Reversible robust data hiding based on wavelet filters modification

Multimedia Tools and Applications - In this paper, a new robust reversible data hiding method is proposed. The method is designed based on wavelet modifications which result in a scalable data...     

Brushless DC motor drives supplied by PV power system based on Z-source inverter and FL-IC MPPT controller

This paper discusses operation performance of a water pumping system consist of a brushless dc (BLDC) motor coupled a centrifugal pump and accompanying a Z-source inverter (ZSI) fed by a photovoltaic (PV) array, to be improved. Despite conventional double-stage power converters, this paper proposes utilizing a single-stage ZSI to extract the maximum power of the PV array and supply the BLDC motor simultaneously. Utilizing the ZSI provides some inherent advantages such as high efficiency and low cost, which is very promising for PV systems due to its novel voltage buck/boost capability. In addition, in order to precisely perform the maximum power point tracking (MPPT) of the PV array the fuzzy logic-incremental conductance (FL-IC) MPPT scheme is proposed. The proposed FL-IC MPPT scheme provides enough modification to the conventional IC method to enjoy an appropriate variable step size MPPT control signal for the ZSI. Moreover, direct torque control (DTC) is found more effective in comparison with hysteresis current control with current shaping to drive the BLDC motor, because it benefits from faster torque response, reduced torque ripple, less sensitivity to parameters variations, and simple implementation. In the mean time, due to the frequently variations of the PV power generation; delivered mechanical power to the centrifugal pump is variable. Thus, the BLDC motor should be driven with variable reference speed. In order to improve the speed transient response of the BLDC motor and enhance the energy saving aspect of the system, it should enjoy a high quality dynamic response characteristic. Therefore, to realize these purposes, particle swarm optimization (PSO) has been proposed to regulate the proportional-integral-derivative (PID) parameters of the BLDC motor speed controller. The system configuration, operation principle and control methods are presented in detail. Finally, the proposed system was simulated in different operation conditions of the PV array by computer simulations and the effectiveness of the proposed control strategies has been validated by comparative studies and simulation results.     

Salient object detection using local,global and high contrast graphs

In this paper, we propose a novel multi-graph-based method for salient object detection in natural images. Starting from image decomposition via a superpixel generation algorithm, we utilize color, spatial and background label to calculate edge weight matrix of the graphs. By considering superpixels as the nodes and region similarities as the edge weights, local, global and high contrast graphs are created. Then, an integration technique is applied to form the saliency maps using degree vectors of the graphs. Extensive experiments on three challenging datasets show that the proposed unsupervised method outperforms the several different state-of-the-art unsupervised methods.     

Effect of filtered wood smoke treatment on chemical and microbial changes in mahi mahi fillets

ABSTRACT:  A study was performed to investigate the effect of filtered wood smoke processing on quality and safety of mahi mahi compared to no treatment. Skinless mahi mahi fillet portions were either treated with filtered smoke (FS) or left untreated for 24 h, followed by either (a) aerobic storage at 4 °C for 8 d or (b) freezing for 30 d (–25 °C) followed by thawing and aerobic storage at 4 °C for 8 d. Results show that treating mahi mahi fillets with FS increased ( P < 0.05) a * values (redness) of the muscle and stabilized it during frozen storage. The redness did, however, decay ( P < 0.05) rapidly on cold storage for both defrosted and fresh filtered-smoke-treated products, and reached initial (presmoking) redness levels in 2 d. The FS process overall significantly ( P < 0.05) improved microbial stability of the product. Stability toward lipid oxidation was also significantly ( P < 0.05) increased for the FS products compared to untreated products, particularly after defrosting. Sensory studies supported the microbial and lipid oxidation findings, showing that products treated with FS were better accepted and had increased ( P < 0.05) shelf life over the untreated products. The shelf life was, however, compromised when microbial levels increased; that is, the process did not mask microbial spoilage; the spoilage did become evident in the sensory trials.     

Flexible, highly scalable, object-based wavelet image compression algorithm for network applications

The authors propose a highly scalable image compression scheme based on the set partitioning in hierarchical trees (SPIHT) algorithm. The proposed algorithm, called highly scalable SPIHT (HS-SPIHT), adds the spatial scalability feature to the SPIHT algorithm through the introduction of multiple resolution-dependent lists and a resolution-dependent sorting pass. It keeps the important features of the original SPIHT algorithm such as compression efficiency, full SNR scalability and low complexity. The flexible bitstream of the HS-SPIHT encoder can easily be adapted to various resolution requirements at any bit rate. The parsing process can be carried out on-the-fly without decoding the bitstream by a simple parser (transcoder) that forms a part of a smart network. The HS-SPIHT algorithm is further developed for fully scalable coding of arbitrarily shaped visual objects. The proposed highly scalable algorithm finds applications in progressive web browsing, visual databases and especially in image transmission over heterogeneous networks.     

Reversible Data Hiding in Encryption Domain based on two dimensional histogram shifting and secure encryption system

Multimedia Tools and Applications - Reversible Data Hiding in Encryption Domain (RDHED) can be used in different areas such as secure remote sensing or telemedicine. Since data structure is...     

Multi-atlas based neonatal brain extraction using atlas library clustering and local label fusion

Multimedia Tools and Applications - Brain extraction is one of the most important preprocessing steps in cerebral magnetic resonance (MR) image analysis. Brain extraction from neonatal MR images is...     

Region-based scalable self-recovery for salient-object images

Self-recovery is a tamper-detection and image recovery methods based on data hiding. It generates two types of data and embeds them into the original image: authentication data for tamper detection and reference data for image recovery. In this paper, a region-based scalable self-recovery (RSS) method is proposed for salient-object images. As the images consist of two main regions, the region of interest (ROI) and the region of non-interest (RONI), the proposed method is aimed at achieving higher reconstruction quality for the ROI. Moreover, tamper tolerability is improved by using scalable recovery. In the RSS method, separate reference data are generated for the ROI and RONI. Initially, two compressed bitstreams at different rates are generated using the embedded zero-block coding source encoder. Subsequently, each bitstream is divided into several parts, which are protected through various redundancy rates, using the Reed-Solomon channel encoder. The proposed method is tested on 10 000 salient-object images from the MSRA database. The results show that the RSS method, compared to related methods, improves reconstruction quality and tamper tolerability by approximately 30% and 15%, respectively.     

A wavelet-based classification of hyperspectral images using Schroedinger eigenmaps

This article proposes a new algorithm for hyperspectral image classification. The proposed method is a spectral–spatial method based on wavelet transforms, kernel minimum noise fraction (KMNF) and spatial–spectral Schroedinger eigenmaps (SSSE). To overcome the computation complexity, one-dimensional discrete wavelet transform (1D-DWT) is applied in spectral domain. To reduce noise, KMNF coefficients are extracted in wavelet space. To solve time-consuming problem, 2D-DWT coefficients are employed in spatial space. Hence, the combination of 1D-DWT, KMNF, and 2D-DWT is suggested to create SSSE features. The classification is carried out by a Support Vector Machine (SVM) classifier. Experimental results show that classification accuracy and time consumption are effectively improved compared to the state-of-the art reported spectral–spatial SVM-based methods.     

Salient object detection method using random graph

In this paper, a bottom-up salient object detection method is proposed by modeling image as a random graph. The proposed method starts with portioning input image into superpixels and extracting color and spatial features for each superpixel. Then, a complete graph is constructed by employing superpixels as nodes. A high edge weight is assigned into a pair of superpixels if they have high similarity. Next, a random walk prior on nodes is assumed to generate the probability distribution on edges. On the other hand, a complete directed graph is created that each edge weight represents the probability for transmitting random walker from current node to next node. By considering a threshold and eliminating edges with higher probability than the threshold, a random graph is created to model input image. The inbound degree vector of a random graph is computed to determine the most salient nodes (regions). Finally, a propagation technique is used to form saliency map. Experimental results on two challenging datasets: MSRA10K and SED2 demonstrate the efficiency of the proposed unsupervised RG method in comparison with the state-of-the-art unsupervised methods.