nils-herrmann/clean_scirepeval huggingface dataset:
Supercapacitor-based embedded hybrid solar/wind harvesting system architectures

Off-grid medium-power (1–10 W) systems require either battery-or supercapacitor-based ambient energy harvesting for sustaining their operation. Supercapacitor-based harvesters are advantageous in autonomous field systems due to their extended lifetime, easy power management, and low maintenance requirement; however, they can reach only up to 10% of the energy density of rechargeable batteries. To overcome this energy density challenge, hybrid power sources, such as solar or wind, can be advantageously utilized in harvesting systems. The complementary power supply characteristics of solar and wind can substantially reduce the required supercapacitor buffer size compared with solar-only or wind-only systems. In the literature, no supercapacitor-based hybrid harvesting system design exists for 1–10 W range. In this paper, we develop and experimentally validate three different categories of supercapacitor-based harvesting systems that are capable of simultaneously harvesting solar/wind (hybrid) power sources.
Transfer learning via attributes for improved on-the-fly classification

Retrieving images for an arbitrary user query, provided in textual form, is a challenging problem. A recently proposed method addresses this by constructing a visual classifier with images returned by an internet image search engine, based on the user query, as positive images while using a fixed pool of negative images. However, in practice, not all the images obtained from internet image search are always pertinent to the query; some might contain abstract or artistic representation of the content and some might have artifacts. Such images degrade the performance of on-the-fly constructed classifier. We propose a method for improving the performance of on-the-fly classifiers by using transfer learning via attributes. We first map the textual query to a set of known attributes and then use those attributes to prune the set of images downloaded from the internet. This pruning step can be seen as zero-shot learning of the visual classifier for the textual user query, which transfers knowledge from the attribute domain to the query domain. We also use the attributes along with the on-the-fly classifier to score the database images and obtain a hybrid ranking. We show interesting qualitative results and demonstrate by experiments with standard datasets that the proposed method improves upon the baseline on-the-fly classification system.
Wafer level embedding technology for 3D wafer level embedded package

This paper presents the development of wafer level embedding process for a three dimensional (3D) embedded micro wafer level package (EMWLP). Wafer level embedding process was carried out by using compression molding machine and low-cost granular epoxy molding compound (EMC). Various molding process parameters such as molding time and temperature and three EMCs of different CTEs were analyzed to achieve reliable 3D EMWLP. Several molding process issues, such as warpage, die-sweep, EMC penetration, and die-shift, were faced during embedding process development. A large warpage of more than 1 mm and die-shift of more than 600 µm were found to occur in reconstructed molded wafer. Wafer level embedding process was optimized to reduce warpage and die-shift problems. A significant reduction in warpage (∼ 30 %) and die-shift (∼ 88 %) were achieved after embedding process optimization. The detail of process optimization is presented in the paper. Reconstructed molded wafers were subjected to various reliability tests, such as thermal cycle (TC), moisture sensitivity test-level 3 (MST-L3), and highly accelerated stress test (HAST). Scanning acoustic microscopy (SAM) analysis of molded wafers was carried out to analyze the void formation and delamination in molded wafers. No major void or delamination was observed in reconstructed wafer after molding as well as after reliability tests.
Automatic Registration of Wide Area Motion Imagery to Vector Road Maps by Exploiting Vehicle Detections

To enrich large-scale visual analytics applications enabled by aerial wide area motion imagery (WAMI), we propose a novel methodology for accurately registering a geo-referenced vector roadmap to WAMI by using the locations of detected vehicles and determining a parametric transform that aligns these locations with the network of roads in the roadmap. Specifically, the problem is formulated in a probabilistic framework, explicitly allowing for spurious detections that do not correspond to on-road vehicles. The registration is estimated via the expectation-maximization (EM) algorithm as the planar homography that minimizes the sum of weighted squared distances between the homography-mapped detection locations and the corresponding closest point on the road network, where the weights are estimated posterior probabilities of detections being on-road vehicles. The weighted distance minimization is efficiently performed using the distance transform with the Levenberg-Marquardt nonlinear least-squares minimization procedure, and the fraction of spurious detections is estimated within the EM framework. The proposed method effectively sidesteps the challenges of feature correspondence estimation, applies directly to different imaging modalities, is robust to spurious detections, and is also more appropriate than feature matching for a planar homography. Results over three WAMI data sets captured by both visual and infrared sensors indicate the effectiveness of the proposed methodology: both visual comparison and numerical metrics for the registration accuracy are significantly better for the proposed method as compared with the existing alternatives.
Decomposition of fluorescent illuminant spectra for accurate colorimetry

Fluorescent lamps are widely used in the office environment and in common desktop scanners. These lamps are characterized by the nearly monochromatic emission lines present in their radiant spectrum. Due to the presence of spectral lines special care is required in the computation of tristimulus values under these illuminants. Accurate computation of the tristimulus values can be performed digitally by modeling the radiance as the sum of a continuous spectrum emitted by the phosphors and weighted impulses corresponding to the emission lines. The paper considers a scheme for estimating the continuous spectrum and the strengths of the impulses from measurements of the spectrum with band limited sensors. The impact of noise in measured data on the accuracy of the final color computation is also considered.<<ETX>>
Multiplexed clustered-dot halftone watermarks using bi-directional phase modulation and detection

We present a method for embedding and detection of visual watermark patterns in printed images that use clustered-dot halftones in the printing process. The method allows two independent watermark patterns to be multiplexed, i.e. embedded in the same region of the printed image, thereby offering an improvement over prior techniques. The watermark patterns are embedded via phase modulation along the two periodicity directions of the halftone. Each embedded pattern can be visually detected, without interference from the other watermark, when the printed image, or a scan thereof, is overlaid with a decoder mask consisting of periodic lines oriented along the corresponding halftone periodicity direction. We experimentally demonstrate that our proposed multiplexing technique is effective. We also present analysis that demonstrates that the embedding has minimal visual impact and explains the pattern observed in the watermark detection process.
Per-Colorant-Channel Color Barcodes for Mobile Applications: An Interference Cancellation Framework

We propose a color barcode framework for mobile phone applications by exploiting the spectral diversity afforded by the cyan (C), magenta (M), and yellow (Y) print colorant channels commonly used for color printing and the complementary red (R), green (G), and blue (B) channels, respectively, used for capturing color images. Specifically, we exploit this spectral diversity to realize a three-fold increase in the data rate by encoding independent data in the C, M, and Y print colorant channels and decoding the data from the complementary R, G, and B channels captured via a mobile phone camera. To mitigate the effect of cross-channel interference among the print-colorant and capture color channels, we develop an algorithm for interference cancellation based on a physically-motivated mathematical model for the print and capture processes. To estimate the model parameters required for cross-channel interference cancellation, we propose two alternative methodologies: a pilot block approach that uses suitable selections of colors for the synchronization blocks and an expectation maximization approach that estimates the parameters from regions encoding the data itself. We evaluate the performance of the proposed framework using specific implementations of the framework for two of the most commonly used barcodes in mobile applications, QR and Aztec codes. Experimental results show that the proposed framework successfully overcomes the impact of the color interference, providing a low bit error rate and a high decoding rate for each of the colorant channels when used with a corresponding error correction scheme.
End-to-end color printer calibration by total least squares regression

Neugebauer modeling plays an important role in obtaining end-to-end device characterization profiles for halftone color printer calibration. This paper proposes total least square (TLS) regression methods to estimate the parameters of various Neugebauer models. Compared to the traditional least squares (LS) based methods, the TLS approach is physically more appropriate for the printer modeling problem because it accounts for errors in the measured reflectance of both the primaries and the modeled samples. A TLS method based on print measurements from single-colorant step-wedges is first developed. The method is then extended to incorporate multicolorant print measurements using an iterative algorithm. The LS and TLS techniques are compared through tests performed on two color printers, one employing conventional rotated halftone screens and the other using a dot-on-dot halftone screen configuration. Our experiments indicate that the TLS methods yield a consistent and significant improvement over the LS-based techniques for model parameter estimation. The gains from the TLS method are particularly significant when the number of patches for which measured data is available is limited.
Vehicle tracking in Wide area motion imagery: A facility location motivated combinatorial approach

We propose a practical combinatorial approach for addressing the challenging task of vehicle tracking in Wide area motion imagery (WAMI) by leveraging a pixel accurate co-registered vector road-map. Specifically, guided by the co-registered road network, we obtain a sparse trellis graph linking each vehicle detection (VD) in a WAMI frame with a road reachable VD in the next frame, which then allows us to enumerate all possible hypotheses tracks. The globally optimal selection of tracks over a K frame window is then formulated as a minimum cost K capacitated facility location problem, where each hypothesis track is allocated VDs in each frame and assigned a cost that models desirable properties of vehicle track. Computation of optimized combinations of jointly feasible tracks that minimize the total cost for the tracks becomes feasible in our formulation for moderate values of K by utilizing available solvers for the facility location problem. The approach automatically selects the optimal number of tracks and provides flexibility in defining costs for tracks globally across the K frames. Vehicle tracking results obtained over test WAMI datasets indicate that our proposed method provides significant better performance than two other state of the art alternatives.
Hardwired data security using smart-metric algorithm

Data security is one of the major concerns in today's scenario. However, due to availability of information related to networks and free software, hackers can easily hack into different computers without even having much in-depth knowledge of the network architecture. It is very hard to protect our valuable data from others and it is impossible to restrict hackers by installing firewalls and other security software. As software is always having some kind of weak points, which can be exploited by technically equipped people, The solution to this problem is by securing the hardware. In hardwired security, we are proposing a hardware control unit attached with motherboard, due to which we can give permission to different users of that computer according to access rights. The users are identified using smart-metric finger print identification algorithm, it require multiple fingerprints in authentication which prevents the re-use of the fingerprint. It also decreases the probability of successful reproduction of phony fingers which will provide a new level of data security.
Continuous Phase Modulated Halftones and Their Application to Halftone Data Embedding

We propose a generalization of periodic clustered-dot halftones, wherein the phase of the halftones is modulated using a secondary signal. The process is accomplished using an analytic halftone threshold function and allows halftones to be generated with different phase variation in different regions of the printed page. We demonstrate that ensuring continuity of the phase assures that the resulting halftone images are free from visible artifacts despite the modulation in phase. We present, applications of the proposed method to halftone data embedding, wherein the changes in phase or in frequency encode the embedded information. For the frequency embedding, using continuous phase modulation, we also consider the limitations on signals that are embedded within our framework. For both applications, we demonstrate how the embedded signals may be recovered from the printed halftones either using the image as a self-reference which reveals the watermark when it is shifted and overlaid with itself or by employing a separate transparency mask
Fast Localization of Autonomous Vehicles Using Discriminative Metric Learning

In this paper, we report a novel algorithm for localization of autonomous vehicles in an urban environment using orthographic ground reflectivity map created with a three-dimensional (3D) laser scanner. It should be noted that the road paint (lane markings, zebra crossing, traffic signs etc.) constitute the distinctive features in the surface reflectivity map which are generally sparse as compared to the non-interesting asphalt and the off-road portion of the map. Therefore, we propose to project the reflectivity map to a lower dimensional space, that captures the useful features of the map, and then use these projected feature maps for localization. We use discriminative metric learning technique to obtain this lower dimensional space of feature maps. Experimental evaluation of the proposed method on real data shows that it is better than the standard image matching techniques in terms of accuracy. Moreover, the proposed method is computationally fast and can be executed at real-time (10 Hz) on a standard CPU.
Image analysis pipeline for characterizing photolytic degradation in daguerreotypes

We describe an image analysis pipeline for the minimally invasive analysis and characterization of light-induced degradation in daguerreotypes. To our knowledge, this is the first time that quantitative characterization and temporal analysis of the photolytic degradation has been described for daguerreotypes. We measure the impact of degradation using microscopic image capture before and after exposure of a small sacrificial region of the daguerreotype to light. The image analysis pipeline compensates for changes in capture position between the pre and post-exposure images and measures the effects of the degradation in the regions under test. Our results show that photolytic degradation follows a profile that is approximated as the sum of two exponentials with a time constants about 0.41 and 0.003 min-1, with variation across regions.
Improved energy-efficient chain-based routing protocol for edge-based wireless sensor networks

Wireless sensor networks (WSNs) are made up of large number of sensor nodes with restricted power resources and storage capacity. As it is necessary to utilize the limited power of each node very efficiently. Therefore it is essential to design an improved chain-based routing protocol for enhancing the lifetime of the network. Among all the existing routing protocols, chain-based routing is one of the most suitable routing method, but there are still some drawbacks like that redundant (detour) transmission and propagation delay are concord with existing schemes presented in literature. To overcome this problem, in this paper we propose a new chain based routing protocol called improved chain-based routing protocol for edge-based WSN (improved CHIRON). In existing CHIRON, area of all groups are not same and group's area increases with moving away from base station (BS). So that the farthest group having longer chain length as compare to nearest group. This will produce larger delay in data propagation between sensor nodes and base station. In our proposed approach the network model is designed such that the groups which are farthest from base station has same chain length like nearest groups therefore data propagation delay becomes less in our proposed approach and overall network lifetime is improved. In this paper we discussed the network model of our proposed protocol for distributing the load of whole network uniformly, we divide the network region into smaller group areas of equal sizes.
Pitch and duration modification for speech watermarking

We propose a speech watermarking algorithm based on the modification of the pitch (fundamental frequency) and duration of the quasi-periodic speech segments. Natural variability of these speech features allows watermarking modifications to be imperceptible to the human observer. On the other hand, the significance of these features makes the system robust to common signal processing operations and low data-rate source excitation based speech coders. This class of coders is particularly obstructive for conventional audio watermarking algorithms when applied to speech signals. A pitch synchronous overlap and add (PSOLA) algorithm is used for pitch and duration modifications in the watermark embedding phase. Experiments with multiple speech codecs show very good robustness with low data-rate (5-8 kbps) speech coders.
Distributed Estimation and Coding: A Sequential Framework Based on a Side-Informed Decomposition

We propose a sequential framework for the distributed multiple-sensor estimation and coding problem that decomposes the problem into a series of side-informed source coding problems and enables construction of good codecs. Our construction relies on a separation result for the simplified scenario where information from one sensor is to be sent to the CP that already has information regarding the desired signal. We show that the optimal encoder decoder combination, in this setting, can be decomposed, without loss in performance, into two steps: A first preprocessing step to extract relevant information from the indirect observation with consideration of the side information, followed by a second step of side-informed encoding of the preprocessed output. A recursive exploit of the decomposition coupled with side-informed transform coding allows us to construct encoders by reusing scalar Wyner-Ziv codecs. We develop a numerical procedure for obtaining bounds delineating the best achievable performance for the proposed sequential framework and construct and demonstrate a practical codec in the proposed framework that achieves empirical performance close to the bound. Furthermore, we also compare the bounds for the sequential scheme against bounds for a number of alternate schemes-for some of which codec constructions are obvious and others for which codec constructions are inobvious. Our results show that, in most cases, our codec exceeds the performance bound of schemes offering obvious constructions. The achievable bound for our sequential framework is also shown to be close to a general nonconstructive distributed bound that does not impose the sequential constraint indicating that the sequential approach may not cause a significant performance compromise.
State-of-charge estimation for supercapacitors: A Kalman filtering formulation

Supercapacitors are an attractive option for energy buffering because of their high efficiency, durability, and low environmental impact. For energy-aware applications, it is desirable to accurately estimate the buffered energy. Under conditions of varying energy supply and demand, estimation of buffered energy by using only the supercapacitor terminal voltage is inaccurate because this does not fully comprehend the physical state of charge. To address this problem, we present a Kalman filtering formulation, using the accepted three-branch circuit model for supercapacitors. Compared with an ideal capacitor, the physically-motivated three-branch model provides a much more accurate representation of the state of charge via three internal state voltages associated with short, medium, and long term charging constants. The proposed Kalman formulation tracks these unobservable internal states. This methodology demonstrates a significantly more accurate estimate of the buffered energy as compared with the alternative models of ideal capacitance or a recursive computation of the stored energy. Simulations conducted with variations that approximate recorded solar intensity profiles, our proposed approach has an error of 1% compared with 31% and 85% for the respective alternative models.
Optimized Elliptic Curve Cryptography for wireless sensor networks

Recent technical advances have made it possible to develop such devices, which are small in size, cheap and capable of wireless communication using radio interfaces and provide pervasive services to the user. Sensor networks are the extreme examples of such ubiquous and pervasive computer paradigm. The constraints imposed on the wireless sensor networks make it more prone to cost of computation. So, the major issue is to reduce the computation cost by implementing some optimization technique, so that the overall efficiency of the system increases without any loss of information. In this paper, we list out few optimization techniques that can be used in Elliptic Curve Cryptography for wireless sensor networks.
Swift: Scalable weighted iterative sampling for flow cytometry clustering

Flow cytometry (FC) is a powerful technology for rapid multivariate analysis and functional discrimination of cells. Current FC platforms generate large, high-dimensional datasets which pose a significant challenge for traditional manual bivariate analysis. Automated multivariate clustering, though highly desirable, is also stymied by the critical requirement of identifying rare populations that form rather small clusters, in addition to the computational challenges posed by the large size and dimensionality of the datasets. In this paper, we address these twin challenges by developing a two-stage scalable multivariate parametric clustering algorithm. In the first stage, we model the data as a mixture of Gaussians and use an iterative weighted sampling technique to estimate the mixture components successively in order of decreasing size. In the second stage, we apply a graph-based hierarchical merging technique to combine Gaussian components with significant overlaps into the final number of desired clusters. The resulting algorithm offers a reduction in complexity over conventional mixture modeling while simultaneously allowing for better detection of small populations. We demonstrate the effectiveness of our method both on simulated data and actual flow cytometry datasets.