varunr14/newsummary huggingface dataset:
An efficient and reliable stress computation algorithm is presented which is based on implicit integration of the local evolution equations of multiplicative finite strain plasticity viscoplasticity . The algorithm is illustrated by an example involving a combined nonlinear isotropic kinematic hardening numerous backstress tensors are employed for a better description of the material behavior . The considered material model exhibits the so called weak invariance under arbitrary isochoric changes of the reference configuration and the presented algorithm retains this useful property . Even more the weak invariance serves as a guide in constructing this algorithm . The constraint of inelastic incompressibility is exactly preserved as well . The proposed method is first order accurate . Concerning the accuracy of the stress computation the new algorithm is comparable to the Euler Backward method with a subsequent correction of incompressibility and the classical exponential method . Regarding the computational efficiency the new algorithm is superior to the EBMSC and EM . Some accuracy tests are presented using parameters of the aluminum alloy 5754 O and the 42CrMo4 steel . FEM solutions of two boundary value problems using MSC.MARC are presented to show the correctness of the numerical implementation . 


Summary: A model of finite strain viscoplasticity based on Lion s approach is considered. The discretized system of constitutive relations is reduced to a scalar equation. The approach exploits the notion of the weak invariance of the material model. The symmetry incompressibility and weak invariance are exactly retained. The accuracy and stability of the algorithm are tested numerically.
Wireless sensor networks may be used for the surveillance of large systems or in the management of disasters such as forest fires or oil spills . In some of these applications the topology of the network is going to be dynamic a connected system can evolve into a clustered system that presents a break of the global connectivity . Using a collector which will regularly visit the disjoint clusters enables to restore a discreet connectivity . A session layer protocol is proposed to reconstitute a consistent global information system . It enables the collector to reconstruct the communication context with the previously visited clusters knowing they could have moved merged or have split . A node sensor model has been integrated into the Riverbed Opnet Modeler network simulation environment . Simulations show the benefits of the protocol and particularly how it provides a better trajectory planning of the collector . 


Summary: Collection of measurements on a dynamic and non connected wireless sensor network. Proposition of a session protocol to reconstitute a consistent information system. Definition of a clustering metric and of a contextual inheritance relation. Simulations of the protocol behavior on Riverbed Opnet Modeler.
Finding new techniques to accelerate electromagnetic simulations has become a necessity nowadays due to its frequent usage in industry . As they are mainly based on domain discretization EM simulations require solving enormous systems of linear equations simultaneously . Available software based solutions do not scale well with the increasing number of equations to be solved . As a result hardware accelerators have been utilized to speed up the process . We introduce using hardware emulation as an efficient solution for EM simulation core solvers . Two different scalable architectures are implemented to accelerate the solver part of an EM simulator based on the Gaussian Elimination and the Jacobi iterative methods . Results show that the performance gap between presented solutions and software based ones increases as the number of equations increases . For example solving 2 002 000 equations using our Clustered Jacobi design in single floating point precision achieved a speed up of 100.88x and 35.24x over pure software implementations represented by MATLAB and the ALGLIB C package respectively . 


Summary: We present an emulation based accelerator for EM simulations in metamaterials. We propose two hardware designs to solve systems of linear equations from the FEM. Both presented designs are run on a physical Veloce 1 emulator from Mentor Graphics. Timing results exceed the performance of several software based solvers.
This paper proposes a novel unambiguous correlation function for composite binary offset carrier signal tracking based on partial correlations . In the proposed scheme first we partition sub carriers of the CBOC signal into partial sub carriers and subsequently we obtain partial correlations by correlating the partial sub carriers with the received CBOC signal . Finally a novel unambiguous correlation function with no side peak is constructed by combining the partial correlations in a specially designed way . Unlike the conventional schemes the proposed scheme does not require any auxiliary signal and from numerical results it is found to offer a better tracking performance than those of the conventional schemes in terms of the tracking error standard deviation and multipath error envelope . 


Summary: An unambiguous tracking scheme is proposed for CBOC 6 1 1 11 signal. The proposed scheme combines the partial correlations in a specially designed way. The autocorrelation side peaks are completely removed by the proposed scheme. Better TESD and MEE performances are achieved over those of the conventional schemes.
Accurate surface estimation is a critical step for autonomous robot navigation on a rough terrain . In this paper we present a new method for estimating the surface of an unknown arbitrarily shaped terrain from the range data . The terrain modeling problem is generally formulated as the estimation of a function whose zero set corresponds to the surface to be reconstructed . A Laser range scanner has been built for acquisition of range data . The range data from the scanner samples the terrain unevenly and is more sparse for distant regions from the sensor . The paper describes the surface estimation problem as a max margin based formulation of a non stationary kernel function and minimizes the objective function using sub gradient method . Unlike other methods additional geometric ray based information is used to eliminate the unnecessary bumps on the surface and increase the precision . The experimental results validate the robustness of the proposed approach . 


Summary: The problem of detecting the surface of an unknown arbitrarily shaped scene from a set of points is proposed. Range data is obtained from in house developed Laser range scanner. The surface estimation problem is described as a max margin based formulation of a kernel function and solving the objective function using sub gradient method. Additional geometric ray based information is used to eliminate the unnecessary bumps on the surface and increase the precision.
A memory efficient field programmable gate array method is described that facilitates the processing of the continuous wavelet transform arithmetic operations . The CWT computations were performed in Fourier space and implemented on FPGA following several optimization schemes . First the adapted wavelet function was stored in a lookup table instead of computing the equation each time . Second the utilization of FPGA memory was highly optimized by only storing the nonzero values of the wavelet function . This reduces 89 of the memory storage and allows fitting the entire design into the FPGA . Third the design decreases the number of multiplications and shortens the time to produce the CWT coefficients . The proposed design was tested using EEG data and demonstrated to be suitable for extracting features from the event related potentials . Fourth wavelet function scales were eliminated which saves further resources . The achieved computation speed allows for real time CWT application . 


Summary: We design highly optimized architecture for continuous wavelet transform in FPGA. Lookup tables are highly recommended in case of implementing complex algorithms. Zero free wavelet function and scale reduction save extensive amount of block RAM. Spartan 3AN FPGA device is suitable for real time EEG analysis with the CWT.
In this work we present a statistical treatment of stress life data drawn from a collection of records of fatigue experiments that were performed on 75S T6 aluminum alloys . Our main objective is to predict the fatigue life of materials by providing a systematic approach to model calibration model selection and model ranking with reference to S N data . To this purpose we consider fatigue limit models and random fatigue limit models that are specially designed to allow the treatment of the run outs . We first fit the models to the data by maximum likelihood methods and estimate the quantiles of the life distribution of the alloy specimen . To assess the robustness of the estimation of the quantile functions we obtain bootstrap confidence bands by stratified resampling with respect to the cycle ratio . We then compare and rank the models by classical measures of fit based on information criteria . We also consider a Bayesian approach that provides under the prior distribution of the model parameters selected by the user their simulation based posterior distributions . We implement and apply Bayesian model comparison methods such as Bayes factor ranking and predictive information criteria based on cross validation techniques under various a priori scenarios . 


Summary: Several models are calibrated to the 75S T6 data set by means of the maximum likelihood method. Classical measures of fit based on information criteria are used to compare and rank models. Bayesian approach is considered to analyze some models under two different a priori scenarios. Bayes factor and predictive information criteria are used to compare Bayesian models.
Foreign fibers in cotton seriously affect the quality of cotton products. Online detection systems of foreign fibers based on machine vision are the efficient tools to minimize the harmful effects of foreign fibers. The optimum feature set with small size and high accuracy can efficiently improve the performance of online detection systems. To find the optimal feature sets a two stage feature selection algorithm combining IG Information Gain approach and BPSO Binary Particle Swarm Optimization is proposed for foreign fiber data. In the first stage IG approach is used to filter noisy features and the BPSO uses the classifier accuracy as a fitness function to select the highly discriminating features in the second stage. The proposed algorithm is tested on foreign fiber dataset. The experimental results show that the proposed algorithm can efficiently find the feature subsets with smaller size and higher accuracy than other algorithms. 


Summary: A two stage selection algorithm combining IG and BPSO for foreign fiber data. IG can filter noisy features and the BPSO can select the highly discriminating features. The experiments show that our method can find the subsets with small size and high accuracy. The optimum subsets are significant for online detection based on machine vision of foreign fibers in cotton.
Blood travels throughout the body and thus its flow is modulated by changes in body condition . As a consequence the wrist pulse signal contains important information about the status of the human body . In this work we have employed signal processing techniques to extract important information from these signals . Radial artery pulse pressure signals are acquired at wrist position noninvasively for several subjects for two cases of interest viz . before and after exercise and before and after lunch . Further analysis is performed by fitting a bi modal Gaussian model to the data and extracting spatial features from the fit . The spatial features show statistically significant changes between the groups for both the cases which indicates that they are effective in distinguishing the changes taking place due to exercise or food intake . Recursive cluster elimination based support vector machine classifier is used to classify between the groups . A high classification accuracy of 99.71 is achieved for the exercise case and 99.94 is achieved for the lunch case . This paper demonstrates the utility of certain spatial features in studying wrist pulse signals obtained under various experimental conditions . The ability of the spatial features in distinguishing changing body conditions can be potentially used for various healthcare applications . 


Summary: Wrist pulse signals are analyzed using spatial features obtained from a bi modal Gaussian model. Statistically significant group differences are found for two cases before and after lunch before and after exercise. A recursive cluster elimination based support vector machine classifier is used for classification. High classification accuracy is obtained for both exercise case 99.71 and lunch case 99.94 . There is tangible scope for using these results in various healthcare applications.
In cognitive radio networks it is well known that the cooperative spectrum sensing can overcome damaging effects of fading and shadowing . However it also increases the amount of energy consumption which is a critical factor in low powered wireless communications . In this paper based on bi threshold energy detection we maximize the network throughput such that the energy consumption is below a predefined value and also sufficient protection of primary users against interference is guaranteed . Convex optimization analysis is presented to jointly obtain the optimal values of sensing time and detection thresholds . Simulation results show that the proposed method is very flexible such that a good tradeoff between achievable throughput and energy efficiency can be established while it often outperforms the conventional sensing method significantly . 


Summary: A bi threshold cooperative spectrum sensing method for energy efficiency and throughput enhancement is proposed. We present a convex optimization analysis. The method shows a good tradeoff between energy consumption and throughput. Both energy efficiency and throughput can be improved for a wide range of SNRs.
The ever growing needs of Big Data applications are demanding challenging capabilities which can not be handled easily by traditional systems and thus more and more organizations are adopting High Performance Computing to improve scalability and efficiency . Moreover Big Data frameworks like Hadoop need to be adapted to leverage the available resources in HPC environments . This situation has caused the emergence of several HPC oriented MapReduce frameworks which benefit from different technologies traditionally oriented to supercomputing such as high performance interconnects or the message passing interface . This work aims to establish a taxonomy of these frameworks together with a thorough evaluation which has been carried out in terms of performance and energy efficiency metrics . Furthermore the adaptability to emerging disks technologies such as solid state drives has been assessed . The results have shown that new frameworks like DataMPI can outperform Hadoop although using IP over InfiniBand also provides significant benefits without code modifications . 


Summary: Analysis and evaluation of several HPC oriented MapReduce frameworks on a cluster. Proposal of a taxonomy to classify these frameworks according to their characteristics. Experimental configuration using several workloads cluster sizes networks and disk technologies. Evaluation in terms of performance and energy efficiency. Results useful to select a suitable MapReduce framework and to identify desirable characteristics for future ones.
Virtual fixtures can be used in haptic enabled hydraulic telemanipulators to facilitate certain tasks . Using this concept however the operator may tend to move the master fast due to relying on the virtual fixture . As a result the slave manipulator could start to lag due to latency in the hydraulic actuation control system . This paper describes how to mitigate the position errors between master and slave robots by overlaying an augmentation force on the master that is collinear but opposite of the master instantaneous velocity . The magnitude of this force is proportional to the position error at the slave end effector . Experiments conducted on a teleoperated hydraulic manipulator to perform several live line maintenance tasks show that the augmented scheme exhibits less position error at the slave side better task quality but longer task completion time as compared to the virtual fixture alone . 


Summary: We introduce the concept of augmented virtual fixtures to reduce tracking error. We experimentally validate effectiveness of the proposed scheme. The application targeted in this paper is live transmission line maintenance. The proposed scheme shows a marked improvement over the virtual fixture mode alone. Specifically for presented experiments our scheme reduces position error by 71 .
This paper proposes an accelerating correlation based image alignment using CPUs for time critical applications in automatic optical inspection . In order to improve computation efficiency the image pyramid search scheme is combined with the parallel computation . The image pyramid search scheme is employed first to quickly find certain objects in both monochrome and color images with rotation translation and scaling . Sub pixel accuracy is then used to attain the more accurate results at the sub pixel level . In our experimental results rotation accuracy is smaller than 0.218 and the speed is increased between 277 and 20 841 times . According to translation rotation and scaling tests the errors of rotation translation and scaling are 0.2 2.07pixel and 0.55 respectively . These results show that the proposed method is suitable for dealing with correlation based image alignment for time critical applications in automatic optical inspection . 


Summary: We propose an accelerating CPU based correlation based image alignment. The image pyramid search scheme is combined with the parallel computation. Sub pixel accuracy is used to attain the more accurate image alignment. It can align the accurate pose of the template image within the inspected image.
This paper proposes an unambiguous correlation function applicable to generic sine phased binary offset carrier signal tracking . In the proposed scheme first we view the BOC sub carrier pulse as a sum of multiple rectangular pulses . Then we obtain partial correlation functions by correlating the multiple rectangular pulses with the received signal and subsequently construct two symmetric correlation functions by combining the partial correlation functions in a specialized way . Finally we generate an unambiguous correlation function by combining the two symmetric correlation functions . The proposed correlation function has a sharper main peak and consequently provides a better tracking performance than those of the conventional correlation functions in terms of the tracking error standard deviation . 


Summary: Ambiguity in tracking is the major problem in binary offset carrier signal tracking. The ambiguity in tracking is caused by the BOC correlation side peaks. A correlation function with no side peaks is proposed for generic sine phased BOC. The correlation function removes the ambiguity and improves the tracking performance.
The main constraint of wireless sensor networks is the limited and generally irreplaceable power source of the sensor nodes . Therefore designing energy saving routing algorithm is one of the most focused research issues . In this paper we propose an energy aware routing algorithm for cluster based WSNs . The algorithm is based on a clever strategy of cluster head selection residual energy of the CHs and the intra cluster distance for cluster formation . To facilitate data routing a directed virtual backbone of CHs is constructed which is rooted at the sink . The proposed algorithm is also shown to balance energy consumption of the CHs during data routing process . We prove that the algorithm achieves constant message and linear time complexity . We test the proposed algorithm extensively . The experimental results show that the algorithm outperforms other existing algorithms in terms of network lifetime energy consumption and other parameters . 


Summary: A new energy aware routing algorithm has been proposed for cluster based wireless sensor networks. It achieves O 1 message complexity per sensor node and O n time complexity for a WSN having n sensor nodes. It efficiently forms the directed virtual backbone of cluster heads to facilitate data routing to the sink. It is successful in balancing the relaying load among the sensor nodes with respect to their residual energy.
Multiple Input Multiple Output system plays a major role in the fourth generation wireless systems to provide high data rates . In this paper a blind channel estimation approach has been proposed for finding the channel length based on the signals received from the MIMO transceiver . The resultant MIMO channel length information is utilized for estimation of the channel impulse response of the system . The estimation is used for adaptation of the equalizer weights based on the proposed Constant Modulus Algorithm which has reduced the fading and multipath propagation resulting from Inter Symbol Interference . The performance of the proposed system has been analyzed in terms of Mean Square Error and Symbol Error Rate for various Signal to Noise Ratio . 


Summary: The channel length is blindly estimated and the channel impulse response is determined. The cost function specifies the signal noise level. Equalizer updates its tap weights based on the cost function of the proposed CMA. With 4 taps the proposed CMA achieves a faster convergence rate. The proposed CMA exhibit better MSE and reduced error rate than other CMA techniques.
This study analyzes the vibration signals of fault induction motors for establishing an intelligent motor fault diagnosis system by using an extension neural network . Extension theory and a neural network are combined to construct the motor fault diagnosis system which identifies the most likely fault types in motors . First the vibration signal spectra of the 10 most common fault types are measured and organized into individual motor fault models . Subsequently according to the motor fault data representative characteristic frequency spectra are identified and the correlation between the motor fault types and their corresponding characteristic frequency spectra are established to develop the motor fault diagnosis system . Finally the test results confirm that the proposed motor fault diagnosis system is fast requires less training data and demonstrates first rate identification capability . 


Summary: This study analyzes the vibration signals of the 10 most common motor fault types. Extension theory and NN are combined to build the motor fault diagnosis system. The proposed motor fault diagnosis system is fast requires less training data. The proposed fault diagnosis system exhibits first rate identification capability.