Substation inspection is not only one of the most important means of ensuring safe and stable operation of equipment, but also a significant way of timely detecting the dominant and recessive defect of the equipment, and preventing the accidents. This paper presents the virtual video and real-time data demonstration used for smart substation inspection based on the latest wearable services technology (such as Google glass).Google Glass is hands-free and uses augmented reality and voice activation to project useful data into our field of vision.A key use-case of google glass could be to allow a field crew out in the substation to be able to identify all relevant data for the network at the crew's current location. From this interface, they could navigate through all the data for a transformer, such as voltage, current, temperature etc. and identify its location in the GIS and view a single-line diagram, query into its asset history, maintenance history, manufacturer information and catalogue etc.

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The presence of relatively high-level background noise in a telecommunication channel may lower the perceived voice quality of speech signals as well as degrade in-band signaling. The challenge is to reduce the noise to a satisfactory level while minimizing the use of computational resources.This paper describes an ef cient noise reduction algorithm and its implementation on a high-performance Digital Signal Processor (DSP) based on the Freescale StarCore TM SC3400 core.The NR implementation methodology takes advantage of the StarCore TM architecture and Code Warrior TM development tools to reduce engineering efforts. After performing code optimization by exploring a mix of highlevel and machine-level languages, the NR computational cost is reduced to approximately 0.6 Millions of Cycles Per Second for the narrow-band applications (i.e., G.711 with 8kHz sampling rate).The algorithm has been evaluated using different approaches, including subjective evaluation by expert listeners. An overall noise reduction of 10-12dB (for standard setting of 13dB noise reduction threshold) has been achieved for most natural-speech signals polluted with stationary noise. The noise reduction component has also been evaluated using wideband signals (16kHz sampling rate). The machine cycle count increased to 1 MCSP (approximately) while the overall noise reduction of 9-12dB was achieved without observing adverse side effects related to voice quality.