nils-herrmann/clean_scirepeval huggingface dataset:
Personal Authentication Using Hand Vein Triangulation and Knuckle Shape

This paper presents a new approach to authenticate individuals using triangulation of hand vein images and simultaneous extraction of knuckle shape information. The proposed method is fully automated and employs palm dorsal hand vein images acquired from the low-cost, near infrared, contactless imaging. The knuckle tips are used as key points for the image normalization and extraction of region of interest. The matching scores are generated in two parallel stages: (i) hierarchical matching score from the four topologies of triangulation in the binarized vein structures and (ii) from the geometrical features consisting of knuckle point perimeter distances in the acquired images. The weighted score level combination from these two matching scores are used to authenticate the individuals. The achieved experimental results from the proposed system using contactless palm dorsal-hand vein images are promising (equal error rate of 1.14%) and suggest more user friendly alternative for user identification.
Stereo-disparity estimation using a supervised neural network

We deal with the problem of determining disparity in gray-level stereoimage-pairs, by treating it as a nonlinear classification problem, and invoking Marr and Poggio's (October 1976) neighborhood criterion. To this end, we propose the application of an artificial neural network (ANN). The main contribution of the paper is believed to be the use of neurons which are trained to be disparity selective, and thereby dispensing with the standard assumptions made about the neighborhood. The disparity estimates so obtained for random-dot and natural stereoimage-pairs are comparable to those found in the literature. Whereas Khotanzad et al. (March 1993) used a multi-layer perceptron (MLP) in order to learn the constraints of a cooperative stereo algorithm for binary, random-dot stereograms, we employ a single layer ANN. Further, in our scheme, the ANN weights adapt themselves to the neighborhood, and are able to learn the constraints successfully
Optimization of hybrid PV/wind energy system using Meta Particle Swarm Optimization (MPSO)

When the stand alone energy system having photovoltaic panels only or wind turbine only are compared with the hybrid PV/wind energy systems, the hybrid systems are more economical and reliable. Hybrid system can be able to adapt the effects due to climate changes. This paper presents a Artificial Intelligent method to design the hybrid PV/wind system. While meeting the basic requirements of the stand-alone energy system, the objective function for cost of hybrid system is constructed, this includes initial costs, yearly operating costs and maintenance costs. The hybrid system consists of photovoltaic panels, wind turbines and storage batteries. Due to the complexity of hybrid energy system with nonlinear integral planning, Meta Particle Swarm Optimization is used to solve the problem. By use of MPSO operation strategy, the global optimal searching ability of the proposed algorithm is improved. The improved PSO can avoid the possibility of local minimum trap. The method mentioned in this article is proved to be effective using an example of hybrid energy system. Finally, the optimal solution is received using proposed MPSO method.
Design and Analysis of MEMS Pressure Transmitter Using Mach–Zehnder Interferometer and Artificial Neural Networks

Micro-electro mechanical systems (MEMS) designing is used to produce component having very small dimensions. In this paper, MEMS-based pressure sensors working using the capacitive sensing technique are worked upon. Furthermore, using neural networks the voltage output produced is linearized. This decreases the non-linearity error. One possible limitation of this electrical circuit is the inability to be used in high temperature regions. This limitation has been overcome by implementing an electro-optic technique by means of which the electrical signal is converted to optical signal. This novelty does not only give the added benefit of being able to be used in inflammable regions but also eases transmission in remote locations. This is done by Mach–Zehnder interferometer whose simulations of intensity variation are studied using OptiBPM software.
Electrical and optical properties of RF sputtered zinc oxide films

Zinc oxide films were deposited by RF sputtering using an oxide target with a room-temperature substrate. Dry air and argon were used as ambients. The target-to-substrate (T-S) distance was varied between 3 cm and 7 cm. The as-grown films were highly resistive and there was no marked difference in the electrical, optical, and structural properties for the films grown at different (T-S) distances and in different ambients.<<ETX>>
1–7 GHz wideband low loss SPDT switch MMIC

A low loss wideband (1-7GHz) SPDT switch using 0.13μm GaAs/InAlAs MHEMT MMIC process is designed, fabricated and tested both on-wafer and in test fixture. The measured results are in close agreement with specifications and simulations, which exhibit insertion loss of 1.05 ±0.05dB, with isolation of 28dB, minimum. All three ports of this SPDT switch are well matched having return loss of more than 14 dB because of series-shunt topology. A series capacitor is also introduced in the common arm working as a DC block. The simulated 1-dB compression takes place at input power of more than +18dBm at 7 GHz and IIP3 is better than +33.5dBm. Being gate voltages of -1.5V and 0V, negligible (<;1mW) power is consumed while operating this switch. This SPDT switch measuring less than 1 mm2 in area enables on chip integration with high performance redundant Ka-band down-converter system-on-chip (SoC). Low loss, high isolation, compactness are the key features of this switch MMIC.
A proposed symmetric and balanced 11-T SRAM cell for lower power consumption

Static random access memories (SRAMs) comprise an increasingly large portion of modern very large scale integrated (VLSI) circuits. The increasing importance of embedded SRAM is due to its low circuit activity factor, leading to low active power density, and productivity of design. The power consumption has become an important issue and has lead to the development of numerous schemes aimed at limiting that component of power during both standby and active operation. In the present paper, we have proposed a symmetric 11-T SRAM cell to reduce power consumption during read/write operation. We have simulated the designed circuit with the help of Tanner EDA tools for 0.25µm technology and simulated results were compared with 6T and ZA cell. It was found that proposed cell consumes 28% lower power than conventional 6T during write mode. During read operation, the average power saving is around 33% compared to conventional cell. The write delay is more in our proposed cell. By choosing width of tail transistors are equal to 3µm, the write delay of the proposed cell can be equated to conventional cell.
An approach to use polarimetric signature for land cover classification

The aim of this paper is to explore the information obtain from the fully polarimetric SAR data. Fully polarimetric SAR data contain both magnitude and phase information therefore it contains great potential about target classification. By using both parameter amplitude and phase we can distinguishes different types of scattering mechanism. For fully utilization of polarimetric SAR data, polarization signatures are used which utilizes the different orientation angle. Polarization signature is a 3-D plot of the received backscattered intensity as a function of ellipticity and orientation angle of antenna. Polarization signatures of urban area are equivalent to dihedral corner reflector which shows the double bounce, polarization signature of water is equivalent to trihedral which shows single bounce and short vegetation shows the polarizations signatures equivalent to dipole at different orientation angle. In this paper, by utilizing the fully polarimetric ALOS-PALSAR data, polarization signatures are extracted at different angles and their capability to classify different land cover classes like; urban, water, short vegetation, tall vegetation and bare soil are explored. The scattering mechanism of generated elliptical and linear polarized images for above mentioned land cover classes is also analyzed and on the basis of their scattering mechanism, decision tree classification (DTC) algorithm has been proposed and performance of the algorithm is also compared with other supervised and unsupervised classification techniques.
Modified Iterative Reconstruction Algorithm for Spiral MRI

The MRI imaging is an efficient tool for disease diagnosis, and the features of the taken image has much importance. The reconstruction of such images needs much attention. The main limitation of MRI imaging is its long scan time. The time taken for acquisition can be reduced by non Cartesian acquisition. Spiral MRI is a non Cartesian MRI imaging and is less affected by motion and flow artifacts. Still, reconstruction of images from non-Cartesian MRI data will suffer from reconstruction errors and in-homogeneity artifacts. This will adversely affect the disease diagnosis. Algorithms like conjugate phase method, Field map calculating Methods, iterative Next Neighbor re-gridding algorithms etc were proposed for solving such issues. Among these, iterative Next Neighbor re-gridding algorithm proved efficient. The main focus of this paper is on efficient reconstruction of spiral MRI image with an improved Iterative next neighbor algorithm which will result in comparatively better picture quality.
Identification of surface defects in textured materials using wavelet packets

This paper investigates a new approach for the detection of surface defects, in textured materials, using wavelet packets. Every inspection image is decomposed with a family of real orthonormal wavelet bases. The wavelet packet coefficients from a set of dominant frequency channels containing significant information are used for the characterization of textured images. A fixed number of shift invariant measures from the wavelet packet coefficients are computed. The magnitude and position of these shift invariant measures in a quadtree representation forms the feature set for a two-layer neural network classifier. The neural net classifier classifies these feature vectors into either of defect or defect-free classes. The experimental results suggest that this proposed scheme can successfully identify the defects, and can be used for automated visual inspection.
Accident avoidance of Fighter Pilots by stress monitoring with wireless sensors in BAN

Wearable health monitoring is novel information technology that will be able to support early detection of abnormal conditions and prevention of its serious consequences. It is useful in areas like lifestyle, assisted living, sports and entertainment functionalities, toxic levels in the lungs of a fire-fighter, radiation exposure in war zones etc. Real time stress monitoring of fighter pilots through wearable wireless sensors in body area network are the vital issue for researchers to acquire potential knowledge through data mining of all gathered information. The statistics reveal that 40% of total flying accidents in IAF occur due to human error[1]. This paper proposes the workable concept to provide some valuable insights of human behavior under high dasiagpsila conditions. The proposed system can synergize the information from multiple sensors, warn the user in the case of emergencies, and provide feedback during supervised recovery or normal activity.
A Novel Technique for Robust Image Registration Using Log Polar Transform

This paper describes a hierarchical image registration algorithm for affine motion recovery. The algorithm estimates the affine transformation parameters necessary to register any two digital images misaligned due to rotation, scale, shear, and translation. The parameters are computed iteratively in a coarse-to-fine hierarchical framework using a variation of the Levenberg-Marquadt nonlinear least squares optimization method. This approach yields a robust solution that precisely registers images with sub pixel accuracy. A log-polar registration module is introduced to accommodate arbitrary rotation angles and a wide range of scale changes. This serves to furnish a good initial estimate for the optimization-based affine registration stage. We demonstrate the hybrid algorithm on pairs of digital images subjected to large affine motion.
Economic Analysis and Power Management of a Small Autonomous Hybrid Power System (SAHPS) Using Biogeography Based Optimization (BBO) Algorithm

In this study, Biogeography Based Optimization (BBO) algorithm is developed for the prediction of the optimal sizing coefficient of Small Autonomous Hybrid Power System (SAHPS) in remote areas. BBO algorithm is used to evaluate optimal component sizing and operational strategy by minimizing the total cost of SAHPS, while guaranteeing the availability of energy. Due to the complexity of the SAHPS design with nonlinear integral planning, BBO algorithm is used to solve the problem. The developed BBO Algorithm has been applied to design the wind/PV/hydro hybrid energy systems to supply a colony located in the area of Jaipur, Rajasthan (India) during the period of January, 2010 to January 2011. It is clear from the results that the proposed BBO method has excellent convergence property, requires less computational time and can avoid the shortcoming of premature convergence of other optimization techniques to obtain a better solution.
F-slot dual bands EMSA for wireless communication

The microstrip patch antenna modiflcations have been being conducted over approximately four decades by different simulation tools. In this paper, an elliptical microstrip antenna (EMSA) is considered as a reference one and the introduction of an F-cut on it has brought improvements in the various parameters in both the operating bands with the centre frequencies of 3.6 GHz (S-Band) and 5.3 GHz (C-Band) covering the wireless interoperability microwave access (WiMAX) and wireless local area network (WLAN) fields respectively. The put forth elliptical patch antenna at Rogers RT /duroid 5880 simulated by HFSS 13 can be used for the wireless fidelity (WiFi) applications also. The proposed antenna has exhibited the return losses of −36.5 dB and −23 dB and the wider radiation pattern with the maxima of field strength being approximated 24 dB and 21.83 dB with appreciable gain of 6.3 dB and 4.1dB at 3.6 GHz and 5.3 GHz, respectively.
Comparison and combination of iris matchers for reliable personal identification

The biometric identification approaches using iris images are receiving increasing attention in the literature. Several methods for the automated iris identification have been presented in the literature and those based on the phase encoding of texture information are suggested to be the most promising. However, there has not been any attempt to combine these phase preserving approaches to achieve further improvement in the performance. This paper presents a comparative study of the performance from the iris identification using log-Gabor, Haar wavelet, DCT and FFT based features. Our experimental results suggest that the performance from the Haar wavelet and log Gabor filter based phase encoding is the most promising among all the four approaches considered in this work. Therefore the combination of these two matchers is most promising, both in terms of performance and the computational complexity. Our experimental results from the all 411 users (CASIA v3) and 224 users (IITD v1) database illustrate significant improvement in the performance that is not possible with either of these approaches individually.
An Accurate Iris Segmentation Framework Under Relaxed Imaging Constraints Using Total Variation Model

This paper proposes a novel and more accurate iris segmentation framework to automatically segment iris region from the face images acquired with relaxed imaging under visible or near-infrared illumination, which provides strong feasibility for applications in surveillance, forensics and the search for missing children, etc. The proposed framework is built on a novel total-variation based formulation which uses l1 norm regularization to robustly suppress noisy texture pixels for the accurate iris localization. A series of novel and robust post processing operations are introduced to more accurately localize the limbic boundaries. Our experimental results on three publicly available databases, i.e., FRGC, UBIRIS.v2 and CASIA.v4-distance, achieve significant performance improvement in terms of iris segmentation accuracy over the state-of-the-art approaches in the literature. Besides, we have shown that using iris masks generated from the proposed approach helps to improve iris recognition performance as well. Unlike prior work, all the implementations in this paper are made publicly available to further advance research and applications in biometrics at-d-distance.
Suspecting Less and Doing Better: New Insights on Palmprint Identification for Faster and More Accurate Matching

This paper introduces a generalized palmprint identification framework to unify several state-of-art 2D and 3D palmprint methods. Through this framework, we argue that the methods employing one-to-one matching strategy and binary representation for feature are more effective for palmprint identification. The analysis for the first argument is based on a statistical matching model and is supported by outperforming results on several publicly available 2D palmprpint databases. These two arguments are further evaluated for 3D palmprint matching and used to introduce a new method for encoding 3D palmprint feature. The proposed 3D feature is binary and more efficiently computed. It encodes the 3D shape of palmprint to either convex or concave. The experimental results on two publicly available, from contactless and contact-base 3D palmprint database of 177 and 200 subjects, respectively, outperform the state-of-the-art methods. This paper also provides our palmprint matching algorithm(s) in public domain, unlike the previous work in this area, which will help to further advance research efforts in this area.
Modeling and Test Case Generation of Inter-component Communication in Android

Currently, there is a lack of tools or techniques which can clearly handle the complexity related to inter-component communication while developing Android applications. We propose a conceptual model which represents the inter-component communication at a higher abstraction level. We also propose a technique to derive test case from the model. The model can be useful in handling complexity at various stages of software engineering process. Mainly, it can be used for testing and analysis of inter-component communication in Android applications which we have demonstrated through experiment.
A 3D Feature Descriptor Recovered from a Single 2D Palmprint Image

Design and development of efficient and accurate feature descriptors is critical for the success of many computer vision applications. This paper proposes a new feature descriptor, referred to as DoN, for the 2D palmprint matching. The descriptor is extracted for each point on the palmprint. It is based on the ordinal measure which partially describes the difference of the neighboring points' normal vectors. DoN has at least two advantages: 1) it describes the 3D information, which is expected to be highly stable under commonly occurring illumination variations during contactless imaging; 2) the size of DoN for each point is only one bit, which is computationally simple to extract, easy to match, and efficient to storage. We show that such 3D information can be extracted from a single 2D palmprint image. The analysis for the effectiveness of ordinal measure for palmprint matching is also provided. Four publicly available 2D palmprint databases are used to evaluate the effectiveness of DoN, both for identification and the verification. Our method on all these databases achieves the state-of-the-art performance.
An advanced approach for implementation of real time scheduling algorithm for efficient mass production

Embedded systems are designed to perform real time scheduling (RTS) for various production services and applications. Use of RTS for production manufacturing will provide some significant advantages like Accuracy, High Throughput, Low Congestion & overall fast system response. In this paper we design & discuss algorithm for Chair manufacturing application. It solves a problem of chair manufacturing by multiple workers (cabinetmakers). Their goal is to make four legs, seat and backrest of the chair and assembly all of these parts with minimal time effort. To perform the same our algorithm design such that it will utilize the processor(s) to achieve minimal delay, no missing deadline & high production values with less time-to-market.
Toward More Accurate Iris Recognition Using Cross-Spectral Matching

Iris recognition systems are increasingly deployed for large-scale applications such as national ID programs, which continue to acquire millions of iris images to establish identity among billions. However, with the availability of variety of iris sensors that are deployed for the iris imaging under different illumination/environment, significant performance degradation is expected while matching such iris images acquired under two different domains (either sensor-specific or wavelength-specific). This paper develops a domain adaptation framework to address this problem and introduces a new algorithm using Markov random fields model to significantly improve cross-domain iris recognition. The proposed domain adaptation framework based on the naive Bayes nearest neighbor classification uses a real-valued feature representation, which is capable of learning domain knowledge. Our approach to estimate corresponding visible iris patterns from the synthesis of iris patches in the near infrared iris images achieves outperforming results for the cross-spectral iris recognition. In this paper, a new class of bi-spectral iris recognition system that can simultaneously acquire visible and near infra-red images with pixel-to-pixel correspondences is proposed and evaluated. This paper presents experimental results from three publicly available databases; PolyU cross-spectral iris image database, IIITD CLI and UND database, and achieve outperforming results for the cross-sensor and cross-spectral iris matching.
A framework for interactive endeavor to enhance learning (iEEL)

Recent technologies provide wide opportunities for learning at remote places and enables people collaboration anywhere. In this paper, we present the future advancement in the teaching learning process in India. As mobile phones and tablets are getting more keenly intellective and more widely used than in the precedent decades, they have become a familiar tool in our present environment. Utilizing them as a cognition tool calls for accommodations that are compatible with the concept of communication tools. Various studies suggest that `Digital education' is comparatively more effective than traditional learning. The proposed system uses an inter network for teacher - student interaction and sharing of resources and messages. It allows knowledge-building and learning process to be run in collaborative learning environment. In this system, a student can create, collect and share inculcate data immediately anywhere, anytime. This enables them to play active role in knowledge building process. Hence, we have proposed a combination of m-learning and e-learning based application along with the concept of BYOD (bring your own device) which is advantageous to both the faculties and students of educational institutions for smart learning.