nils-herrmann/clean_scirepeval huggingface dataset:
Synthesis of Floating-Point Addition Clusters on FPGAs Using Carry-Save Arithmetic

A new method to synthesize clusters of floating-point addition operations on FPGAs is presented. Similar to Altera’s floating-point data path compiler, it performs normalization once, at the output of the cluster operation. All significands in the clustered operation are denormalized in parallel with respect to the largest exponent: a fixed-point compressor tree then sums the aligned significands, followed by normalization and rounding. Compared to Altera’s floating-point datapath compiler, our method reduces the critical path delay by as much as 20%, and area by as much as 29% on Altera Stratix III FPGAs.
Carbon NanoTube based logic gates structure for low power consumption at nano-scaled era

As day by day continuing research in the field of nanotechnology, the CMOS manufacturing process scaled down in nano-dimensions at the cost of severe process variations and high leakage current which resulting large power dissipation. Therefore the leakage current and power dissipation becomes increasingly more focused in VLSI circuit design. Carbon NanoTube Field Effect Transistor (CNFETs) is suited best alternatives to the conventional CMOS based devices. During various simulation results, unexpected reduction in process variation, ultra low (nano-scaled) power memory devices and superior improvement of Noise Margin, propagation delay, write-read margin and its stability is found. CNFETs based logic gates are compared with Conventional CMOS and FinFET based logic gates in respect to delay and power consumption.
Methodology for Analyzing ESD-Induced Soft Failure Using Full-Wave Simulation and Measurement

An analysis methodology is presented to investigate soft failures in electronic devices. This methodology combines transmission line pulse (TLP) full-wave simulations with system-level and TLP measurements. Information on susceptible parts in the device under test (DUT) and/or soft failure sensitivity of the integrated circuits (ICs) are obtained from the measurements. Then, the TLP current spreading within the printed circuit board (PCB) of the DUT is simulated. The susceptible signals can be determined by comparing the simulated voltages and/or currents at the signal terminations with the measured threshold values. If the simulated voltages and/or currents are higher than the threshold values, the signal is considered susceptible and a soft failure may occur in the DUT. Using the obtained information from simulations and measurements, the root causes of soft failures can be identified. Further, by utilizing full-wave simulations, the design of the product can be modified to reduce the electrostatic discharge (ESD) noise on the susceptible signals, and consequently prevent soft failures. The proposed analysis methodology is applied to a tablet which suffers from soft failure. The root cause of the soft failure is identified, and countermeasures are designed against the ESD-induced soft failure.
Effect of thermal annealing on electrical stability of TIPS-pentacene flexible OFETs

Effect of thermal annealing on the performance of flexible OFETs was investigated. Annealing at low temperatures up to 50 °C enhanced the molecular ordering and granular arrangement leading to improved device performance, however, a permanent degradation was induced at high temperature annealing due to loss in crystallinity and crack generation. An annealed device exhibited a larger decay in drain current than a pristine device under constant bias stress. Under combined effects of mechanical and thermal stress, effect of thermal stress was found to be dominant at higher annealing temperatures, resulting in monotonic performance degradation.
Accurate Iris Recognition at a Distance Using Stabilized Iris Encoding and Zernike Moments Phase Features

Accurate iris recognition from the distantly acquired face or eye images requires development of effective strategies, which can account for significant variations in the segmented iris image quality. Such variations can be highly correlated with the consistency of encoded iris features and knowledge that such fragile bits can be exploited to improve matching accuracy. A nonlinear approach to simultaneously account for both local consistency of iris bit and also the overall quality of the weight map is proposed. Our approach therefore more effectively penalizes the fragile bits while simultaneously rewarding more consistent bits. In order to achieve more stable characterization of local iris features, a Zernike moment-based phase encoding of iris features is proposed. Such Zernike moments-based phase features are computed from the partially overlapping regions to more effectively accommodate local pixel region variations in the normalized iris images. A joint strategy is adopted to simultaneously extract and combine both the global and localized iris features. The superiority of the proposed iris matching strategy is ascertained by providing comparison with several state-of-the-art iris matching algorithms on three publicly available databases: 1) UBIRIS.v2; 2) FRGC; and 3) CASIA.v4-distance. Our experimental results suggest that proposed strategy can achieve significant improvement in iris matching accuracy over those competing approaches in the literature, i.e., average improvement of 54.3%, 32.7%, and 42.6% in equal error rates, respectively, for UBIRIS.v2, FRGC, and CASIA.v4-distance.
Hand-Geometry Recognition Using Entropy-Based Discretization

The hand-geometry-based recognition systems proposed in the literature have not yet exploited user-specific dependencies in the feature-level representation. We investigate the possibilities to improve the performance of the existing hand-geometry systems using the discretization of extracted features. This paper proposes employing discretization of hand-geometry features, using entropy-based heuristics, to achieve the performance improvement. The performance improvement due to the unsupervised and supervised discretization schemes is compared on a variety of classifiers: k-NN, naive Bayes, SVM, and FFN. Our experimental results on the database of 100 users achieve significant improvement in the recognition accuracy and confirm the usefulness of discretization in hand-geometry-based systems
Performance Analysis Of Slip Power Recovery Scheme Employing Two Inverter Topologies

This paper presents an approach for performance analysis of an induction motor utilizing the characteristics of slip power recovery schemes (SPRS). The performance is analyzed by using a three phase bridge recovery inverter without chopper and inverter with chopper. The simulation test for a 5.4 hp wound rotor induction motor is carried out on MATLAB/SIMULINK environment. The results of simulation are studied, analyzed and compared. It has been observed from the simulation results that the power factor and efficiency of the slip power recovery scheme using inverter with chopper has better values than the slip power recovery scheme using inverter without chopper.
Low-loss AlInN/GaN microwave switch

A report is presented on the first low-loss monolithic microwave integrated circuit switch using AlInN/GaN heterostructure field effect transistors. Owing to significantly lower sheet resistance compared to the conventional AlGaN/GaN heterostructure (215 against 280 Q/□), and lower contact resistance (0.27 against 0.5 Ω×mm), the microwave switch based on AlInN/GaN shows lower insertion loss (0.8 against 0.9 dB at 6 GHz) and higher isolation (40 against 35 dB at 6 GHz). The achieved performance compares favourably or exceeds published results for this frequency range; even better performance can be achieved considering ultimate parameters for AlInN/GaN heterostructures.
Security for IoT: An effective DTLS with public certificates

The IoT (Internet of Things) is a scenario in which things, people, animal or any other object can be identified uniquely and have the ability to send or receive data over a network. With the IPV6 the address space has been increased enormously, favors allocation of IP address to a wide range of objects. In near future the number of things that would be connected to internet will be around 40 million. In this scenario it is expected that it will play a very vital role in business, data and social processes in which devices will interact among themselves and with the surrounding by interchanging information . If this information carries sensitive data then security is an aspect that can never be ignored. This paper discusses some existing security mechanism for IoT and an effective DTLS mechanism that makes the DTLS security more robust by employing public certificates for authentication. We can use a Certificate authority that can give the digital certificates to both the client and server and can increase the effectiveness of this communication. This work aims to introduce a CA for the communication and to provide some results that can show its improved performance in contrast to the pre-shared key communication.
A low power IoT network for smart agriculture

Traditional agriculture is transforming into smart agriculture due to the prominence of the Internet of Things (IoT). Low-cost and low-power are the key factors to make any IoT network useful and acceptable to the farmers. In this paper, we have proposed a low-power, low-cost IoT network for smart agriculture. For monitoring the soil moisture content, we have used an in-house developed sensor. In the proposed network, the IITH mote is used as a sink and sensor node which provides low-power communication. We have evaluated our network with state of the art networks, proposed for agriculture monitoring. Power and cost are the two metrics used for evaluation of these networks. Results show that the proposed network consumes less power and has on average 83% prolonged lifetime at a lower cost compared to previously proposed network in the agriculture field.
Diagonal slotted diamond shaped dual circularly polarized microstrip patch antenna with dumbbell aperture coupling

Improvement in ellipticity bandwidth with a diagonal slotted diamond shaped microstrip patch antenna is analyzed in this paper. The antenna is electro-magnetically fed using two orthogonally oriented dumbbell shaped apertures fed by a 3 dB 90deg hybrid for dual polarization operation. The radiating element is a 45deg rotated square patch (aspect ratio a/b = 1.015) forming a diamond. The antenna is designed to transmit and receive the two differently circularly polarized wide band signals simultaneously with a single antenna. It is observed that by cutting an electrically small rectangular slot along one of the diagonal of the square patch significantly improves the ellipticity bandwidth without altering its impedance bandwidth. The current distribution of the patch remains unaltered and hence the radiation pattern is also not affected. The use of a thick, low cost dielectric foam yields further improvement in bandwidth. A 2:1 impedance bandwidth of 26.5% and 3 dB axial ratio bandwidth of 20.4% has been achieved in simulation with the above geometry. An improvement of 5% in impedance bandwidth and 9% in ellipticity bandwidth is obtained over a patch of similar geometry with conventional rectangular shaped apertures. When compared to a non-slotted patch with dumbbell apertures the improvement in axial ratio bandwidth is of the order of 5% while the impedance bandwidth remains unaltered. The use of dielectric foam makes the antenna light weight and low cost. The above features make it an ideal element for wide band application as required for personal, mobile satellite and wireless communication systems
Separation of Sources From Single-Channel EEG Signals Using Independent Component Analysis

The electroencephalogram (EEG) signals are often mixed with several sources such as electrooculogram and electromyogram signals. Independent component analysis (ICA) is often used to separate the sources from the multichannel EEG signals. Recently, the use of portable EEG devices has gained much attention due to their low power consumption and the ability to record the EEG signals in home environment. However, these systems are equipped with single or few EEG channels, and hence the direct application of ICA is not possible. In this paper, we proposed an efficient technique to separate the sources from single-channel EEG signals by combining the singular spectrum analysis (SSA) and ICA techniques. In this technique, the single-channel EEG data are first decomposed into multivariate data using SSA. Later, ICA is applied on the multivariate data to extract the source signals. In order to validate the performance of the proposed technique, we carried out the simulation on synthetic and real life EEG signals. In addition, we have also studied the performance of the proposed technique for detecting the seizures. The performance measures of a seizure detection classifier such as receiver operating curve, true positive rate, and false positive rate are obtained and found that the proposed technique exhibits better performance compared with the existing methods.
A review on lightweight cryptography algorithms for data security and authentication in IoTs

Internet of Things (IoTs) comprises of a cluster of resource constrained devices, sensors and machines connected with each other and communicating over the internet. Due to frequent exchange of confidential data over the internet, IoTs become susceptible to various attacks (such as eavesdropping, denial of service, fabrication attacks) and to resolve these attacks security is required. In this paper, a brief discussion on the various IoT applications and architectures has been done. Further, the security concerns regarding information sharing and attacks have been highlighted. To overcome from these attacks safety measures regarding data security and authentication are discussed in detail resulting in use of cryptography as a solution. The comparative analysis of various lightweight encryption and authentication algorithms is carried out. The comparative analysis results show that the lightweight algorithms have good performance as compared to conventional cryptography algorithm in terms of memory requirement, their operations, and power consumption. Also, some research directions defined in which further work can be done on lightweight cryptography algorithms.
Scattered context grammar in hindi linguistics

The Hindi language is considered amongst the richest morphological language because of the various forms generated from a single word form. In this paper, dependencies among various words in Hindi sentences are entertained using scattered context grammar. This paper also includes the rules which are to be considered while converting nouns, adjectives and verbs from their singular form to plural form using Transducers and hence the application of scattered context grammar is explored in using nouns, adjectives and verbs in the sentences. This paper also covers a step higher of scattered context grammar which is transformational scattered context grammar.
Evaluation of the Interleaved Source Coding (ISC) Under Packet Correlation

Network impairments such as delay and packet losses have severe impact on the presentation quality of many predictive video sources. Prior researches have shown efforts to develop packet loss resilient coding methods to overcome such impairments for real-time streaming applications. Interleaved source coding (ISC) is one of the error resilient coding methods, which is based on an optimum interleaving of predictive video coded frames transmitted over a single erasure channel. ISC employs a Markov decision process (MDP) and a corresponding dynamic programming algorithm to identify the optimal interleaving pattern for a given channel model and a transmitting sequence. ISC has shown to significantly improve the overall quality of predictive video coded stream over a lossy channel without complex modifications to standard video coders. In this paper, ISC is evaluated over channels with memory. In particular, we analyze the impact of packet correlation of the popular Gilbert model on ISC-based packet video over a wide range of packet loss probabilities. Simulations have shown that ISC advances the traditional method as either the loss rate increases or the packet correlation decreases
Generalized multiscale seam carving

With the abundance and variety of display devices, novel image resizing techniques have become more desirable. Content-aware image resizing (retargeting) techniques have been proposed that show improvement over traditional techniques such as cropping and resampling. In particular, seam carving has gained attention as an effective solution, using simple filters to detect and preserve the high-energy areas of an image. Yet, it stands to be more robust to a variety of image types. To facilitate such improvement, we recast seam carving in a more general framework and in the context of filter banks. This enables improved filter design, and leads to a multiscale model that addresses the problem of scale of image features. We have found our generalized multiscale model to improve on the existing seam carving method for a variety of images.
Detecting Malware Outbreaks Using a Statistical Model of Blackhole Traffic

Internet blackholes have emerged as very effective tools for monitoring changes in the Internet's traffic behavior. Prior studies have shown that traffic observed at a blackhole contains valuable information about emerging malware. While blackhole traffic has been effectively used for attack forensics, a systematic method of leveraging this traffic for online Internet- scale anomaly detection is not available. In this paper, we propose a novel technique to detect malware outbreaks using deviations in a robust statistical model of a blackhole's traffic. First, we introduce a novel and accurate Piecewise Poisson process Model (PPM) of traffic observed at an Internet Motion Sensor (IMS) blackhole which provides a statistical quantification of the intensity or rate of incoming traffic at a blackhole, which can in turn be used to detect malware outbreaks. After establishing the accuracy of the proposed PPM model, we develop a regression model that can characterize variations in the PPM's traffic rates. Once an accurate model of traffic rates is in place, malware outbreaks can be detected using deviations from the model's likely statistical patterns. After removing simple deterministic patterns, we observe that a blackhole's traffic rate residuals have a skewed and heavy-tailed behavior. Consequently, we employ a stable distribution that models variations in traffic rate residuals with very high accuracy. Finally, we propose an online detection mechanism that utilizes deviations from the rate residual distribution of blackhole traffic data to detect malware outbreaks. Experimental results using the IMS data for approximately one year show that the proposed mechanism accurately detects malware outbreaks in a timely manner.
Translation-Invariant Contourlet Transform and Its Application to Image Denoising

Most subsampled filter banks lack the feature of translation invariance, which is an important characteristic in denoising applications. In this paper, we study and develop new methods to convert a general multichannel, multidimensional filter bank to a corresponding translation-invariant (TI) framework. In particular, we propose a generalized algorithme agrave trous, which is an extension of the algorithme agrave trous introduced for 1-D wavelet transforms. Using the proposed algorithm, as well as incorporating modified versions of directional filter banks, we construct the TI contourlet transform (TICT). To reduce the high redundancy and complexity of the TICT, we also introduce semi-translation-invariant contourlet transform (STICT). Then, we employ an adapted bivariate shrinkage scheme to the STICT to achieve an efficient image denoising approach. Our experimental results demonstrate the benefits and potential of the proposed denoising approach. Complexity analysis and efficient realization of the proposed TI schemes are also presented
A multiresolution approach to binary tree representations of images

A multiresolution method for constructing a BSP (binary space partitioning) tree is introduced. This approach derives a hierarchy (pyramid) of scale-space images from the original image. In this hierarchy, a BSP tree of an image is built from other trees representing low-resolution images of the pyramid. A low-resolution image BSP tree serves as an initial guess to construct a higher-resolution image tree. Due to filtering when constructing the pyramid, details are discarded. As a result, a more robust segmentation is obtained. Moreover a significant computational advantage is achieved.<<ETX>>
Image super-resolution via Dual-Manifold Clustering and Subspace Similarity

In this paper, we consider the problem of example based single image super-resolution. Our main contribution is introducing a new framework that makes no assumption about the structural similarity between the high-resolution (HR) and low-resolution (LR) manifolds. Instead, we use a subspace affinity measure to exploit the similarity between each HR and LR subspace. First, we train both LR and HR manifolds independently, and then, by using subspace similarity we find the closest HR subspace to each LR subspace. Each patch from the LR test image is projected onto the LR trained manifold to find the closest LR subspace. Finally, the corresponding HR subspace is selected to reconstruct the HR version of the test patch. We refer to the proposed framework Dual-Manifold Clustering and Subspace Similarity (DMCSS). The experimental results showed that DMCSS achieves clear visual improvements and an average of 1dB improvement in PSNR over state-of-the-art algorithms in this field.
Delay Constraint Error Control Protocol for Real-Time Video Communication

Real-time video communication over wireless channels is subject to information loss since wireless links are error-prone and susceptible to noise. Popular wireless link-layer protocols, such as retransmission (ARQ) based 802.11 and hybrid ARQ methods provide some level of reliability while largely ignoring the latency issue which is critical for real-time applications. Therefore, they suffer from low throughput (under high-error rates) and large waiting-times leading to serious degradation of video playback quality. In this paper, we develop an analytical framework for video communication which captures the behavior of real-time video traffic at the wireless link-layer while taking into consideration both reliability and latency conditions. Using this framework, we introduce a delay constraint packet embedded error control (DC-PEEC) protocol for wireless link-layer. DC-PEEC ensures reliable and rapid delivery of video packets by employing various channel codes to minimize fluctuations in throughput and provide timely arrival of video. In addition to theoretically analyzing DC-PEEC, the performance of the proposed scheme is analyzed by simulating real-time video communication over ldquorealrdquo channel traces collected on 802.11 b WLANs using H.264/AVC JM14.0 video codec. The experimental results demonstrate performance gains of 5-10 dB for different real-time video scenarios.