems are poorly matched for e-textile prototyping. This paper presents the ThreadBoard, a tool that supports rapid prototyping of e-textile circuits. With rapid prototyping, students can test circuit designs and identify circuitry errors prior to their sewn project. We present the design process used to iteratively create the ThreadBoard’s layout, with the goal of improving its usability for e-textile creators.

We propose a new Exokernel-like layer to allow mutually untrusting physically deployed services to efficiently share the resources of a data center. We believe that such a layer offers not only efficiency gains, but may also enable new economic models, new applications, and new security-sensitive uses. A prototype (currently in active use) demonstrates that the proposed layer is viable, and can support a variety of existing provisioning tools and use cases.

Data centers are an indispensable part of today's IT infrastructure. To keep pace with modern computing needs, data centers continue to grow in scale and consume increasing amounts of power. While prior work on data centers has led to significant improvements in their energy-efficiency, detailed measurements from these facilities' operations are not widely available, as data center design is often considered part of a company's competitive advantage. However, such detailed measurements are critical to the research community in motivating and evaluating new energy-efficiency optimizations. In this paper, we present a detailed analysis of a state-of-the-art 15MW green multi-tenant data center that incorporates many of the technological advances used in commercial data centers. We analyze the data center's computing load and its impact on power, water, and carbon usage using standard effectiveness metrics, including PUE, WUE, and CUE. Our results reveal the benefits of optimizations, such as free cooling, and provide insights into how the various effectiveness metrics change with the seasons and increasing capacity usage. More broadly, our PUE, WUE, and CUE analysis validate the green design of this LEED Platinum data center.

Cooperative Positioning (CP) techniques are used to enhance the performance of positioning through sharing position-related data among a number of agents. These agents are usually users with mobility and, possibly, the infrastructure node(s). In CP systems, position-related data are shared among the participating nodes using a communication medium. Inter-node distance is a common parameter considered in CP techniques, especially for those in the environments with limited Global Navigation Satellite System (GNSS) visibility including dense urban areas and indoor environments. Radio ranging based on Received Signal Strength (RSS) is popular among researchers for its simplicity. However, the accuracy of this method is far beyond the requirements of CP systems. Here, we introduce Carrier Frequency Offset (CFO) as a potential observable to improve RSS ranging. Regardless of the content of the data communicated among the agents, RSS and CFO always exist in the communication signal. Therefore, the results of this work can be applied to improve the performance of any other CP method. Here, a hybrid CFO-RSS ranging method is presented to improve the accuracy of RSS ranging. The experimental results show up to 80% accuracy improvement over RSS using the proposed hybrid CFO-RSS technique. Although the examples used here are for outdoor situations, the outcomes are directly applicable indoors or any situation where GNSS signals are not available.

An integrated positioning solution termed ‘collaborative positioning’ employs multiple location sensors with different accuracy on different platforms for sharing of their absolute and relative localizations. Typical application scenarios are dismounted soldiers, swarms of UAV's, team of robots, emergency crews and first responders. The stakeholders of the solution (i.e., mobile sensors, users, fixed stations and external databases) are involved in an iterative algorithm to estimate or improve the accuracy of each node's position based on statistical models. This paper studies the challenges to realize a public and low-cost solution, based on mass users of multiple-sensor platforms. For the investigation field experiments revolved around the concept of collaborative navigation, and partially indoor navigation. For this purpose different sensor platforms have been fitted with similar type of sensors, such as geodetic and low-cost high-sensitivity GNSS receivers, tactical grade IMU's, MEMS-based IMU's, miscellaneous sensors, including magnetometers, barometric pressure and step sensors, as well as image sensors, such as digital cameras and Flash LiDAR, and ultra-wide band (UWB) receivers. The employed platforms in the tests include a train on a building roof, mobile mapping vans, a personal navigator and a foot tracker unit. In terms of the tests, the data from the different platforms are recorded simultaneously. Several field experiments conducted in a week at the University of Nottingham are described and investigated in the paper. The personal navigator and a foot tracker unit moved on the building roof, then trough the building down to where it logged data simultaneously with the vans, all of them moving together and relative to each other. The platforms then logged data simultaneously covering various accelerations, dynamics, etc. over longer trajectories. Promising preliminary results of the field experiments showed that a positioning accuracy on the few meter level can be achieved for the navigation of the different platforms.

Semantic Sensor Web infrastructures use ontology-based models to represent the data that they manage; however, up to now, these ontological models do not allow representing all the characteristics of distributed, heterogeneous, and web-accessible sensor data. This paper describes a core ontological model for Semantic Sensor Web infrastructures that covers these characteristics and that has been built with a focus on reusability. This ontological model is composed of different modules that deal, on the one hand, with infrastructure data and, on the other hand, with data from a specific domain, that is, the coastal flood emergency planning domain. The paper also presents a set of guidelines, followed during the ontological model development, to satisfy a common set of requirements related to modelling domain-specific features of interest and properties. In addition, the paper includes the results obtained after an exhaustive evaluation of the developed ontologies along different aspects (i.e., vocabulary, syntax, structure, semantics, representation, and context).

Automatic differentiation is the primary means of obtaining analytic derivatives from a numerical model given as a computer program. Therefore, it is an essential productivity tool in numerous computational science and engineering domains. Computing gradients with the adjoint (also called reverse) mode via source transformation is a particularly beneficial but also challenging use of automatic differentiation. To date only ad hoc solutions for adjoint differentiation of MPI programs have been available, forcing automatic differentiation tool users to reason about parallel communication dataflow and dependencies and manually develop adjoint communication code. Using the communication graph as a model we characterize the principal problems of adjoining the most frequently used communication idioms. We propose solutions to cover these idioms and consider the consequences for the MPI implementation, the MPI user and MPI-aware program analysis. The MIT general circulation model serves as a use case to illustrate the viability of our approach.

The Open/ADF tool allows the evaluation of derivatives of functions defined by a Fortran program. The derivative evaluation is performed by a Fortran code resulting from the analysis and transformation of the original program that defines the function of interest. Open/ADF has been designed with a particular emphasis on modularity, flexibility, and the use of open source components. While the code transformation follows the basic principles of automatic differentiation, the tool implements new algorithmic approaches at various levels, for example, for basic block preaccumulation and call graph reversal. Unlike most other automatic differentiation tools, Open/ADF uses components provided by the Open/AD framework, which supports a comparatively easy extension of the code transformations in a language-independent fashion. It uses code analysis results implemented in the OpenAnalysis component. The interface to the language-independent transformation engine is an XML-based format, specified through an XML schema. The implemented transformation algorithms allow efficient derivative computations using locally optimized cross-country sequences of vertex, edge, and face elimination steps. Specifically, for the generation of adjoint codes, Open/ADF supports various code reversal schemes with hierarchical checkpointing at the subroutine level. As an example from geophysical fluid dynamics, a nonlinear time-dependent scalable, yet simple, barotropic ocean model is considered. OpenAD/F's reverse mode is applied to compute sensitivities of some of the model's transport properties with respect to gridded fields such as bottom topography as independent (control) variables.

Abstract Typical weather and climate models need a software tool to couple sub‐scale model components. The high‐performance computing requirements and a variety of model interfaces make the development of such a coupling tool very challenging. In this paper, we describe the approach of the Earth System Modeling Framework, in particular its component and coupling mechanism, and present the results of three cross‐organization model interoperability experiments. Copyright © 2006 John Wiley & Sons, Ltd.

Field programmable memory BIST controllers are becoming a necessity to target manufacturing defects in embedded memories. For 65nm and below, random defects are not the only ones affecting the yield of a process. Systematic as well as parametric defects are now the predominant causes of memory failures and have to be addressed. Conventional memory BIST algorithms are usually targeted to catch random defects. In order to catch such systematic and parametric defects, it is necessary to have the flexibility to apply new algorithms to embedded memories after manufacturing. In this paper, a field programmable memory BIST architecture is proposed to support multiple loops within a test step of an algorithm, including nested loops. These controllers, therefore, guarantee supporting complex algorithm necessary to target defects during failure analysis that could help yield ramp up or reduce test escapes. In addition, the proposed architecture is modular in nature and allows optimizing the complexity of the controller along with area and performance

This paper presents a new approach for performing logic BIST diagnosis exclusively using MISR signatures. Unlike conventional logic BIST diagnosis approaches which require either huge test time or complicated logic BIST design and ATE flow, signature based diagnosis does not require dynamically changing MISR operations for each failing device. Our experimental data shows that signature based diagnosis can achieve similar diagnosis resolution with manageable diagnosis run time while eliminating most of the complexity associated with the traditional approach to logic BIST diagnostics

We describe and analyze the performance of a cluster of personal computers dedicated to coupled climate simulations.This climate modeling system performs comparably to state-of-the-art supercomputers and yet is affordable by individual research groups, thus enabling more spontaneous application of high-end numerical models to climate science.The cluster's novelty centers around the Arctic Switch Fabric and the StarT-X network interface, a systemarea interconnect substrate developed at MIT.A significant fraction of the interconnect's hardware performance is made available to our climate model through an application-specific communication library.In addition to reporting the overall application performance of our cluster, we develop an analytical performance model of our application.Based on this model, we define a metric, Potential Floating-Pointing Performance, which we use to quantify the role of high-speed interconnects in determining application performance.Our results show that a high-performance interconnect, in conjunction with a light-weight application-specific library, provides efficient support for our fine-grain parallel application on an otherwise general-purpose commodity system. 1