• Smart Modularized Advanced Reusable Telemeter (SMART)

      Daniels, R. M.; Sheaffer, D. A.; Sandia National Laboratories (International Foundation for Telemetering, 1992-10)
      The SMART (Smart Modularized Advanced Reusable Telemeter) is an advanced telemetry system. The SMART system enhances the quality of a weapon system by providing an adaptable built-in telemetry capability for the weapon. Existing weapon telemetry systems are centralized, separate components which require many fault-prone interconnections. This system reduces the number of interconnections and provides higher performance than current systems. The modular system uses a high data-rate serial data link that connects remote measurement modules located throughout the unit-under-test. A smart processor is used to analyze and compress data from the various modules prior to transmission, making more effective use of the telemetry bandwidth. The smart processing unit also adapts the measurement units for changing test conditions on-the-fly. The system will allow more complete testing of the weapon system and solve a broader range of problems. The goal of the SMART project is to utilize the most advanced technology to overcome the current design methodologies that have perpetuated shortcomings in present systems. This project is being conceptualized to encompass a broader range of telemetry applications beyond the present weapon systems at Sandia.
    • TRANSIENT REDUCTION ANALYSIS using NEURAL NETWORKS (TRANN)

      Larson, P. T.; Sheaffer, D. A.; Sandia National Laboratories (International Foundation for Telemetering, 1992-10)
      Our telemetry department has an application for a data categorization/compression of a high speed transient signal in a short period of time. Categorization of the signal reveals important system performance and compression is required because of the terminal nature of our telemetry testing. Until recently, the hardware for the system of this type did not exist. A new exploratory device from Intel has the capability to meet these extreme requirements. This integrated circuit is an analog neural network capable of performing 2 billion connections per second. The two main advantages of this chip over traditional hardware are the obvious computation speed of the device and the ability to compute a three layer feed-forward neural network classifier. The initial investigative development work using the Intel chip has been completed. The results from this proof of concept will show data categorization/compression performed on the neural network integrated circuit in real time. We will propose a preliminary design for a transient measurement system employing the Intel integrated circuit.