This paper presents the design and development of a Silicon Carbide (SiC) technology-based Non-Inverting Buck-Boost (NIBB) DC-DC power conversion unit for harsh space environment application with very high temperature (HT) (>150°C) and high radiation level. This work evaluates the capability of SiC bare dies for high temperature (>150°C) power electronics. The selection of critical passive components of the power converter is done through rigorous characterization of their performance metrics such as capacitance, inductance, leakage current, etc. under influence of increasing operating temperature. In this work, the design, prototype development, operation, and testing of a 100kHz, 100W NIBB DC-DC converter over the 25° - 200° C ambient temperature range is demonstrated. The designed converter provides up to a 3.5A output load and convert the input side battery voltage levels of 28V, 120V, and 160V to a configurable output voltage from 30V to 48V, used as a standard for NASA space missions. The converter power stage, including the power semiconductor devices, inductor, and ceramic input and output filter capacitors, were placed inside a temperature controlled chamber for testing and temperature-variant characterization. The MOSFET gate drive circuit, input power source, and output load were placed external to the environmental chamber. Results at 200°C environment report a peak full load efficiency of 91.3%, which validates the developed converter architecture to be a suitable candidate for high-frequency HT power conversion.

Since electrostatic discharge (ESD) protection devices comprise the “front end,” or interface between an integrated circuit and the system bus, they may be driven into nonlinearity when the system operates in a high-power microwave (HPM) environment. We have experimentally studied HPM effects in CMOS-based ESD devices, and showed that asymmetry in the large-signal responses of NMOS and PMOS ESD protection devices drives spurious bias shifts in the circuit. These shifts have been shown elsewhere to produce serious secondary effects such as increased power consumption, state errors and instability. Using an in-house developed physics-based drift-diffusion simulator, we present some fundamental imbalances in the NMOS and PMOS device behavior and performance.

In this paper, we present a new energy analysis method for evaluating energy consumption of embedded sensor nodes at the application level and the network level. Then we apply the proposed energy analysis method to develop new energy management schemes in order to maximize lifetime for Wireless Sensor Network Building Energy Monitoring Systems (WSNBEMS). At the application level, we develop a new design approach that uses dataflow techniques to model the application-level interfacing behavior between the processor and sensors on an embedded sensor node. At the network level, we analyze the energy consumption of the IEEE 802.15.4 MAC functionality. Based on our techniques for modeling and energy analysis, we have implemented an optimized WSNBEMS for a real building, and validated our energy analysis techniques through measurements on this implementation. The performance of our implementation is also evaluated in terms of monitoring accuracy and energy consumption savings. We have demonstrated that by applying the proposed scheme, system lifetime can be improved significantly without affecting monitoring accuracy.

Wireless sensor network (WSN) applications have been studied extensively in recent years. Such applications involve resource-limited embedded sensor nodes that have small size and low power requirements. Based on the need for extended network lifetimes in WSNs in terms of energy use, the energy efficiency of computation and communication operations in the sensor nodes becomes critical. Digital Signal Processing (DSP) applications typically require intensive data processing operations and as a result are difficult to implement directly in resource-limited WSNs. In this article, we present a novel design methodology for modeling and implementing computationally intensive DSP applications applied to wireless sensor networks. This methodology explores efficient modeling techniques for DSP applications, including data sensing and processing; derives formulations of Energy-Driven Partitioning (EDP) for distributing such applications across wireless sensor networks; and develops efficient heuristic algorithms for finding partitioning results that maximize the network lifetime. To address such an energy-driven partitioning problem, this article provides a new way of aggregating data and reducing communication traffic among nodes based on application analysis. By considering low data token delivery points and the distribution of computation in the application, our approach finds energy-efficient trade-offs between data communication and computation.

Distributed sensor system applications (e.g., wireless sensor networks) have been studied extensively in recent years. Such applications involve resource-limited embedded sensor nodes that communicate with each other through self-organizing protocols. Depending on application requirements, distributed sensor system design may include protocol and prototype implementation. Prototype implementation is especially useful in establishing and maintaining system functionality as the design is customized to satisfy size, energy, and cost constraints. In this paper, we present a streamlined, application-specific approach to incorporating fault tolerance into a TDMA-based distributed sensor system for line-crossing recognition. The objective of this approach is to prevent node failures from translating into failures in the overall system. Our approach is specialized and light-weight so that fault tolerance is achieved without significant degradation in energy efficiency. We also present an asynchronous handshaking approach for providing synchronization between the transceiver and digital processing subsystem in sensor node. This provides a general method for achieving such synchronization with reduced hardware requirements and reduced energy consumption compared to conventional approaches, which rely on generic interface protocols. We demonstrate the capabilities of our approaches to fault tolerance and transceiver-processor integration through experiments involving a complete prototype wireless sensor network test-bed, and a distributed line-crossing recognition application that runs on this test-bed.

Many application-specific wireless sensor network (WSN) systems require small size and low power features due to their limited resources, and their use in distributed, wireless environments. In this paper, we present a light-weight distributed algorithm for line-crossing recognition, together with its analysis, implementation, and experimental evaluation within a prototype wireless sensor network platform. The algorithm is developed in conjunction with a TDMA-based communication protocol such that the proposed system provides for low duty cycle and energy efficient operation. An accurate lifetime model is proposed with consideration of detailed energy usage to analyze and estimate the system lifetime. Our experimental results demonstrate the accuracy of this lifetime model, and its utility in optimizing network implementation. The design and experimental evaluation of our prototype network demonstrates the compactness and functionality of the proposed distributed WSN system for line-crossing recognition.

Wireless sensor network (WSN) applications have been studied extensively in recent years. Such applications involve resource-limited embedded sensor nodes that have small size and low power requirements. Based on the need for extended network lifetimes in WSNs in terms of energy use, the energy efficiency of computation and communication operations in the embedded sensor nodes becomes critical. Digital signal processing (DSP) applications typically require intensive data processing operations. They are difficult to apply directly in resource-limited WSNs because their operational complexity can strongly influence the network lifetime. In this paper, we present a design methodology for modeling and implementing DSP applications applied to wireless sensor networks. This methodology explores efficient modeling techniques for DSP applications, including acoustic sensing and data processing; derives formulations of energy-driven partitioning for distributing such applications across wireless sensor networks; and develops efficient heuristic algorithms for finding partitioning results that maximize the network lifetime. A case study involving a speech recognition system demonstrates the capabilities of our proposed methodology.