varunr14/newsummary huggingface dataset:
In a wireless sensor network where sensors are arranged into a flat topology sensors near the sink consume much more energy than sensors at the boundary of the network . Sensors near the sink relay many packets than far away sensors to the sink . After these sensors expire energy holes are created near the sink . Therefore other sensors can not reach to the sink and the network becomes disconnected . In this paper we propose some strategies that can balance energy consumption of the deployed sensors and reduce energy holes from the network by balancing the communication load as equally as possible . We performed extensive experiments on the proposed algorithm using various network scenarios and compared it with other existing algorithms . The experimental results verify the effectiveness and feasibility of the proposed work in terms of network lifetime energy consumption and other important network parameters . 


Summary: Load management strategy for large scale wireless sensor networks. Energy efficient on demand clustering strategy. Load balancing data routing mechanism. Load balancing data routing mechanism reduces energy holes from the network. Experimental results for verifying the effectiveness and feasibility of the proposed work.
Widely deployed real time embedded systems can improve the performance of industrial applications but these systems also face the critical challenge of providing high quality security in an unpredictable network environment . We measure the time and energy consumptions of commonly used cryptographic algorithms on a real embedded platform and introduce a method to quantify the security risk of real time applications . We propose a Dynamic Security Risk Management mechanism to manage the aperiodic real time tasks for networked industrial applications . Inspired by the feedback design philosophy DSRM is designed as a two level control mechanism . The upper level component makes efforts to admit or reject the arrival tasks and assigns the reasonable security level for each admitted task . With three proportional feedback controllers at the lower level the security level of each ready task can be adjusted adaptively according to the dynamic environments . Simulation results show the superiority of the proposed mechanism . 


Summary: Introducing security critical applications based on embedded control server systems for industrial networks. Establishing the security aware task security overhead and risk models for aperiodic real time applications. Combining the soft real time and security requirements into a unified framework. Deploying proportional controllers to achieve satisfied fine grained control.
This paper presents a new particle swarm optimization algorithm called the PSO IAC algorithm to resolve the goal of reaching with the obstacle avoidance problem for a 6 DOF manipulator of the home service robot . The proposed PSO IAC algorithm integrates the improved adaptive inertia weight and the constriction factor with the standard PSO . Both the free space and obstacle avoidance states are established for evaluations in computer simulations and real time experiments . The performance comparisons of the PSO IAC algorithm with respect to the existing inertia weighted PSO constriction factor based PSO constriction factor and inertia weighted PSO and adaptive inertia weighted PSO algorithms are examined . Simulation results indicate that the PSO IAC algorithm provides the fastest convergence capability . Finally the proposed control scheme can make the manipulator of the home service robot arrive at the goal position with and without obstacles in all real time experiments . 


Summary: A new adaptive PSO method is proposed and verified by simulations and a real robot. Our proposed method has been successful applied to three dimensional obstacle avoidance with manipulator for the home service robot. Both the free space and obstacle avoidance states are established for evaluations in computer simulations and real time experiments. Our PSO IAC algorithm has achieved outstanding performance compared to other methods in these experiments.
Face detection is one of the most important parts of biometrics and face analysis science . In this paper a novel multi stage face detection method is proposed which can remarkably detect faces in different images with different illumination conditions variety of poses and disparate sizes . The idea is to utilize a preprocessing step to filter many non face windows by means of a skin segmentation procedure in order to boost the speed of the detection and also utilize the color information as much as possible . Subsequently candidate windows are fed to a Local Hierarchical Pattern generator unit where a new texture pattern is produced . Based on this pattern a kernel probability map is calculated for each window and by summing probabilities of all kernels and comparing it with a predefined threshold decision is made about content of the window . Not only does this algorithm effectively eliminate many non face regions but it is also capable of detecting faces with relatively acceptable rate in different conditions . 


Summary: A multi stage face detection method is proposed using color and texture information. Using skin detection candidate windows are extracted. Candidate windows are verified based on combination of KPM and LHP. Both qualitative and quantitative results confirm the merit of the algorithm.
Hierarchical clustering technique is an effectual topology control methodology in Wireless Sensor Networks . This technique is used to increase the life time of the network by reducing the number of transmissions towards the base station . We propose and validate a new routing protocol termed as Sleep awake Energy Efficient Distributed clustering algorithm . We divide the network sensing field into three energy regions because in SEED cluster heads are communicating directly with the base station . The cluster heads of the high energy region are communicating with the base station through a longer distance and paying extra energy cost as compared to the cluster heads of the low energy region . Same application base sensor nodes form sub clusters to decrease the number of transmissions towards the base station . In every round one node from these sub clusters nodes awake and transmit the data and the rest of them sleep to save available resources . We select six criteria to check the performance of our algorithm . The simulation results show that SEED achieves longer network life time and high throughput as compared to the existing clustering protocols . 


Summary: A new energy efficient routing algorithm has been proposed for cluster based wireless sensor networks. SEED achieves O 1 message exchange complexity per sensor node and time complexity for cluster formation is O N for WSN having N sensor nodes. We introduce sub clustering in SEED to decrease the redundant data transmissions towards the base station. The proposed model utilizes sleep awake awareness to save the available power.
In this paper we discuss the application of two dimensional linear cellular automata rules with the help of fuzzy heuristic membership function to the problems of edge detection in image processing applications . We proposed an efficient and simple thresholding technique of edge detection based on fuzzy cellular automata transition rules optimized by Particle Swarm Optimization method . Finally we present some results of the proposed linear rules for edge detection to the selected 22 images from the Berkeley Segmentation Dataset and compare with some classical Sobel and Canny results . Also Baddeley Delta Metric is used for the performance index to compare the results . 


Summary: This study is the application of 2D linear cellular automata CA rules with the help of fuzzy membership function to the problems of edge detection. An efficient and simple thresholding technique of edge detection based on CA transition rules optimized by Particle Swarm Optimization method PSO is proposed. Results of the proposed to the selected 22 images from the Berkeley Segmentation Dataset BSDS are presented. Comparison with some classical Sobel and Canny results is included. Baddeley Delta Metric BDM is used for the performance index to compare the results.
This paper presents a new medical image enhancement method that adjusts the fractional order according to the dynamic gradient feature of the entire image . The presented method can extract the edges of an image accurately and enhance them while preserving smooth areas and weak textures these improvements can be particularly helpful to doctors diagnoses . The primary contribution of this paper is the Adaptive Fractional Differential Algorithm which uses the improved Otsu algorithm to segment the edges textures and smooth areas of images . This algorithm allows the optimal fractional order of each pixel to be obtained using an adaptive fractional differential function constructed based on the area feature of image . As a result the image can be enhanced adaptively . Experimental results show that for medical images AFDA shows better image enhancement than other methods by making edges clearer and textures richer . 


Summary: We use the improved Otsu algorithm to segment medical images. The function of order v is constructed based on the features of image areas. The fractional differential order of each pixel is obtained by the function. Each pixel of medical image is processed by adaptive fractional differential mask. The edge of medical image is enhanced while the weak texture is preserved.
We extend the hierarchical multiscale design framework of Nakshatrala et al . to nonlinear elastodynamics wherein we use topology optimization to design material micro structures to achieve desired energy propagation in nonlinear elastic material systems subjected to impact loading . As in Part I a well posed topology optimization problem is obtained via relaxation to design the macroscale which requires homogenization theory to relate the macroscopic homogenized response to its micro structure and via restriction to design the microscale to obtain a well defined micro structural length scale . It is assumed that the primary wavelengths of interest are much longer than the micro structural length scale and hence the effective properties are computed using the static homogenization theory . An adjoint sensitivity analysis is performed to compute the derivatives of the objective function with respect to the micro structural design parameters and a gradient based optimization algorithm is used to update the design . The numerical implementation of the computationally challenging terminal value adjoint problems is discussed and a structural design example for tailored energy propagation is provided . 


Summary: Adjoint sensitivity analysis for nonlinear elastodynamics. Composite design for desired energy propagation. Multiscale topology optimization.
Energy efficiency EE maximization with limited interference to the primary user PU is one of the primary concerns in cognitive radio networks CRNs . To achieve this objective we first propose an algorithm to select less spatially correlated secondary users SUs to lessen the shadowing effect in wireless environment. Further the aid of parametric transformation with the Lagrangian duality theorem in our proposed algorithm called Novel Iterative Dinkelbach method NIDM is used to optimise both sensing time and transmission power allocation of the SUs for maximising EE under the constraints of maximum transmission power interference to the PU overall outage of secondary transmission and minimum data rate requirement. Extensive simulation results demonstrate the effectiveness of our proposed algorithm. It is also observed that our proposed scheme outperforms the other existing schemes in enhancing the EE with the same system parameters. 


Summary: Less spatial correlated secondary users SUs are selected based on Hungarian method. The joint optimization of sensing time and transmission power of SUs is achieved through Novel Iterative Dinkelbach method NIDM algorithm. The combination of parametric transformation with the Lagrangian duality provides better performance with lesser computational complexity. The proposed method is validated through the simulation results.
This work proposes a hybrid modelling technique for efficient analysis of poroelastic materials which are widely used for noise reduction in acoustic problems . By combining the finite element method and the wave based method in a direct manner the proposed hybrid technique maximises the advantages and compensates the drawbacks of both numerical methods . The considered poroelastic domain described by Biot s theory is divided into two groups of domains according to their geometrical characteristics and boundary conditions . The group with complex geometries and or boundary conditions leading to singularities is discretised into a large number of small finite elements . The other group consisting of large geometrically moderate poroelastic domains is partitioned into wave based subdomains where the field variables are expanded with analytical poroelastic wave functions . Both groups modelled by the finite element method and the wave based method respectively are combined in a hybrid framework in this work to ensure their interacting dynamic behaviours . The properties of the hybrid model are investigated and are compared to existing modelling methods for some numerical examples . The proposed direct hybrid modelling technique provides stable predictions and exhibits fast convergence performances for the analysis of poroelastic materials especially when singularities arise in the poroelastic domain . 


Summary: A hybrid modelling technique for efficient analysis of Biot s poroelastic materials. Efficient analysis performance for addressing singularities in poroelastic domain. Domain with singularities and or complex geometry discretised into finite elements. Geometrically moderate domain partitioned into convex wave based subdomains. Direct interface coupling of finite element and wave based poroelastic domains.
When wireless sensor networks are deployed in areas inaccessible by human beings security becomes extremely important as they are prone to different types of malicious attacks . We propose a scheme to build a security mechanism in a query processing paradigm within WSNs with clustered architecture . This work aims to preserve the basic security features such as confidentiality and integrity as well as to protect from replay attack in presence of mote class attacker . Considering the limitations of such an attacker the probability of attacking cluster head and member nodes is higher than attacking the base station . Paying attention to this fact in all communication between cluster head and member nodes the key is neither transmitted nor pre deployed . Performance of the scheme is evaluated and compared through qualitative and quantitative analyses results show the present scheme s dominance over the competing schemes . 


Summary: Low overhead secured query processing mechanism in clustered WSN environment. Preserves security features confidentiality integrity and defence against replay attack. Query is sent from base station to cluster heads in encrypted form. The cluster heads register their member nodes by exchanging messages. The cluster heads send aggregated responses to the base station.
Due to the limited network bandwidth a noise robust low bit rate compression scheme of Mel frequency cepstral coefficients MFCCs is desired for distributed speech recognition DSR services. In this paper we present an efficient MFCCs compression method based on weighted least squares W LS polynomial approximation through the exploitation of the high correlation across consecutive MFCC frames. Polynomial coefficients are quantized by designing a tree structured vector quantization TSVQ based scheme. Recognition experiments are conducted on the noisy Aurora 2 database under both clean and multi condition training modes. The results show that the proposed W LS encoder slightly exceeds the ETSI advanced front end ETSI AFE baseline system for bit rates ranging from 1400 bps to 1925 bps under clean training mode. However a negligible degradation is observed in case of multi condition training mode around 0.6 and 0.2 at 1400 bps and 1925 bps respectively . Furthermore the obtained performance generally outperforms the ETSI AFE source encoder at 4400 bps under clean training and provides similar performance at 1925 bps under multi condition training. 


Summary: A low bit rate source coding scheme for distributed speech recognition DSR systems is proposed. The algorithm is based on weighted least squares W LS polynomial approximation. The efficiency of the algorithm is tested with the noisy Aurora 2 database for bit rates ranging from 1400 bps to 1925 bps. The obtained results generally outperform the ETSI AFE encoder for clean training and provide similar performance at 1925 bps for multi condition training.
Advanced medical diagnosing and research requires precise information which can be obtained from measured electrophysiological data e.g . electroencephalogram and electrocardiograph . However they are often contaminated with noise and a variety of bioelectric signals called artefacts e.g . electromyography . These noise and artefacts which need to be reduced make it difficult to distinguish normal from abnormal physiological activity . Electromagnetic contamination of recorded signals represents a major source of noise in electrophysiology and impairs the use of recordings for research . In this paper we present an effective method for cancelling 50Hz interference using a radial basis function Wiener hybrid filter . One of the main points of this paper is the hybridization of the RBF filter and a Wiener filter to make full use of both merits . The effectiveness and validity of those filters are verified by applying them to ECG and EEG recordings . The results show that the proposed method is able to reduce powerline interference from the noisy ECG and EEG signals more accurately and consistently in comparison to some of the state of the art methods and this method can be efficiently used with very low signal to noise ratios while preserving original signal waveform . 


Summary: A method based on radial basis function and Wiener filter system is proposed for filtering powerline in biomedical recordings. The proposed solution addresses both ECG and EEG recordings. Several simulations have demonstrated the enhancement of the proposed method in comparison to other techniques. The results suggest that clinical information can be maintained. This method provides the best approach for obtaining both more signal reduction and low distortion of the signal results.
A new recurrent wavelet fuzzy neural network RWFNN with adaptive learning rates is proposed to control the rotor position on the axial direction of a thrust magnetic bearing TMB mechanism in this study. First the dynamic analysis of the TMB with differential driving mode DDM is derived. Because the dynamic characteristics and system parameters of the TMB mechanism are high nonlinear and time varying the RWFNN which integrates wavelet transforms with fuzzy rules is proposed to achieve precise positioning control of the TMB. For the designed RWFNN controller the online learning algorithm is derived using back propagation method. Moreover since the improper selection of learning rates for the RWFNN will deteriorate the control performance an improved particle swarm optimization IPSO is adopted to adapt the learning rates of the RWFNN on line. Numerical simulations show the validity of TMB system using the proposed RWFNN controller with IPSO under the occurrence of uncertainties. 


Summary: A new recurrent wavelet fuzzy neural network RWFNN controller is proposed. RWFNN is adopted to control the rotor position of a thrust magnetic bearing TMB . The online learning algorithm of RWFNN is derived using back propagation method. The adaptive learning rates are performed via improved particle swarm optimization. Numerical simulations show the validity of TMB using the RWFNN controller.
The clarity and accuracy of echocardiography images are greatly reduced by speckle noise. Noise suppression however is difficult to achieve without also obscuring both rapidly moving structures and object edges. This research seeks to address these challenges by introducing a novel filtering framework based on temporal information and sparse representation. The proposed method involves smoothing intensity variation time curves IVTCs assessed in each pixel. Using an over complete dictionary that contains prototype signal atoms IVTCs can be reconstructed as linear combinations of a few of these atoms. After a comprehensive comparison of sparse recovery algorithms three were selected for our method Bayesian Compressive Sensing BCS the Bregman iterative algorithm and Orthogonal Matching Pursuit OMP . The performance of the proposed method was then evaluated and compared with other speckle reduction filters. The experimental results demonstrate that the proposed algorithm can be used to achieve better preserved edges and reduce blurring. 


Summary: Noise reduction in echocardiography images is proposed. Filtering framework is based on temporal information and sparse representation. Proposed method consists of smoothing intensity variation time curves assessed in each pixel. A smooth version of signal can be reconstructed by using a proper sparse recovery which is followed by an adaptive thresholding method to locate the most important atoms. After a comprehensive comparison of sparse recovery algorithms three were selected for our method Bayesian Compressive Sensing BCS Bregman Iterative algorithm and Orthogonal Matching Pursuit OMP . The proposed method preserves the edges and rapidly moving structures.
XACML policies which are widely adopted for defining and controlling dynamic access among Web cloud services are becoming more complex in order to handle the significant growth in communication and cooperation between individuals and composed services . However the large size and complexity of these policies raise many concerns related to their correctness in terms of flaws conflicts and redundancies presence . This paper addresses this problem through introducing a novel set and semantics based scheme that provides accurate and efficient analysis of XACML policies . First our approach resolves the complexity of policies by elaborating an intermediate set based representation to which the elements of XACML are automatically converted . Second it allows to detect flaws conflicts and redundancies between rules by offering new mechanisms to analyze the meaning of policy rules through semantics verification by inference rule structure and deductive logic . All the approach components and algorithms realizing the proposed analysis semantics have been implemented in one development framework . Experiments carried out on synthetic and real life XACML policies explore the relevance of our analysis algorithms with acceptable overhead . Please visit http www.azzammourad.org projects to download the framework . 


Summary: We provide policy analysis scheme to detect access contradictions among web services. We propose semantic based policy analysis through deductive logic and inference rules. We present flaw conflict and redundancy detection algorithms for XACML policy analysis. We show through experiments that SBA XACML provides efficient detection mechanisms.