EURASIP Journal on Advances in Signal Processing

We address the problem of timing-based localization in wireless networks, when an unknown fraction of data is corrupted by non-ideal propagation conditions. While timing-based techniques can enable accurate localization, they are sensitive to corrupted data. We develop a robust method that is applicable to a range of localization techniques, including time-of-arrival, time-difference-of-arrival and time-difference in schedule-based transmissions. The method is distribution-free, is computationally efficient and requires only an upper bound on the fraction of corrupted data, thus obviating distributional assumptions on the corrupting noise. The robustness of the method is demonstrated in numerical experiments.

Digital watermarking is well known as enabling technology to prove ownership on copyrighted material, detect originators of illegally made copies, monitor the usage of the copyrighted multimedia data and analyze the spread spectrum of the data over networks and servers. Research has shown that data hiding techniques can be applied successfully to other application areas like manipulations recognition. In this paper, we show our innovative approach for integrating watermark and cryptography based methods within a framework of new application scenarios spanning a wide range from dedicated and user specific services, "Try&Buy" mechanisms to general means for long-term customer relationships. The tremendous recent efforts to develop and deploy ubiquitous mobile communication possibilities are changing the demands but also possibilities for establishing new business and commerce relationships. Especially we motivate annotation watermarks and aspects of M-Commerce to show important scenarios for access control. Based on a description of the challenges of the application domain and our latest work we discuss, which methods can be used for establishing services in a fast convenient and secure way for conditional access services based on digital watermarking combined with cryptographic techniques. We introduce an example scenario for digital audio and an overview of steps in order to establish these concepts practically.

This paper proposes a novel approach for improving the speech of a single speaker in noisy reverberant environments. The proposed approach is based on using a beamformer with a large number of virtual microphones with the suggested arrangement on an open sphere. Our method takes into account virtual microphone signal synthesizing using the non-parametric sound field reproduction in the spherical harmonics domain and the popular weighted prediction error method. We obtain entirely accurate beam steering towards a known source location with more directivity. The suggested approach is proven to perform effectively not just in boosting the directivity factor but also in terms of improving speech quality as measured by subjective metrics like the PESQ. In comparison to current research in the area of speech enhancement by beamformer, our experiments reveal more noise and reverberation suppression as well as improved quality in the enhanced speech samples due to the usage of virtual beam rotation in the fixed beamformer. Text for this section.

We propose a new method for speech source separation that is based on directionally-disjoint estimation of the transfer functions between microphones and sources at different frequencies and at multiple times. The spatial transfer functions are estimated from eigenvectors of the microphones' correlation matrix. Smoothing and association of transfer function parameters across different frequencies are performed by simultaneous extended Kalman filtering of the amplitude and phase estimates. This approach allows transfer function estimation even if the number of sources is greater than the number of microphones, and it can operate for both wideband and narrowband sources. The performance of the proposed method was studied via simulations and the results show good performance.

Once the Internet of Things was proposed, it has received great attention from all walks of life and has become one of the top ten technologies that change the world. Therefore, more and more people are engaged in the research of the Internet of Things, after the unremitting efforts of all seniors. Now the Internet of Things has been applied to every aspect of our lives. However, in the application process of the Internet of Things, the protection of personal privacy will undoubtedly be involved. If this problem is not effectively resolved, it will become a major obstacle to the development of the Internet of Things. At present, the research of fully homomorphic technology has attracted great attention from the cryptography community. You can directly calculate the encrypted text encryption to obtain the output and decrypt the output. The result is the same as the output of the unencrypted plain text. This article first comprehensively describes the solution to the privacy protection problem in the existing Internet of Things, and then proposes to apply the fully homomorphic technology to the Internet of Things to make the services provided by the network more secure. Through the analysis of the basic composition and architecture of the existing Internet of Things system, a privacy protection interaction model for the Internet of Things is established, which uses a completely homomorphic technology. On this basis, the algorithm for implementing simple homomorphic encryption is improved, and general homomorphic encryption theory is proposed for some security issues. After using this method to encrypt privacy, the success rate of cracking dropped by 24%.

In this paper, we introduce a new self-adaptive algorithm for segmenting human skin regions in color images. Skin detection and segmentation is an active research topic, and many solutions have been proposed so far, especially concerning skin tone modeling in various color spaces. Such models are used for pixel-based classification, but its accuracy is limited due to high variance and low specificity of human skin color. In many works, skin model adaptation and spatial analysis were reported to improve the final segmentation outcome; however, little attention has been paid so far to the possibilities of combining these two improvement directions. Our contribution lies in learning a local skin color model on the fly, which is subsequently applied to the image to determine the seeds for the spatial analysis. Furthermore, we also take advantage of textural features for computing local propagation costs that are used in the distance transform. The results of an extensive experimental study confirmed that the new method is highly competitive, especially for extracting the hand regions in color images.

Fast multidimensional NMR is important in chemical shift assignment and for studying structures of large proteins. We present the first method which takes advantage of the sparsity of the wavelet representation of the NMR spectra and reconstructs the spectra from partial random measurements of its free induction decay (FID) by solving the following optimization problem: min subject to , where is a given observation vector, a random sampling operator, denotes the Fourier transform, and an orthogonal 2D wavelet transform. The matrix is a given matrix such that . This problem can be solved by general-purpose solvers; however, these can be prohibitively expensive in large-scale applications. In the settings of interest, the underlying solution is sparse with a few nonzeros. We show here that for large practical systems, a good approximation to the sparsest solution is obtained by iterative thresholding algorithms running much more rapidly than general solvers. We demonstrate the applicability of our approach to fast multidimensional NMR spectroscopy. Our main practical result estimates a four-fold reduction in sampling and experiment time without loss of resolution while maintaining sensitivity for a wide range of existing settings. Our results maintain the quality of the peak list of the reconstructed signal which is the key deliverable used in protein structure determination.

We developed a system that automatically authenticates offline handwritten signatures using the discrete Radon transform (DRT) and a hidden Markov model (HMM). Given the robustness of our algorithm and the fact that only global features are considered, satisfactory results are obtained. Using a database of 924 signatures from 22 writers, our system achieves an equal error rate (EER) of 18% when only high-quality forgeries (skilled forgeries) are considered and an EER of 4.5% in the case of only casual forgeries. These signatures were originally captured offline. Using another database of 4800 signatures from 51 writers, our system achieves an EER of 12.2% when only skilled forgeries are considered. These signatures were originally captured online and then digitally converted into static signature images. These results compare well with the results of other algorithms that consider only global features.

Digital image manipulation software is now readily available on personal computers. It is therefore very simple to tamper with any image and make it available to others. Insuring digital image integrity has therefore become a major issue. Watermarking has become a popular technique for copyright enforcement and image authentication. The aim of this paper is to present an overview of emerging techniques for detecting whether image tampering has taken place. Compared to the techniques and protocols for security usually employed to perform this task, the majority of the proposed methods based on watermarking, place a particular emphasis on the notion of content authentication rather than strict integrity. In this paper, we introduce the notion of image content authentication and the features required to design an effective authentication scheme. We present some algorithms, and introduce frequently used key techniques.