Enhancement of Wyner-Ziv video coding for wireless channels

Wyner-Ziv video coding (WZVC) is a fast emerging video coding technique for wireless video sensor networks. WZVC moves the complexity from the encoder (sensor) to the decoder (receiver). This thesis proposes few enhancements to solve challenging problems in WZVC, namely 1) handling impairments of a fading wireless channel in WZVC environment, 2) investigating rate penalty of WZVC in wireless fading channels, 3) adaptive encoder rate control using inter-frame cross-correlation properties, and 4) better side information estimation with a bit-based noise variance computation technique. In case (1), the decoder metric values in WZVC are calculated with respect to the correlation noise, channel noise, and fading. Multiple input multiple output (MIMO) diversity scheme is studied with WZVC for improving the reconstructed video output. Simulation results show that the average peak signal to noise ratio (PSNR) of Foreman video is improved by ≈ 3 dB with the configuration of 2I4O compared to single input single output (SISO) channel at SNR = 2 dB. In case (2), expressions for the rate penalty are analytically derived under different wireless channel conditions. WZVC scheme with receiver diversity (WZVC-RD) is proposed and also demonstrated that it alleviates the channel-induced rate penalty. Simulation results show that with adequate diversity, the channel induced rate penalty can be almost completely eliminated, i.e., the average rate penalty is reduced to be less than 20 Kbps in WZVC-RD for Foreman video at SNR = 2 dB. In case (3), theoretical rate-distortion behavior of conditional decoding is compared with that of the practical WZVC. An encoder rate control algorithm is proposed with respect to the cross-correlation threshold (CCTH) at the decoder, where the definition of the threshold is derived based on cross-correlation statistics of the key frames. Additionally, an adaptive threshold algorithm (ATHA) is proposed to improve the PSNR versus rate relationship. In case (4), correlation noise variance estimation is proposed with respect to the bit pattern of each pixel; it is named as “bit-based noise variance”. PSNR improvement of ≈ 4 dB is observed for the Foreman video frames with higher correlation noise.

Enhancement of Wyner-Ziv video coding for wireless channels

Wyner-Ziv video coding (WZVC) is a fast emerging video coding technique for wireless video sensor networks. WZVC moves the complexity from the encoder (sensor) to the decoder (receiver). This thesis proposes few enhancements to solve challenging problems in WZVC, namely 1) handling impairments of a fading wireless channel in WZVC environment, 2) investigating rate penalty of WZVC in wireless fading channels, 3) adaptive encoder rate control using inter-frame cross-correlation properties, and 4) better side information estimation with a bit-based noise variance computation technique. In case (1), the decoder metric values in WZVC are calculated with respect to the correlation noise, channel noise, and fading. Multiple input multiple output (MIMO) diversity scheme is studied with WZVC for improving the reconstructed video output. Simulation results show that the average peak signal to noise ratio (PSNR) of Foreman video is improved by ≈ 3 dB with the configuration of 2I4O compared to single input single output (SISO) channel at SNR = 2 dB. In case (2), expressions for the rate penalty are analytically derived under different wireless channel conditions. WZVC scheme with receiver diversity (WZVC-RD) is proposed and also demonstrated that it alleviates the channel-induced rate penalty. Simulation results show that with adequate diversity, the channel induced rate penalty can be almost completely eliminated, i.e., the average rate penalty is reduced to be less than 20 Kbps in WZVC-RD for Foreman video at SNR = 2 dB. In case (3), theoretical rate-distortion behavior of conditional decoding is compared with that of the practical WZVC. An encoder rate control algorithm is proposed with respect to the cross-correlation threshold (CCTH) at the decoder, where the definition of the threshold is derived based on cross-correlation statistics of the key frames. Additionally, an adaptive threshold algorithm (ATHA) is proposed to improve the PSNR versus rate relationship. In case (4), correlation noise variance estimation is proposed with respect to the bit pattern of each pixel; it is named as “bit-based noise variance”. PSNR improvement of ≈ 4 dB is observed for the Foreman video frames with higher correlation noise.

Auto- versus Cross-Correlation Noise in Periodically Driven Quantum Coherent Conductors

Expressing currents and their fluctuations at the terminals of a multi-probe conductor in terms of the wave functions of carriers injected into the Fermi sea provides new insight into the physics of electric currents. This approach helps us to identify two physically different contributions to shot noise. In the quantum coherent regime, when current is carried by non-overlapping wave packets, the product of current fluctuations in different leads, the cross-correlation noise, is determined solely by the duration of the wave packet. In contrast, the square of the current fluctuations in one lead, the autocorrelation noise, is additionally determined by the coherence of the wave packet, which is associated with the spread of the wave packet in energy. The two contributions can be addressed separately in the weak back-scattering regime, when the autocorrelation noise depends only on the coherence. Analysis of shot noise in terms of these contributions allows us, in particular, to predict that no individual traveling particles with a real wave function, such as Majorana fermions, can be created in the Fermi sea in a clean manner, that is, without accompanying electron–hole pairs.

A Source Number Estimation Algorithm Based on Data Local Density and Fuzzy C-Means Clustering

An advanced source number estimation (SNE) algorithm based on both fuzzy C-means clustering (FCM) and data local density (DLD) is proposed in this paper. The DLD of an eigenvalue refers to the number of eigenvalues within a specific neighborhood of this eigenvalue belonging to the data covariance matrix. This local density essentially as the one-dimensional sample feature of the FCM is extracted into the SNE algorithm based on FCM and can enable to improve the probability of correct detection (PCD) of the SNE algorithm based on the FCM especially for low signal-to-noise ratio (SNR) environment. Comparison experiment results demonstrate that compared to the SNE algorithm based on the FCM and other similar algorithms, our proposed algorithm can achieve highest PCD of the incident source number in both cases of spatial white noise and spatial correlation noise.

Probing signals with ZACZ for GPRonboard of unmanned aerial vehicle

<p><span>Modern ground penetrating radars (</span><span>GPR</span><span>), designed to determine the thickness of ice or search for the occurrence of aquifers in arid regions of the Earth, are installed either on helicopters or on the earth's surface. The use of a helicopter is economically expensive, and the installation of GPRs on the earth's surface is of a local nature. Modern GPRs mainly use video pulse probing signals and probing signals with linear frequency modulation. These signals have correlation noise, which makes it difficult to obtain a high-quality radar image. In this work, we propose to use a signal with a zero autocorrelation zone (ZACZ) as a probing signal for GPR installed on an unmanned aerial vehicle. In work, a polyphase probing signal with a ZACZ is synthesized and a comparative analysis of the correlation characteristics of the synthesized signal with the optimal phase-code shift keyed signal is carried out. </span></p>

Improving TDWZ Correlation Noise Estimation: A Deep Learning based Approach

Transform domain Wyner-Ziv video coding (TDWZ) has shown its benefits in compressing video applications with limited resources such as visual surveillance systems, remote sensing and wireless sensor networks. In TDWZ, the correlation noise model (CNM) plays a vital role since it directly affects to the number of bits needed to send from the encoder and thus the overall TDWZ compression performance. To achieve CNM with high accurate for TDWZ, we propose in this paper a novel CNM estimation approach in which the CNM with Laplacian distribution is adaptively estimated based on a deep learning (DL) mechanism. The proposed DL based CNM includes two hidden layers and a linear activation function to adaptively update the Laplacian parameter. Experimental results showed that the proposed TDWZ codec significantly outperforms the relevant benchmarks, notably by around 35% bitrate saving when compared to the DISCOVER codec and around 22% bitrate saving when compared to the HEVC Intra benchmark while providing a similar perceptual quality.