Sharif Digital Repository

Noise interference and data loss are two major problems that affect the processing results of image data transmission and storage. Restoration of the lost information of an image based on the existing information is the essence of inpainting. In this paper a new algorithm based on Sample and Hold interpolation and Iteration is proposed for reconstructing damaged images from existing regions and is compared to some other methods. The experimental results show the superiority of the visual quality and PSNR performance of the proposed method. It is observed that this approach can efficiently fill in the holes with visually plausible information  

This paper proposes a novel fusion method for visible and infrared images. The infrared and visible samples are obtained by a sampling pattern such as star and spiral. Then, the samples are fused according to fusion rules. Finally, the proposed method is applied to fuse the infrared and visible samples. The proposed method is iterative and a significant advantage of it, besides its superior performance, is that it is faster than the previous compressive sensing based fusion methods. The simulation results confirm the success of the proposed method for fusion of infrared and visible images. © 2018 IEEE  

In this paper, a semi-blind multiplicative watermarking approach for audio and speech signals has been presented. At the receiver end, the optimal maximum likelihood (ML) detector aided by the archived information for Gaussian and Laplacian signals in noisy environment is designed and implemented. The performance of the proposed scheme is analytically calculated and verified by simulation. Then, we adapt the proposed scheme to speech and audio signals. To improve robustness, the algorithm is applied to low frequency components of the host signal. Besides, the power of the watermark is controlled elegantly to have inaudibility using perceptual evaluation of audio quality (PEAQ) and perceptual... 

In this paper, a semi-blind multiplicative watermarking approach for audio and speech signals has been presented. At the receiver end, the optimal Maximum Likelihood (ML) detector aided by the channel side information for Gaussian and Laplacian signals in noisy environment is designed and implemented. The performance of the proposed scheme is analytically calculated and verified by simulation. Then, we adapt the proposed scheme to speech and audio signals. To improve robustness, the algorithm is applied to low frequency components of the host signal. Besides, the power of the watermark is controlled elegantly to have inaudibility using Perceptual Evaluation of Audio Quality (PEAQ) and... 

Reconstructing a signal corrupted by impulsive noise is of high importance in several applications, including impulsive noise removal from images, audios and videos, and separating texts from images. Investigating this problem, in this paper we propose a new method to reconstruct a noise-corrupted signal where both signal and noise are sparse but in different domains. We apply our algorithm for impulsive noise (Salt- and-Pepper Noise (SPN) and Random-Valued Impulsive Noise (RVIN) removal from images and compare our results with other notable algorithms in the literature. Simulation indicates show that our algorithm is not only simple and fast, but also it outperforms the other... 

In this letter, we propose a sparsity promoting feedback acquisition and reconstruction scheme for sensing, encoding and subsequent reconstruction of spectrally sparse signals. In the proposed scheme, the spectral components are estimated utilizing a sparsity-promoting, sliding-window algorithm in a feedback loop. Utilizing the estimated spectral components, a level signal is predicted and sign measurements of the prediction error are acquired. The sparsity promoting algorithm can then estimate the spectral components iteratively from the sign measurements. Unlike many batch-based compressive sensing algorithms, our proposed algorithm gradually estimates and follows slow changes in the... 

In a distributed algorithm several processing nodes communicate with each other and incorporate in order to reach a common goal. Each unit has its own locally-observed environment while it can either help or mislead other units when sharing its information. This could be either intentional or simply due to the observation noise. Previous works mainly focus on locating the agents properly and assigning weights based on stationary environment in order to minimize the impact of noisy nodes. We, however, propose a method capable of assigning combination weights in accordance with momentary performance of the neighboring nodes. In order to demonstrate this capability, the proposed algorithm has... 

With the advent of the Internet of Things (IoT), the development of self-powered sensors has received much attention. Introducing triboelectric nanogenerators (TENGs) as a power source that converts mechanical movement into electrical signals has been admired recently. Moreover, the monitoring of humidity has become enormously essential in several technological contexts from environment monitoring to biomedical applications, thus joining these two subjects provides a huge benefit in achieving self-powered humidity sensors. Here, in this research, facile, low-priced and self-powered humidity sensors are fabricated utilizing transition-metal dichalcogenides (TMD) nanosheets. Semi-vertical SnS2... 

QBism is one of the main candidates for an epistemic interpretation of quantum mechanics. According to QBism, the quantum state or the wavefunction represents the subjective degrees of belief of the agent assigning the state. But, although the quantum state is not part of the furniture of the world, quantum mechanics grasps the real via the Born rule which is a consistency condition for the probability assignments of the agent. In this paper, we evaluate the plausibility of recent criticism of QBism. We focus on the consequences of the subjective character of the quantum state, the issue of realism and the problem of the evolution of the quantum state in QBism. In particular, drawing upon... 

To investigate the biomarker detection capability of the 2D MoSe2 monolayer as the lung cancer detection sensor, the interaction between the biomarker molecules and the surface of the sensor has been studied by first-principles calculations in three scenarios namely i) pristine MoSe2, ii) Nb-doped MoSe2, iii) Nb-decorated MoSe2 monolayers. In this study, it is proposed that Nb-decorated MoSe2 is a promising biosensor for detecting the two most prominent lung cancer biomarkers in the breath namely 2-butanone (C4H8O) and 1-propanol (C3H8O). The adsorption energy, charge transfer, and the equivalent sensitivity for C3H8O adsorption on Nb-decorated MoSe2 were calculated as −1.907 eV, 0.026e, and... 

The authors propose asynchronous level crossing (LC) A/D converters for low redundancy voice sampling. They propose to utilise the family of iterative methods with adaptive thresholding (IMAT) for reconstructing voice from non-uniform LC and adaptive LC (ALC) samples thereby promoting sparsity. The authors modify the basic IMAT algorithm and propose the iterative method with adaptive thresholding for level crossing (IMATLC) algorithm for improved reconstruction performance. To this end, the authors analytically derive the basic IMAT algorithm by applying the gradient descent and gradient projection optimisation techniques to the problem of square error minimisation subjected to sparsity. The... 

In this paper, we propose a new method to reconstruct a signal corrupted by noise where both signal and noise are sparse but in different domains. The main contribution of our algorithm is its low complexity; it has much lower run-time than most other algorithms. The reconstruction quality of our algorithm is both objectively (in terms of PSNR and SSIM) and subjectively better or comparable to other state-of-the-art algorithms. We provide a cost function for our problem, present an iterative method to find its local minimum, and provide the analysis of the algorithm. As an application of this problem, we apply our algorithm for Salt-and-Pepper noise (SPN) and Random-Valued Impulsive Noise... 

An analysis is presented of the effective electromagnetic parameters of high-permittivity, anisotropic artificial dielectrics which are built by stacking arrays of metallic elements and conventional dielectric films, with adjacent arrays shifted with respect to each other. The effective parameters of the artificial dielectric are extracted from the scattering coefficients of plane electromagnetic waves which are normally or obliquely incident on a slab of the artificial material with finite thickness. These coefficients are derived from the generalised scattering matrix of a single layer of metallic elements which is computed using the integral equation technique. Both two-dimensional and... 

Semiconductor gas sensors have been developed so far on empirical bases, but now recent innovative materials for advancing gas sensor technology have been made available for further developments. Two-dimensional (2D) materials have gained immense attention since the advent of graphene. This attention inspired researchers to explore a new family of potential 2D materials. The superior structural, mechanical, optical and electrical properties of 2D materials made them attractive for next-generation smart device applications. There are considerable improvements and research studies on graphene, molybdenum disulfide (MoS2), tungsten disulfide (WS2), tin sulfide (SnS2), black phosphorus and other... 

In order to plan precise treatment or accurate tumor removal surgery, brain tumor segmentation is critical for detecting all parts of tumor and its surrounding tissues. To visualize brain anatomy and detect its abnormalities, we use multi-modal Magnetic Resonance Imaging (MRI) as input. This paper introduces an efficient and automated algorithm based on the 3D bit-plane neighborhood concept for Brain Tumor segmentation using a rule-based learning algorithm. In the proposed approach, in addition to using intensity values in each slice, we consider sets of three consecutive slices to extract information from 3D neighborhood. We construct a Rule base using sequential covering algorithm. Through... 

In order to plan precise treatment or accurate tumor removal surgery, brain tumor segmentation is critical for detecting all parts of tumor and its surrounding tissues. To visualize brain anatomy and detect its abnormalities, we use multi-modal Magnetic Resonance Imaging (MRI) as input. This paper introduces an efficient and automated algorithm based on the 3D bit-plane neighborhood concept for Brain Tumor segmentation using a rule-based learning algorithm. In the proposed approach, in addition to using intensity values in each slice, we consider sets of three consecutive slices to extract information from 3D neighborhood. We construct a Rule base using sequential covering algorithm. Through... 

Glioma is one of the most frequent primary brain tumors in adults that arise from glial cells. Automatic and accurate segmentation of glioma is critical for detecting all parts of tumor and its surrounding tissues in cancer detection and surgical planning. In this paper, we present a reliable classification framework for detection and segmentation of abnormal tissues including brain glioma tumor portions such as edemas and tumor core. This framework learns weighted features extracted from the 3D cubic neighborhoods regarding to gray-level differences that indicate the spatial relationships among voxels. In addition to intensity values in each slice, we consider sets of three consecutive... 

Space environments have a significant influence on advanced composite structures and adhesive joints. Degradation in the mechanical properties of aerospace materials changes the dynamic behavior of the structures and adhesive joints. In this paper, a typical tubular adhesive joint with material degradation due to geostationary orbit (GEO) thermal cycles has been studied numerically with Python scripts. Adhesive joint geometry and boundary conditions are the main parametric study parameters. The results show that the first non–zero natural frequencies of the clamped-free tubular adhesive joint decreased due to mechanical property degradation. A dynamic behavior comparison of the degradation... 

Glioma is a common type of tumor that develops in the brain. Due to many differences in the shape and appearance, accurate segmentation of glioma for identifying all parts of the tumor and its surrounding tissues in cancer detection is a challenging task in cancer detection. In recent researches, the combination of atlas-based segmentation and machine learning methods have presented superior performance over other automatic brain MRI segmentation algorithms. To overcome the side effects of limited existing information on atlas-based segmentation, and the long training and the time consuming phase of learning methods, we proposed a semi-supervised learning framework by introducing a... 

This paper presents a novel Cyber-Physical System (CPS) equipped with an advanced Distributed Model Predictive Control (DMPC) method with reduced order computational complexity, zero steady-state error, reduced start-up energy consumption and improved transient response for the optimal heating-cooling of buildings. The satisfactory application of this method for the optimal heating-cooling of a large-scale (6-story) building with 40 rooms is illustrated. Smart Industrial Internet of Things (IIoT) -based thermostats, a gateway and a general Quadratic Programming (QP) solver are developed. Using this hardware set-up, the simulation results for the 6-story building are verified in a small scale...