Feedforward data-aided phase noise estimation from a DCT basis expansion

This contribution deals with phase noise estimation from pilot symbols. The phase noise process is approximated by an expansion of discrete cosine transform (DCT) basis functions containing only a few terms. We propose a feedforward algorithm that estimates the DCT coefficients without requiring detailed knowledge about the phase noise statistics. We demonstrate that the resulting (linearized) mean-square phase estimation error consists of two contributions: a contribution from the additive noise, that equals the Cramer-Rao lower bound, and a noise independent contribution, that results front the phase noise modeling error. We investigate the effect of the symbol sequence length, the pilot symbol positions, the number of pilot symbols, and the number of estimated DCT coefficients it the estimation accuracy and on the corresponding bit error rate (PER). We propose a pilot symbol configuration allowing to estimate any number of DCT coefficients not exceeding the number of pilot Symbols, providing a considerable Performance improvement as compared to other pilot symbol configurations. For large block sizes, the DCT-based estimation algorithm substantially outperforms algorithms that estimate only the time-average or the linear trend of the carrier phase. Copyright (C) 2009 J. Bhatti and M. Moeneclaey

An enhanced method based on intermediate significant bit technique for watermark images

Intermediate Significant Bit digital watermarking technique (ISB) is a new approved technique of embedding a watermark by replacing the original image pixels with new pixels. This is done by ensuring a close connection between the new pixels and the original, and at the same time, the watermark data can be protected against possible damage. One of the most popular methods used in watermarking is the Least Significant Bit (LSB). It uses a spatial domain that includes the insertion of the watermark in the LSB of the image. The problem with this method is it is not resilient to common damage, and there is the possibility of image distortion after embedding a watermark. LSB may be used through replacing one bit, two bits, or three bits; this is done by changing the specific bits without any change in the other bits in the pixel. The objective of this thesis is to formulate new algorithms for digital image watermarking with enhanced image quality and robustness by embedding two bits of watermark data into each pixel of the original image based on ISB technique. However, to understand the opposite relationship between the image quality and robustness, a tradeoff between them has been done to create a balance and to acquire the best position for the two embedding bits. Dual Intermediate Significant Bits (DISB) technique has been proposed to solve the existing LSB problem. Trial results obtained from this technique are better compared with the LSB based on the Peak Signal to Noise Ratio (PSNR) and Normalized Cross Correlation (NCC). The work in this study also contributes new mathematical equations that can study the change on the other six bits in the pixel after embedding two bits

Imputation of Rainfall Data Using the Sine Cosine Function Fitting Neural Network

Missing rainfall data have reduced the quality of hydrological data analysis because they are the essential input for hydrological modeling. Much research has focused on rainfall data imputation. However, the compatibility of precipitation (rainfall) and non-precipitation (meteorology) as input data has received less attention. First, we propose a novel pre-processing mechanism for non-precipitation data by using principal component analysis (PCA). Before the imputation, PCA is used to extract the most relevant features from the meteorological data. The final output of the PCA is combined with the rainfall data from the nearest neighbor gauging stations and then used as the input to the neural network for missing data imputation. Second, a sine cosine algorithm is presented to optimize neural network for infilling the missing rainfall data. The proposed sine cosine function fitting neural network (SC-FITNET) was compared with the sine cosine feedforward neural network (SCFFNN), feedforward neural network (FFNN) and long short-term memory (LSTM) approaches. The results showed that the proposed SC-FITNET outperformed LSTM, SC-FFNN and FFNN imputation in terms of mean absolute error (MAE), root mean square error (RMSE) and correlation coefficient (R), with an average accuracy of 90.9%. This study revealed that as the percentage of missingness increased, the precision of the four imputation methods reduced. In addition, this study also revealed that PCA has potential in pre-processing meteorological data into an understandable format for the missing data imputation

Real-Time Monitoring of Video Quality in IP Networks

This paper investigates the problem of assessing the quality of video transmitted over IP networks. Our goal is to develop a methodology that is both reasonably accurate and simple enough to support the large-scale deployments that the increasing use of video over IP are likely to demand. For that purpose, we focus on developing an approach that is capable of mapping network statistics, e.g., packet losses, available from simple measurements, to the quality of video sequences reconstructed by receivers. A first step in that direction is a loss-distortion model that accounts for the impact of network losses on video quality, as a function of application-specific parameters such as video codec, loss recovery technique, coded bit rate, packetization, video characteristics, etc. The model, although accurate, is poorly suited to large-scale, on-line monitoring, because of its dependency on parameters that are difficult to estimate in real-time. As a result, we introduce a relative quality metric (rPSNR) that bypasses this problem by measuring video quality against a quality benchmark that the network is expected to provide. The approach offers a lightweight video quality monitoring solution that is suitable for large-scale deployments. We assess its feasibility and accuracy through extensive simulations and experiments

Flexi-WVSNP-DASH: A Wireless Video Sensor Network Platform for the Internet of Things

abstract: Video capture, storage, and distribution in wireless video sensor networks (WVSNs) critically depends on the resources of the nodes forming the sensor networks. In the era of big data, Internet of Things (IoT), and distributed demand and solutions, there is a need for multi-dimensional data to be part of the Sensor Network data that is easily accessible and consumable by humanity as well as machinery. Images and video are expected to become as ubiquitous as is the scalar data in traditional sensor networks. The inception of video-streaming over the Internet, heralded a relentless research for effective ways of distributing video in a scalable and cost effective way. There has been novel implementation attempts across several network layers. Due to the inherent complications of backward compatibility and need for standardization across network layers, there has been a refocused attention to address most of the video distribution over the application layer. As a result, a few video streaming solutions over the Hypertext Transfer Protocol (HTTP) have been proposed. Most notable are Apple’s HTTP Live Streaming (HLS) and the Motion Picture Experts Groups Dynamic Adaptive Streaming over HTTP (MPEG-DASH). These frameworks, do not address the typical and future WVSN use cases. A highly flexible Wireless Video Sensor Network Platform and compatible DASH (WVSNP-DASH) are introduced. The platform's goal is to usher video as a data element that can be integrated into traditional and non-Internet networks. A low cost, scalable node is built from the ground up to be fully compatible with the Internet of Things Machine to Machine (M2M) concept, as well as the ability to be easily re-targeted to new applications in a short time. Flexi-WVSNP design includes a multi-radio node, a middle-ware for sensor operation and communication, a cross platform client facing data retriever/player framework, scalable security as well as a cohesive but decoupled hardware and software design.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Estimation of signal distortion using effective sampling density for light field-based free viewpoint video

In a light field-based free viewpoint video (LF-based FVV) system, effective sampling density (ESD) is defined as the number of rays per unit area of the scene that has been acquired and is selected in the rendering process for reconstructing an unknown ray. This paper extends the concept of ESD and shows that ESD is a tractable metric that quantifies the joint impact of the imperfections of LF acquisition and rendering. By deriving and analyzing ESD for the commonly used LF acquisition and rendering methods, it is shown that ESD is an effective indicator determined by system parameters and can be used to directly estimate output video distortion without access to the ground truth. This claim is verified by extensive numerical simulations and comparison to PSNR. Furthermore, an empirical relationship between the output distortion (in PSNR) and the calculated ESD is established to allow direct assessment of the overall video distortion without an actual implementation of the system. A small scale subjective user study is also conducted which indicates a correlation of 0.91 between ESD and perceived quality