An energy-efficient encryption mechanism for NVM-based main memory in mobile systems

Emerging non-volatile memory (NVM) has been considered as the most promising candidate of DRAM for future main memory design in mobile devices. NVM-based main memory exhibits attractive features, such as byte-addressability, low standby power, high density and near DRAM performance. However, the nature of non-volatility makes NVM vulnerable to be attacked by malicious programs. Though several data encryption techniques have been proposed to solve this problem, they do not consider the limited resources in mobile systems. To address this issue, in this paper, we propose an energy-efficient encryption mechanism, named MobiLock, to effectively enhance the security of NVM-based main memory in mobile systems. The basic idea is to enhance the encryption and decryption performance by utilizing cache and concurrency mechanisms, respectively. To achieve this, we first develop a cache mechanism to cache the encrypted intermediate data (i.e., PAD) whose plaintexts are updated frequently, for accelerating decryption and reducing recomputation of PAD. We then propose a concurrency mechanism to read the ciphertext in NVM and calculate the PAD simultaneously, to reduce the decryption latency. The evaluation results show that our technique can effectively reduce encryption energy consumption and decryption latency, respectively.     

Interactive multi-image colour visualization for hyperspectral imagery

Data visualization can accelerate data processing so that enormous quantities of data can be utilized effectively. Visualization of data can achieve image communication between people and data as well as between people to help observers get information hidden in data, providing a tool for discovery and understanding of scientific law. To solve the problem of multi-image and multi-modality image display in the field of remote sensing, an interactive colour visualization method for hyperspectral imagery (HSI) is proposed in this article. This method visualizes complex information of original HSI data through different fusion results of multiple images in a colour space, which is under the interactive control of the observers. By gradually determining predetermined points, observers can obtain a relatively satisfying image blending mode, output an image with clearer interested target, and obtain the corresponding mixing coefficient of images. The proposed method can also solve the problem that traditional visualization methods only display information from three bands in one image, and conduct information mining in HSI with a certain purpose based on the demands of users. In addition, this approach is also applicable for visualization of other types of multi-modal imagery.     

Fault Detection and Diagnosis in dynamic systems using Weightless Neural Networks

This work examines Fault Detection and Diagnosis (FDD) based on Weightless Neural Networks (WNN) with applications in univariate and multivariate dynamic systems. WNN use neurons based on RAM (Random Access Memory) devices. These networks use fast and flexible learning algorithms, which provide accurate and consistent results, without the need for residual generation or network retraining, and therefore they have great potential use for pattern recognition and classification (Ludermir, Carvalho, Braga, de Souto, 1999). The proposed system firstly executes the selection of attributes (in the multivariable case) and does the time series mapping of the data. In the intermediate stage, the WNN performs the detection and diagnosis per class. The network outputs are then passed through a clustering filter in the final stage of the system, if a diagnosis per fault groups is necessary. The system was tested with two case studies: one was an actual application for the temperature monitoring of a sales gas compressor in a natural gas processing unit; and the other one uses simulated data for an industrial plant, known in the literature as “Tennessee Eastman Process”. The results show the efficiency of the proposed systems for FDD with classification accuracies of up to 98.78% and 99.47% for the respective applications.     

Hyperspectral image compression based on lapped transform and Tucker decomposition

In this paper, we present a hyperspectral image compression system based on the lapped transform and Tucker decomposition (LT-TD). In the proposed method, each band of a hyperspectral image is first decorrelated by a lapped transform. The transformed coefficients of different frequencies are rearranged into three-dimensional (3D) wavelet sub-band structures. The 3D sub-bands are viewed as third-order tensors. Then they are decomposed by Tucker decomposition into a core tensor and three factor matrices. The core tensor preserves most of the energy of the original tensor, and it is encoded using a bit-plane coding algorithm into bit-streams. Comparison experiments have been performed and provided, as well as an analysis regarding the contributing factors for the compression performance, such as the rank of the core tensor and quantization of the factor matrices.     

Morphological segmenting and neighborhood pixel-based locality preserving projection on brain fMRI dataset for semantic feature extraction: an affective computing study

Two specific chemical receptive fields of brain, namely the amygdala and the orbital-frontal cortex, are related to valence and arousal in medical experiments. Functional magnetic resonance imaging (fMRI), which is a noninvasive, repeatable, and atomical tool for medical imaging in clinic system, was widely used in affective computing; however, it faces its dataset processing difficulty for dimensional reduction as well as for decreasing the computational complexity. In addition, features extraction from those de-dimensionality datasets is a challenging issue. The current work solved the de-dimensionality issue by using some preprocessing algorithms including clustering, morphological segmenting, and locality preserving projection. In order to keep useful information in fMRI dataset for reduction process, improved neighborhood pixel-based locality preserving projection (NP-LPP) algorithm was addressed and continuously for feature extraction operating using Otsu weighted sum of histogram. Furthermore, a modified covariance power spectral density (MC-PSD) separately in an fMRI Valence–Arousal experiments was measured. The results were analyzed and compared with affective norms English words system. The experiments established that the proposed methods of NP-LPP effectively simplified high complexity of fMRI, and Otsu weighted sum of histogram exhibited superior performance for features extraction compared to the MC-PSD through the calculation root mean standard error. The current proposed method provided a potential application and promising research direction on human semantic retrieval through medical imaging dataset.

     

Methodology for automatic bioacoustic classification of anurans based on feature fusion

The automatic recognition of anurans by their calls provides indicators of ecosystem health and habitat quality. This paper presents a new methodology for the acoustic classification of anurans using a fusion of frequency domain features, Mel and Linear Frequency Cepstral Coefficients (MFCCs and LFCCs), with time domain features like entropy and syllable duration through intelligent systems. This methodology has been validated in three databases with a significant number of different species proving the strength of this approach. First, the audio recordings are automatically segmented into syllables which represent different anuran calls. For each syllable, both types of features are computed and evaluated separately as in previous works. In the experiments, a novel data fusion method has been used showing an increase of the classification accuracy which achieves an average of 98.80% ± 2.43 in 41 anuran species from AmphibiaWeb database, 96.90% ± 3.57 in 58 frogs from Cuba and 95.48% ± 4.97 in 100 anurans from southern Brazil and Uruguay; reaching a classification rate of 95.38% ± 5.05 for the aggregate dataset of 199 species.     

Adaptive multilevel modulation for grooming in elastic cloud optical networks

In order to mitigate the mismatch of granularities between fixed grid and client traffic, the elastic optical network (EON) was proposed by using orthogonal frequency division multiplexing. In EONs, the bandwidth variable transponder adjusts the number of bits per symbol, so that an optical signal generates by using just enough sub-carriers each with appropriate modulation level. Owing to the advantage of line-rate adaption above, the application of cloud computing has witnessed rapid growth in EONs. However, bandwidth variable transponders consume more power compared with ordinary ones, which will lead to a power-thirsty EON if no effective measure is taken. As a result, the green grooming was proposed for EONs. Unfortunately, the adaptive multilevel modulation was neglected in the current works focusing on green grooming. Thus, in this paper, we design a novel modulation adaptive grooming with guaranteeing transmission performances in green EONs. The distance-adaptive spectrum resource allocation is applied to the green grooming algorithm previously designed by us for EONs. The simulation results show that the adaptive multilevel modulation plays an important role on saving spectrum and power consumption for the green grooming in EONs, because the spectral bandwidth can be saved by increasing the number of bits per symbol to transmit the same data rate.     

A modified ant colony system for finding the expected shortest path in networks with variable arc lengths and probabilistic nodes

The problem of finding the expected shortest path in stochastic networks, where the presence of each node is probabilistic and the arc lengths are random variables, have numerous applications, especially in communication networks. The problem being NP-hard we use an ant colony system (ACS) to propose a metaheuristic algorithm for finding the expected shortest path. A new local heuristic is formulated for the proposed algorithm to consider the probabilistic nodes. The arc lengths are randomly generated based on the arc length distribution functions. Examples are worked out to illustrate the applicability of the proposed approach.     

Efficient implementation of chaotic image encryption in transform domains

The primary goal of this paper is security management in data image transmission and storage. Because of the increased use of images in industrial operations, it is necessary to protect the secret data of the image against unauthorized access. In this paper, we introduce a novel approach for image encryption based on employing a cyclic shift and the 2-D chaotic Baker map in different transform domains. The Integer Wavelet Transform (IWT), the Discrete Wavelet Transform (DWT), and the Discrete Cosine Transform (DCT) are exploited in the proposed encryption approach. The characteristics of the transform domains are studied and used to carry out the chaotic encryption. A comparison study between the transform-domain encryption approaches in the presence of attacks shows the superiority of encryption in the DWT domain.