Classification of DoS Attacks in Smart Underwater Wireless Sensor Network

As per the most recent literature, Orthogonal Frequency Division Multiplexing (OFDM), a multi access technique, is considered most suitable for the 3G, 4G and 5G techniques in high speed wireless communication. What made OFDM most popular is its ability to deliver high bandwidth efficiency and superior data rate. Besides it, high value of peak to average power ratio (PAPR) and Inter Carrier Interference (ICI) are the challenges to tackle down via appropriate mitigation scheme. As a research contribution in the present work, an improved self-cancellation (SC) technique is designed and simulated through Simulink to mitigate the effect of ICI. This novel proposed technique (Improved SC) is designed over discrete wavelet transform (DWT) based OFDM and compared with conventional SC scheme over different channel conditions i.e. AWGN and Rayleigh fading environments. It is found that proposed DWT-OFDM with Improved SC scheme outperforms conventional SC technique significantly, under both AWGN and Rayleigh channel conditions. Further, in order to justify the novelty in the research contribution, a Split-DWT based Simulink model for Improved SC scheme is investigated to analyse the BER performance. This Split-DWT based Simulink model presented here foretells the future research potential in wavelet hybridization of OFDM to side-line ICI effects more efficiently.

     

A branch-and-bound algorithm to minimize total weighted completion time on identical parallel machines with job release dates

In this paper, we consider an identical parallel machine scheduling problem with release dates. The objective is to minimize the total weighted completion time. This problem is known to be strongly NP-hard. We propose some dominance properties and two lower bounds. We also present an efficient heuristic. A branch-and-bound algorithm, in which the heuristic, the lower bounds and the dominance properties are incorporated, is proposed and tested on a large set of randomly generated instances.     

Investigating the effect of varying block size on power and energy consumption of GPU kernels

The Journal of Supercomputing - Power consumption is likely to remain a significant concern for exascale performance in the foreseeable future. In addition, graphics processing units (GPUs) have...     

Dynamic and scalable multi-level trust management model for Social Internet of Things

The Internet of Things (IoT) is a paradigm that has made everyday objects intelligent by offering them the ability to connect to the Internet and communicate. Integrating the social component into IoT gave rise to the Social Internet of Things (SIoT), which has helped overcome various issues such as heterogeneity and navigability. In this kind of environment, participants compete to offer a variety of attractive services. Nevertheless, some of them resort to malicious behaviour to spread poor-quality services. They perform so-called Trust-Attacks and break the basic functionality of the system. Trust management mechanisms aim to counter these attacks and provide the user with an estimate of the trust degree they can place in other users, thus ensuring reliable and qualified exchanges and interactions. Several works in literature have interfered with this problem and have proposed different Trust-Models. The majority tried to adapt and reapply Trust-Models designed for common social networks or peer-to-peer ones. That is, despite the similarities between these types of networks, SIoT ones present specific peculiarities. In SIoT, users, devices and services are collaborating. Devices entities can present constrained computing and storage capabilities, and their number can reach some millions. The resulting network is complex, constrained and highly dynamic, and the attacks-implications can be more significant. In this paper, we propose DSL-STM a new dynamic and scalable multi-level Trust-Model, specifically designed for SIoT environments. We propose multidimensional metrics to describe and SIoT entities behaviours. The latter are aggregated via a Machine Learning-based method, allowing classifying users, detecting attack types and countering them. Finally, a hybrid propagation method is suggested to spread trust values in the network, while minimizing resource consumption and preserving scalability and dynamism. Experimentation made on various simulated scenarios allows us to prove the resilience and performance of DSL-STM.

     

An industrial portrait background removal solution based on knowledge infusion

Background removal of an identity (ID) picture consists in separating the foreground (face, body, hair and clothes) from the background of the image. It is a necessary groundwork for all modern identity documents that also has many benefits for improving ID security. State of the art image processing techniques encountered several segmentation issues and offer only partial solutions. It is due to the presence of erratic components like hairs, poor contrast, luminosity variation, shadow, color overlap between clothes and background. In this paper, a knowledge infused approach is proposed that hybridizes smart image processing tasks and prior knowledge. The research is based on a divide and conquer strategy aiming at simulating the sequential attention of human when performing a manual segmentation. Knowledge is infused by considering the spatial relation between anatomic elements of the ID image (face feature, forehead, body and hair) as well as their “signal properties”. The process consists in first determining a convex hull around the person’s body including all the foreground while keeping very close to the contour between the background and the foreground. Then, a body map generated from biometric analysis associated to an automatic grab cut process is applied to reach a finer segmentation. Finally, a heuristic-based post-processing step consisting in correcting potential hair and fine boundary issues leads to the final segmentation. Experimental results show that the newly proposed architecture achieves better performances than tested current state-of-the-art methodologies including active contours, generalist popular deep learning techniques, and also two other ones considered as the smartest for portrait segmentation. This new technology has been adopted by an international company as its industrial ID foreground solution.

     

Abiotic stress of ZnO‐PEG,ZnO‐PVP,CuO‐PEG and CuO‐PVP nanoparticles enhance growth,sweetener compounds and antioxidant activities in shoots of Stevia rebaudiana Bertoni

Nanoparticles are known to play remarkable role as abiotic stress elicitors in plants. This study reports the comparative analysis of effects produced by capped [zinc oxide (ZnO)‐polyethylene glycol (PEG), ZnO‐polyvinyl pyrrolidone (PVP), copper oxide (CuO)‐PEG, CuO‐PVP] and uncapped (ZnO and CuO) nanoparticles on the medicinal plant, Stevia rebaudiana raised in vitro for the production of commercially important sweetener compounds. In context of shoot organogenesis, ZnO‐PEG, ZnO‐PVP, CuO‐PEG, CuO‐PVP were employed to the growth medium that resulted in increased growth parameters, and larger content of steviol glycosides as compared to the shoots raised in medium containing ZnO and CuO, revealed by high‐performance liquid chromatography. In the meanwhile, non‐enzymatic antioxidant activities including total phenolic content, total flavonoid content, total antioxidant capacity, total reducing power and 2,2‐diphenyl‐1‐picryl hydrazyl‐free radical scavenging activity were calculated and showed comparatively greater amounts in shoots grown in medium containing capped ZnO or CuO nanoparticles. Furthermore, the ZnO and its derivatives revealed to be more reactive at 1 mg/l of concentration. Whereas, the CuO and its derivatives produced greater response on Stevia at 10 mg/l concentration of nanoparticles. This study paves the way for more such studies encompassing capped and uncapped nanoparticles and their ultimate effect on in‐vitro grown plant tissues for the production of active metabolites on industrial scale.Inspec keywords: nanoparticles, chromatography, resins, polymers, zinc compounds, copper compounds, food technology, agricultureOther keywords: nanoparticles, sweetener compounds, antioxidant activities, Stevia rebaudiana Bertoni, abiotic stress elicitors, zinc oxide‐polyethylene glycol, zinc oxide‐polyvinyl pyrrolidone, copper oxide‐polyethylene glycol, medicinal plant, shoot organogenesis, steviol glycosides, high‐performance liquid chromatography, nonenzymatic antioxidant activities, total phenolic content, total flavonoid content, total antioxidant capacity, total reducing power, 2,2‐diphenyl‐1‐picryl hydrazylfree radical scavenging activity, derivatives, active metabolite production, copper oxide‐polyvinyl pyrrolidone     

Hypersonic flight vehicle trajectory optimization using pattern search algorithm

In this work, trajectory optimization of an aerodynamically controlled hypersonic boost glide class of flight vehicle is presented. In order to meet the mission constraints such as controllability, skin temperature, and terminal conditions etc., the trajectory is optimized using a pattern search algorithm with the lift to drag (L/D) ratio as a control parameter. It is brought out that the approach offers a viable tool for optimizing trajectories for the considered class of vehicles. Further, the effects of the constraints on trajectory shape and performance are studied and the analysis is used to bring out an optimal vehicle configuration at the initial stage of the design process itself. The research also reveals that the pattern search algorithm offers superior performance in comparison with the genetic algorithm for this class of optimization problem.     

Augmented photoelectrochemical response of CdS/ZnS quantum dots sensitized hematite photoelectrode

A visible light active and stable photoelectrode has been developed by depositing a passivating layer of ZnS QDs on CdS QDs sensitized hematite photoelectrode (Hematite‐CdS/ZnS) for PEC generation of hydrogen. Photoelectrochemical properties, in terms of stability and efficiency, have been investigated on the various hematite photoelectrodes sensitized with CdS QDs and CdS/ZnS QDs by varying number of SILAR cycles. I–V characteristics show that two layers of ZnS QDs deposited over three layers of CdS could enhance PEC response of hematite and efficiency by a factor of 3 and 11 respectively. Chronoamperometry measurement ensures that after adding a layer of ZnS QDs, CdS sensitized hematite film turns out to be a stable photoelectrode in the electrolyte. Prepared photoelectrodes have been characterized by XRD, SEM, HRTEM and UV–Vis spectrophotometer for various structural, morphological and optical properties to analyze PEC results. Mott–Schottky analysis and incident photon to current conversion efficiency (IPCE) measurements of sensitized hematite photoelectrode supported the improved PEC response of CdS/ZnS QDs sensitized hematite thin films. Copyright © 2016 John Wiley & Sons, Ltd.     

Chaos based key expansion function for block ciphers

The Key Expansion Function is a vital constituent component of any block cipher. Many of Key Expansion Functions generate subkeys through the algorithms which are based on Feistel or Substitution Permutation Network (SPN) structures against which cryptanalytic methods have been well researched. In this very paper, an efficient method for generating subkeys based on chaotic maps has been suggested. The phenomenon behind the proposed Key Expansion Function is the mixing property of Tent Map. Using chaotic binary sequences, the proposed Key Expansion Function satisfies the specific statistical and cryptographic properties of chaotic generators. A new Bit Extraction Technique based on IEEE-754 Floating-point Standard (binary32) is used to extract the bits of subkeys from the chaotic binary sequences. The generated subkeys are then analyzed. The results show that the given Chaos-based Key Expansion Function is well protected and fully strengthened in all respects.