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Mobile phone can serve as powerful tool for world-wide communication. A system is developed to remotely monitor process through spoken commands using mobile. Mel cepstrum features are extracted from spoken words. Learning Vector Quantization Neural Network is used for recognition of various words used in the command. The accuracy of spoken commands is about 98%. A text message is generated and sent to control system mobile in form of SMS. On receipt of SMS, control system mobile informs AVR micro-controller based card, which performs specified task. The system alerts user in case of occurrence of any abnormal conditions like power failure, loss of control, etc. Other applications where this approach can be extended are also discussed.

In modern short range wireless applications, RF, Bluetooth and UWB communication schemes are recommended with respect to power consumption, low/high data rates and location capability. Though special bandwidth is not allocated to UWB, coexistence of such signal in the available RF environment is a critical issue and hence analysis of UWB signal with respect to interference is very much required in dedicated WPAN networks. In this paper, effect of UWB interference for existing systems such as GPS, FWA, UMTS, DCS1800 is analyzed. Communication path distance is directly proportional to the UWB signal pulse width. To correctly define the UWB signal this functionality is realized by composing UWB signal and results are simulated using MATLAB.

This paper presents the implementation of near numerically intensive algorithms. The internal program optimal Electrocardiogram (ECG) classifier based on Multilayer memory is structured so that a total of eight instructions can be Perceptron Neural Networks (MLP NN). In the present fetched every cycle. With a clock rate of 150MHz, the C6711 investigations the optimized MLP NN based classifier is designed and implemented for detection of normal and abnormal ECG. is capable of ng eight 32-bi instucis every Some dominant unique features of ECG are extracted using or 6.66 ns e bor indes the co71c Digital Signal Processing tools to optimize the MLP NN model. floating-point digital signal processor and a 16-bit codec For this, MLP NN network is used to maximize accuracy under AD535 for input and output. The onboard codec AD535 uses a the constraints of minimum network dimension so that its sigma-delta technology that provides analog-to-digital hardware implementation further requires minimum number of conversion and digital-to-analog conversion. A fixed sampling components to satisfy real time constraints and low power rate of 8kHz is used for the analog-to-digital converter. The consumption. The classification accuracy of MLP NN is found classifier thus designed is implemented on TMS320C6711 very good even after repeating the simulation experiments a processor based Digital Signal Processing (DSP) board [4]-number of times on different data partitions. The MLP NN thus [6].designed has been implemented on the TMS320C6711 processor.

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