Miracle-dz/UniFAR-FTUs huggingface dataset:
A feature based approach for license plate-recognition of Indian number plates

In this paper a method for vehicle license plate identification is implemented and analyzed, on the basis of a novel adaptive image segmentation technique conjunction with character recognition. A novel method for license plate localization based on texture and edge information is proposed. The whole process is divided into two parts: candidate extraction and candidate verification. In the first part, the license plate being extracted from complex environment, several candidate areas instead of one with the max texture information are extracted. In the second part, autocorrelation based binary image and projection algorithm are used to verify the plate candidates. Adaptive median filter is applied to remove the noise from the image. Image processing technique such as edge detection, thresholding, resampling and filtering have been used to locate and isolate the license plate and the characters. The system can recognize single line number plates under widely varying illumination conditions with a success rate of about 80%.
WiseHAUL: An SDN-empowered Wireless Small Cell Backhaul testbed

The deployment of dense networks of Small Cells (SC) is one of the key components of 5G mobile networks and will pose several challenges to the backhaul network. Software-Defined Networking (SDN) is a key technique to cope with the increased management complexity of such heterogeneous deployments, helping in the task of achieving a system-wide network management, which includes the 5G mobile wireless backhaul. Therefore, it is fundamental to devise the tools for the evaluation of 5G backhauling SDN architectures. This paper presents WiseHAUL: an SDN-empowered Wireless Small Cell Backhaul testbed. We present the design considerations of WiseHAUL, deployed in the form of an hypercube mesh that combines heterogeneous wireless technologies with the final goal of conducting SDN research and experimentation. Preliminary experimental results serve to identify the main research challenges faced by canonical SDN management techniques in an all-wireless backhaul deployment, hence acting as key drivers for further redesign and testbed development towards hybrid SDN models.
Imaging the sources and full extent of the sodium tail of the planet Mercury

Observations of sodium emission from Mercury can be used to describe the spatial and temporal patterns of sources and sinks in the planet's surface‐boundary‐exosphere. We report on new data sets that provide the highest spatial resolution of source regions at polar latitudes, as well as the extraordinary length of a tail of escaping Na atoms. The tail's extent of ∼1.5 degrees (nearly 1400 Mercury radii) is driven by radiation pressure effects upon Na atoms sputtered from the surface in the previous ∼15 hours. Wide‐angle filtered‐imaging instruments are thus capable of studying the time history of sputtering processes of sodium and other species at Mercury from ground‐based observatories in concert with upcoming satellite missions to the planet. Plasma tails produced by photo‐ionization of Na and other gases in Mercury's neutral tails may be observable by in‐situ instruments.
Improving Recommendation Accuracy by Combining Trust Communities and Collaborative Filtering

With the booming of online social networks, social trust has been used to cluster users in recommender systems. It has been proven to improve the recommendation accuracy when trust communities are integrated into memory-based collaborative filtering algorithms. However, existing trust community mining methods only consider the trust relationships, regardless of the distrust information. In this paper, considering both the trust and distrust relationships, a SVD signs based community mining method is proposed to process the trust relationship matrix in order to discover the trust communities. A modified trust metric which considers a given user's expertise level in a community is presented to obtain the indirect trust values between users. Then some missing ratings of the given user are complemented by the weighted average preference of his/her trusted neighbors selected in the same community during the random walk procedures. Finally, the prediction for a given item is generated by the conventional collaborative filtering. The comparison experiments on Epinions data set demonstrate that our approach outperforms other state-of-the-art methods in terms of RMSE and RC.
Observed strengthening of interbasin exchange via the Indonesian seas due to rainfall intensification

A proxy of the Indonesian Throughflow (ITF) transport, developed using in situ hydrographic measurements along with assimilations, shows a significant strengthening trend during the past decade. This trend is due to a freshening and subsequent increase in the halosteric component of the ITF transport associated with enhanced rainfall over the Maritime Continent over the same period. The strengthening of the ITF transport leads to a significant change in heat and freshwater exchange between the Pacific and Indian Oceans and contributes to the warming and freshening of the eastern Indian Ocean. The combined effect of the ITF transport of mass and freshwater along with tropical rainfall plays a very important role in the climate system.
SAM: A Semantic-Aware Multi-tiered Source De-duplication Framework for Cloud Backup

Existing de-duplication solutions in cloud backup environment either obtain high compression ratios at the cost of heavy de-duplication overheads in terms of increased latency and reduced throughput, or maintain small de-duplication overheads at the cost of low compression ratios causing high data transmission costs, which results in a large backup window. In this paper, we present SAM, a Semantic-Aware Multitiered source de-duplication framework that first combines the global file-level de-duplication and local chunk-level deduplication, and further exploits file semantics in each stage in the framework, to obtain an optimal tradeoff between the deduplication efficiency and de-duplication overhead and finally achieve a shorter backup window than existing approaches. Our experimental results with real world datasets show that SAM not only has a higher de-duplication efficiency/overhead ratio than existing solutions, but also shortens the backup window by an average of 38.7%.
Independent Component Analysis Involving Autocorrelated Sources With an Application to Functional Magnetic Resonance Imaging

Independent component analysis (ICA) is an effective data-driven method for blind source separation. It has been successfully applied to separate source signals of interest from their mixtures. Most existing ICA procedures are carried out by relying solely on the estimation of the marginal density functions, either parametrically or nonparametrically. In many applications, correlation structures within each source also play an important role besides the marginal distributions. One important example is functional magnetic resonance imaging (fMRI) analysis where the brain-function-related signals are temporally correlated. In this article, we consider a novel approach to ICA that fully exploits the correlation structures within the source signals. Specifically, we propose to estimate the spectral density functions of the source signals instead of their marginal density functions. This is made possible by virtue of the intrinsic relationship between the (unobserved) sources and the (observed) mixed signals. Our methodology is described and implemented using spectral density functions from frequently used time series models such as autoregressive moving average (ARMA) processes. The time series parameters and the mixing matrix are estimated via maximizing the Whittle likelihood function. We illustrate the performance of the proposed method through extensive simulation studies and a real fMRI application. The numerical results indicate that our approach outperforms several popular methods including the most widely used fastICA algorithm. This article has supplementary material online.
Zone-Based Feature Extraction Techniques and SVM for Handwritten Gurmukhi Character Recognition

In this paper we present an overview of Feature Extraction techniques for off-line recognition of isolated Gurumukhi characters recognition. Selection of Feature Extraction method is probably the single most important factor in achieving high performance in pattern recognition. Our paper presents Zone based approach which is the combination of image centroid zone and zone centroid zone of numeral/character image. In image centroid zone character is divided into n equal zone and then image centroid and the average distance from character centroid to each zones/grid/boxes present in image is calculated. Similarly, in zone centroid zone character image is divided into n equal zones and centroid of each zones/boxes/grid and average distance from zone centroid to each pixel present in block/zone/grid is calculated. We have used SVM for subsequent classifier and recognition purpose. We obtained 95.11% recognition accuracy.
Exploiting 3D faces in biometric forensic recognition

The analysis of 3D face data is very promising in improving the identification performances of individuals. In this paper two algorithms will be presented to match 3D face shapes (3D to 3D) and also to compare a synthetically generated 2D view with a given 2D face image (3D to 2D). The algorithm to match 3D shapes is based on the extraction and comparison of invariant features from the 3D face template. In order to perform the 3D to 2D matching an algorithm is used to generate a 2D view of the face which is as close as possible to the given face image. Salient features are extracted from the two face images and are used to determine the similarity of the faces. To simulate a real forensic environment, a small database of 3D faces has been acquired. The dataset will be used to test the identification performances in both cases.
Coverage and connectivity preserving routing in wireless sensor networks: A new approach

These days wireless sensor networks handle a widespread range of monitoring tasks using their sensing and vision capabilities. These tasks may consist of very complex functions and large calculations. In order to make network alive for a long duration, these sensors have to take decision in judicious and efficient manner. The increased lifetime results into increased connectivity. Also, the intellectual use of energy resources provides the nodes better connectivity to the base station. The different clustering protocols such as Low Energy Adaptive Clustering Hierarchy (LEACH) are used in order to gain energy efficiency. The LEACH uses probability as the key attribute to elect the new cluster head. In this paper, a new approach based on the frequency of a node selected as cluster head for the selection of new cluster head is proposed which is also more efficient than the former one. The proposed approach for cluster head selection results in increased lifetime and thus, in increased connectivity. Simulation results show that in terms of different parameters like number of live nodes, dead nodes and residual energy our proposed strategy outperforms existing conventional sensor network protocols.
Comparison of discrete- and continuous-state stochastic methods to model neuronal signal transduction

Several stochastic methods have been developed for the simulation of biochemical reactions. The best known stochastic reaction method is the Gillespie stochastic simulation algorithm which, in this study, is compared to two types of stochastic differential equation models. As a test case, we use a neuronal signal transduction network of 110 reactions and 63 chemical species. We concentrate on showing when stochastic methods are especially needed and how distributions from different stochastic methods differ. We conclude that stochastic differential equations are not suitable for use in volumes on the order of spines, and that even in dendritic and soma regions a portion of the chemical species will exhibit negative excursions. For this reason, a hybrid deterministic and stochastic method may be most applicable to the larger volumes while the Gillespie stochastic simulation algorithm is needed for spines and subspinal volumes. In addition, a grid computing solution is needed for larger volumes to reduce the computation time to tractable levels.
Corporate Governance and Innovative Performance: Evidence from US publicly Traded Manufacturing Firms

This study investigates the relationship between corporate governance and corporate innovation performance. For this purpose, we using COMPUSTAT, patents data and corporate governance quality data to test our hypothesis. We uncover a positive relationship between corporate governance quality, GQ index which is a measure for shareholder right, and innovation outcome, as well governed firm have higher innovation productivity. We conclude that corporate governance quality is an important determinant of innovation performance.
Development / Plasticity / Repair Fibroblast Growth Factor-Inducible-14 Is Induced in Axotomized Neurons and Promotes Neurite Outgrowth

For successful nerve regeneration, a coordinated shift in gene expression pattern must occur in axotomized neurons. To identify genes participating in axonal regeneration, we characterized mRNA expression profiles in dorsal root ganglia (DRG) before and after sciatic nerve transection. Dozens of genes are differentially expressed after sciatic nerve injury by microarray analysis. Induction of SOX11, FLRT3, myosin-X, and fibroblast growth factor-inducible-14 (Fn14) mRNA in axotomized DRG neurons was verified by Northern analysis and in situ hybridization. The Fn14 gene encodes a tumor necrosis-like weak inducer of apoptosis (TWEAK) receptor and is dramatically induced in DRG neurons after nerve damage, despite low expression in developing DRG neurons. Fn14 expression in PC12 cells is also upregulated by nerve growth factor treatment. Overexpression of Fn14 promotes growth cone lamelipodial formation and increases neurite outgrowth in PC12 cells. These Fn14 effects are independent of the ligand, TWEAK. Fn14 colocalizes with the Rho family GTPases, Cdc42 and Rac1. Furthermore, Fn14 physically associates with Rac1 GTPase in immunoprecipitation studies. The neurite outgrowth-promoting effect of Fn14 is enhanced by Rac1 activation and suppressed by Rac1 inactivation. These findings suggest that Fn14 contributes to nerve regeneration via a Rac1 GTPase-dependent mechanism.
An algorithm for computing partial derivatives of the value function by simulation

The problems involving incentive compatibility constrains in the form of participation constrains have received wide attention in the literature due to the recent advances in the dynamic optimization techniques. Often the optimality conditions for this class of problems involve partial derivatives of the optimal value function with respect to some of the endogenous state variables. In this note we suggest an algorithm for computing these partial derivatives by simulation. The attractive features of the algorithm include its rather wide scope of applicability and simplicity of implementation. Furthermore,
Complex Empirical Decomposition method in radar signal processing

In this paper we analyze possible application of complex Empirical Mode Decomposition (EMD) algorithm to the radar signal analysis. Presented method is applied to the analysis of experimental High Frequency Surface Wave (HFSW) radar data. Detailed procedure of extracting target signal from clutter is described. It is shown that complex EMD can extract target signal in heavy clutter environment and achieve better results compared to original, real valued EMD.
Efficient computation for the noisy MAX

Díez's algorithm for the noisy MAX is very efficient for polytrees, but when the network has loops, it has to be combined with local conditioning, a suboptimal propagation algorithm. Other algorithms, based on several factorizations of the conditional probability of the noisy MAX, are not as efficient for polytrees but can be combined with general propagation algorithms such as clustering or variable elimination, which are more efficient for networks with loops. In this article we propose a new factorization of the noisy MAX that amounts to Díez's algorithm in the case of polytrees and at the same time is more efficient than previous factorizations when combined with either variable elimination or clustering. © 2003 Wiley Periodicals, Inc.
High-Efficiency Microwave and mm-Wave Stacked Cell CMOS SOI Power Amplifiers

Design and implementation of high-efficiency microwave and mm-wave CMOS silicon-on-insulator (SOI) power amplifiers (PAs) based on a stacked cell approach is presented. Two stacked cell PAs have been implemented in GlobalFoundries 45-nm CMOS SOI technology. The first PA operating at K-band (24-28 GHz) is designed with three stacked triple Cascode cells. Each cell uses three standard transistors with separate layout. At 24 GHz, the K-band PA biased under a supply voltage of 10.8 V measures a maximum linear power gain of 13 dB, a saturated output power PSAT of 25.3 dBm, a -1-dB output power P1dB of 23.8 dBm, and a peak power-added efficiency (PAE) of 20%. The second PA targeted at U-band frequencies is designed with two stacked triple Cascode cells. Transistors in each cell have a combined layout that reduces parasitic capacitances, leading to significant improvement in the PAE at mm-wave frequencies. The U-band PA operates from 42 to 54 GHz. At 46 GHz, and under a supply voltage of 6 V, it measures a saturated output power (PSAT) of 22.4 dBm, a linear gain of 17.4 dB, and an unprecedented peak PAE of 42%.
New insight on the frequency dependence of TDDB in high-k/metal gate stacks

This paper deals with the oxide breakdown (BD) under positive gate voltage in nMOS Devices. First, bulk current is shown to be more sensitive than gate current for breakdown event detection. Then, since test interruption is shown to induce possible error in TBD evaluation, a methodology with an on the fly detection of breakdown is proposed for both DC and AC stresses. Finally, a discussion on the impact of charge trapping/detrapping is opened.
Structure and Polymorphism of the UL 6 Portal Protein of Herpes Simplex Virus Type 1 Benes

Structure and Polymorphism of the UL6 Portal Protein of Herpes Simplex Virus Type 1 Benes L. Trus, Naiqian Cheng, William W. Newcomb, Fred L. Homa, Jay C. Brown, and Alasdair C. Steven* Imaging Sciences Laboratory, Division of Computational Bioscience, Center for Information Technology, and Laboratory of Structural Biology Research, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, Maryland; Department of Microbiology and Cancer Center, University of Virginia Health System, Charlottesville, Virginia; and Department of Molecular Genetics and Biochemistry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
Harborview assessment for risk of mortality: an improved measure of injury severity on the basis of ICD-9-CM.

BACKGROUND There have been several attempts to develop a scoring system that can accurately reflect the severity of a trauma patient's injuries, particularly with respect to the effect of the injury on survival. Current methodologies require unreliable physiologic data for the assignment of a survival probability and fail to account for the potential synergism of different injury combinations. The purpose of this study was to develop a scoring system to better estimate probability of mortality on the basis of information that is readily available from the hospital discharge sheet and does not rely on physiologic data. METHODS Records from the trauma registry from an urban Level I trauma center were analyzed using logistic regression. Included in the regression were Internation Classification of Diseases-9th Rev (ICD-9CM) codes for anatomic injury, mechanism, intent, and preexisting medical conditions, as well as age. Two-way interaction terms for several combinations of injuries were also included in the regression model. The resulting Harborview Assessment for Risk of Mortality (HARM) score was then applied to an independent test data set and compared with Trauma and Injury Severity Score (TRISS) probability of survival and ICD-9-CM Injury Severity Score (ICISS) for ability to predict mortality using the area under the receiver operator characteristic curve. RESULTS The HARM score was based on analysis of 16,042 records (design set). When applied to an independent validation set of 15,957 records, the area under the receiver operator characteristic curve (AUC) for HARM was 0.9592. This represented significantly better discrimination than both TRISS probability of survival (AUC = 0.9473, p = 0.005) and ICISS (AUC = 0.9402, p = 0.001). HARM also had a better calibration (Hosmer-Lemeshow statistic [HL] = 19.74) than TRISS (HL = 55.71) and ICISS (HL = 709.19). Physiologic data were incomplete for 6,124 records (38%) of the validation set; TRISS could not be calculated at all for these records. CONCLUSION The HARM score is an effective tool for predicting probability of in-hospital mortality for trauma patients. It outperforms both the TRISS and ICD9-CM Injury Severity Score (ICISS) methodologies with respect to both discrimination and calibration, using information that is readily available from hospital discharge coding, and without requiring emergency department physiologic data.
Genetic Algorithms Adaptive Control for an Underactuated System

In this paper we develop an intelligent controller on the basis of Genetic Algorithms for the stabilization of a class of Underactuated Mechanical Systems. First we develop a static State Feedback Controller with inner noncollocated partial feedback linearization loop to stabilize the nominal system. Second, we develop a Genetic algorithm controller to maintain system stabilization and to adapt the controller variables to the changes of system parameters. Simulations results made on a 2-DOF whirling pendulum are given to illustrate our approach. Copyright c © 2003-2005 Yang’s Scientific Research Institute, LLC. All rights reserved.
Tumor Recognition in Wireless Capsule Endoscopy Images Using Textural Features and SVM-Based Feature Selection

Tumor in digestive tract is a common disease and wireless capsule endoscopy (WCE) is a relatively new technology to examine diseases for digestive tract especially for small intestine. This paper addresses the problem of automatic recognition of tumor for WCE images. Candidate color texture feature that integrates uniform local binary pattern and wavelet is proposed to characterize WCE images. The proposed features are invariant to illumination change and describe multiresolution characteristics of WCE images. Two feature selection approaches based on support vector machine, sequential forward floating selection and recursive feature elimination, are further employed to refine the proposed features for improving the detection accuracy. Extensive experiments validate that the proposed computer-aided diagnosis system achieves a promising tumor recognition accuracy of 92.4% in WCE images on our collected data.
Optimising Flash non-volatile memory using machine learning: a project overview

While near ubiquitous, the physical principles of Flash memory mean that its performance degrades with use. During fabrication and operation, its ability to be repeatedly programmed/erased (endurance) needs to be balanced with its ability to store information over months/years (retention). This project overview describes how our modelling of data we obtain experimentally from Flash chips uniquely allows us to optimise the settings of their internal configuration registers, thereby mitigating these problems.