ANT-colony based disjoint set assortment in wireless sensor networks

Wireless Networks - Wireless sensor network (WSN) consists of small sized devices containing different sensors to monitor physical, environmental and medical conditions during surveillance of...     

Quasi-static compressive behaviour of aluminium cenosphere syntactic foams

In this paper, cenosphere particles embedded in AA2014 aluminium matrix are used to fabricate syntactic foam by stir casting method. The particle size is about 100?µm and foam density is about 1990?kg?m^?3. Compression tests at strain rate 0.001/s are performed on foam samples to characterise their mechanical properties which are then used in numerical analysis on commercial finite element analysis software ABAQUS/CAE with isotropic elastic-plastic material model. Experimental and numerical results show good conformity in deformation behaviour with elastic and plateau zones showing average deviations less than 5% and 20%, respectively. Foams showed high yield stress and energy absorption capabilities that can be useful in making blast and impact resistant structures.     

Facile Deposition of Silver Nanoparticles on Photonic Cellulose Nanocrystals Films: A Study on Solvent Stability and Post Antibacterial Activity

Cellulose nanocrystals (CNCs) incorporated with silver nanoparticles (AgNPs) photonic films have drawn considerable attention due to their plasmonic chiroptical activity. However, the exploitation of some fundamental properties for practical use such as the affinity analysis of metal nanoparticles attached to the surface of photonic films according to the solvent compatibility and antibacterial activity under physical conditions has yet not been studied. Hence, a facile process of in situ deposition of AgNPs into the chiral structure of CNC films is proposed. CNC photonic films, cross-linked by glutaraldehyde are prepared. This interaction generated the solvents-stable photonic film with a considerable amount of unreacted aldehyde functional groups that facilitates the reduction of Ag salt to AgNPs. The formed AgNPs in the photonic films show excellent stability over immersion in various polar and non-polar solvents. The post-solvent treated photonic films display excellent contact-based antibacterial behavior against gram-negative Escherichia coli.     

Phase diagrams for oil/methanol/ether mixtures

One-phase transmethylations of vegetable oils with methanol to form methyl esters occur considerably faster than conventional two-phase reactions. Addition of simple ethers is an efficient method for producing a single phase. Ternary phase diagrams have been determined at 23°C for oil/methanol/ether mixtures; these are useful when applying the one-phase method across a wide range of conditions. Soybean, canola, palm, and coconut oils were used in combination with five ethers, namely, tetrahydrofuran (THF), 1,4-dioxane (DO), diethyl ether (DE), diisopropyl ether (DI), andtert-butyl methyl ether (TBM). All five ethers can produce miscibility for all methanol/oil compositions. The ether/methanol volumetric ratios required for miscibility at a methanol/soybean or canola oil volumetric ratio of 0.20 (5.4 molar ratio) at 23°C are: THF, 1.15; DO, 1.60; DE, 1.38 DI, 1.57; and TBM, 1.57. For THF, this results in one-phase mixtures that contain 65 vol% oil. Soybean and canola oil form identical diagrams. Palm oil requires slightly less ether at the lower methanol concentrations, but coconut oil requires considerably less across the whole concentration range. Acid-catalyzed reactions, when performed at the boiling point of the most volatile component, require less ether than predicted from the diagrams.     

Minimizing conditional-value-at-risk for stochastic scheduling problems

This paper introduces the use of conditional-value-at-risk (CVaR) as a criterion for stochastic scheduling problems. This criterion has the tendency of simultaneously reducing both the expectation and variance of a performance measure, while retaining linearity whenever the expectation can be represented by a linear expression. In this regard, it offers an added advantage over traditional nonlinear expectation-variance-based approaches. We begin by formulating a scenario-based mixed-integer program formulation for minimizing CVaR for general scheduling problems. We then demonstrate its application for the single machine total weighted tardiness problem, for which we present both a specialized l-shaped algorithm and a dynamic programming-based heuristic procedure. Our numerical experimental results reveal the benefits and effectiveness of using the CVaR criterion. Likewise, we also exhibit the use and effectiveness of minimizing CVaR in the context of the parallel machine total weighted tardiness problem. We believe that minimizing CVaR is an effective approach and holds great promise for achieving risk-averse solutions for stochastic scheduling problems that arise in diverse practical applications.     

Deep learning approaches for speech emotion recognition: state of the art and research challenges

Speech emotion recognition (SER) systems identify emotions from the human voice in the areas of smart healthcare, driving a vehicle, call centers, automatic translation systems, and human-machine interaction. In the classical SER process, discriminative acoustic feature extraction is the most important and challenging step because discriminative features influence the classifier performance and decrease the computational time. Nonetheless, current handcrafted acoustic features suffer from limited capability and accuracy in constructing a SER system for real-time implementation. Therefore, to overcome the limitations of handcrafted features, in recent years, variety of deep learning techniques have been proposed and employed for automatic feature extraction in the field of emotion prediction from speech signals. However, to the best of our knowledge, there is no in-depth review study is available that critically appraises and summarizes the existing deep learning techniques with their strengths and weaknesses for SER. Hence, this study aims to present a comprehensive review of deep learning techniques, uniqueness, benefits and their limitations for SER. Moreover, this review study also presents speech processing techniques, performance measures and publicly available emotional speech databases. Furthermore, this review also discusses the significance of the findings of the primary studies. Finally, it also presents open research issues and challenges that need significant research efforts and enhancements in the field of SER systems.

     

Graph attention-based collaborative filtering for user-specific recommender system using knowledge graph and deep neural networks

Knowledge and Information Systems - Collaborative filtering suffers from the issues of data sparsity and cold start. Due to which recommendation models that only rely on the user–item...     

A systematic mapping of semi-formal and formal methods in requirements engineering of industrial Cyber-Physical systems

Journal of Intelligent Manufacturing - The requirements engineering of Industrial Cyber-Physical Systems is extremely challenging due to large system sizes, component heterogeneity, involvement of...     

An Improved Hybrid Indoor Positioning Algorithm via QPSO and MLP Signal Weighting

Accurate location or positioning of people and self-driven devices in large indoor environments has become an important necessity The application of increasingly automated self-operating moving transportation units, in large indoor spaces demands a precise knowledge of their positions. Technologies like WiFi and Bluetooth, despite their low-cost and availability, are sensitive to signal noise and fading effects. For these reasons, a hybrid approach, which uses two different signal sources, has proven to be more resilient and accurate for the positioning determination in indoor environments. Hence, this paper proposes an improved hybrid technique to implement a fingerprinting based indoor positioning, using Received Signal Strength information from available Wireless Local Area Network access points, together with the Wireless Sensor Networks technology. Six signals were recorded on a regular grid of anchor points, covering the research space. An optimization was performed by relative signal weighting, to minimize the average positioning error over the research space. The optimization process was conducted using a standard Quantum Particle Swarm Optimization, while the position error estimate for all given sets of weighted signals was performed using a Multilayer Perceptron (MLP) neural network. Compared to our previous research works, the MLP architecture was improved to three hidden layers and its learning parameters were finely tuned. These experimental results led to the 20% reduction of the positioning error when a suitable set of signal weights was calculated in the optimization process. Our final achieved value of 0.725 m of the location incertitude shows a sensible improvement compared to our previous results.     

Dynamic S-Box Generation Using Novel Chaotic Map with Nonlinearity Tweaking

A substitution box (S-Box) is a crucial component of contemporary cryptosystems that provide data protection in block ciphers. At the moment, chaotic maps are being created and extensively used to generate these S-Boxes as a chaotic map assists in providing disorder and resistance to combat cryptanalytical attempts. In this paper, the construction of a dynamic S-Box using a cipher key is proposed using a novel chaotic map and an innovative tweaking approach. The projected chaotic map and the proposed tweak approach are presented for the first time and the use of parameters in their working makes both of these dynamic in nature. The tweak approach employs cubic polynomials while permuting the values of an initial S-Box to enhance its cryptographic fort. Values of the parameters are provided using the cipher key and a small variation in values of these parameters results in a completely different unique S-Box. Comparative analysis and exploration confirmed that the projected chaotic map exhibits a significant amount of chaotic complexity. The security assessment in terms of bijectivity, nonlinearity, bits independence, strict avalanche, linear approximation probability, and differential probability criteria are utilized to critically investigate the effectiveness of the proposed S-Box against several assaults. The proposed S-Box’s cryptographic performance is comparable to those of recently projected S-Boxes for its adaption in real-world security applications. The comparative scrutiny pacifies the genuine potential of the proposed S-Box in terms of its applicability for data security.