Deep convolution network for surveillance records super-resolution

Multimedia Tools and Applications - The aim of image super resolution (SR) is to recover low resolution (LR) input image or video to a visually desirable high-resolution (HR) one. The task of...     

Deep semantic preserving hashing for large scale image retrieval

Multimedia Tools and Applications - Hashing approaches have got a great attention because of its efficient performance for large-scale images. This paper, aims to propose a deep hashing method...     

Measurement and modeling of solubility of H2S in aqueous diisopropanolamine solution

Modeling of solubility of acid gases in aqueous alkanolamine solutions is essential for design of an absorber for natural gas sweetening. In this work an apparatus similar to the device of Hayduk and Chen (1970), which was improved by Pahlavanzadeh and Motahhari (1997), for the measurement of gas solubility data by the synthetic method was used. The solubility of hydrogen sulfide in aqueous diisopropanolamine (DIPA) solution in mass concentration range of 30–40% for 101,325 Pa pressure and for temperature ranging from 313–343 K was reported. The obtained experimental solubility data of H₂S in aqueous solutions of DIPA was used to predict the different interaction parameters of modified UNIQUAC-NRF model for calculating the activity coefficients. For nonideality of species in liquid phase, the UNIQUAC-NRF equation with ion-pair approach was applied. For long range interaction, the Pitzer-Debye-Huckel term was used.     

Encapsulation of palladium nanoparticles by multiwall carbon nanotubes‐graft‐poly(citric acid) hybrid materials

Citric acid was polymerized onto the surface of functionalized multiwall carbon nanotubes (MWCNT‐COOH) and MWCNT‐graft‐poly(citric acid) (MWCNT‐g‐PCA) hybrid materials were obtained. Due to the grafted poly(citric acid) branches, MWCNT‐g‐PCA hybrid materials not only were soluble in water but also were able to trap water soluble metal ions. Reduction of trapped metal ions in the polymeric shell of MWCNT‐g‐PCA hybrid materials by reducing agents such as sodium borohydride led to encapsulated metal nanoparticles on the surface of MWCNT. Herein palladium nanoparticles were encapsulated and transported by MWCNT‐g‐PCA hybrid materials (MWCNT‐g‐PCA‐EPN) and their application as nanocatalyst toward Heck reaction in different conditions was investigated. The catalytic activity of palladium ions supported by MWCNT‐g‐PCA hybrid materials (MWCNT‐g‐PCA‐PdCl₂) toward Heck reactions is much more than for MWCNT‐g‐PCA‐EPN. Structure, characteristics and catalytic activity of synthesized systems was investigated using spectroscopy and microscopy methods. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2010     

Electromagnetic properties of electrospun Fe3O4/carbon composite nanofibers

In order to develop multifunctional nanofibers, the electrical conductivity and magnetic properties of Fe₃O₄/carbon composite nanofibers have been examined. Polyacrylonitrile (PAN) is used as a matrix to produce magnetic composite nanofibers containing different amounts of magnetite (Fe₃O₄) nanoparticles. Electrospun composite nanofibers were thermally treated to produce electrically conductive and magnetically permeable composite carbon nanofibers. The composite nanofibers were characterized using scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffractometry (XRD), Raman spectroscopy, four-point probe and Superconducting Quantum Interference Device (SQUID). Uniform nanofibers were obtained with successful transferring of magnetic properties of Fe₃O₄ into the as-spun composite nanofibers. The electromagnetic properties were tuned by adjusting the amount of Fe₃O₄ in the matrix and carbonization process. The electrical conductivity, magnetic moment and also magnetic hysteresis rise up by adding Fe₃O₄ and increasing carbonization temperature. The high surface area provided by the ultrafine fibrous structures, the flexibility and tuneable electromagnetic properties are expected to enable the expansion of the design options for a wide rage of electronic devices.     

Strengthening the Security of EPC C-1 G-2 RFID Standard

In this paper, we analyze the security of AZUMI protocol which is compliant with EPC-Class-1 Generation-2 standard and recently has been proposed by Peris et al. This protocol is an improvement to a protocol proposed by Chen and Deng which has been cryptanalysed by Peris et al. and Kapoor and Piramuthu. However, our security analysis clearly shows that the designers were not successful in their attempt to improve Chen and Deng protocol. More precisely, we present an efficient passive attack to disclose the tag and the reader secret parameters, due to PRNG and the length of the values. In addition, we present a simple tag impersonation attack against this protocol. The success probability of all attacks are almost “1” and the cost of given attacks are at most eavesdropping two sessions of protocol. However, the given secrets disclosure attack also requires $O(2^{16})$ off-line evaluations of a $PRNG$ function. To counteract such flaws, we improve the AZUMI protocol by applying some minor modifications so that it provides the claimed security properties.     

A multi-stage stochastic programming approach for production planning with uncertainty in the quality of raw materials and demand

Motivated by the challenges encountered in sawmill production planning, we study a multi-product, multi-period production planning problem with uncertainty in the quality of raw materials and consequently in processes yields, as well as uncertainty in products demands. As the demand and yield own different uncertain natures, they are modelled separately and then integrated. Demand uncertainty is considered as a dynamic stochastic data process during the planning horizon, which is modelled as a scenario tree. Each stage in the demand scenario tree corresponds to a cluster of time periods, for which the demand has a stationary behaviour. The uncertain yield is modelled as scenarios with stationary probability distributions during the planning horizon. Yield scenarios are then integrated in each node of the demand scenario tree, constituting a hybrid scenario tree. Based on the hybrid scenario tree for the uncertain yield and demand, a multi-stage stochastic programming (MSP) model is proposed which is full recourse for demand scenarios and simple recourse for yield scenarios. We conduct a case study with respect to a realistic scale sawmill. Numerical results indicate that the solution to the multi-stage stochastic model is far superior to the optimal solution to the mean-value deterministic and the two-stage stochastic models.     

A New Lightweight User Authentication and Key Agreement Scheme for WSN

The Internet of Things (IoT) is one of the most up-to-date and newest technologies that allows remote control of heterogeneous networks and has a good outlook for industrial applications. Wireless sensor networks (or in brief WSNs) have a key role on the Internet of industrial objects. Due to the limited resources of the sensor nodes, designing a balanced authentication scheme to provide security in reasonable performance in wireless sensor networks is a major challenge in these applications. So far, several security schemes have been presented in this context, but unfortunately, none of these schemes have provided desired security in reasonable cost. In 2017, Khemissa et al. proposed a security protocol for mutual authentication between sensor node and user in WSNs, however, in this paper we show that this protocol is not safe enough in the confrontation of desynchronization, user impersonation and gateway impersonation attacks. The proposed attacks succeed with the probability of one and to be realized only require an execution of the protocol. Given merits of the Khemissa et al.’s protocol, we also improved their protocol in such a way that provides suitable level of security, and also we prove its security using two formal ways, i.e. BAN logic and also the Scyther tool. We also argue informally about the improved protocol’s security.

     

Feature extraction using discrete cosine transform and discrimination power analysis with a face recognition technology

Discrete cosine transform (DCT) is a powerful transform to extract proper features for face recognition. After applying DCT to the entire face images, some of the coefficients are selected to construct feature vectors. Most of the conventional approaches select coefficients in a zigzag manner or by zonal masking. In some cases, the low-frequency coefficients are discarded in order to compensate illumination variations. Since the discrimination power of all the coefficients is not the same and some of them are discriminant than others, so we can achieve a higher true recognition rate by using discriminant coefficients (DCs) as feature vectors. Discrimination power analysis (DPA) is a statistical analysis based on the DCT coefficients properties and discrimination concept. It searches for the coefficients which have more power to discriminate different classes better than others. The proposed approach, against the conventional approaches, is data-dependent and is able to find DCs on each database. The simulations results of the various coefficient selection (CS) approaches on ORL and Yale databases confirm the success of the proposed approach. The DPA-based approaches achieve the performance of PCA/LDA or better with less complexity. The proposed method can be implemented for any feature selection problem as well as DCT coefficients. Also, a new modification of PCA and LDA is proposed namely, DPA-PCA and DPA-LDA. In these modifications DCs which are selected by DPA are used as the input of these transforms. Simulation results of DPA-PCA and DPA-LDA on the ORL and Yale database verify the improvement of the results by using these new modifications.