Implementation of low-density parity-check codes decoder for CCSDS standard

The complexity/performance balanced decoder for low-density parity-check （LDPC） codes is preferred in practical wireless communication systems. A low complexity LDPC decoder for the Consultative Committee for Space Data Systems （CCSDS） standard is achieved in DSP. An ap- proximate decoding algorithm, normalized rain-sum algorithm, is used in the implementation for its low amounts of computation. To reduce the performance loss caused by the approximation, the pa- rameters of the normalized min-sum algorithm are determined by calculating and finding the mini- mum value of thresholds through density evolution. The minimum value which indicates the best per- formance of the decoding algorithm is corresponding with the optimized parameters. In implementa- tion, the memory cost is saved by decomposing the parity-check matrix into submatrices to store and the computation of passing message in decoding is accelerated by using the intrinsic function of DSP. The performance of the decoder with optimized factors is simulated and compared with the ideal BP decoder. The result shows they have about the same performance.

Practical Considerations for Implementing Adaptive Acoustic Noise Cancellation in Commercial Earbuds

Active noise cancellation has become a prominent feature in contemporary in-ear personal audio devices.However,due to constraints related to component arrangement,power consumption,and manufacturing costs,most commercial products utilize fixed-type controller systems as the basis for their active noise control algorithms.These systems offer robust performance and a straightforward structure,which is achievable with cost-effective digital signal processors.Nonetheless,a major drawback of fixed-type controllers is their inability to adapt to changes in acoustic transfer paths,such as variations in earpiece fitting conditions.Therefore,adaptive-type active noise control systems that employ adaptive digital filters are considered as the alternative.To address the increasing system complexity,design concepts and implementation strategies are discussed with respect to actual hardware limitations.To illustrate these considerations,a case study showcasing the implementation of a filtered-x least mean square-based active noise control algorithm is presented.A commercial evaluation board accommodating a low-cost,fixed-point digital signal processor is used to simplify operation and provide programming access.The earbuds are obtained from a commercial product designed for noise cancellation.This study underscores the importance of addressing hardware constraints when implementing adaptive active noise cancellation,providing valuable insights for real-world applications.

Two simple memristive maps with adaptive energy regulation ano digital signal process verification

Mathematical models can produce desired dynamics and statistical properties with the insertion of suitable nonlinear terms,while energy characteristics are crucial for practical application because any hardware realizations of nonlinear systems are relative to energy flow.The involvement of memristive terms relative to memristors enables multistability and initial-dependent property in memristive systems.In this study,two kinds of memristors are used to couple a capacitor or an inductor,along with a nonlinear resistor,to build different neural circuits.The corresponding circuit equations are derived to develop two different types of memristive oscillators,which are further converted into two kinds of memristive maps after linear transformation.The Hamilton energy function for memristive oscillators is obtained by applying the Helmholz theorem or by mapping from the field energy of the memristive circuits.The Hamilton energy functions for both memristive maps are obtained by replacing the gains and discrete variables for the memristive oscillator with the corresponding parameters and variables.The two memristive maps have rich dynamic behaviors including coherence resonance under noisy excitation,and an adaptive growth law for parameters is presented to express the self-adaptive property of the memristive maps.A digital signal process(DSP)platform is used to verify these results.Our scheme will provide a theoretical basis and experimental guidance for oscillator-to-map transformation and discrete map-energy calculation.