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In this paper, a two-stage content-based image retrieval (CBIR) approach is proposed to improve the retrieval performance. To develop a general retrieval scheme which is less dependent on domain-specific knowledge, the discrete cosine transform (DCT) is employed as a feature extraction method. In establishing the database, the DC coefficients of Y, U and V components are quantized such that the feature space is partitioned into a finite number of grids, each of which is mapped to a grid code (GC). When querying an image, at coarse classification stage, the grid-based classification (GBC) and the distance threshold pruning (DTP) serve as a filter to remove those candidates with widely distinct features. At the fine classification stage, only the remaining candidates need to be computed for the detailed similarity comparison. The experimental results show that both high efficacy and high efficiency can be achieved simultaneously using the proposed two-stage approach.

In this article, a new minimum spanning tree-based method for shape description and matching is proposed. Its properties are checked through the problem of graphical symbols recognition. Recognition invariance in front shift and multi-oriented noisy objects was studied in the context of small and low resolution binary images. The approach seems to have many desirable properties, even if the construction of graphs induces an expensive algorithmic cost. In order to reduce time computing, an alternative solution based on image compression concepts is provided. The recognition is realized in a compact space, namely the Discrete Cosine space. The use of block discrete cosine transform is discussed and justified. The experimental results led on the GREC2003 database show that the proposed method is characterized by a good discrimination power, a real robustness to noise, with an acceptable time computing. The position with a reference approach like Zernike moments is also investigated to measure the relevance of the proposed technique.

This paper assesses the arithmetic benefits provided by the Residue Number System (RNS) for building Digital Signal Processing (DSP) systems with Field-Programmable Logic (FPL) technology. The quantifiable benefits of this approach are studied in the context of a new Fast Cosine Transform (FCT) architecture enhanced by using the Quadratic Residue Number System (QRNS). The system reduces the number of adders and multipliers required for the N-point Discrete Cosine Transform (DCT) and provides high throughput. For an FPL-based implementation, the proposed design gets significant improvements over an equivalent 2C structure. By using up to 6-bit moduli, an overall increase in the system performance of about 140% is achieved. If this speed increase is considered along with the penalty in device resources, the presented QRNS-based FCT system provides an improvement in the area-delay figure factor of about 20%. Finally, the conversion overhead was carefully studied and it was found that the quantifiable benefits of the proposed design are not affected when converters are included.

In order to protect audio files, we propose in this paper a new integration scheme for blind audio file watermarking. The goal is to find a compromise between capacity and imperceptibility in order to hide as much data as possible while minimizing file degradation. This integration scheme is implemented in the three insertion domains: spatial, frequency and multi-resolution domains. For the spatial-domain integration, the mark is inserted directly into the data samples. For the frequency-domain integration, a Discrete Cosine Transform is applied to the audio frames; after the thresholding and quantification step, the watermark is inserted into the Discrete Cosine Transform coefficients to obtain the watermarked file. For the multi-resolution-domain insertion, a single-level Discrete Wavelet Transform is applied using the scaling low-pass filter and wavelet high-pass filter. The watermark integration is then performed using the obtained AC coefficients. The proposed concealment process combines three values to integrate two bits and only one may be modified, which reduces the probability of change unlike other approaches. This implies less modification and therefore less distortion of the host file; this explains the good Signal-to-Noise Ratio obtained of more than 59dB for the spatial-domain integration and therefore a reasonable imperceptibility. An evaluation of the watermark’s robustness demonstrates that the proposed schemes generate reasonably robust watermarked samples against various attacks with a high-quality watermark with normalized cross-correlation greater than 0.9 for the three insertion domains.

In this work, we proposed a robust and blind watermarking approach to adequately secure medical images exchanged in telemedicine. This approach ensures the traceability and integrity of the medical and essential image for data security in the field of telemedicine. In this paper, a blind watermarking method is proposed to adequately secure the electronic patient records. The integration of the watermark will be carefully performed by combining the parity of the successive values. This innovative approach will be typically implemented in the three insertion domains: spatial, frequency and multi-resolution. For the spatial domain, the watermark will be integrated into the colorimetric values of the image. In the frequency domain, the watermark bits will be substituted to the DCT coefficient’s least significant bit. For the multi-resolution domain insertion, after calculating a DWT, the obtained LL sub-band coefficients will be used for the integration process. After comparing our approaches to the various recent works in the three domains, the obtained results demonstrate that our proposed approach offers a good imperceptibility for the frequency and spatial domains insertion.

This paper presents a vision based technique to identify the unspoken phones using a small camera that is located on the headset of the speaker. The system is based on temporal integration of the video data to generate motion history image (MHI). The paper proposes the use of global features to classify the MHI and compares the use of image moments with Discrete Cosine Transform (DCT). A comparison between Zernike moments (ZM) with DCT indicates that while the accuracy of classification for both techniques is very comparable (96% for ZM and 94% for DCT) when there is no relative motion between the camera and the mouth, ZM is resilient to rotation of the camera and continues to gives good results despite rotation but DCT is sensitive to rotation.

Based on the accuracy of the system and its resilience to movement artefacts such as rotation, the authors propose the use of such a system for human computer interface. Such a system could be invaluable when it is important to communicate without making a sound, such as giving passwords when in an open office or in public spaces.

Transmitting images over the wireless channel require that optimum compression ratio be maintained along with good image quality. This becomes a critical issue especially at higher values of bit error rate (BER). Joint photographic experts group (JPEG) standard and its successor JPEG 2000 provide excellent compression ratio but image reconstruction becomes highly difficult under extreme noise conditions. We present a mapping technique which gives better compression as compared to the existing techniques. Also, its performance is excellent even for higher bit error rates (BERs). This is supported by the results presented for JPEG, JPEG 2000, as well as mapping technique under fading channel conditions. Moreover, it is observed that the presence of high levels of noise has negligible effect on the reconstruction of images encoded using mapping technique.

Conventional techniques for security of data, designed by using only one of the security mechanisms, cryptography or steganography, are suitable for limited applications only. In this paper, we propose a crypto-stego system that would be appropriate for secure transmission of different forms of data. In the proposed crypto-stego system, we present a mechanism to provide secure transmission of data by multiple safety measures, firstly by applying encryption using Affine Transform and Discrete Cosine Transform (DCT) and then merging this encrypted data with an image, randomly chosen from a set of available images, and sending the image so obtained to the receiver at the other end through the network. The data to be sent over a communication channel may be a gray-scale or colored image, or a text document (doc, .txt, or .pdf file). As it is encrypted and sent hidden in an image, it avoids any attention to itself by the observers in the network. At the receiver’s side, reverse transformations are applied to obtain the original information. The experimental results, security analysis and statistical analysis for gray-scale images, RGB images, text documents (.doc, .txt, .pdf files), show robustness and appropriateness of the proposed crypto-stego system for secure transmission of the data through unsecured network. The security analysis and key space analysis demonstrate that the proposed technique is immune from cryptanalysis.

This paper proposes a compression algorithm using octonary repetition tree (ORT) based on run length encoding (RLE). Generally, RLE is one type of lossless data compression method which has duplication problem as a major issue due to the usage of code word or flag. Hence, ORT is offered instead of using a flag or code word to overcome this issue. This method gives better performance by means of compression ratio, i.e. 99.75%. But, the functioning of ORT is not good in terms of compression speed. For that reason, physical- next generation secure computing (PHY-NGSC) is hybridized with ORT to raise the compression speed. It uses an MPI-open MP programming paradigm on ORT to improve the compression speed of encoder. The planned work achieves multiple levels of parallelism within an image such as MPI and open MP for parallelism across a group of pictures level and slice level, respectively. At the same time, wide range of data compression like multimedia, executive files and documents are possible in the proposed method. The performance of the proposed work is compared with other methods like accordian RLE, context adaptive variable length coding (CAVLC) and context-based arithmetic coding (CBAC) through the implementation in Matlab working platform.

In this paper, we apply classical non-uniform L1 formula and the compact difference scheme for solving linear fractional systems with Neumann boundary conditions. A novelty and simple demonstration strategy is presented on the convergence analysis in the discrete maximum norm. Moreover, based on the special properties of the resulting coefficient matrix, diagonalization technique and discrete cosine transform (DCT) are adopted to speed up the convergence rate of the proposed method. In addition, the numerical scheme is also extended to the three-dimensional (3D) case. Several numerical experiments are given to support our findings.

We present a preliminary investigation of compression of segmented 3D seismic volumes for the rendering purposes. Promising results are obtained on the base of 3D discrete cosine transforms followed by the SPIHT coding scheme. An accelerated version of the algorithm combines 1D discrete cosine transform in vertical direction with the 2D wavelet transform of horizontal slices. In this case the SPIHT scheme is used for coding the mixed sets of cosine-wavelet coefficients.

Trajectory representation model has been proposed to describe non-rigid deformation. An optimal trajectory space finding algorithm for 3D non-rigid structure from motion (OTSF-NRSFM) based on this model also has been proposed. However, the influence of the wavelet basis selection on the OTSF-NRSFM algorithm has still not been studied. To help OTSF-NRSFM researchers select wavelet basis properly, we investigated the influences of wavelet basis selection. Two typical wavelet bases, DCT basis and WHT basis, are discussed in this paper. The spectrum properties of wavelet basis and feature point trajectory, trajectory representation results on synthetic shark data, OTSF-NRSFM reconstruction results on synthetic data and real data are analyzed. The results show that the wavelet selection has much influence on OTSF-NRSFM reconstruction results of some non-rigid feature points, which have complicated trajectory. This paper gives researchers some inspiration about wavelet basis selection in OTSF-NRSFM algorithm.

DNA-binding proteins (DBPs) perform an influential role in diverse biological activities like DNA replication, slicing, repair, and transcription. Some DBPs are indispensable for understanding many types of human cancers (i.e. lung, breast, and liver cancer) and chronic diseases (i.e. AIDS/HIV, asthma), while other kinds are involved in antibiotics, steroids, and anti-inflammatory drugs designing. These crucial processes are closely related to DBPs types. DBPs are categorized into single-stranded DNA-binding proteins (ssDBPs) and double-stranded DNA-binding proteins (dsDBPs). Few computational predictors have been reported for discriminating ssDBPs and dsDBPs. However, due to the limitations of the existing methods, an intelligent computational system is still highly desirable. In this work, features from protein sequences are discovered by extending the notion of dipeptide composition (DPC), evolutionary difference formula (EDF), and K-separated bigram (KSB) into the position-specific scoring matrix (PSSM). The highly intrinsic information was encoded by a compression approach named discrete cosine transform (DCT) and the model was trained with support vector machine (SVM). The prediction performance was further boosted by the genetic algorithm (GA) ensemble strategy. The novel predictor (DBP-GAPred) acquired 1.89%, 0.28%, and 6.63% higher accuracies on jackknife, 10-fold, and independent dataset tests, respectively than the best predictor. These outcomes confirm the superiority of our method over the existing predictors.

In this paper we propose a new appearance based system which consists of two stages: visual speech feature extraction and classification, followed by recognition of the extracted feature, thereby the result is a complete lip-reading system. This lip-reading system employs our Hyper Column Model (HCM) approach to extract and classify the visual features and uses the Hidden Markov Model (HMM) for recognition. This paper addresses mainly the first stage; i.e. feature extraction and classification. We investigate the HCM performance to achieve feature extraction and classification and then compare the performance when replacing HCM with Fast Discrete Cosine Transform (FDCT). Unlike FDCT, HCM could extract the entire features without any loss. Also the experiments have shown that HCM is generally better than FDCT and provides a good distribution of the phonemes in the feature space for recognition purposes. For fair comparison, two databases are exploited with three different sets of resolution for each database. One of these two databases is designed to include shifted and scaled objects. Experiments reveal that HCM is capable of recovering and dealing with such image restrictions whereas the effectiveness of FDCT drops drastically especially for new subjects.

Pseudospectral time domain (PSTD) methods are widely used in many branches of acoustics for the numerical solution of the wave equation, including biomedical ultrasound and seismology. The use of the Fourier collocation spectral method in particular has many computational advantages. However, the use of a discrete Fourier basis is also inherently restricted to solving problems with periodic boundary conditions. Here, a family of spectral collocation methods based on the use of a sine or cosine basis is described. These retain the computational advantages of the Fourier collocation method but instead allow homogeneous Dirichlet (sound-soft) and Neumann (sound-hard) boundary conditions to be imposed. The basis function weights are computed numerically using the discrete sine and cosine transforms, which can be implemented using $O(NlogN)$ operations analogous to the fast Fourier transform. Practical details of how to implement spectral methods using discrete sine and cosine transforms are provided. The technique is then illustrated through the solution of the wave equation in a rectangular domain subject to different combinations of boundary conditions. The extension to boundaries with arbitrary real reflection coefficients or boundaries that are nonreflecting is also demonstrated using the weighted summation of the solutions with Dirichlet and Neumann boundary conditions.

Computer vision abilities of autonomous systems have a significant computational demand on the underlying hardware. We research on methods to reduce the amount of data provided to Artificial Neural Networks (ANN)-based image classification. We consider transformation techniques, as many visual sensors inherently provide hardware components for the Discrete Cosine Transform (DCT). The focus in this paper is on a fast prediction phase, at the cost of memory and a more sophisticated training phase. In particular, we partition data in the frequency domain, define the problem by mathematical programming and train a set of distinct ANNs. An online algorithm selects the smallest feasible ANN in the prediction phase.

Digital watermark protocol technique can prove the origination of a digital media, such as video, audio, or still image, so it is used for copyright protection. One problem with traditional digital watermark techniques is that if an illegal copy of the media is found it is impossible to determine who distribute it. In order to solve this problem, an undeniable digital watermark protocol is proposed in this paper. In this protocol, both the seller and the buyer generate the sequence of digital watermark jointly based on zero-knowledge proof. In cases where an unauthorized copy is found, it must the buyer distribute it. Experimental results show that the proposed scheme is robust against some image processing and lossy compression.