varunr14/newsummary huggingface dataset:
A Mobile Ad hoc Network is an infrastructure less collection of nodes that are powered by portable batteries . Consumption of energy is the major constraint in a wireless network . This paper presents a new algorithm called Energy Aware Span Routing Protocol that uses energy saving approaches such as Span and the Adaptive Fidelity Energy Conservation Algorithm . Energy consumption is further optimized by using a hardware circuit called the Remote Activated Switch to wake up sleeping nodes . These energy saving approaches are well established in reactive protocols . However there are certain issues to be addressed when using EASRP in a hybrid protocol especially a proactive protocol . Simulation results for the EASRP protocol show an increase in energy efficiency of 12.2 and 17.45 compared with EAZRP and ZRP respectively . The EASRP protocol also proves to be effective in by producing a better packet delivery ratio for low and high density networks as measured by the NS 2 simulation tool . 


Summary: A new Energy Aware Span Routing Protocol EASRP for wireless ad hoc networks is proposed. Proposed protocol can minimize utilization of energy source by combining energy saving approaches Span and AFECA. It uses the Remote Activated Switch and wakes up the sleeping nodes during inactive time for reduce latency problem. The performance parameter of proposed protocol is tested under Network Simulator 2.
This study proposes a novel optical fiber sensor network using three dimensional wavelength time spatial optical code division multiple access scheme . The proposed 3 D modified quadratic congruence M matrix coding scheme overcome the restriction of single pulse per row inherent in traditional three dimensional codes and maintain a high signal to noise ratio even when source power is low . The 3 D system is implemented using optical switches fiber Bragg gratings optical splitter and optical combiner . The noises of phase induced intensity noise and multiple access interference in the decoding mechanism can be suppressed by using a spectral spreading scheme and balance detection in the receivers . By constructing 3 D codes using bipolar pseudorandom codes rather than unipolar codes provides a significant increase in the maximum permissible number of active sensor nodes . 


Summary: The OFS network using 3 D wavelength time spatial optical CDMA scheme. OFS have advantages of high bandwidth and resist electromagnetic interference. The 3 D codes by extending modified quadratic congruence and M matrix codes. Overcoming the restriction of single pulse per row in traditional technology. The noises of PIIN and MAI can be suppressed by balance detection scheme.
We consider turbo coded multi carrier double space time transmit diversity DSTTD system that employs orthogonal frequency division multiplexing OFDM for the transmission of acoustic signals in underwater communication where acoustic interference and ambient noise are the major channel deficiencies. DSTTD employs two space time block codes at the transmitter. At each receiver we implement space time Block Nulling detection technique to increase throughput. We consider the iterative decoding algorithm at the receiver to alleviate the effects of ambient noise and acoustic interference. Further we implement multi carrier modulation technique to mitigate the effects of multipath propagation. We investigate the effects of eleven tap delay pertaining to shallow water channel model for the DSTTD OFDM system. Our simulation results reveal that our considered system with Block Nulling technique provides better bit error rates for lower signal to noise ratio when compared to a minimum mean square error detector based system. Further it achieves higher throughput with fewer computations at the receivers. 


Summary: We consider DSTTD OFDM system for underwater communication. Non linear multi user detection based on Block Nulling increases throughput. Transmitter uses turbo code as an encoder and the receiver decodes iteratively. Multi carrier modulation mitigates the effects of multipath propagation. This system provides better bit error rates performance with lower signal to noise ratio.
Embedding secret information into a cover media and extracting the information and the image without any distortion is known as reversible watermarking RW . This paper analyzes the performance of hybrid local prediction error based RW using difference expansion DE . The cover medical image is split into non overlapping blocks. The border pixels in each block are predicted using median edge detection MED prediction. The other pixels in the block are predicted using least square prediction and the prediction error is expanded. The secret data are embedded into the cover medical image corresponding to the prediction error using the DE method. The predictor is also embedded into the cover medical image to recover the data at detection without any additional information. The simulation results show that this method achieves better watermarked image quality and high embedding capacity when compared to other classical prediction methods Median MED Rhombus and Gradient Adjusted Prediction. 


Summary: A hybrid local prediction error based difference expansion reversible watermarking algorithm for hiding data into medical images is presented. The performance of hybrid local algorithm with median median edge gradient adjusted and local prediction algorithms is compared. The hybrid local prediction algorithm has the highest frequency of zero prediction error. The PSNR and embedding capacity are improved in the hybrid local algorithm.
To improve the precision of low cost vehicle mounted global position system GPS this paper presents the multi source information fusion algorithm of vehicle navigation which is based on the interacting multiple model IMM . Considering vehicle kinematics and dynamic characteristics as well as its braking capacity in extreme accelerating situations this study establishes the clothoid model which shows longitudinal and lateral constant speed motion and the Adaptive Current Statistics ACS model which shows the acceleration of a vehicle. Through the parametric estimation of interactivity filtering and updating of probability the vehicle trajectory is predicted within a period of time and high precision dead reckoning is therefore achieved. Comparative analysis shows that the above algorithm can improve the precision of vehicle mounted GPS inertial navigation system. 


Summary: Clothoid model which shows longitudinal and lateral constant speed motion and the Adaptive Current Statistics model which shows the accelerating of the vehicle. Enhancing the positioning accuracy by the ACS model and clothoid model. IMM is better to improve the position accuracy within the limited cost.
This paper presents multi quantized local binary patterns for facial gender classification. For encoding the gray level difference GLD between a reference pixel and its neighbors local binary pattern employs a binary quantization which retains the sign of GLD but discards the magnitude information. To improve the discrimination capability the proposed method utilizes both the sign and magnitude components by performing multi level vector quantization of GLD. Each quantized level is then separately encoded to generate multiple local binary patterns. The proposed method is evaluated on four publicly available datasets FERET PAL CASIA and FEI through extensive experiments. Comparison of performance with various existing methods clearly demonstrated that the proposed method has advantages such as higher discrimination power improved noise robustness and better generalization capability. 


Summary: Multi level quantization scheme is proposed to enhance discrimination power of LBP. The proposed method utilizes both the sign and magnitude components from GLD. The results of the experiments validate the robustness of the proposed method. Thorough evaluation of four popular features LBP CLBP ELBP and LTP is presented.
The main goal of this paper is to propose and implement an experimental fully automatic face recognition system which will be used to annotate photographs during insertion into a database . Its main strength is to successfully process photos of a great number of different individuals taken in a totally uncontrolled environment . The system is available for research purposes for free . It uses our previously proposed SIFT based Kepenekci approach for the face recognition because it outperforms a number of efficient face recognition approaches on three large standard corpora . The next goal is proposing a new corpus creation algorithm that extracts the faces from the database and creates a facial corpus . We show that this algorithm is beneficial in a preprocessing step of our system in order to create good quality face models . We further compare the performance of our SIFT based Kepenekci approach with the original Kepenekci method on the created corpus . This comparison proves that our approach significantly outperforms the original one . The last goal is to propose two novel supervised confidence measure methods based on a posterior class probability and a multi layer perceptron to identify incorrectly recognized faces . These faces are then removed from the recognition results . We experimentally validated that the proposed confidence measures are very efficient and thus suitable for our task . 


Summary: We proposed and implemented a new face corpus creation algorithm. We created a new facial corpus from the data of the Czech News Agency. We evaluated a novel face recognition method the SIFT based Kepenekci approach. We proposed and evaluated two novel confidence measure techniques. We proposed implemented and evaluated the fully automatic face recognition system.
In this work we intend to address the limitation of our earlier particle method namely the Moving Particle Pressure Mesh method in handling arbitrary shaped flow boundaries . The application of the Cartesian pressure mesh system adopted in our original MPPM method which serves as the main key in recovering the divergence free velocity condition for incompressible flow in the framework of particle method is rather limited to rectangular flow domain . Here the hybrid unstructured pressure mesh is adopted to remove the geometrical constraint of our earlier MPPM method . Coupled with the moving particle strategy in the Moving Particle Semi implicit method the new method is named as the Unstructured Moving Particle Pressure Mesh method in the current work . A consistent Laplacian model namely the Consistent Particle Method recently reported in the open literature is incorporated as well in the framework of UMPPM for discretizing the viscous term on the scattered particle cloud while its implicit form is solved in the current work for overall robustness . Finally we shall verify our UMPPM method with a series of benchmark solutions available from the literatures including those obtained from the commercial code . It is appealing to find that the numerical solutions of UMPPM compare well with the benchmark solutions . In some cases the accuracy of our UMPPM is better than that of the existing particle method such as Smoothed Particle Hydrodynamics . 


Summary: Extension of MPPM particle mesh method to handle complex flow domain. Extension of MPPM to handle non isothermal buoyant flow. The viscous term in MPPM is treated by an implicit and consistent Laplacian model. No ghost particles are used to model no slip wall boundary. Accuracy enhancement in MPPM can be achieved via particle adaptation.
This paper addresses the use of isogeometric analysis to solve solid mechanics problems involving nearly incompressible materials . The present work is focused on extension of two field mixed variational formulations in both small and large strains to isogeometric analysis . Inf sup stable displacement pressure combinations for mixed formulations are developed based on the subdivision property of NURBS . Stability and convergence properties of the proposed displacement pressure combinations are illustrated by computing numerical inf sup constants and error norms . The performance of the proposed formulations is assessed by studying several benchmark examples involving nearly incompressible and incompressible elastic and elasto plastic materials in both small and large strain regime . 


Summary: Inf sup stable displacement pressure combinations are developed based on subdivision properties of NURBS. The inf sup stability is proven numerically by evaluating inf sup constants. Optimal convergence rates are obtained for linear problems. Stable and accurate solutions can be obtained with fewer load steps.
Interactive image segmentation aims to extract user specified regions from the background . In this paper an efficient two stage region merging based method is proposed for interactive image segmentation . An image is first over segmented into many super pixels using a bottom up method . The color histogram is exploited to represent each super pixel and the Bhattacharyya coefficient is computed to measure the similarity of two adjacent super pixels . Then some strokes denoting the desired object and background are manually labeled by the user on the over segmented image . With the labeled seed super pixels a merging strategy is designed to realize adaptive region merging . The whole merging process is divided into two stages which are repeatedly executed until no new merging occurs . In the first stage some unlabelled super pixels are merged into the labeled foreground or background super pixels if the labeled ones are their nearest neighbors . In the second stage any two unlabelled super pixels are merged together if one super pixel is the nearest neighbor of the other . Extensive experiments are conducted to evaluate the performance of the proposed method . The results show that the proposed method can extract the object reliably and quickly from the background . 


Summary: Two stage region merging interactive image segmentation. User stroke labeled seed super pixels. Adaptive region merging.
Active contours or snakes have a wide range of applications in object segmentation which use an energy minimizing spline to extract objects borders. Classical snakes have several drawbacks such as the initial contour sensitivity and convergence ability to local minima. Many approaches based on active contours are put forward to addressing these problems. However these approaches have limitation that they all depend too much on the amplitude of edge gradient and abandon directional information. This can lead to poor convergence toward the object boundary in the presence of strong background edges and cluttered noises. To deal with these issues we first propose a novel external force called adaptive edge preserving generalized gradient vector flow based on component based normalization CN AEGGVF which can adaptively adjust the process of diffusion according to the local characteristics of an image and preserve weak edges by adding the gradient information of an image. The experimental results show that the new model provides much better results than other approaches in terms of noise robustness weak edge preserving and convergence. Secondly an improved multi step decision model based on CN AEGGVF is presented which added new effective weighting function to attenuate the magnitudes of unwanted edges and adopted narrow band method to reduce time complexity. The novel method is analyzed visually and qualitatively on nature image dataset. Experimental results and comparisons against other methods show that the proposed method has better segmentation accuracy than other comparative approaches. 


Summary: A multi step decision model based on adaptive edge preserving generalized gradient vector flow using component based normalization for snake model is proposed. The proposed algorithm presents a novel external force which provides better results than other approaches in terms of noise robustness weak edge preserving and convergence. An improved multi step decision model based on this novel external force is adopted which adds new effective weighting function to attenuate the magnitudes of unwanted edges and adopts narrow band method to reduce time complexity. Experimental results and comparisons against other methods show that the proposed method has better segmentation accuracy than other comparative approaches.
In this paper we present a multi fidelity extension of non intrusive polynomial chaos based on regression for uncertainty quantification purposes . The proposed method uses the principle of a global correction function from a previous similar method that uses spectral projection to estimate the coefficients . Due to its usage of regression to estimate the coefficients the present method offers high flexibility in the sampling and generation of the polynomial basis . The method takes advantage of a nested sampling plan to create the samples for the low fidelity and correction expansions where the high fidelity samples are a subset of the LF ones . To build the polynomial basis a total order or hyperbolic truncation strategy is used with a highly flexible combination of the LF and correction polynomial expansions . The method is demonstrated on some artificial test problems and aerodynamic problems of the Euler flow around an airfoil and common three dimensional research models . In order to derive the strategies for successful MF approximation the effect of the correlation and the errors between the LF and HF functions is also studied . The results show that high correlation and moderately low errors are important to improve the MF approximation s accuracy . On a common research model problem the MF approach with partially converged simulations as the LF samples can successfully reduce the computational cost to about 40 for similar accuracy compared to an approach using a single HF expansion . 


Summary: We present a multi fidelity MF extension of non intrusive polynomial chaos based on regression for uncertainty quantification purposes. The proposed method uses the principle of a global correction function nested sampling plan and uses regression to estimate the coefficients. The results show that high correlation and moderately low errors between the low and high fidelity functions are important to improve the MF approximation s accuracy. On aerodynamic problem a computational cost saving of about 60 can be obtained by using the present method.
In this paper an improved particle swarm optimization guidance IPSOG is proposed. The particle swarm optimization guidance PSOG is presented to solve nonlinear and dynamic missile guidance problems. However the miss distance MD tends to be large. The objective function of the relative distance in the PSOG leads the missile to the current position of a target. Therefore the PSOG is similar to pursuit guidance. In the IPSOG a new objective function for the PSOG is introduced to improve the guidance performance. The line of sight LOS rate is taken as the objective function. The fitness function is then evaluated according to the defined objective function. Numerical simulation results show that the guidance performance of the IPSOG is better than the PSOG. 


Summary: The proposed is an improved PSO guidance law which is called IPSOG for a missile. The principle of IPSOG is based on PSO algorithm and parallel navigation. Both Pbest and Gbest that are evaluated by LOS rate should close to zero in IPSOG. Compared the IPSOG with the PSOG the IPSOG has better guidance performance.
Wavelet packet acoustic features are found to be very promising in unvoiced phoneme classification task but they are less effective to capture periodic information from voiced speech . This motivated us to develop a wavelet packet based feature extraction technique that signifies both the periodic and aperiodic information . This method is based on parallel distributed processing technique inspired by the human speech perception process . This front end feature processing technique employs Equivalent Rectangular Bandwidth filter like wavelet speech feature extraction method called Wavelet ERB Sub band based Periodicity and Aperiodicity Decomposition . Winer filter is used at front end to minimize the noise for further processing . The speech signal is filtered by 24 band ERB like wavelet filter banks and then the output of each sub band is processed through comb filter . Each comb filter is designed individually for each sub band to decompose the signal into periodic and aperiodic features . Thus it carries the periodic information without losing certain important information like formant transition incorporated in aperiodic features . Hindi phoneme classification experiments with a standard HMM recognizer under both clean training and multi training condition is conducted . This technique shows significant improvement in voiced phoneme class without affecting the performance of unvoiced phoneme class . 


Summary: 24 subband WP decomposition according to the auditory ERB scale. Proposed wavelet subband specific periodic and aperiodic decomposition. Wiener filter is used at frontend for noise minimization. Hindi phoneme classification task has been carried out. Proposed technique outperforms others classify voiced phonemes.
A wireless ad hoc network consists of a set of wireless devices . The wireless devices are capable of communicating with each other without the assistance of base stations . Space Division Multiple Access is a new technology designed to optimize the performance of current and future mobile communication systems . In this paper an SDMA based MAC protocol for wireless ad hoc networks with smart antennas is proposed . The proposed protocol exploits the SDMA system to allow reception of more than one packet from spatially separated transmitters . Using SDMA technology provides collision free access to the communication medium based on the location of a node . The proposed protocol solves the hidden terminal problem the exposed terminal problem and the deafness problem . Simulation results demonstrate the effectiveness of the proposed S MAC in improving throughput and increasing spatial channel reuse . 


Summary: An SDMA based MAC protocol S MAC for wireless ad hoc networks is proposed. Exploiting the creation of spatial channels to enhance network capacity. Providing collision free access to the communication medium based on the location of a node. Improving the performance of throughput and spatial channel reuse.
In general in order for individuals to take part in a lottery they must purchase physical lottery tickets from a store . However due to the popularity and portability of smart phones this paper proposes a lottery entry purchase protocol for joint multi participants in a mobile environment . This method integrates cryptology including elliptic curve cryptography and public key infrastructure enabling users to safely and fairly join a lottery via a mobile device . The lottery organization involves an untraceable tamperproof decryptor to generate the winning numbers and the generation of those winning numbers is fair and publicly verifiable . All participants share an equal probability of winning the prize . Subsequently a comparison table shows that the proposed protocol can withstand attacks and efficiently satisfy the known requirements in a mobile environment . In addition this study also ensures public verification and mutual authentication . 


Summary: This study proposes a fair and secure protocol allowing users to safely and fairly purchase lottery tickets by smart phone. The proposed lottery protocol can satisfy the fairness accuracy and public verification requests in a mobile environment. The proposed protocol can prevent the various malicious attacks discussed above with low computation costs. The proposed protocol can achieve the multi participants in a mobile environment.