We propose a decentralized but collaborative attribute-based tracing mechanism (a signer-identifying mechanism) for group signatures. Instead of a central tracing party in our scheme, a set of tracers satisfying the attribute set used for generating the group signature can identify the signer. Thus our proposal limits the parties who can identify the signer. On the other hand, it decentralized the tracing authority.

Threats and risks against supply chains are increasing and a framework to add the trustworthiness of supply chain has been considered. In this framework, organisations in the supply chain validate the conformance to the pre-defined requirements. The results of validations are linked each other to achieve the trustworthiness of the entire supply chain. In this paper, we further consider this framework for data supply chains. First, we implement the framework and evaluate the performance. The evaluation shows 500 digital evidences (logs) can be checked in 0.28 second. We also propose five methods to improve the performance as well as five new functionalities to improve usability. With these functionalities, the framework also supports maintaining the certificate chain.

The current study is focused on automatic speech emotion recognition, and particularly on the effect of additive noise and reverberation on speech emotion recognition. The emotional clean speech is produced by four professional actors, who simulate the neutral, joy, anger, and sadness emotions. To produce noisy emotional speech data, white Gaussian noise is superimposed onto the clean speech at several signal-to-noise ratio (SNR) levels. Concerning the reverberant emotional speech data, a technique is applied which is based on convolution of clean speech data with impulse responses recorded in several environments with different reverberation times. The four emotions are recognized using i-vectors, along with probabilistic linear discriminant analysis (PLDA), widely used in speaker recognition and adapted here for speech emotion recognition. When noisy and reverberant emotional speech data are recognized using clean models, the recognition rates are significantly decreased compared to the clean test data. To address this problem, a method based on multi-style training is applied, which utilizes training data of several SNR levels (different to the SNR level of the test data, therefore SNR-independent), or training on data with different reverberation times. Using multi-style training to recognize emotions in noisy or reverberant environments, the recognition rates are significantly increased, and the differences compared to the clean case are statistically not significant. Furthermore, the i-vector paradigm based classification method is compared with a baseline Gaussian mixture models (GMM) based method, and it demonstrates superior performance.

This paper proposes a fast and robust content-based copy detection scheme. Our proposal consists of a new compact feature, efficient keyframe selection and adaptive mask-based feature comparison. Firstly, a block-level luminance centroid is binarized into a 32-bit quadrant feature for fast and robust feature comparison. Subsequently, a new keyframe selection method is adopted to enhance pairwise independence between unrelated video segments in addition to choosing stable keyframes. Finally, a block-level mask-based feature comparison method is introduced to compare only stable features. Experimental results show our scheme improves recall by 0.1 at the same precision 0.9 and the processing speed in feature comparison of the proposed scheme is about twice as fast as that of conventional schemes.

This paper proposes a novel CT image coding concept named LOI which stands for level of interest and coding methods based on that concept. The well-known ROI (region of interest) coding gives priority to pixels in particular regions of an image when encoding or decoding the image. Similarly, LOI coding gives priority to the pixels having a particular pixel level. LOI is ideal for coding of CT images and offers remarkably high coding efficiency. This paper proposes two types of LOI coding methods. One type is the floating point method and the other is the integer method. This paper verifies that the both types improve coding efficiency of pixels in the LOI window. This paper also shows that adequate image quality is obtained even if the viewing window is different from the LOI window.

This paper proposes novel techniques for reducing tiling artifacts and encoding time which are two of the largest problems with the JPEG2000 encoder. The proposed method detects tiling artifacts during the coding process and reduces them automatically using tile-boundary adaptive post-quantization. It also provides a higher speed JPEG2000 coding process. This accelerated performance was obtained by predicting the truncation points using the entropy of wavelet transform coefficients prior to the arithmetic coding. This encoding method also requires no additional processing in the decoder. In the experiment, it was subjectively confirmed that tiling artifacts were greatly reduced and that the coding process was considerably accelerated.

This paper proposes the tile size conversion method for the wavelet image transcoding gateway and a set of methods to reduce the tile boundary artifacts caused by the conversion.In the wavelet image coding system represented by JPEG2000, pictures are usually divided into one or more tiles and each tile then transformed separately. On low memory terminals such as mobile terminals, some decoders are likely to have limits on what tile sizes they can decode. Assuming a system using these limited decoders, methods were investigated for converting the tile size quickly and automatically at the gateway when image data with a non-decodable tile size is received at the gateway from another system. Furthermore, tile boundary artifacts reduction methods are investigated. This paper proposes a basic method, which creates a tiled wavelet coefficient sequence from a non-tiled wavelet coefficient sequence. This paper also verifies the validity of the proposed scheme by implementing it with a 5x3 reversible filter and a 9x7 irreversible filter.

Universal learning networks (ULNs) have been proposed, which are a super set of any kinds of supervised learning networks. One of the important features of the ULNs is that the ULNs have a systematic algorithm for calculating higher order derivatives of the criterion function with respect to parameters. Both robust control and chaos control methods using the second order derivatives of the ULNs have also been proposed. In this paper, a new control design method of the nonlinear systems is proposed, which is an extension of the above robust control method in terms of the stability as well as the quick response of the systems.

This paper proposes a direct sequence/code division multiple access (DS/CDMA) cellular system using channel activation and soft power control techniques to increase both the forward and reverse link capacities. To equivalently control the received signal to interference and noise power ratio (CINR) to be constant, the proposed system employs a channel activation technique in which higher channel coding gain and small processing gain are employed with a shorter burst length during higher CINR period, and a lower channel coding gain and larger processing gain are employed with longer burst during lower CINR. Moreover, the proposed system employs a soft power control technique to enhance the advantages of the channel activation technique. Computer simulation confirms that the proposed system can achieve a higher system capacity with a smaller soft hand-off margin and a smaller average transmitter power.