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  • Blind three dimensional deconvolution via convex optimization
    Multidimens. Syst. Signal Process. (IF 2.338) Pub Date : 2020-01-14
    Shayan Shojaei, Farzan Haddadi

    In this paper we discuss recovering two signals from their convolution in 3 dimensions. One of the signals is assumed to lie in a known subspace and the other one is assumed to be sparse. Various applications such as super resolution, radar imaging, and direction of arrival estimation can be described in this framework. We introduce a method to estimate parameters of a signal in a low-dimensional subspace which is convolved with another signal comprised of some impulses in time domain. We transform the problem to a convex optimization in the form of a positive semi-definite program using lifting and the atomic norm. We demonstrate that unknown parameters can be recovered by lowpass observations. Numerical simulations show excellent performance of the proposed method.

  • Coherent wide-band signals DOA estimation by the new CTOPS algorithm
    Multidimens. Syst. Signal Process. (IF 2.338) Pub Date : 2020-01-08
    Abbas Asadzadeh, Seyed Mohammad Alavi, Mahmood Karimi, Hadi Amiri

    Direction of arrival estimation is one of the most important issues in array signals processing. In this paper, a new method is presented for direction estimation of coherent wide-band signals. First of all, signal eigenvalues which have information of the direction of coherent signals, are extracted as primary vectors in each frequency bin. Afterwards, by constructing the innovative matrix H from signal eigenvalues, the de-correlation process is performed and the linear independent vectors of sources are extracted from SVD decomposition. Finally, by choosing transfer matrix, the signal subspace of the reference frequency is transferred to other frequency bins and the direction estimation process is performed by using TOPS algorithm. The proposed method does not need any knowledge of the number of sources and initial estimation of arrival angles. According to the simulation results, the CTOPS new method has a better performance than TOPS method in the presence of correlated sources.

  • Secure data hiding by fruit fly optimization improved hybridized seeker algorithm
    Multidimens. Syst. Signal Process. (IF 2.338) Pub Date : 2020-01-08
    R. Roselin Kiruba, T. Sree Sharmila

    Abstract The recent growth of World Wide Web (WWW) and development of the next-generation internet facilitates a huge amount of data being conveniently transmitted via wireless networks. The sensitive information transmitted is potentially vulnerable in the communication channel like wireless networks. Unauthorized users could potentially intercept and negatively exploit the true intent of the information being exchanged between legitimate users. The efficient steganography techniques are very useful to prevent such undesirable interception of information. In this work, we propose and evaluate an efficient image steganography using Fruit Fly Optimization hybridized Improved Seeker (FOIS) algorithm. The FOIS provides information security and safeguards the medical data to avoid medical related cybercrimes. FOIS efficiently determines the optimal locations of pixels adaptively in the spatial domain of the cover image. Initially, the cover image is divided into n blocks of \(8 \times 8\), on which a permutation combination is applied to find the number of blocks for further processing. This method improves the image quality and secures data. The secret messages are embedded in each block using optimal pixels selection and Least Significant Bit (LSB) of Discrete Cosine Transform coefficients. Moreover, in order to ensure seamless communication over an insecure communication channel, a dual cryptosystem model is developed which consist of the proposed steganography scheme and Rivest Cipher (RC4) cryptosystem. This work validates the security level of the stego image, and finally the performance is compared with state-of-the-art methods such as LSB, Particle Swarm Optimization and Genetic Algorithm. The performance assessment reveals that the proposed steganography model outperforms other optimization based approaches in terms of Peak Signal-to-Noise Ratio, embedding capacity and imperceptibility.

  • Stability of one-dimensioned spatially interconnected systems
    Multidimens. Syst. Signal Process. (IF 2.338) Pub Date : 2019-12-17
    Olivier Bachelier, Thomas Cluzeau, Francisco José Silva Alvarez, Nima Yeganefar

    Abstract This article is dedicated to the stability of one-dimensioned spatially interconnected systems. More precisely, it focuses on systems which results of the interconnection of a possibly large number of cells (continuous subsystems). This note is restricted to the case where cells are just distributed along a line. The global system can then be seen as a mixed continuous–discrete 2D Roesser system but with implicit discrete dynamics along the space dimension. Recent results on the stability of 2D Roesser models are exploited and adapted to derive a sufficient condition for such a system to be stable. The condition seems to be close to necessity if not necessary. It is tractable since it is expressed in terms of linear matrix inequalities. The novelty clearly lies in the reduction of the conservatism of the proposed analysis.

  • Factorizations for a class of multivariate polynomial matrices
    Multidimens. Syst. Signal Process. (IF 2.338) Pub Date : 2019-12-17
    Dong Lu, Dingkang Wang, Fanghui Xiao

    This paper investigates how to factorize a class of multivariate polynomial matrices. We prove that an \(l\times m\) multivariate polynomial matrix admits a matrix factorization with respect to a given polynomial if the polynomial and all the \((l-1)\times (l-1)\) reduced minors of the matrix generate a unit ideal. This result is a generalization of a theorem in Liu et al. (Circuits Syst Signal Process 30(3):553–566, 2011). Based on three main theorems presented in the paper and a constructive algorithm proposed by Lin et al. (Circuits Syst Signal Process 20(6):601–618, 2001), we give an algorithm which can be used to factorize more multivariate polynomial matrices. In addition, an illustrative example is given to show the effectiveness of the proposed algorithm.

  • Epileptic high-frequency oscillations: detection and classification
    Multidimens. Syst. Signal Process. (IF 2.338) Pub Date : 2019-12-14
    Shun-Chi Wu, Chen-Wei Chou, Chien Chen, Shang-Yeong Kwan, Yung-Chih Su

    High-frequency oscillations (HFOs) in intracranial electroencephalograms of patients with epilepsy are regarded as promising biomarkers of epileptogenic zones. Their detection and classification can be achieved by visual assessment or automated approaches, although manual processing of large recordings can be laborious. As a result, an automated analysis scheme is indispensable to enable the clinical use of HFOs. In this paper, we present a two-stage strategy to detect and classify HFOs, which starts with a threshold-based approach to detect plausible HFO events followed by an event classification to discriminate different oscillations. Unlike existing approaches, the detection process in the proposed schemes starts by calculating various multi-channel features that allow interrelations among electrodes to be exploited for detection. On this basis, the detection thresholds are set epoch-by-epoch, relying on a two-component Gaussian mixture model to avoid threshold overestimation. The events deemed to be plausible HFOs are then subjected to classification. By simultaneously examining the raw data and time-frequency maps of these events, they are ultimately sorted into the following categories: HFOs, spikes, and spikes with HFOs, so that the oscillations solely caused by filtering sharp transients can be discriminated. Experimental results using simulated data and intracranial recordings from three epileptic patients demonstrate that our proposed schemes achieve promising sensitivity and precision, especially when the noise level is high.

  • Nonuniform sampling for random signals bandlimited in the linear canonical transform domain
    Multidimens. Syst. Signal Process. (IF 2.338) Pub Date : 2019-11-21
    Haiye Huo, Wenchang Sun

    Abstract In this paper, we mainly investigate the nonuniform sampling for random signals which are bandlimited in the linear canonical transform (LCT) domain. We show that the nonuniform sampling for a random signal bandlimited in the LCT domain is equal to the uniform sampling in the sense of second order statistic characters after a pre-filter in the LCT domain. Moreover, we propose an approximate recovery approach for nonuniform sampling of random signals bandlimited in the LCT domain. Furthermore, we study the mean square error of the nonuniform sampling. Finally, we do some simulations to verify the correctness of our theoretical results.

  • Expanded coprime array for DOA estimation: augmented consecutive co-array and reduced mutual coupling
    Multidimens. Syst. Signal Process. (IF 2.338) Pub Date : 2019-11-19
    Yunfei Wang, Wang Zheng, Xiaofei Zhang, Jinqing Shen

    Generalized coprime structure decomposes the interleaved subarrays in the conventional coprime array by introducing a displacement and the resulting CADiS, i.e. coprime array with displaced subarrays, configuration can enlarge the minimum adjacent spacing between elements to multiples of half-wavelength, which is considerably attractive in alleviating mutual coupling effect. However, the difference co-array that CADiS yields is fractured, which greatly deteriorates direction of arrival (DOA) estimation performance and achievable degrees of freedom of the algorithms based on consecutive co-array, e.g. spatial smoothing technique and Toeplitz matrix method. In this paper, from the mutual coupling effect and difference co-array perspective, we propose an expanded coprime array (ECA) structure by two steps. The first step is to further augment the displacement between the subarrays of CADiS to suppress the mutual coupling effect. The second step is to relocate a proper number of rightmost elements to concatenate the dominant consecutive co-array generated by CADiS to enhance the consecutive difference co-array. Specifically, we provide the closed-form expressions of resulting consecutive difference co-array, the number of relocated elements and their positions. Furthermore, different from the spatial smoothing based methods, we employ Toeplitz matrix property to directly construct the full-rank covariance matrix of the received data from the consecutive co-array with a lower computational cost and present the Toeplitz-MUSIC algorithm to testify the effectiveness and superiority of the proposed ECA structure.

  • A novel efficient image encryption algorithm based on affine transformation combine with linear fractional transformation
    Multidimens. Syst. Signal Process. (IF 2.338) Pub Date : 2019-11-06
    Dawood Shah, Tariq Shah, Sajjad Shaukat Jamal

    Algebraic structures and their hardware–software implementation gain considerable attention in the field of information security and coding theory. Research progress in the applications of arithmetic properties of algebraic structures is being frequently made. These structures are mostly useful in improvement of the cryptographic algorithms. A novel technique is given to design a cryptosystem responsible for lossless for image encryption. The proposed scheme is for the RGB image whose pixels are considered as 24 binary bits, accordingly a unique arrangement for the construction of S-boxes over a Galois field \( GF\left( {2^{9} } \right) \) is employed. Consequently, it generates multiple different S-boxes with excellent cryptographic characteristic and hence confusing process of the cryptosystem has been working. Whereas the diffusion process in this cryptosystem is based on Affine transformation over a unit elements of an integers modulo ring \( {\mathbb{Z}}_{n} \). The scrambling of the image data through the Affine transformation escalate the security asset, avoid computational effort and abbreviated the time complexity. In addition, the simulation test and comparative scrutinize illustrate that the proposed scheme is highly sensitive, large keyspace, excellent statistical properties and secure against differential attacks. Therefore, the proposed algorithm is valuable for confidential communication. Furthermore, due to the arithmetic properties of algebraic structures, the proposed scheme would be easily implemented, secure and fast enough to be utilized in real-world applications.

  • Energy centroid clustering algorithm to enhance the network lifetime of wireless sensor networks
    Multidimens. Syst. Signal Process. (IF 2.338) Pub Date : 2019-11-04
    Sathyapriya Loganathan, Jawahar Arumugam

    Abstract Wireless sensor networks (WSN) consists of dedicated sensors, which monitor and record various physical and environmental conditions like temperature, pollution levels, humidity etc. WSN is compatible with several applications related to environmental and healthcare monitoring. The sensor nodes have a limited battery life and are deployed in hostile environments. Recharging or replacement of the batteries in the sensor nodes are very difficult after deployment in inaccessible areas where energy is an important factor for continuous network operation. Energy efficiency is a major concern in the wireless sensor networks as it is important for maintaining network operation. In this paper, an energy efficient clustering algorithm based energy centroid and energy threshold has been proposed for wireless sensor networks. Here each cluster is designed to own 25% of the sensor nodes using distance centroid algorithm. Cluster head selection is based on the energy centroid of each cluster and energy threshold of the sensor nodes. Communication between the sink node and cluster head uses distance of separation as a parameter for reducing the energy consumption. The result obtained shows an average increase of 53% in energy conservation and network lifetime compared to Leach-B, Park Approach, EECPK-means Approach and MPST Approach.

  • A fast and high accurate image copy-move forgery detection approach
    Multidimens. Syst. Signal Process. (IF 2.338) Pub Date : 2019-11-02
    Xiang-Yang Wang, Chao Wang, Li Wang, Li-Xian Jiao, Hong-Ying Yang, Pan-Pan Niu

    Copy-move is one of the most common image forgeries, wherein one or more region are copied and pasted within the same image. The motivations of such forgery include hiding an element in the image or emphasizing a particular object. Copy-move image forgery is more challenging to detect than other types, such as splicing and retouching. Keypoint based copy-move forgery detection extracts image keypoints and uses local visual features to identify duplicated regions, which exhibits remarkable performance with respect to memory requirement and robustness against various attacks. However, these approaches fail to handle the cases when copy-move forgeries only involve small or smooth regions, where the number of keypoints is very limited. Also, they generally have higher time costs owing to complex feature descriptor and more error matching points. To tackle these challenges, we propose a fast and effective copy-move forgery detection method through adaptive keypoint extraction and processing, introducing fast robust invariant feature, and filtering out the wrong pairs. Firstly, the uniform distribution keypoints are extracted adaptively from the forged image by employing the fast approximated LoG filter and performing the uniformity processing. Then, the image keypoints are described using fast robust invariant feature and matched through the Rg2NN algorithm. Finally, the falsely matched pairs are removed by employing the segmentation based candidate clustering, and the duplicated regions are localized using optimized mean-residual normalized production correlation. We conduct extensive experiments to evaluate the performance of the proposed scheme, in which encouraging results validate the effectiveness of the proposed technique, in comparison with the state-of-the-art approaches recently proposed in the literature.

  • PAC-Bayesian framework based drop-path method for 2D discriminative convolutional network pruning
    Multidimens. Syst. Signal Process. (IF 2.338) Pub Date : 2019-10-19
    Qinghe Zheng, Xinyu Tian, Mingqiang Yang, Yulin Wu, Huake Su

    Abstract Deep convolutional neural networks (CNNs) have demonstrated its extraordinary power on various visual tasks like object detection and classification. However, it is still challenging to deploy state-of-the-art models into real-world applications, such as autonomous vehicles, due to their expensive computation costs. In this paper, to accelerate the network inference, we introduce a novel pruning method named Drop-path to reduce model parameters of 2D deep CNNs. Given a trained deep CNN, pruning paths with different lengths is achieved by ordering the influence of neurons in each layer on the probably approximately correct (PAC) Bayesian boundary of the model. We believe that the invariance of PAC-Bayesian boundary is an important factor to guarantee the generalization ability of deep CNN under the condition of optimizing as much as possible. To the best of our knowledge, this is the first time to reduce model size based on the generalization error boundary. After pruning, we observe that the convolutional kernels themselves become sparse, rather than some being removed directly. In fact, Drop-path is generic and can be well generalized on multi-layer and multi-branch models, since parameter ranking criterion can be applied to any kind of layer and the importance scores can still be propagated. Finally, Drop-path is evaluated on two image classification benchmark datasets (ImageNet and CIFAR-10) with multiple deep CNN models, including AlexNet, VGG-16, GoogLeNet, and ResNet-34/50/56/110. Experimental results demonstrate that Drop-path achieves significant model compression and acceleration with negligible accuracy loss.

  • Multi-scale RoIs selection for classifying multi-spectral images
    Multidimens. Syst. Signal Process. (IF 2.338) Pub Date : 2019-10-11
    Ayan Seal, Angel Garcia-Pedrero, Debotosh Bhattacharjee, Mita Nasipuri, Mario Lillo-Saavedra, Ernestina Menasalvas, Consuleo Gonzalo-Martin

    The applications of object-based image analysis (OBIA) in remote sensing studies have received a considerable amount of attention over the recent decade due to dramatically increasing of the spatial resolution of satellite imaging sensors for earth observation. In this study, an unsupervised methodology based on OBIA paradigm for the estimation of multi-scale training sets for land cover classification is proposed. The proposed method consists of selection of valid region of interests in an unsupervised way and its characterization using some attributes in order to form meaningful and reliable training sets for supervised classification of different land covers of a satellite image. Multi-scale image segmentation is a prerequisite step for estimation of multi-scale training sets. However, scale selection remains a challenge in multi-scale segmentation. In this work, we propose a method to determine the appropriate segmentation scale for each land cover with the help of prior knowledge in the form of in-situ data. The proposed method is further discussed and validated through multi-scale segmentation using quick shift and random forest algorithms on two multi-spectral images captured using Worldview-2 sensor. Experimental results indicate that the proposed method qualitatively and quantitatively outperforms three state-of-the-art methods.

  • IVA using complex multivariate GGD: application to fMRI analysis
    Multidimens. Syst. Signal Process. (IF 2.338) Pub Date : 2019-10-09
    Rami Mowakeaa, Zois Boukouvalas, Qunfang Long, Tülay Adali

    Abstract Examples of complex-valued random phenomena in science and engineering are abound, and joint blind source separation (JBSS) provides an effective way to analyze multiset data. Thus there is a need for flexible JBSS algorithms for efficient data-driven feature extraction in the complex domain. Independent vector analysis (IVA) is a prominent recent extension of independent component analysis to multivariate sources, i.e., to perform JBSS, but its effectiveness is determined by how well the source models used match the true latent distributions and the optimization algorithm employed. The complex multivariate generalized Gaussian distribution (CMGGD) is a simple, yet effective parameterized family of distributions that account for full second- and higher-order statistics including noncircularity, a property that has been often omitted for convenience. In this paper, we marry IVA and CMGGD to derive, IVA-CMGGD, with a number of numerical optimization implementations including steepest descent, the quasi-Newton method Broyden–Fletcher–Goldfarb–Shanno (BFGS), and its limited-memory sibling limited-memory BFGS all in the complex-domain. We demonstrate the performance of our algorithm on simulated data as well as a 14-subject real-world complex-valued functional magnetic resonance imaging dataset against a number of competing algorithms.

  • Mixed near-field and far-field source localization revised: propagation loss included
    Multidimens. Syst. Signal Process. (IF 2.338) Pub Date : 2019-10-08
    Pourya Behmandpoor, Farzan Haddadi

    Abstract This paper is concerned with source localization when path loss is taken into account. We modify multiple signal classification method to localize near-field sources whose received power is different in the sensors of the array due to path loss. Traditional methods fail to localize the sources, and they also fail to separate the bearing estimation and the range estimation of the sources, when path loss is considered. We suggest a T-shaped array avoiding multidimensional search to estimate source location parameters, separately. At the first step, the ranges of the signal sources are estimated, and then at the second step, the directions of arrival of the sources are estimated using the respective ranges determined at the first stage. The performance of the proposed method is assessed when mixed near-field and far-field sources coexist. Simulation results are presented to show the superior performance of the proposed algorithm compared to the existing techniques and the Cramer–Rao bound.

  • SFFS–SVM based prostate carcinoma diagnosis in DCE-MRI via ACM segmentation
    Multidimens. Syst. Signal Process. (IF 2.338) Pub Date : 2019-10-03
    Chuan-Yu Chang, Kathiravan Srinivasan, Hui-Ya Hu, Yuh-Shyan Tsai, Vishal Sharma, Punjal Agarwal

    Abstract The prostate carcinoma is amongst the most commonly occurring cancers in Taiwanese males. Moreover, it is one of the chief reasons for cancer deaths among Taiwanese men, and early diagnosis of prostate cancer is vital for effective treatment. In this work, a diagnosis model for identifying the prostate carcinoma in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is proposed. The urologists utilize the DCE-MRI as a support mechanism for better diagnosis of the carcinoma development in the prostate. Gadolinium is utilized as the contrast agent for the DCE-MRI data, and it was injected once and the time series data were captured at distinct time intervals of 0, 20, 60, and 100 s correspondingly. Primarily, after pre-processing the DCE-MRI information, the prostate data is segmented by employing the active contour model. Subsequently, 136 features are extracted from the segmented prostrate expanse of the DCE-MRI data, and the relative intensity change curve is computed. Afterward, Fisher’s discriminant ratio and sequential forward floating selection is deployed for choosing ten highly discriminative features. Lastly, the segmented prostate regions are classified into two groups, namely: tumor and normal classes by employing the support vector machine classifier. The experimental results elucidate that the proposed system is superior on the subject of accuracy, sensitivity, and specificity when compared with specific existing methods. Additionally, the proposed system also demonstrates a 94.75% accuracy. Moreover, this signifies the fact that the proposed method for analyzing the DCE data has shown prodigious prospects in the prostate carcinoma diagnosis.

  • Content-based blur image retrieval using quaternion approach and frequency adder LBP
    Multidimens. Syst. Signal Process. (IF 2.338) Pub Date : 2019-04-16
    Komal Nain Sukhia, M. Mohsin Riaz, Abdul Ghafoor

    The paper presents a content based image retrieval scheme based on feature extraction and weighing. Features are extracted using frequency adder based local binary pattern and blur detection metric which are then optimally combined using a weighing scheme. Simulations are performed on modified Wang and KTH-TIPS databases, which include images from four different classes of blur respectively. Comparison of simulation results with the state-of-the-art techniques show better retrieval precision and recall values for proposed technique.

  • An improved signal processing algorithm for VSF extraction
    Multidimens. Syst. Signal Process. (IF 2.338) Pub Date : 2019-01-08
    Xiaolin Liang, Hao Zhang, Tingting Lu, Han Xiao, Guangyou Fang, Thomas Aaron Gulliver

    Abstract Contactless detection of human beings via extracting vital sign features (VSF) is a perfect technology by employing an ultra-wideband radar. Only using Fourier transform, it is a challenging task to extract VSF in a complex environment, which can cause a lower signal to noise ratio (SNR) and significant errors due to the harmonics. This paper proposes an improved signal processing algorithm for VSF extraction via analyzing the skewness and standard deviation of the collected impulses. The discrete windowed Fourier transform technique is used to estimate the time of arrival of the pulses. The frequency of human breathing movements is obtained using an accumulation scheme in frequency domain, which can better cancel out the harmonics. The capabilities of removing clutters and improving SNR are validated compared with several well-known methods experimentally.

  • An optimal weighted averaging fusion strategy for remotely sensed images
    Multidimens. Syst. Signal Process. (IF 2.338) Pub Date : 2019-02-25
    Madheswari Kanmani, Venkateswaran Narasimhan

    Abstract Image fusion plays a vital role in providing better visualization of remotely sensed image data. Most earth observation satellites have sensors that provide both high spatial resolution panchromatic (PAN) images and low resolution multispectral (MS) images. In this paper, we propose a new fusion algorithm that optimally combines spectral information from MS image and spatial information from the PAN image of the same scene to create a single comprehensive fused image. As the performance of the fusion scheme relies on the choice of fusion rule, the proposed algorithm is based on a weighted averaging fusion rule that uses optimal weights obtained from brain storm optimization (BSO) algorithm for the fusion of high frequency and low frequency coefficients obtained by applying Curvelet transform to the source images. The objective function in BSO is formulated with twin objectives of maximizing the entropy and minimizing the root mean square error. The fusion results are compared with the existing fusion techniques, such as Brovey, principal component analysis, discrete wavelet transform, non sub-sampled contourlet transform, and intensity hue saturation. From the experimental results and analysis, the proposed fusion algorithm gives a better fusion performance in terms of subjective and objective measures than the traditional algorithms. As a benefit, the proposed fusion scheme preserves spectral information of the MS image with increased spatial resolution and edge information.

  • Multi-path convolutional neural network for lung cancer detection
    Multidimens. Syst. Signal Process. (IF 2.338) Pub Date : 2018-11-23
    Worku Jifara Sori, Jiang Feng, Shaohui Liu

    Lung cancer is the leading cause of death among cancer-related death. Like other cancers, the finest solution for lung cancer diagnosis and treatment is early screening. Automatic CAD system of lung cancer screening from Computed Tomography scan mainly involves two steps: detect all suspicious pulmonary nodules and evaluate the malignancy of the nodules. Recently, there are many works about the first step, but rare about the second step. Since the presence of pulmonary nodules does not absolutely specify cancer, the morphology of nodules such as shape, size, and contextual information has a sophisticated relationship with cancer, the screening of lung cancer needs a careful investigation on each suspicious nodule and integration of information of all nodules. We propose deep CNN architecture which differs from those traditionally used in computer vision to solve this problem. First, the suspicious nodules are generated with the modified version of U-Net and then the generated nodules become an input data for our model. The proposed model is a multi-path CNN which exploits both local features as well as more global contextual features simultaneously to automatically detect lung cancer. To this end, the model used three paths, each path employed different receptive field size which helps to model distant dependencies (short and long-range dependencies of the neighboring pixels). Then, to further upgrade our model performance, we concatenate features from the three paths. This balance the receptive field size effect and makes our model more adaptable to the variability of shape, size, and contextual information among nodules. Finally, we also introduce a retraining phase system that permits us to tackle difficulties related to the imbalance of image labels. Experimental results on Kaggle Data Science Bowl 2017 challenge shows that our model is better adaptable to the described inconsistency among nodules size and shape, and also obtained better detection results compared to the recently published state of the art methods.

  • Output feedback stabilization of two-dimensional fuzzy systems
    Multidimens. Syst. Signal Process. (IF 2.338) Pub Date : 2018-11-21
    Lizhen Li, Zhiping Lin, Yuan Chai, Jingjing Cai

    This paper is concerned with the output feedback stabilization of two-dimensional discrete fuzzy systems described by the Fornasini–Marchesini second model. Based on the fuzzy-basis-dependent Lyapunov function, a new criterion is proposed for the fuzzy static output feedback (SOF) controller, which is expressed as strict linear matrix inequalities and hence numerically tractable. The main advantage of the developed SOF control scheme is that no constraints are imposed on system matrices, which is expected to have a wider range of applications. The applicability and the advantage of the proposed results are shown through two numerical examples.

  • Image encryption using sparse coding and compressive sensing
    Multidimens. Syst. Signal Process. (IF 2.338) Pub Date : 2019-02-25
    R. Ponuma, R. Amutha

    Abstract An encryption algorithm based on sparse coding and compressive sensing is proposed. Sparse coding is used to find the sparse representation of images as a linear combination of atoms from an overcomplete learned dictionary. The overcomplete dictionary is learned using K-SVD, utilizing non-overlapping patches obtained from a set of images. Compressed sensing is used to sample data at a rate below the Nyquist rate. A Gaussian measurement matrix compressively samples the plain image. As these measurements are linear, chaos based permutation and substitution operations are performed to obtain the cipher image. Bit-level scrambling and block substitution is done to confuse and diffuse the measurements. Simulation results verify the performance of the proposed technique against various statistical attacks.

  • Mobile cloud based-framework for sports applications
    Multidimens. Syst. Signal Process. (IF 2.338) Pub Date : 2019-03-06
    Zahid Mahmood, Nargis Bibi, Muhammad Usman, Uzair Khan, Nazeer Muhammad

    Smartphones are increasingly becoming popular due to the wide range of capabilities, such as Wi-Fi connectivity, video acquisition, and navigation. Some of these applications require large computational power, memory, and long battery life. Sports entertainment applications executed on smartphones is the future paradigm shift that will be enabled by the mobile cloud computing environments. Many times mobile users request multiple mobile services in workflows to fulfill their complex requirements. To investigate such issues, we develop a mobile cloud based framework that detects and retrieves player statistics on a mobile phone during live cricket. The proposed framework is divided into several services and each service is either executed locally or on the cloud. Our approach considers the dependencies among different services and aims to optimize the execution time and energy consumption for executing the services. Due to the applied offloading strategy, the proposed framework turns the smartphones smarter by significantly reducing the execution burden and energy consumption of the smartphone. Experimental results are promising and show feasibility of the proposed framework to be deployed in several related applications using techniques of computer vision and machine learning.

  • A stochastic analysis of distance estimation approaches in single molecule microscopy - quantifying the resolution limits of photon-limited imaging systems.
    Multidimens. Syst. Signal Process. (IF 2.338) Pub Date : 2014-06-17
    Sripad Ram,E Sally Ward,Raimund J Ober

    Optical microscopy is an invaluable tool to visualize biological processes at the cellular scale. In the recent past, there has been significant interest in studying these processes at the single molecule level. An important question that arises in single molecule experiments concerns the estimation of the distance of separation between two closely spaced molecules. Presently, there exists different experimental approaches to estimate the distance between two single molecules. However, it is not clear as to which of these approaches provides the best accuracy for estimating the distance. Here, we address this problem rigorously by using tools of statistical estimation theory. We derive formulations of the Fisher information matrix for the underlying estimation problem of determining the distance of separation from the acquired data for the different approaches. Through the Cramer-Rao inequality, we derive a lower bound to the accuracy with which the distance of separation can be estimated. We show through Monte-Carlo simulations that the bound can be attained by the maximum likelihood estimator. Our analysis shows that the distance estimation problem is in fact related to the localization accuracy problem, the latter being a distinct problem that deals with how accurately the location of an object can be determined. We have carried out a detailed investigation of the relationship between the Fisher information matrices of the two problems for the different experimental approaches considered here. The paper also addresses the issue of a singular Fisher information matrix, which presents a significant complication when calculating the Cramer-Rao lower bound. Here, we show how experimental design can overcome the singularity. Throughout the paper, we illustrate our results by considering a specific image profile that describe the image of a single molecule.

  • Fisher information matrix for branching processes with application to electron-multiplying charge-coupled devices.
    Multidimens. Syst. Signal Process. (IF 2.338) Pub Date : 2012-10-11
    Jerry Chao,E Sally Ward,Raimund J Ober

    The high quantum efficiency of the charge-coupled device (CCD) has rendered it the imaging technology of choice in diverse applications. However, under extremely low light conditions where few photons are detected from the imaged object, the CCD becomes unsuitable as its readout noise can easily overwhelm the weak signal. An intended solution to this problem is the electron-multiplying charge-coupled device (EMCCD), which stochastically amplifies the acquired signal to drown out the readout noise. Here, we develop the theory for calculating the Fisher information content of the amplified signal, which is modeled as the output of a branching process. Specifically, Fisher information expressions are obtained for a general and a geometric model of amplification, as well as for two approximations of the amplified signal. All expressions pertain to the important scenario of a Poisson-distributed initial signal, which is characteristic of physical processes such as photon detection. To facilitate the investigation of different data models, a "noise coefficient" is introduced which allows the analysis and comparison of Fisher information via a scalar quantity. We apply our results to the problem of estimating the location of a point source from its image, as observed through an optical microscope and detected by an EMCCD.

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