nils-herrmann/clean_scirepeval huggingface dataset:
A primal-dual proximal splitting approach for restoring data corrupted with poisson-gaussian noise

A Poisson-Gaussian model accurately describes the noise present in many imaging systems such as CCD cameras or fluorescence microscopy. However most existing restoration strategies rely on approximations of the Poisson-Gaussian noise statistics. We propose a convex optimization algorithm for the reconstruction of signals degraded by a linear operator and corrupted with mixed Poisson-Gaussian noise. The originality of our approach consists of considering the exact continuous-discrete model corresponding to the data statistics. After establishing the Lipschitz differentiability of the Poisson-Gaussian log-likelihood, we derive a primal-dual iterative scheme for minimizing the associated penalized criterion. The proposed method is applicable to a large choice of penalty terms. The robustness of our scheme allows us to handle computational difficulties due to infinite sums arising from the computation of the gradient of the criterion. The proposed approach is validated on image restoration examples.
A majorize-minimize memory gradient algorithm applied to X-ray tomography

Tomography is an image reconstruction task that may be viewed as a linear inverse problem akin to deconvolution. Recent progresses in optimization methods have made it possible to formulate this task so that fewer projections and higher amounts of noise can be dealt with, making use of a-priori information and domain constraints. In this article, we investigate 3MG, a new optimization method that is highly flexible and effective. In particular, we propose and compare convex and non-convex regularization potentials on both synthetic and real images. We further investigate the possibility to deal with continuous angular integration, i.e. where projections rays are no longer straight lines, but cones. This is encountered in a variety of real-life situations, but is difficult or impossible to deal with exactly using traditional reconstruction algorithms. We show that in this situation it may be beneficial to acquire fewer projections than would be required using classical methods.
Periodic area-of-motion characterization for bio-medical applications

Many bio-medical applications involve the analysis of sequences for motion characterization. In this article, we consider 2D+t sequences where a particular motion (e.g. a blood flow) is associated with a specific area of the 2D image (e.g. an artery) but multiple motions may exist simultaneously in the same sequences (e.g. there may be several blood vessels present, each with their specific flow). The characterization of this type of motion typically involves first finding the areas where motion is present, followed by an analysis of these motions: speed, regularity, frequency, etc. In this article, we propose a methodology called “area-of-motion characterization” suitable for simultaneously detecting and characterizing areas where motion is present in a sequence. We can then classify this motion into consistent areas using unsupervised learning and produce directly usable metrics for various applications. We illustrate this methodology for the analysis of cilia motion on ex-vivo human samples, and we apply and validate the same methodology for blood flow analysis in fish embryo.
Performance of TCP and the concept of heterogeneity in the future communication network

TCP is an end to end transmission control protocol was originally tuned for wired network. As the random packet loss due to bit error is not negligible in wireless network, it causes a sever performance degradation of TCP under wireless environment. In the paper TCP is exclusively studied. Here in the paper a heterogeneous network have been considered. Challenges of TCP in such network have been well emphasized in the paper. Different versions of TCP have been explained and compared on the basis of simulation results
Design and Development of Multi-Die Laterally Placed and Vertically Stacked Embedded Micro-Wafer-Level Packages

Two embedded micro-wafer-level packages (EMWLP) with 1) laterally placed and 2) vertically stacked thin dies are designed and developed. Three-dimensional stacking of thin dies is demonstrated as progressive miniaturization driver for multi-chip EMWLP. Both the developed packages have dimensions of 10 mm × 10 mm × 0.4 mm and solder ball pitch of 0.4 mm. As part of the development several key processes like thin die stacking, 8-in wafer encapsulation using compression molding, low-temperature dielectric with processing temperature less than 200°C have been developed. The EMWLP components success fully pass 1000 air to air thermal cycling (-40°C to 125°C), unbiased highly accelerated stress testing (HAST) and moisture sensitivity level (MSL3) tests. Developed EMWLP also show good board level TC (>; 1000 cycles) and drop test reliability results. Integration of thin film passives like inductors and capacitors are also demonstrated on EMWLP platform. Developed thin film passives show a higher Q-factor when compared to passives on high resistivity silicon platform. Thermo-mechanical simulation studies on developed EMWLP demonstrate that systemic control over die, RDL, and package thicknesses can lead to designs with improved mechanical reliability.
Fusing structure from motion and lidar for dense accurate depth map estimation

We present a novel framework for precisely estimating dense depth maps by combining 3D lidar scans with a set of uncalibrated camera RGB color images for the same scene. Rough estimates for 3D structure obtained using structure from motion (SfM) on the uncalibrated images are first co-registered with the lidar scan and then a precise alignment between the datasets is estimated by identifying correspondences between the captured images and reprojected images for individual cameras from the 3D lidar point clouds. The precise alignment is used to update both the camera geometry parameters for the images and the individual camera radial distortion estimates, thereby providing a 3D-to-2D transformation that accurately maps the 3D lidar scan onto the 2D image planes. The 3D to 2D map is then utilized to estimate a dense depth map for each image. Experimental results on two datasets that include independently acquired high-resolution color images and 3D point cloud datasets indicate the utility of the framework. The proposed approach offers significant improvements on results obtained with SfM alone.
See and listen: Score-informed association of sound tracks to players in chamber music performance videos

Both audio and visual aspects of a musical performance, especially their association, are important for expressing players' ideas and for engaging the audience. In this paper, we present a framework for combining audio and video analyses of multi-instrument chamber music performances to associate players in the video to the individual separated instrument sources from the audio, in a score-informed fashion. The instrument sources are first separated using a score-informed source separation techniques. The individual sources are then associated with different players in the video by correlating the onset instants of their aligned score tracks with the players' motion detected using optical flow. Experiments on 19 musical pieces with varying polyphony show that the proposed method obtains the correct association for 17 pieces, and an accuracy of 89.2% of the association of all individual tracks. The approach enables novel music enjoyment experiences by allowing users to target an audio source by clicking on the player in the video to separate/enhance it.
An economical design of an AUV for navigation and Ocean Floor Research

In this paper we propose a design of an Autonomous Underwater Vehicle that can perform some primary functions and can be of great relevance to numerous underwater predicaments for the researchers. We are focussing on fields like Vehicle Hydrodynamics, Underwater Propulsion, Underwater Sensing, Mechanical Systems, Underwater Acoustics and Power Systems. Formulating our design on all the above characteristics we wish to focus on divisions of Defence and Ocean floor Research.
Hierarchical watermarking for secure image authentication with localization

Several fragile watermarking schemes presented in the literature are either vulnerable to vector quantization (VQ) counterfeiting attacks or sacrifice localization accuracy to improve security. Using a hierarchical structure, we propose a method that thwarts the VQ attack while sustaining the superior localization properties of blockwise independent watermarking methods. In particular, we propose dividing the image into blocks in a multilevel hierarchy and calculating block signatures in this hierarchy. While signatures of small blocks on the lowest level of the hierarchy ensure superior accuracy of tamper localization, higher level block signatures provide increasing resistance to VQ attacks. At the top level, a signature calculated using the whole image completely thwarts the counterfeiting attack. Moreover, "sliding window" searches through the hierarchy enable the verification of untampered regions after an image has been cropped. We provide experimental results to demonstrate the effectiveness of our method.
Simulation of error trapping decoders on a fading channel

In this paper, we evaluate the performance of error trapping decoding when used to protect data over a mobile radio channel. The channel is represented by the Gilbert-Elliott (GE) model, with its parameters related to the physical quantities determining the fading statistics. Simulations are used to evaluate codeword and bit error probabilities for three different error correcting codes (linear block codes) for a number of scenarios. Using simplifying assumptions, analytical expressions are derived for the codeword error probabilities and these are shown to be in excellent agreement with simulation results.
Interference cancellation in cognitive radio networks using block diagonalization

In this paper, we propose a Multi-user Multiple-Input Multiple-Output (MU-MIMO) based Cognitive Radio network where the unlicensed secondary uses (SU) can simultaneously use the spectrum which is used by a licensed primary user (PU). The interference caused by the secondary user to the primary user has been cancelled. This is done by using Block Diagonalization (BD) followed by QR-MRL in order to cancel the interference caused by PU antennas. Previous works, such as ZF (Zero Forcing)-BD and MMSE (Minimum Mean Squared Error)-BD are succeed to cancel the interference among multiple users. Here, the performance comparison has been made in terms of Bit Error Rate (BER) plots for ZF-BD, MMSE-BD and QR-MRL-BD. Also, these techniques have been compared with BER plot obtained when SUs interfere with the PUs. The results show that the BD method is able to cancel the interference caused by the SUs successfully and the QR-MRL method which is an ordered successive interference cancellation(OSIC) method effectively cancels the interference caused among user's antennas. A significant improvement in BER plot for QR-MRL-BD is observed. Also, a simulink model for ZF-BD has been discussed for future hardware implementation.
A Regularized Model-Based Optimization Framework for Pan-Sharpening

Pan-sharpening is a common postprocessing operation for captured multispectral satellite imagery, where the spatial resolution of images gathered in various spectral bands is enhanced by fusing them with a panchromatic image captured at a higher resolution. In this paper, pan-sharpening is formulated as the problem of jointly estimating the high-resolution (HR) multispectral images to minimize an objective function comprised of the sum of squared residual errors in physically motivated observation models of the low-resolution (LR) multispectral and the HR panchromatic images and a correlation-dependent regularization term. The objective function differs from and improves upon previously reported model-based optimization approaches to pan-sharpening in two major aspects: 1) a new regularization term is introduced and 2) a highpass filter, complementary to the lowpass filter for the LR spectral observations, is introduced for the residual error corresponding to the panchromatic observation model. To obtain pan-sharpened images, an iterative algorithm is developed to solve the proposed joint minimization. The proposed algorithm is compared with previously proposed methods both visually and using established quantitative measures of SNR, spectral angle mapper, relative dimensionless global error in synthesis, Q, and Q4 indices. Both the quantitative results and visual evaluation demonstrate that the proposed joint formulation provides superior results compared with pre-existing methods. A software implementation is provided.
Mechanical characterization of next generation eWLB (embedded wafer level BGA) packaging

Integrated Circuits fabricated on silicon are assembled in different forms of electronic packages and are used extensively in electronic products such as personal, portable, healthcare, entertainment, industrial, automotive, environmental and security systems. Current and future demand for these electronic systems in terms of performance, power consumption, reliable system at a reasonable price are met by developing advanced/appropriate silicon process technology, innovative packaging solutions with use of chip-package-system co-design, low cost materials, advanced assembly and reliable interconnect technologies. In this article packaging evolution for hand held application is discussed with special focus on next generation chip embedding technology called eWLB in detail. Currently 1st generation eWLB technology is available in the industry. This paper will highlight some of the recent advancements in component level and board level reliability of next generation eWLB technologies including multi-RDL, thin eWLB, extra large eWLB with multi-chip. Standard JEDEC tests were carried out to investigate component level reliability and both failure analysis was performed to investigate potential structural defects. Daisychain eWLBs were assembled with different package size and different configuration as like thin or multi-RDL or multi-die. Test vehicles were also tested for drop and TCoB (Temperature on Board) reliability in industry standard test conditions. Next generation test vehicles passed both drop and TCoB tests. There was more than 50% improvement of characteristic lifetime with thinned eWLB in TCoB test because of its enhanced flexibility of package. This paper also presents study of package warpage behavior with temperature profile which is important for understanding of mechanical behavior of next generation 3D eWLBs.
Discriminative spatial saliency for image classification

In many visual classification tasks the spatial distribution of discriminative information is (i) non uniform e.g. person `reading' can be distinguished from `taking a photo' based on the area around the arms i.e. ignoring the legs and (ii) has intra class variations e.g. different readers may hold the books differently. Motivated by these observations, we propose to learn the discriminative spatial saliency of images while simultaneously learning a max margin classifier for a given visual classification task. Using the saliency maps to weight the corresponding visual features improves the discriminative power of the image representation. We treat the saliency maps as latent variables and allow them to adapt to the image content to maximize the classification score, while regularizing the change in the saliency maps. Our experimental results on three challenging datasets, for (i) human action classification, (ii) fine grained classification and (iii) scene classification, demonstrate the effectiveness and wide applicability of the method.
In-situ calibration of accelerometers in body-worn sensors using quiescent gravity

As the cost, size and power required by sensor devices decrease, an increasing range of applications are possible. We focus on the application of tracking accelerometer data from body worn sensors over long durations in health monitoring applications. Body worn sensors must be compact for the convenience of the patient and low power to support extended operation, but these benefits can come at the cost of accuracy. To mitigate this loss of accuracy we first examine the ability of calibration to improve accuracy. Next we propose an in-situ method for calibrating the accelerometers using the quiescent acceleration due to gravity as a calibration signal. This in-situ calibration uses only the stored data from the duration the sensor is worn by the patient and does not require any extra procedures or measurements from the physical sensor devices. Compared to a manual three-axis calibration technique, the proposed calibration can be applied to data without the need for specific calibration procedures or even access to the original sensor and provides comparable or better accuracy.
Vehicle Tracking in Wide Area Motion Imagery via Stochastic Progressive Association Across Multiple Frames

Vehicle tracking in wide area motion imagery (WAMI) relies on associating vehicle detections across multiple WAMI frames to form tracks corresponding to individual vehicles. The temporal window length, i.e., the number <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> of sequential frames, over which associations are collectively estimated poses a tradeoff between accuracy and computational complexity. A larger <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> improves performance because the increased temporal context enables the use of motion models and allows occlusions and spurious detections to be handled better. The number of total hypotheses tracks, on the other hand, grows exponentially with increasing <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula>, making larger values of <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> computationally challenging to tackle. In this paper, we introduce stochastic progressive association across multiple frames, an iterative approach that progressively grows <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> with each iteration to improve estimated tracks by exploiting the enlarged temporal context while keeping computation manageable through two novel approaches for pruning association hypotheses. First, guided by a road network, accurately co-registered to the WAMI frames, we disregard unlikely associations that do not agree with the road network. Second, as <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> is progressively enlarged at each iteration, the related increase in association hypotheses is limited by revisiting only the subset of association possibilities rendered open by stochastically determined dis-associations for the previous iteration. The stochastic dis-association at each iteration maintains each estimated association according to an estimated probability for confidence, obtained via a probabilistic model. Associations at each iteration are then estimated globally over the <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> frames by (approximately) solving a binary integer programming problem for selecting a set of compatible tracks. Vehicle tracking results obtained over test WAMI data sets indicate that our proposed approach provides significant performance improvements over the state-of-the-art alternatives.
A New Modified Playfair Algorithm Using CBC

Security is required in order to defend information from unauthorized person on the network. Therefore, securely exchanging data over the internet is vital. In this context, cryptography is used that transmute facts from its under stable form into an indecipherable form by using various encryption techniques. Play air cipher is the form of block cipher which has no limit on the number of characters in a message it can do, but it operates on block of characters encrypting and decrypting two characters at a time cipher. In this, the plain text digraphs are converted to cipher text digraphs and vice versa using a pre-shared key. This is achieved by performing several operations column wise row wise and by creating rectangular form. This paper deals with a new solution approach to overcome the shortcomings of the Play air algorithm. In this paper, the presented Play air CBC encryption mechanism makes the cryptanalysis complex. The encrypted text obtained is almost unreadable.
Object Category Detection by Statistical Test of Hypothesis

We propose a novel framework for object detection and localization in images containing appreciable clutter and occlusions. The problem is cast in a statistical hypothesis testing framework. The image under test is converted into a set of local features using affine invariant local region detectors, described using the popular SIFT descriptor. Due to clutter and occlusions, this set is expected to contain features which do not belong to the object. We sample subsets of local features from this set and test for the alternate hypothesis of object present against the null hypothesis of object absent. Further, we use a method similar to the recently proposed spatial scan statistic to refine the object localization estimates obtained from the sampling process. We demonstrate the results of our method on the two datasets TUD Motorbikes and TUD Cars. TUD Cars database has background clutter. TUD Motorbikes dataset is recognized to have substantial variation in terms of scale, background, illumination, viewpoint and occlusions.
An information transmission measure for the analysis of effective connectivity among cortical neurons

We present a methodology for detecting effective connections between simultaneously recorded neurons using an information transmission measure to identify the presence and direction of information flow from one neuron to another. Using simulated and experimentally-measured data, we evaluate the performance of our proposed method and compare it to the traditional transfer entropy approach. In simulations, our measure of information transmission outperforms transfer entropy in identifying the effective connectivity structure of a neuron ensemble. For experimentally recorded data, where ground truth is unavailable, the proposed method also yields a more plausible effective connectivity structure than transfer entropy.
Automated Coverage Register Access Technology on UVM Framework for Advanced Verification

VLSI Designs are getting more complex with the advancements in technologies. These design rules are forcing a large number of components on a single chip. The number of registers is also increasing in SOC/IP designs. The conventional verification techniques fail to provide consistent results when design consists of a large number of registers. The proposed architecture establishes a platform to automate the verification of complex intellectual properties with multiple register access. It improvises the results as compared to SystemVerilog and conventional Universal Verification Technology (UVM) testbench. This paper focuses on advanced verification scenarios using coverage automation and Register Access Technology (RAT) on UVM verification environment. The proposed testbench automation incorporates all necessary attributes including the functional correctness of register values. The work exposes the register database in multiple register SoC/IP design. The proposed testbench justifies the functional coverage and fast execution of verification flow as compared to conventional verification methods.