RESUMEN
Underwater sensor nodes are usually deployed by ships, aircraft, etc., in random drops, and there is current movement in the underwater environment, which results in an uneven distribution of sensor nodes and thus, different energy consumption in each area of the network. In addition, the underwater sensor network also has a "hot zone" problem. To address the uneven energy consumption of the network caused by the above problem, the non-uniform clustering algorithm for energy equalization is put forward. Considering the residual energy, density and coverage redundancy of nodes, this algorithm selects the cluster heads and makes them more reasonably distributed. Additionally, according to the selected cluster heads, the size of each cluster is designed to equalize the energy consumption of the network during multi-hop routing. In this process, the residual energy of cluster heads and the mobility of nodes are considered, and real-time maintenance is performed for each cluster. The simulation results demonstrate that the proposed algorithm is effective in prolonging the network lifetime and balancing the network energy consumption; moreover, the network coverage maintenance is better than that of other algorithms.
Asunto(s)
Redes de Comunicación de Computadores , Tecnología Inalámbrica , Simulación por Computador , Algoritmos , Análisis por ConglomeradosRESUMEN
The masking release (MR; i.e., better speech recognition in fluctuating compared with continuous noise backgrounds) that is evident for listeners with normal hearing (NH) is generally reduced or absent for listeners with sensorineural hearing impairment (HI). In this study, a real-time signal-processing technique was developed to improve MR in listeners with HI and offer insight into the mechanisms influencing the size of MR. This technique compares short-term and long-term estimates of energy, increases the level of short-term segments whose energy is below the average energy, and normalizes the overall energy of the processed signal to be equivalent to that of the original long-term estimate. This signal-processing algorithm was used to create two types of energy-equalized (EEQ) signals: EEQ1, which operated on the wideband speech plus noise signal, and EEQ4, which operated independently on each of four bands with equal logarithmic width. Consonant identification was tested in backgrounds of continuous and various types of fluctuating speech-shaped Gaussian noise including those with both regularly and irregularly spaced temporal fluctuations. Listeners with HI achieved similar scores for EEQ and the original (unprocessed) stimuli in continuous-noise backgrounds, while superior performance was obtained for the EEQ signals in fluctuating background noises that had regular temporal gaps but not for those with irregularly spaced fluctuations. Thus, in noise backgrounds with regularly spaced temporal fluctuations, the energy-normalized signals led to larger values of MR and higher intelligibility than obtained with unprocessed signals.