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An energy-efficient discrete cosine transform (DCT) is proposed. It can be used in image compression in wireless visual sensor networks. It is a combination of the recently proposed block discrete cosine transform and a pruned approach. Thus, the computational complexity is reduced significantly. The experimental results show that the proposed transform leads to a significant reduction in computation time at the target sensor node. This will consequently result in a saving in energy consumption. The proposed DCT is very suitable for implementation in wireless sensor networks powered by batteries.

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In this paper, a new real-time approach for audio recognition of waterbird species in noisy environments, based on a Texas Instruments DSP, i.e. TMS320C6713 is proposed. For noise estimation in noisy water bird's sound, a tonal region detector (TRD) using a sigmoid function is introduced. This method offers flexibility since the slope and the mean of the sigmoid function can be adapted autonomously for a better trade-off between noise overvaluation and undervaluation. Then, the features Mel Frequency Cepstral Coefficients post processed by Spectral Subtraction (MFCC-SS) were extracted for classification using Support Vector Machine classifier. A development of the Simulink analysis models of classic MFCC and MFCC-SS is described. The audio recognition system is implemented in real time by loading the created models in DSP board, after being converted to target C code using Code Composer Studio. Experimental results demonstrate that the proposed TRD-MFCC-SS feature is highly effective and performs satisfactorily compared to conventional MFCC feature, especially in complex environment.