varunr14/newsummary huggingface dataset:
Efficient data delivery accompanied with low end to end delay is important in vehicular ad hoc networks and optimal route selection is vital to improve these performance metrics. Different routing algorithms are proposed in VANETs. This paper reviews and analyzes these algorithms briefly and based on the analysis a new opportunistic based routing algorithm OSTD is proposed for urban scenarios. The proposed algorithm severely considers the type of vehicular distribution in the calculation of utility function. This utility function is used to evaluate the routes in the network. The reason of such a severe consideration is expressed and evaluated in the paper. Vehicle s driving path predictability is also used in the algorithm to forward the packet to a more suitable next hop as vehicular mobility is the reflection of human social activity. Simulation results show that OSTD achieves a higher delivery ratio and lower end to end delay and packet loss compared to the other well known protocols. 


Summary: An opportunistic routing algorithm is proposed for the urban scenarios in VANETs. The algorithm is composed of two phases intersection and next hop selection phases. The type of vehicular distribution is used to evaluate the routes in the network. Vehicle s driving path is also used to select the next hop. Packet delivery packet loss packet e2e delay are used to evaluate the protocols. Our algorithm shows better performance compared to the other well known protocols.
Based on previous results on periodic non uniform sampling and using the well known Non Uniform Fourier Transform through Bartlett s method for Power Spectral Density estimation we propose a new smart sampling scheme named the Dynamic Single Branch Non uniform Sampler . The idea of our scheme is to reduce the average sampling frequency the number of samples collected and consequently the power consumption of the Analog to Digital Converter . In addition to that our proposed method detects the location of the bands in order to adapt the sampling rate . In this paper through we show simulation results that compared to classical uniform sampler or existing multi coset based samplers our proposed sampler in certain conditions provides superior performance in terms of sampling rate or energy consumption . It is not constrained by the inflexibility of hardware circuitry and is easily reconfigurable . We also show the effect of the false detection of active bands on the average sampling rate of our new adaptive non uniform sub Nyquist sampler scheme . 


Summary: We have proposed a new smart sampler scheme to reduce the power consumption of the Analog to Digital Converter by reducing its average sampling frequency. Its more efficient in terms of sampling rate than classical sampler uniform or multi coset when the false detection of bands localisation is low.
Street lighting is a ubiquitous utility but sustaining its operation presents a heavy financial and environmental burden . Many schemes have been proposed which selectively dim lights to improve energy efficiency but little consideration has been given to the usefulness of the resultant street lighting system . This paper proposes a real time adaptive lighting scheme which detects the presence of vehicles and pedestrians and dynamically adjusts their brightness to the optimal level . This improves the energy efficiency of street lighting and its usefulness a streetlight utility model is presented to evaluate this . The proposed scheme is simulated using an environment modelling a road network its users and a networked communication system and considers a real streetlight topology from a residential area . The proposed scheme achieves similar or improved utility to existing schemes while consuming as little as 1 2 of the energy required by conventional and state of the art techniques . 


Summary: TALiSMaN detects road users and sets streetlight brightness appropriately. A utility model is detailed to quantify the usefulness of street lighting. Street lighting schemes are evaluated with StreetlightSim. TALiSMaN offers comparable usefulness as conventional lighting schemes. TALiSMaN consumes 2 55 of the energy of conventional state of the art schemes.
This contribution presents B zier extraction of truncated hierarchical splines and the application of the approach to adaptive isogeometric analysis . The developed procedures allow for the implementation of hierarchical splines and NURBS without the need for an explicit truncation of the basis . Moreover standard procedures of adaptive finite element analysis for error estimation and marking of elements are directly applicable due to the strict use of an element viewpoint . Starting from a multi level nested mesh that results from uniform refinement standard B zier extraction is applied to active elements that contribute to the hierarchical approximation . This results in a multi level system of equations without communication between individual hierarchy levels . A hierarchical subdivision operator is developed to recover this communication by transforming the multi level system of equations into a hierarchical system of equations . It is demonstrated that this approach implicitly defines the truncated hierarchical basis in terms of a simple matrix multiplication . In this way the implementation effort is reduced to a minimum as shape function routines and B zier extraction procedures remain unchanged compared to standard isogeometric analysis . The convergence and the computational efficiency of the approach are examined in three different demonstration problems of heat conduction linear elasticity and the phase field modelling of brittle fracture . 


Summary: B zier extraction of truncated hierarchical splines and the application of the approach to adaptive isogeometric analysis are presented. An explicit computation of the truncation is avoided. A strict element viewpoint is adopted in the refinement algorithms which facilitates the application of standard procedures of adaptive finite element analysis. Numerical examples demonstrate optimal convergence rates for problems that involve singularities and strong gradients.
The paper introduces a new automated seizure detection model that integrates Weighted Permutation Entropy and a Support Vector Machine classifier model to enhance the sensitivity and precision of the detection process . The proposed system utilizes the fact that entropy based measures for the EEG segments during epileptic seizure are lower than in normal EEG . The new suggested model better tracks abrupt changes in the signal and assigns less complexity to segments that exhibit regularity or are subjected to noise effects . The Weighted Permutation Entropy algorithm relies on the ordinal pattern of the time series along with the amplitudes of its sample points . The proposed technique is implemented and tested on hundreds real EEG signals and the performance is compared based on sensitivity specificity and accuracy . Various experiments have been applied in different scenarios including healthy with eyes open healthy with eyes closed epileptic patients during no seizure state from two different location of the brain . Other scenarios have been applied accompanied by background simulated noise resulting from physiological and environmental artifacts . Results showed outstanding performance and revealed promising results in terms of discrimination of seizure and seizure free segments . It also manifests high robustness against noise sources . 


Summary: A new automated seizure detection model is proposed. Applying Weighted Permutation Entropy to classify raw and decomposed EEG signals. Simulating physiological and environmental artifacts to test the model robustness against noise. Comparison between Support Vector Machine and Artificial Neural Network classifier. Comparison with other automatics seizure detection models found in literature.
In this paper a discontinuous Galerkin method for a nonlinear shear flexible shell theory is proposed that is suitable for both thick and thin shell analysis . The proposed method extends recent work on Reissner Mindlin plates to avoid locking without the use of projection operators such as mixed methods or reduced integration techniques . Instead the flexibility inherent to discontinuous Galerkin methods in the choice of approximation spaces is exploited to satisfy the thin plate compatibility conditions a priori . A benefit of this approach is that only generalized displacements appear as unknowns . We take advantage of this to craft the method in terms of a discrete energy minimization principle thereby restoring the Rayleigh Ritz approach . In addition to providing a straightforward and elegant derivation of the discrete equilibrium equations the variational character of the method could afford numerous advantages in terms of mesh adaptation and available solution techniques . The proposed method is exercised on a set of benchmarks and example problems to assess its performance numerically and to test for shear and membrane locking . 


Summary: Discontinuous Galerkin method for a nonlinear shear flexible shell theory. Locking free method in the thin shell limit without the use of projection operators. Specially useful in the development of shell fracture methods that incorporate shear fracture mechanisms.
Increasingly companies are adopting service oriented architectures to respond to rapid changes in the market . Even though there are excellent tools and frameworks for service oriented architecture adoption and service development the latest adaptation to context has not been properly dealt with yet . Current approaches are mostly focused on solving context aware issues for web applications only focusing mainly on client side adaptation and there is a clear lack of context taxonomies which facilitate context aware applications . In our previous work we proposed Model Driven Adaptable Services a methodology and tool for the development of context aware services . In this paper we propose two key improvements on MoDAS firstly leveraging the proposal s abstraction level facilitating the use of a larger collection of contexts through the definition of an extensible context taxonomy by means of a metamodel secondly providing additional opportunities for code generation . 


Summary: A context metamodel to classify different types of contexts has been provided. MoDAS was improved through an XML document that facilitates context code generation. MoDAS provides model driven trustworthy and well structured code. The aspect oriented implementation facilitates system evolution and maintenance.
Computer systems are required to process data more rapidly than ever due to recent software and internet technology developments . The server computers work continuously and provide services to many clients simultaneously which results in greater heat production and high temperature that must be managed in order to avoid malfunction and failure of critical hardware . In this study three cooling systems were used comparatively to examine the temperature and performance of the CPU and motherboard . The temperature characteristics and performance of the CPU were tested with a heat sink water cooling system and thermoelectric cooler . According to the test results the thermoelectric cooling system has better cooling performance than the other two systems under continuous operating conditions . Additionally the performance rating of the CPU was the best with a thermoelectric cooler under varying workloads . 


Summary: The cooling efficiency of the specific cooling systems on critical server hardware is investigated. The performance of the cooling systems on server CPU is outlined. The applied cooling systems in the context of temperature and performance are compared. The superior performance rating and cooling efficiency is obtained by the thermoelectric cooler.
Feature selection is a well studied problem in the areas of pattern recognition and artificial intelligence. Apart from reducing computational cost and time a good feature subset is also imperative in improving the classification accuracy of automated classifiers. In this work a wrapper based feature selection approach is proposed using the evolutionary harmony search algorithm whereas the classifiers used are the wavelet neural networks. The metaheuristic algorithm is able to find near optimal solutions within a reasonable amount of iterations. The modifications are accomplished in two ways initialization of harmony memory and improvisation of solutions. The proposed algorithm is tested and verified using UCI benchmark data sets as well as two real life binary classification problems namely epileptic seizure detection and prediction. The simulation results show that the standard harmony search algorithm and other similar metaheuristic algorithms give comparable performance. In addition the enhanced harmony search algorithm outperforms the standard harmony search algorithm. 


Summary: A feature selection approach is proposed using the harmony search algorithm. A new harmony memory initialization is adopted and dynamic parameters are used. The proposed method and other metaheuristic algorithms give comparable performance. The enhanced harmony search algorithm outperforms the standard algorithm.
In this paper an Android based home automation system that allows multiple users to control the appliances by an Android application or through a web site is presented . The system has three hardware components a local device to transfer signals to home appliances a web server to store customer records and support services to the other components and a mobile smart device running Android application . Distributed cloud platforms and Google services are used to support messaging between the components . The prototype implementation of the proposed system is evaluated based on the criteria considered after the requirement analysis for an adequate home automation system . The paper presents the outcomes of a survey carried out regarding the properties of home automation systems and also the evaluation results of the experimental tests conducted with volunteers on running prototype . 


Summary: A cloud based and Android supported scalable home automation system is proposed. The mechanism relies on the distributed Google Cloud Platform. Google Cloud Messaging supports the communication infrastructure in the system. The prototype is evaluated based on a list of criteria for an adequate system.
A fault detection method based on dynamic kernel slow feature analysis is presented in the paper . SFA is a new feature extraction technology which can find a group of slowly varying feature outputs from the high dimensional inputs . In order to analyze the nonlinear dynamic characteristics of the process data DKSFA is presented which applies the augmented matrix to consider the dynamic characteristic and uses kernel slow feature analysis to extract the nonlinear slow features hidden in the observed data . For the purpose of fault detection the D monitoring statistic index is built based on DKSFA model and its confidence limit is computed by kernel density estimation . Simulations on a nonlinear system and Tennessee Eastman benchmark process show that the proposed method has a better fault detection performance compared with the conventional KPCA based method . 


Summary: A nonlinear dynamic process monitoring method is presented. The proposed method can extract the inherent slow features from the high dimensional observed data. A statistic index is built based on slow features to carry out process monitoring. The method is more sensitive to process faults than the conventional KPCA based method.
The paper deals with the level control of a Modified Quadruple Tank Process . In that the interaction is introduced between the bottom two tanks which causes an additional non linear dynamical component of the conventional Quadruple Tank Process . Since the process has an inbuilt and imposed uncertainties a robust Sliding Mode Controller is initially designed to drive the system to the desired operating point via the sliding surface . Further the conventional sliding surface is altered with fractional order dynamics . It makes a Fractional Order SMC and is proposed for both finite time convergence and counteract to the uncertainties present in the system . The undesirable chattering effect is reduced by introducing a novel exponential Multi Level Switching technique in order to protect control valve . Simulation results show the efficacy of the proposed Multi Level Switching FrSMC in terms of fast convergence with high robustness when compared to conventional SMC . 


Summary: Devised a new and complex nonlinear process model named as MQTP. Initial implementation of nonlinear and robust control over the MQTP using SMC. Proposed a FrSMC for Finite Time Convergence with adequate mathematical proofs. Implemented a novel approach for reducing the chattering effect by means of MLS.
In this paper we propose a blind color image watermarking scheme based on quaternion discrete Fourier transform and on an improved uniform log polar mapping . The proposed watermarking scheme embeds dual watermarks one is a meaningful binary image watermark and the other is a bipolar watermark . The former is embedded in the real part of mid frequency QDFT coefficients using quantization index modulation . The latter is used to resynchronize the watermark after the watermarked image has been attacked making the scheme resistant to geometric attacks . In particular the IULPM allows for greater accuracy when estimating the rotation angle of a geometric attack . At the same time the watermark embedding employs the image holistically rather than in a block pattern . Experimental results demonstrate that the proposed scheme achieves better performance of robustness against both common signal operations and geometric attacks compared to other existing schemes . 


Summary: A robust blind color image watermarking scheme is proposed. An improved uniform log polar mapping method is used to resist geometric attacks. A dual watermark is embedded into quaternion discrete Fourier transform domain. The holistic embedding manner rather than block based achieves better robustness.
Automating actions based on collected context from Internet of Things controlled systems is one of the most important requirements of IOT systems this paper seeks to satisfy this requirement by offering a context aware service framework on top of IOT controlled systems . The fault management process in electric power distribution networks is taken here as a case study and used for implementing our proposed framework . We discuss the different aspects that need to be covered in such a framework and its components . The obtained results from simulating and testing the framework show a significant improvement in the task management process compared to the traditional approach . 


Summary: Detailed explanation of an automatic dispatcher its components and functions. Framework structure is based on context awareness integration. with IOT middleware Framework tested in electrical distribution systems as a case study. High progress in the system performance in terms of cost time and effort. Acceleration in faults repairing and increased user satisfaction.
Recognition of age separated face images is a challenging and open research problem. In this paper we propose a facial asymmetry based matching score space MSS approach for recognition of age separated face images. Motivated by its discriminatory information we evaluate facial asymmetry across small and large temporal variations and use asymmetric facial features to recognize age separated face images. We extract three different facial features including holistic feature descriptors using Principal Component Analysis PCA local feature descriptors using Local Binary Patterns LBP and Densely Sampled Asymmetric Features DSAF to represent face images. Then we develop MSS to discriminate genuine and imposter classes using support vector machine SVM as a classifier. Experimental results on three widely used face aging databases the FERET MORPH and FG NET show that proposed approach has superior performance compared to some existing state of the art approaches. 


Summary: Facial asymmetry based approach is proposed to classify age separated face images. Facial asymmetry is measured and evaluated across temporal variations. A 3 D matching scores space is built using holistic local and asymmetric features. SVM is used as classifier to separate genuine and imposter classes in score space. Results show better performance of proposed approach compared to existing methods.
In this paper we propose a client based solution to detect evil twin attacks in wireless local area networks . An evil twin is a kind of rogue Wi Fi access point which has the same SSID name as a legitimate one and is set up by an attacker . After a victim associates his device with an evil twin an attacker can eavesdrop sensitive data forwarded through the evil twin . Most existing detection solutions are administrator based which are used by wireless network administrators to verify whether a given AP is in an authorized list or not . Such administrator based solutions are limited hardly maintained and difficult to protect users 24 7 . Hence we propose a client based detection mechanism called evil twin detector to detect this type of attacks . An evil twin detector changes its wireless network interface card to monitor mode to capture wireless TCP IP packets . Through analyzing captured packets our detector allows client users to easily and precisely detect an evil twin thus avoids threats created by evil twins . Our method does not need to know any authorized AP list and does not rely on data training or machine learning technique . Finally we implement a detecting system on Windows 7 . 


Summary: A client side detection mechanism for evil twins. The mechanism relies on packet forwarding behavior of an evil twin attack. Proposes the first idea of detecting evil twin by operating the wireless network interface controller in monitor mode.
Recent technological trends such as cloud computing wireless communication and wireless sensor networks provide a strong infrastructure and offer a true enabler for health information technology services over the Internet . This system is based on the cloud computing environment integrating mobile communication technology context aware technology and wireless sensor networks to build a mobile web for a personalized health information service which includes two health information recommendation service functions a collaborative recommender and a physiological indicator based recommender . We further propose a hybrid predictive model which combines the Grey Theory and Markov chain to predict the moving object s path . This will decrease the cost which arises from tracking errors and prolong the network s lifetime . From the experiment results of usability it has been discovered that subjects have positive responses towards usability measurement dimensions of the system satisfaction expectation confirmation perceived trust perceived usefulness and perceived value . 


Summary: The development of a Mobile Health Information Recommendation system which integrates Cloud Computing and Context aware technology and applies Collaborative Filtering technique for information recommendation. Through the collaborative recommender users can effectively obtain consistent health information and reduce energy and time consumption.