Signal Processing Reconstruction Research Articles

WIDE-BAND SECURE COMPRESSED SPECTRUM SENSING FOR COGNITIVE RADIO SYSTEMS

Cooperative wide-band spectrum sensing has been considered to enable cognitive radio operation of wireless regional area networks (WRAN) in the UHF and VHF TV broadcasting bands. In this paper, cooperative compressed spectrum sensing is considered to enable fast sensing of the wide-band spectrum. The speed and accuracy of spectrum sensing are improved by further optimization of the compressed sensing receiver, which is done blindly without any prior knowledge of the sensed signal. Enhanced compressed spectrum sensing algorithms are proposed for the cases of individual spectrum sensing and cooperative spectrum sensing. The cooperative signal reconstruction process is modifled to optimally combine the received measurements at the fusion center. A low complexity authentication mechanism, which is inherent to cooperative compressed spectrum sensing, is proposed to make the cognitive radio system immune to adversary attacks.

Compressed Sensing of Complex Sinusoids: An Approach Based on Dictionary Refinement

In the existing compressed sensing (CS) theory, the accurate reconstruction of an unknown signal lies in the awareness of its sparsifying dictionary. For the signal represented by a finite sum of complex sinusoids, however, it is impractical to set a fixed sparsifying Fourier dictionary prior to signal reconstruction due to our ignorance of the signal's component frequencies. To address this, we model the sparsifying Fourier dictionary as a parameterized dictionary, with the sampled frequency grid points treated as the underlying parameters. Consequently, the sparsifying dictionary is refinable during the signal reconstruction process, and its refinement can be accomplished via the adjustment of the frequency grid. Furthermore, based on the philosophy of the variational expectation-maximization (EM) algorithm, we develop a novel recovery algorithm for CS of complex sinusoids. The algorithm achieves joint sparse representation recovery and sparsifying dictionary refinement by successively executing steps of signal coefficients estimation and dictionary parameters optimization. Simulation results under different conditions demonstrate that compared to the state-of-the-art CS recovery methods, the proposed algorithm achieves much higher signal reconstruction accuracy, and yields superior performance both in suppressing additive noise in measurements and in reconstructing signals with closely-spaced component frequencies.

High Quality Integrated Data Reconstruction for Medical Applications

In this paper, the implementation of a high quality integrated data reconstruction model and algorithm has been proposed, especially for medical applications. Patients’ Information acquired at the sending end and reconstructed at the receiving end by using a technique that would be high quality for the signal reconstruction process. A method is proposed in which the reconstruction of data like ECG, audio and other patients’ vital parameters that are acquired in the time-domain and operated in the frequency-domain. Further the data will be reconstructed in the time-domain from the frequency domain where high quality data is required. In this particular case, high quality ensures the distortion less and noiseless recovered baseband signal. This would usually require the application of Fast Fourier Transform (FFT) and Inverse Fast Fourier Transform (IFFT) return the data to the spatial domain. The simulation is performed using Matlab. The Composite baseband signal has been generated by developing a program as well as by acquiring to the workspace. The feature of the method is that it can achieve high-quality integrated data reconstruction and can be associated easily with spatial domain.