EURASIP Journal on Advances in Signal Processing

An adaptive example-based super-resolution (SR) using kernel principal component analysis (PCA) with a novel classification approach is presented in this paper. In order to enable estimation of missing high-frequency components for each kind of texture in target low-resolution (LR) images, the proposed method performs clustering of high-resolution (HR) patches clipped from training HR images in advance. Based on two nonlinear eigenspaces, respectively, generated from HR patches and their corresponding low-frequency components in each cluster, an inverse map, which can estimate missing high-frequency components from only the known low-frequency components, is derived. Furthermore, by monitoring errors caused in the above estimation process, the proposed method enables adaptive selection of the optimal cluster for each target local patch, and this corresponds to the novel classification approach in our method. Then, by combining the above two approaches, the proposed method can adaptively estimate the missing high-frequency components, and successful reconstruction of the HR image is realized.

Motivated by recent results in Joint Source/Channel coding and decoding, we consider the decoding problem of Arithmetic Codes (AC). In fact, in this article we provide different approaches which allow one to unify the arithmetic decoding and error correction tasks. A novel length-constrained arithmetic decoding algorithm based on Maximum A Posteriori sequence estimation is proposed. The latter is based on soft-input decoding using a priori knowledge of the source-symbol sequence and the compressed bit-stream lengths. Performance in the case of transmission over an Additive White Gaussian Noise channel is evaluated in terms of Packet Error Rate. Simulation results show that the proposed decoding algorithm leads to significant performance gain while exhibiting very low complexity. The proposed soft input arithmetic decoder can also generate additional information regarding the reliability of the compressed bit-stream components. We consider the serial concatenation of the AC with a Recursive Systematic Convolutional Code, and perform iterative decoding. We show that, compared to tandem and to trellis-based Soft-Input Soft-Output decoding schemes, the proposed decoder exhibits the best performance/complexity tradeoff. Finally, the practical relevance of the presented iterative decoding system is validated under an image transmission scheme based on the JPEG 2000 standard and excellent results in terms of decoded image quality are obtained.

Nowadays, deep neural networks (DNNs) have been rapidly deployed to realize a number of functionalities like sensing, imaging, classification, recognition, etc. However, the computational-intensive requirement of DNNs makes it difficult to be applicable for resource-limited Internet of Things (IoT) devices. In this paper, we propose a novel pruning-based paradigm that aims to reduce the computational cost of DNNs, by uncovering a more compact structure and learning the effective weights therein, on the basis of not compromising the expressive capability of DNNs. In particular, our algorithm can achieve efficient end-to-end training that transfers a redundant neural network to a compact one with a specifically targeted compression rate directly. We comprehensively evaluate our approach on various representative benchmark datasets and compared with typical advanced convolutional neural network (CNN) architectures. The experimental results verify the superior performance and robust effectiveness of our scheme. For example, when pruning VGG on CIFAR-10, our proposed scheme is able to significantly reduce its FLOPs (floating-point operations) and number of parameters with a proportion of 76.2% and 94.1%, respectively, while still maintaining a satisfactory accuracy. To sum up, our scheme could facilitate the integration of DNNs into the common machine-learning-based IoT framework and establish distributed training of neural networks in both cloud and edge.

Motion compensated prediction (MCP) implemented in most video coding schemes is based on translational motion model. However, nontranslational motions, for example, rotational motions, are common in videos. Higher-order motion model researches try to enhance the prediction accuracy of MCP by modeling those nontranslational motions. However, they require affine parameter estimation, and most of them have very high computational complexity. In this paper, a translational and rotational MCP method using special subsampling in the interpolated frame is proposed. This method is simple to implement and has low computational complexity. Experimental results show that many blocks can be better predicted by the proposed method, and therefore a higher prediction quality can be achieved with acceptable overheads. We believe this approach opens a new direction in MCP research.

Benefits of network coding towards enhancing communication quality, both in terms of robustness or data transmission rates, make it a significant candidate as a future networking technology. Conventionally, network coding is mostly used in wired infrastructures, where transmission errors between nodes are negligible. Capturing the provided benefits of network coding via straightforward extension from wired networks to wireless networks is not trivial. In addition to the challenges introduced through the wireless channel impairments, we can also capture the spatial diversity gain provided by the broadcast nature of the wireless channels. In this work, we design and implement a network-coded cooperation (NCC) system that operates in real time through the use of software-defined radio (SDR) nodes for the first time in the literature. We specifically target wireless networks. Our system is based on orthogonal frequency division multiple access (OFDMA) that provides a practical means to enable high transmission rates through the use of narrowband subcarriers. The developed testbed is composed of three source nodes, a relay node and two destination nodes. The transmission of the proposed NCC-OFDMA system is completed in two phases; the broadcast and the relaying phases. Multiplexing of source nodes’ signals is achieved through OFDMA technique. In the broadcast phase, an OFDMA signal is transmitted to relay and destination nodes. In the relaying phase, the relay node first detects the OFDMA signal, generates network-coded symbols, and then transmits these symbols to destination nodes. At the end of these two phases, the destination nodes determine the source nodes’ signals by using network decoders. The destination nodes make use of both the uncoded and network-coded symbols, which are received in broadcast and relaying phases, respectively. Destination nodes then perform network decoding. Through real-time bit error rate and error vector magnitude measurements, we show that the NCC-OFDMA system can significantly improve the communication quality and robustness, while enabling data transmission between multiple users, as known from theoretical analyses. Some features of this implemented NCC-OFDMA system have the potential to be included in 5G standards, due to the improved radio resource usage efficiency.

Recently, hearing aid systems where the left and right ear devices collaborate with one another have received much attention. Apart from supporting natural binaural hearing, such systems hold great potential for improving the intelligibility of speech in the presence of noise through beamforming algorithms. Binaural beamforming for hearing aids requires an exchange of microphone signals between the two devices over a wireless link. This paper studies two problems: which signal to transmit from one ear to the other, and at what bit-rate. The first problem is relevant as modern hearing aids usually contain multiple microphones, and the optimal choice for the signal to be transmitted is not obvious. The second problem is relevant as the capacity of the wireless link is limited by stringent power consumption constraints imposed by the limited battery life of hearing aids.

Fast burst synchronization is an important requirement in asynchronous communication networks, where devices transmit short data packets in an unscheduled fashion. Such a synchronization is typically achieved by means of a preamble sent in front of the data packet. In this paper, we study fast burst synchronization for power line communication (PLC) systems operating below 500 kHz and transmitting data rates of up to about 500 kbps as it is typical in various PLC network applications. In particular, we are concerned with the receiver processing of the preamble signal and the actual design of preambles suitable for fast burst synchronization in such PLC systems. Our approach is comprehensive in that it takes into account the most distinctive characteristics of the power line channel, which are multipath propagation, highly varying path loss, and disturbance by impulse noise, as well as important practical constraints, especially the need for spectral shaping of the preamble signal and fast adjustment of the automatic gain control (AGC). In fact, we regard the explicit incorporation of these various requirements into the preamble design as the main contribution of this work. We devise an optimization criterion and a stochastic algorithm to search for suitable preamble sequences. A comprehensive performance comparison of a designed and two conventional preambles shows that the designed sequence is superior in terms of (a) fast burst synchronization in various transmission environments, (b) fast AGC adjustment, and (c) compliance of its spectrum with the spectral mask applied to the data transmit signal.

A recently rediscovered sound synthesis method, which is based on feedback amplitude modulation (FBAM), is investigated. The FBAM system is interpreted as a periodically linear time-varying digital filter, and its stability, aliasing, and scaling properties are considered. Several novel variations of the basic system are derived and analyzed. Separation of the input and the modulation signals in FBAM structures is proposed which helps to create modular sound synthesis and digital audio effects applications. The FBAM is shown to be a powerful and versatile sound synthesis principle, which has similarities to the established distortion synthesis methods, but which is also essentially different from them.

Proportionate adaptive algorithms have been proposed recently to accelerate convergence for the identification of sparse impulse response. When the excitation signal is colored, especially the speech, the convergence performance of proportionate NLMS algorithms demonstrate slow convergence speed. The proportionate affine projection algorithm (PAPA) is expected to solve this problem by using more information in the input signals. However, its steady-state performance is limited by the constant step-size parameter. In this article we propose a variable step-size PAPA by canceling the a posteriori estimation error. This can result in high convergence speed using a large step size when the identification error is large, and can then considerably decrease the steady-state misalignment using a small step size after the adaptive filter has converged. Simulation results show that the proposed approach can greatly improve the steady-state misalignment without sacrificing the fast convergence of PAPA.

Cramer-Rao bounds for one- and two-dimensional angle-of-arrival estimation are reviewed for generalized 3-D array geometries. Assuming an elevated sensor array is used to locate sources on a ground plane, we give a simple procedure for drawing x-y location confidence ellipses from the Cramer-Rao covariance matrix. We modify the ordinary bounds for the case of "separate" 1-D estimates and numerically compare this with the full, joint bound. We prove that "separate" processing is optimal for a Uniform Cross Array with a single source, and that it is not optimal for the L-shaped array. A trade-off emerges between location accuracy and array height: for distant sources, increased height generally reduces error. When more than one source is present, significant gains are obtained from joint processing. We also show useful gains for distant sources by adding out-of-plane sensors in an "L + z" configuration with joint processing. These comparisons can aid system designers in deciding between separate and joint processing approaches.