varunr14/newsummary huggingface dataset:
Modeling traffic flow and gathering accurate traffic congestion information are two challenging problems in smart transportation systems . Most of the traffic flow models and velocity estimation methodologies that have been proposed so far gather the data from GPS equipped smart phones and extract the flow model based on GPS sampling . However these approaches tend to fail in real life scenarios due to the insufficient vehicle data and unpredictable dynamics of the flow . Furthermore utilization of GPS sensor leads to a battery drainage and hence reduces the overall system performance . In this paper we propose a new battery friendly data acquisition model to obtain the raw data . We then evaluate our model under various traffic conditions to determine its feasibility in vehicle speed estimation . The proposed model results in 88 location accuracy whereas it reduces the battery consumption by half . 


Summary: Evaluate the effectiveness of using cellular positioning techniques along with GPS sensor based data collection via performing experiments on energy consumption and location accuracy for various data acquisition modes where only GPS positioning only cellular positioning and both of the techniques are executed. Our proposed system is designed to be used for average velocity calculation which is an essential parameter in traffic monitoring systems. Therefore we perform experiments to compute the average velocity by extracting location data with the hybrid data acquisition model. We assume average speed measurements obtained from GPS positioning as the ground truth data to assess the performance of our model. Assess the feasibility of cellular positioning in cases where the GPS sensors are not available for location estimation. In this paper we propose a hybrid acquisition model which applies cellular positioning techniques to obtain the raw location data where GPS sensor is not available or battery of a particular smart phone is too low for location lookup.
In this paper we address the problem of broadcast routing and scheduling of video streaming for delay sensitive applications in backbone wireless mesh networks . Given a source node and a set of destinations we aim to build a broadcast tree and compute an optimal schedule such that the throughput for the source to broadcast streaming data to all the destinations is maximized . We divide the whole period for video broadcast into identical time frames and prove that maximizing the throughput can be converted into minimizing the length of a time frame . We propose a three step method as a solution . Firstly we build the broadcast tree by defining a new routing metric to select relay nodes . Then we use local search method to adjust the tree structure . Last we propose a greedy method to schedule concurrent transmissions . Simulations have demonstrated that our method can improve the performance significantly compared with existing methods . 


Summary: We formulate the problem as a mixed integer quadratically constrained program. We use a three step method to build a broadcast tree and schedule transmissions. We show the effectiveness of our method by simulation.
Progress in the area of environmental sustainability for the mobile computing industry could be achieved by making advancement on two fronts reducing the energy consumed by individual devices throughout their life cycle and reducing the rate at which these devices are discarded . In this work to address both fronts we propose the use of a thin client approach whereby a mobile device relies mainly on the resources at a remote server to carry out application tasks . To assess the benefits of the proposed approach this work develops an analytical model as well as performs an empirical evaluation of performance and energy consumption on Android based smartphones . In terms of energy a reduction of approximately 11 in the average life cycle energy is seen by increasing the device s usage life by even three months through a thin client approach . In terms of performance a thin client device is shown to improve execution by 57 compared to a self reliant device . 


Summary: An analytical model for lifecycle energy consumption of mobile devices is proposed and evaluated. A derivation of the theoretical conditions for energy savings under thin client paradigm. A comparative empirical evaluation of performance and energy consumption of old and new Android based smartphone operating under the thin client paradigm was performed.
We consider the extension of the Nitsche method to the case of fluid structure interaction problems on unfitted meshes . We give a stability analysis for the space semi discretized problem and show how this estimate may be used to derive optimal error estimates for smooth solutions irrespectively of the mesh interface intersection . We also discuss different strategies for the time discretization using either fully implicit or explicit coupling schemes . Some numerical examples illustrate the theoretical discussion . 


Summary: Unfitted finite element method for a fluid structure interaction. Proof of stability and accuracy. Different coupling scheme s for time advancement fully coupled or loosely coupled.
Various signaling techniques have been considered for I O interconnects at 25 Gb s or higher . However they either suffer from high baud rate as in the case of NRZ or an excess signal to noise ratio penalty as in the case of PAM 4 . To overcome these problems a four phase shifted sinusoid symbol signaling method is proposed that can be readily applied to high speed I O transceivers . By using PSS 4 the SNR penalty can be reduced from PAM 4 our theoretical analysis has revealed that the SNR performance of the PSS 4 is 6.5 dB better than that of PAM 4 with comparable bandwidth . The transistor level simulation results have shown that PSS 4 has an average of twice larger vertical eye opening than PAM 4 . In addition the supply voltage sensitivity of PSS 4 is 55 and 20 less than that of PAM 4 and NRZ respectively and the power consumption of PSS 4 is 13.5 lower than that of PAM 4 . 


Summary: Instead of using amplitude modulation a four phase shifted sinusoid symbol signaling PSS 4 is proposed for high speed I O interconnects from the phase aspect. PSS 4 reduces the signal to noise ratio SNR penalty of 6.5 dB from PAM 4. A novel pre emphasis technique is proposed for PSS 4 to improve the spectral performance.
Due to intra flow and inter flow interference problems the throughput performance decreases dramatically in a multi hop wireless network. These two kinds of bandwidth unfair sharing problems could cause serious collisions and congestion hence affecting the performance of multi hop wireless networks. That is to say data packets that need to traverse more hops to arrive at the destination will get lower throughput and result in the inter flow fairness problem. Furthermore the quality of video transmission is especially poor in traditional multi hop wireless network environments. Therefore in this paper we propose a virtual queue management scheme that does not require the modification of any communication protocol. According to the number of flows it adjusts the queue management scheme to achieve each flow s fair sharing of channel resource. It also improves the quality of video transmission. Through NS2 simulations the results show that our proposed scheme can mitigate the inter flow fairness problem and effectively improves the quality of video transmission. 


Summary: Packets that traverse more hops to the destination will get lower throughput and result in the inter flow fairness issue. We propose a virtual queue management scheme that does not require the modification of any communication protocol. According to the number of flows our scheme adjusts the parameters to achieve each flow s fair sharing of channel resource. Our proposed scheme can mitigate the inter flow fairness problem and effectively improves the quality of video transmission.
Compressed sensing recovers a sparse signal from a small set of linear nonadaptive measurements . A sparse signal can be represented by compressed measurements with a reduced number of projections on a set of random vectors . In this paper a multiscale compressed sensing based processing is investigated for an electrocardiogram signal which yields coded measurements . In case of an electrocardiogram signal the coded measurements are expected to retain the clinical information . To achieve this compressed sensing based processing is applied at each wavelet scale and measurements are coded using Huffman coder . The measurements at each scale use random sensing matrix with independent identically distributed entries formed by sampling a Gaussian distribution . The proposed method is evaluated using pathological ECG signals from the CSE database synthetic and normal ECGs . It helps preserve the pathological information and clinical components in compressed signal . The compressed signal quality is evaluated using standard distortion measures and mean opinion score . The MOS values for the signals range from to 8.3 with a wavelet energy based diagnostic distortion value of 9.46 which falls under the excellent category . 


Summary: Multiscale compressive sensing based processing is applied for Electrocardiograms. The wavelet coefficients at different subbands are sparse in nature. Compressed measurements are taken at wavelet scales and measurements are encoded for further compression. Method is evaluated using pathological ECG signals from CSE database synthetic and normal ECGs. Distortion introduced is evaluated by quantitative and qualitative analysis like PRD WEDD RMSE and MOS.
Designing a lightweight secure communication protocol for Wireless Sensor Networks remains a challenging issue since sensor networks are resource limited and are left unattended . Sensor nodes in WSNs are subjected to varying forms of attacks . An adversary may destroy or damage the communications done in multi hop WSNs by means of packet dropping and modification . Hence it is essential to have an efficient cryptographic scheme to protect the communications done in WSNs . This study introduces a Reliable Anonymous Secure Packet forwarding scheme that can prevent not only traffic analysis attack but also the attacks done through compromised forwarding nodes . The mechanisms followed here are effective with low computation and communication overhead . The performance of the proposed scheme is evaluated over NS2 with a series of simulation . The simulated results show that the proposed scheme performs better than other comparable schemes . 


Summary: Establishing an optimal communication path for reliable data delivery. Privacy for sensor readings through security service anonymity. Incremental hashing in establishing secure channel. Symmetric key management with key refreshness.
The existing clustering algorithms are either static or dynamic depending on the frequency of clustering . In static clustering clusters are formed once which reduces the clustering overhead but leads to early energy drain of a few nodes in the network . The network lifetime can be improved by dynamic clustering in which clusters are reformed after every round which increases the clustering overhead . To optimize the parameters including clustering overhead network lifetime energy hole FND and LND in WSN a hybrid unequal clustering with layering protocol is proposed . The HUCL is a hybrid of static and dynamic clustering approaches . In HUCL the network is divided into layers and clusters of various sizes . The cluster heads are selected based on available energy the distance to the sink and the number of neighbors . Once the cluster is formed the same structure is maintained for a few rounds . The data are forwarded to the sink through a multi hop layer based communication with an in network data compression algorithm . In comparison with the existing protocols the HUCL balances energy and achieves a good distribution of clusters extends the lifetime of the network and avoid the energy hole problem . 


Summary: Low overhead date collection framework for WSN to mitigate the energy hole problem. The clusters formed by this protocol are unequal size clusters. Hybrid of static and dynamic clustering for maximizing lifetime in WSN. In network data compression by cluster heads is proposed.
This paper investigates an optimal adaptive rate and power transmission algorithms for Orthogonal Frequency Division Multiplexing based Cognitive Radio systems . The aim was to study the problem of maximizing the overall rate achieved by the Secondary User while keeping the interference powers introduced by the SU on the spectrum band of Primary User s below the specified thresholds and considering the total transmit power budget constraints . In addition the novel suboptimal power allocation algorithm was proposed and consequently the maximum modulation level according to allocated power based on maximizing the overall achievable rate was obtained . The performance of the proposed suboptimal algorithm is compared with the optimal and existing algorithms including uniform loading and water filling algorithms . Numerical results revealed that the proposed suboptimal algorithm had a better performance than the uniform loading and water filling algorithms . 


Summary: Investigate adaptive rate and power allocation in OFDM based Cognitive Radio system. Investigate an optimal adaptive rate and power transmission algorithms. Proposed novel suboptimal algorithm for power allocation. Compare proposed suboptimal algorithm with optimal and conventional algorithms.
Variational decomposition has been widely used in image denoising however it can t distinguish texture from noise well . Replacing the fixed parameter in the decomposition with a monotone increasing sequence and iteratively taking the residual of the previous step as the input to decompose we propose a multiscale variational decomposition model in this paper . Unlike the fixed scale decomposition the new model can decompose the input image into a sum of a series of features with different scales . So texture can be distinguished from noise . In addition we prove the nontrivial property and the convergence of this multiscale decomposition and introduce a hybrid iteration algorithm that combines the first order primal dual algorithm with the gradient decent method to numerically solve the multiscale decomposition model . Numerical results validate the effectiveness of the proposed model . Furthermore we apply this multiscale decomposition for image hierarchical restoration . Compared with the classical hierarchical decomposition hierarchical wavelet decomposition and fixed scale decomposition our model has better performance for both synthetic and real images in terms of PSNR and MSSIM . 


Summary: We propose a multiscale variational decomposition model. We apply the proposed model for hierarchical image restoration and reconstruction. We prove the nontrivial property and the convergence of our model. Experimental results verify the correctness of the theoretical analysis.
In this paper we present a novel pose and expression invariant approach for 3D face registration based on intrinsic coordinate system characterized by nose tip horizontal nose plane and vertical symmetry plane of the face . It is observed that distance of nose tip from 3D scanner is reduced after pose correction which is presented as a quantifying heuristic for proposed registration scheme . In addition motivated by the fact that a single classifier can not be generally efficient against all face regions a two tier ensemble classifier based 3D face recognition approach is presented which employs Principal Component Analysis for feature extraction and Mahalanobis Cosine matching score for classification of facial regions with weighted Borda Count based combination and a re ranking stage . The performance of proposed approach is corroborated by extensive experiments performed on two databases GavabDB and FRGC v2.0 confirming effectiveness of fusion strategies to improve performance . 


Summary: A 3D face registration and recognition approach is proposed. Pose correction is evaluated through various correction parameters. Face range images are divided into different regions and features are extracted. Ensemble classifier is used to fuse results using features from different regions. Results show mark improvement in registration accuracy and recognition rates.
This work evaluates the performance of a NURBS based isogeometric finite element formulation for solving stationary acoustic problems in two dimensions . An initial assessment is made by studying eigenvalue problems for a square and a circular domain . The spectral approximation properties of NURBS functions of varying order are compared to those of conventional polynomials and are found to be superior yielding more accurate representations of eigenvalues as well as eigenmodes . The higher smoothness of NURBS shape functions yields better approximations over an extended frequency range when compared to conventional polynomials . Two numerical case studies including a geometrically complex domain are used to benchmark the method versus the traditional finite element method . A convergence analysis confirms the higher efficiency of the isogeometric method on a per degree of freedom basis . Simulations over a wider frequency range also illustrate that the method suffers less from the dispersion effects that deteriorate the acoustic response towards higher frequencies . The tensor product structure of NURBS however also imposes practical considerations when modelling a complex geometry consisting of multiple patches . 


Summary: The performance of NURBS based IGA for time harmonic acoustics in 2D is studied. NURBS functions exhibit better spectral approximation properties than polynomials. IGA converges faster than conventional FEM on a per DOF basis. IGA suffers less from the pollution effect than conventional FEM. IGA also performs well for complex geometries and impedance boundary conditions.
Nonlocal means based denoising is an efficient and simple method for image sequence denoising which has been applied successfully in many image sequence denoising applications . We extend the method for image sequence denoising using Zernike moments called NLM ZMs which provides better denoising performance as compared to NLM based approach . In addition the proposed method is faster because the number of computations needed for block matching and weight computation are significantly reduced . The NLM approach uses the photometric distance between two image patches for determining the similarity distance . In the proposed approach low order ZMs are used for the block matching process resulting in better denoising performance at a much lower computation cost . Detailed experimental results are provided to demonstrate better performance and higher speed of the proposed approach as compared to the NLM approach . The results are also compared with the state of the art NLM based image sequence denoising methods and the denoising results are observed to be competitive with higher speed . 


Summary: Image sequence denoising using NLM and Zernike moments proposed. Significant improvement in image sequence denoising achieved. Denoising results are comparable to state of the art methods with much faster speed. Proposed method is faster by a factor lying between 11 and 12. Better rates of improvement achieved at higher levels of noise.
This study developed a Kinect microphone array based method for the voice based control of humanoid robot exhibitions through speech and speaker recognition. A support vector machine SVM a Gaussian mixture model GMM and dynamic time warping DTW were used for speaker verification speaker identification and speech recognition respectively they were effectively combined for realizing advanced voice based control of humanoid robot exhibitions. Speech recognition capability was enhanced by using the Kinect microphone array and by combining the DTW based recognition decisions associated with all the microphones through a fuzzy control scheme. A humanoid robot with the proposed voice based control can be controlled through voice commands by authenticated users. The robot first verifies the authenticity of the personal operator following which it identifies the operator and validates the command. Subsequently it executes the command if both the user and command are valid. Experimental results demonstrated the effectiveness and accuracy of the proposed method. 


Summary: Kinect microphone array based voice control for operating a robot is proposed. Speech and speaker recognition are effectively combined for fine robot control. Kinect fuzzy DTW with an accurately designed fuzzy controller is proposed.
In this work a novel comparative method for highly brittle materials such as aragonite crystals is proposed which provides an efficient and accurate in sight understanding for multi scale fracture modeling . In particular physically motivated molecular dynamics simulations are performed to model quasi static brittle crack propagation on the nano scale and followingly compared to macroscopic modeling of fracture using the phase field modeling technique . A link between the two modeling schemes is later proposed by deriving PFM parameters from the MD atomistic simulations . Thus in this combined approach MD simulations provide a more realistic meaning and physical estimation of the PFM parameters . The proposed computational approach that encompasses mechanics on discrete and continuum levels can assist multi scale modeling and easing for instance the simulation of biological materials and the design of new materials . 


Summary: A link between molecular and continuum models for brittle fracture is proposed. Parameters obtained from the molecular scale are used in the continuum approach. Under this approach the phase field parameters acquire an entirely physical meaning. This approach can assist multi scale modeling of materials.
This paper presents a new approach for verifying user identity in pervasive environments using a non intrusive behaviour tracking technique that offers minimum interruption to the user s activities . The technique termed Non intrusive Identity Assertion System uses knowledge of how the user uses the environment s services to infer their identity . The technique monitors user behaviour through identifying certain types of activity without the need for detailed tracking of user behaviour thus minimising intrusion on the user s normal activities . The technique was evaluated using a simulated environment to assess its reliability . Simulation results show that the technique can be applied in various situations such as strict and relaxed security settings by applying different security policies . They also show that the technique is particularly effective where the environment has a mixture of high and low security resources in which case reliability could exceed 99 . 


Summary: A user centric unobtrusive approach for verifying the user s identity is proposed. The approach uses knowledge about the user s behaviour to infer their identity. The approach uses a simple numerical algorithm to assert the user s identity.