Lina Stankovic

Scalable Video Multicast Using Expanding Window Fountain Codes

Fountain codes were introduced as an efficient and universal forward error correction (FEC) solution for data multicast over lossy packet networks. They have recently been proposed for large scale multimedia content delivery in practical multimedia distribution systems. However, standard fountain codes, such as LT or Raptor codes, are not designed to meet unequal error protection (UEP) requirements typical in real-time scalable video multicast applications. In this paper, we propose recently introduced UEP expanding window fountain (EWF) codes as a flexible and efficient solution for real-time scalable video multicast. We demonstrate that the design flexibility and UEP performance make EWF codes ideally suited for this scenario, i.e., EWF codes offer a number of design parameters to be ldquotunedrdquo at the server side to meet the different reception criteria of heterogeneous receivers. The performance analysis using both analytical results and simulation experiments of H.264 scalable video coding (SVC) multicast to heterogeneous receiver classes confirms the flexibility and efficiency of the proposed EWF-based FEC solution.

On a Training-Less Solution for Non-Intrusive Appliance Load Monitoring Using Graph Signal Processing

With ongoing large-scale smart energy metering deployments worldwide, disaggregation of a households total energy consumption down to individual appliances using analytical tools, also known as non-intrusive appliance load monitoring (NALM), has generated increased research interest lately. NALM can deepen energy feedback, support appliance retrofit advice, and support home automation. However, despite the fact that NALM was proposed over 30 years ago, there are still many open challenges with respect to its practicality and effectiveness at low sampling rates. Indeed, the majority of NALM approaches, supervised or unsupervised, require training to build appliance models, and are sensitive to appliance changes in the house, thus requiring regular re-training. In this paper, we tackle this challenge by proposing an NALM approach that does not require any training. The main idea is to build upon the emerging field of graph signal processing to perform adaptive thresholding, signal clustering, and pattern matching. We determine the performance limits of our approach and demonstrate its usefulness in practice. Using two open access datasets-the US REDD data set with active power measurements downsampled to 1 min resolution and the UK REFIT data set with 8-s resolution, we demonstrate the effectiveness of the proposed method for typical smart meter sampling rate, with the state-of-the-art supervised and unsupervised NALM approaches as benchmarks.

Non-intrusive appliance load monitoring using low-resolution smart meter data

We propose two algorithms for power load disaggregation at low-sampling rates (greater than 1sec): a low-complexity, supervised approach based on Decision Trees and an unsupervised method based on Dynamic Time Warping. Both proposed algorithms share common pre-classification steps. We provide reproducible algorithmic description and benchmark the proposed methods with a state-of-the-art Hidden Markov Model (HMM)-based approach. Experimental results using three US and three UK households, show that both proposed methods outperform the HMM-based approach and are capable of disaggregating a range of domestic loads even when the training period is very short.

Compressive image sampling with side information

Compressive sampling is a novel framework that exploits sparsity of a signal in a transform domain to perform sampling below the Nyquist rate. In this paper, we apply compressive sampling to reduce the sampling rate of images/video. The key idea is to exploit the intra- and inter-frame correlation to improve signal recovery algorithms. The image is split into non-overlapping blocks of fixed size, which are independently compressively sampled exploiting sparsity of natural scenes in the Discrete Cosine Transform (DCT) domain. At the decoder, each block is recovered using useful information extracted from the recovery of a neighboring block. In the case of video, a previous frame is used to help recovery of consecutive frames. The iterative algorithm for signal recovery with side information that extends the standard orthogonal matching pursuit (OMP) algorithm is employed. Simulation results are given for Magnetic Resonance Imaging (MRI) and video sequences to illustrate advantages of the proposed solution compared to the case when side information is not used.

Expanding Window Fountain codes for scalable video multicast

Digital Fountain (DF) codes have recently been suggested as an efficient forward error correction (FEC) solution for video multicast to heterogeneous receiver classes over lossy packet networks. However, to adapt DF codes to low-delay constraints and varying importance of scalable multimedia content, unequal error protection (UEP) DF schemes are needed. Thus, in this paper, Expanding Window Fountain (EWF) codes are proposed as a FEC solution for scalable video multicast. We demonstrate that the design flexibility and UEP performance make EWF codes ideally suited for this scenario, i.e., EWF codes offer a number of design parameters to be ldquotunedrdquo at the server side to meet the different reception conditions of heterogeneous receivers. Performance analysis of H.264 Scalable Video Coding (SVC) multicast to heterogeneous receiver classes confirms the flexibility and efficiency of the proposed EWF-based FEC solution.

Adaptive Correlation Estimation With Particle Filtering for Distributed Video Coding

Distributed video coding (DVC) is rapidly gaining popularity as a low cost, robust video coding solution, that reduces video encoding complexity. DVC is built on distributed source coding (DSC) principles where correlation between sources to be compressed is exploited at the decoder side. In the case of DVC, a current frame available only at the encoder is estimated at the decoder with side information generated from other frames available at the decoder. One of the main challenges in DVC design is that correlation among the source and side information needs to be estimated online and as accurately as possible. Since correlation dynamically changes with the scene, in order to exploit the robustness of DSC code designs, we integrate particle filtering (PF) with standard belief propagation (BP) decoding for inference on one joint factor graph to estimate correlation among source and side information. Correlation estimation is performed online as it is carried out jointly with decoding of the graph-based DSC code. Moreover, we demonstrate our joint bit-plane decoding with adaptive correlation estimation schemes within state-of-the-art DVC systems, which are transform-domain based with a feedback channel for rate adaptation. Experimental results show that our proposed system gives a significant performance improvement compared to the benchmark state-of-the-art DISCOVER codec (including correlation estimation) and the case without dynamic PF tracking, due to improved knowledge of timely correlation statistics via the combination of joint bit-plane decoding and particle-based BP (PBP) tracking.

Image registration using BP-SIFT

Scale Invariant Feature Transform (SIFT) is a powerful technique for image registration. Although SIFT descriptors accurately extract invariant image characteristics around keypoints, the commonly used matching approaches of registration loosely represent the geometric information among descriptors. In this paper, we propose an image registration algorithm named BP-SIFT, where we formulate keypoint matching of SIFT descriptors as a global optimization problem and provide a suboptimum solution using belief propagation (BP). Experimental results show significant improvement over conventional SIFT-based matching with reasonable computation complexity.

Cooperative network-coding system for wireless sensor networks

Describes a cooperative network coding system for wireless sensor networks. In this paper, we propose two practical power) and bandwidth)efficient systems based on amplify)and)forward (AF) and decode)and)forward (DF) schemes to address the problem of information exchange via a relay. The key idea is to channel encode each source’s message by using a high)performance non)binary turbo code based on Partial Unit Memory (PUM) codes to enhance the bit)error)rate performance, then reduce the energy consumption and increase spectrum efficiency by using network coding (NC) to combine individual nodes’ messages at the relay before forwarding to the destination. Two simple and low complexity physical layer NC schemes are proposed based on combinations of received source messages at the relay. We also present the theoretical limits and numerical analysis of the proposed schemes. Simulation results under Additive White Gaussian Noise, confirm that the proposed schemes achieve significant bandwidth savings and fewer transmissions over the benchmark systems which do not resort to NC. Theoretical limits for capacity and Signal to Noise Ratio behaviour for the proposed schemes are derived. The paper also proposes a cooperative strategy that is useful when insufficient combined messages are received at a node to recover the desired source messages, thus enabling the system to retrieve all packets with significantly fewer retransmission request messages.

Single pixel optical imaging using a scanning MEMS mirror

The paper describes a low-complexity optical imaging system using demagnifying optics, a single scanning MEMS mirror and a single photodetector. Light at visible wavelengths from the object passes through a lens assembly and is incident on a scanning MEMS micromirror. After reflection from the micromirror, a complete image of the object is projected at the image plane of the optical system where a single-element photodetector with a pinhole at its entrance is located. By tilting the micromirror in the x and y directions, the projected image is translated across the image plane in the x and y directions. The photodetector sequentially detects the intensity of different areas of the projected optical image, thereby enabling a digital image to be generated pixel-by-pixel. However, due to the noisy raw image obtained experimentally, an image enhancement algorithm based on iterative-combined wavelet and curvelet denoising has been developed. Using blind image quality indices (BIQI) as an objective performance measure, it is shown that the proposed image enhancement method enhances the raw image by up to 40% and outperforms state-of-the-art denoising methods for up to 10 units of BIQI.

Non-Intrusive Load Disaggregation Using Graph Signal Processing

With the large-scale roll-out of smart metering worldwide, there is a growing need to account for the individual contribution of appliances to the load demand. In this paper, we design a graph signal processing (GSP)-based approach for non-intrusive appliance load monitoring (NILM), i.e., disaggregation of total energy consumption down to individual appliances used. Leveraging piecewise smoothness of the power load signal, two GSP-based NILM approaches are proposed. The first approach, based on total graph variation minimization, searches for a smooth graph signal under known label constraints. The second approach uses the total graph variation minimizer as a starting point for further refinement via simulated annealing. The proposed GSP-based NILM approach aims to address the large training overhead and associated complexity of conventional graph-based methods through a novel event-based graph approach. Simulation results using two datasets of real house measurements demonstrate the competitive performance of the GSP-based approaches with respect to traditionally used hidden Markov model-based and decision tree-based approaches.