nils-herrmann/clean_scirepeval huggingface dataset:
A simple high capacity multiple service solution with controlled QoS for GERAN

Offering multiple services with high spectral efficiency is crucial for 3rd generation cellular systems. Further, for stable system behavior, controlling quality of service (QoS) for offered loads beyond the capacity limit of the systems is required. This paper describes and evaluates a simple, easy-to-plan concept that accomplishes this goal for the GSM/EDGE Radio Access Network (GERAN). Cornerstones of the concept are tight frequency reuse, service-based power setting and power-based admission control. Preliminary results indicate that the proposed solution manages to preserve targeted QoS for a mix of services at excessive traffic loads. Moreover, different operator QoS policies may be realized in a straightforward manner.
Network Densification Impact on System Capacity

With the tremendous growth of smartphone penetration, the demand for increased capacity in cellular networks has escalated. The trend is predicted to continue for years to come. One common way to cope with the ever increasing capacity demand is to add cells and densify the networks. Network densification was used before the introduction of LTE and Heterogeneous Networks, and will be even more emphasized with the introduction of 5G, where higher frequency bands with more challenging propagation characteristics, will be utilized. This study shows that in a confined geographical area with a fixed number of users, the user bit rate always increases with network densification. It is also shown that the capacity increase slows down with an increased number of cells, due to the increasing interference from the additional cells. Also, if the path loss exponent increases with distance, a reduced distance between cells reduces the capacity increase.
Efficient validation/verification of a robust DVB-H link layer

The link layer is one of the key differentiating functions in a DVB-H receiver. In order to differentiate between silicon solutions, manufacturers can choose between basic or advanced link-layer implementation concepts, resulting in modest or excellent data retrieval performance, respectively. Hence, objective performance evaluation, which proves the quality of the link layer implementation, is an essential step to validate product robustness. This paper presents techniques for validation and verification tools to evaluate the DVB-H link-layer robustness. The proposed techniques for robustness testing are capable of generating both regular and extreme signal conditions. Furthermore, we also discuss an efficient analysis tool required to inspect (1) the IP-based output data, (2) Service Information and (3) Program Specific Information of the link layer. Simulation results for improved link-layer performance presented by the tools are validated from a newly developed silicon implementation1.
Advanced High-Power-Density Interior Permanent Magnet Motor for Traction Applications

Electric drive systems, which include electric machines and power electronics, are a key enabling technology for advanced vehicle propulsion systems that reduce the petroleum dependence of the ground transportation sector. To have significant effect, electric drive technologies must be economical in terms of cost, weight, and size while meeting performance and reliability expectations. This paper will provide details of the design, analysis, and testing of an advanced interior permanent magnet (PM) machine that was developed to meet the FreedomCAR 2020 specifications. The 12-slot/10-pole machine has segmented stator structure equipped with fractional-slot nonoverlapping concentrated windings. The rotor has a novel spoke structure/assembly. Several prototypes with different thermal management schemes have been built and tested. This paper will cover the test results for all these prototypes and highlight the tradeoffs between the various schemes. Due to the high machine frequency (~1.2 kHz at the top speed), detailed analysis of various loss components and ways to reduce them will be presented. In addition, due to the high coolant inlet temperature and the fact that the machine is designed to continuously operate at 180 °C, detailed PM demagnetization analysis will be presented. The key novelty in this paper is the advanced rotor structure and the thermal management schemes.
New scalable three-stage motion estimation technique for mobile MPEG encoding

The paper presents a new scalable three-stage motion estimation technique, which includes processing of frames in display order and approximating motion vector fields using multiple references. Quality refinement is added as an optional stage. The complete system provides a flexible framework with a large scalability range in computational effort, resulting in different picture-quality levels or bitrates. Experiments show a scalable computational effort with a factor of 14, resulting in a global variation of 7 dB SNR in picture quality (with the "Stefan" sequence). In high-quality operation, the new method is comparable to a full-search motion estimation with a search window of 32/spl times/32 pixels (or even outperforms it). The innovation provides an excellent starting point for scalability in resource-constrained mobile system design (see Mietens, S. et al.,  2001, vol.3, p.462-5, 2001).
Highly uniform and low-loss passive silicon photonics devices using a 300mm CMOS platform

Using an advanced 300mm CMOS-platform, we report record-low and highly-uniform propagation loss: 0.45±0.12dB/cm for wires, and 2dB/cm for slot waveguides. For WDM devices, we demonstrate channel variation(3-σ) within-wafer and within-device of 6.1nm and 1.2nm respectively.
Data-flow timing models of dynamic multimedia applications for multiprocessor systems

Efficient implementation of complex state-of-the-art multimedia applications on a system-on-chip requires a multi-processor architecture. For this purpose, we have developed modeling techniques for application mapping on predictable multiprocessor systems. In the mapping experiments, it was found that the static nature of synchronous data-flow graphs is not suited for multimedia systems, which are increasingly characterized by dynamic workload. Therefore, we present an extension that also captures the dynamic properties of multimedia applications. We chose MPEG-4 shape-texture decoding, as a complex example for the modeling of an application with dynamic behavior. Experiments with a set of five test sequences show that our model has a high accuracy (3.4%). A key advantage of our modeling method is that it allows to analytically derive the performance characteristics.
Leaky wave enhanced feed arrays for the improvement of the edge of coverage gain in multibeam reflector antennas

The performance of multibeam focal plane arrays feeding a single aperture is usually reduced due to conflicting requirements on the feed elements. Dense packing is usually required to minimize the beam separation, while typically large feed apertures are needed to provide the high feed directivity to reduce spillover losses from the reflector. In this paper the use of dielectric super-layers to shape the radiation pattern of each feed is demonstrated. The shaping is obtained by exciting, according to design, a pair of TE/TM leaky waves. The spillover from the reflector is reduced without physically increasing the dimensions of each single element aperture. A prototype of a feed array composed of 19 waveguides arranged in a hexagonal lattice was designed, manufactured and tested. The measured embedded patterns provided an increase of the edge of coverage gain, with respect to the free space case, of at least 0.6 dB in an operating bandwidth (BW) of ap12%. Moreover when reactive loading of adjacent feeds is adopted the increase in the edge of coverage with respect to the free space case was demonstrated to be larger than 1.6 dB over a 3% BW.
ANDES: grid capacity planning using a bottom-up, profile-based load forecasting approach

Liander, a Distribution Network Operator (DNO) in the Netherlands, faces potential challenges due to changing behaviour of its customers. New techniques such as electrical vehicles, solar PV and heat pumps can have a huge impact on the loads in the grid. This study evaluates ANDES, a load forecast model developed by Alliander that can make forecasts of future grid load for 40 years ahead, for multiple scenarios and from LV-feeders up to HV/MV-substations. In contrast with traditional load forecasting methods, the results of ANDES give insight into future peak loads and new load profiles due to an increased amount of EV, PV and HP in the grid. These results can be used to identify future bottlenecks in the grid and design the appropriate measures.
Rhubarb Identification by Using Temperature-Constrained Cascade Correlation Networks

Temperature-constrained cascade correlation networks (TCCCNs) were used to identify powdered rhubarbs based on their near-infrared spectra. Comparative studies were made by using Latin-partitions and leave-one-out cross validation methods. Results showed that multiple networks with single output predicted generally better than single network with multiple outputs, and that the Multi-TCCCN models with Latin-partitions gave slightly better performance than those with leave-one-out cross validation. Better results with TCCCN models were obtained compared with conventional back-propagation neural networks (BP-NNs). The effects of parameters on correct identification and parameter optimizations were discussed in detail. With optimized neural network training parameters, rhubarb powdered samples investigated were classified by a TCCCN model based on their NIR spectra with 100% accuracy.
TROD: Tracking with occlusion handling and drift correction

We present a tracking framework in which we learn a HOG-based object detector in the first video frame and use this detector to localize the object in subsequent frames. We contribute and improve the tracking on the three following points. First, an occlusion-handling algorithm exploits discriminative information from the detector by dividing the object bounding box into patches and comparing each patch to the object model. Second, a drift-correction technique uses descriptive information of the object by calculating the similarity between the object in the previous frame and its shifted versions in the current frame. Third, a stochastic learning algorithm updates the object detector using single object and single background samples for selected frames only. Experiments with benchmark sequences show that the proposed tracker outperforms state-of-the-art methods on several sequences and has the smallest average location error.
Adaptation of large vocabulary recognition system parameters

The authors report on a series of experiments in which the hidden Markov model baseforms and the language model probabilities were updated from spontaneously dictated speech captured during recognition sessions with the IBM Tangora system. The basic technique for baseform modification consisted of constructing new fenonic baseforms for all recognized words. To modify the language model probabilities, a simplified version of a cache language model was implemented. The word error rate across six talkers was 3.7%. Baseform adaptation reduced the average error rate to 3.5%, and using the cache language model reduced the error rate to 3.2%. Combining both techniques further reduced the error rate to 3.1%-a respectable improvement over the original error rate, especially given that the system was speaker-trained prior to adaptation.<<ETX>>
GAS: the Geostationary Atmospheric Sounder

This paper presents the concept and initial breadboarding results of geostationary atmospheric sounder (GAS), which is being developed by Saab Space AB and Omnisys AB, Sweden, and funded by European Space Agency (ESA). GAS utilizes interferometric synthetic aperture radiometry to obtain desired spatial (30 km) and temporal (nowcasting) resolution for measurement of atmospheric temperature and humidity profiles under all weather conditions. These parameters are decisively important to meteorological and climate models at all time scales.
Monolithic integration of diffractive lenses with LED-arrays for board-to-board free space optical interconnect

Since optical interconnections can severely reduce problems associated with electrical interconnect technology (including bandwidth limitations, electromagnetic cross talk, signal delay and EMI aspects), the development of suitable electrooptic components is of crucial importance for implementation of optical interconnects in future computer systems. This paper addresses the design, modeling, fabrication as well as experimental assessment of LED-arrays, with diffractive lenses etched into the rear side of the LED-substrate. The suitability of such optical sources for board-to-board optical interconnections will be demonstrated.
Microfabrication of 3D terahertz circuitry

Advances in micro-fabrication techniques combined with accurate simulation tools has provided the means for the realisation of complex terahertz circuitry. Silicon micro-machining provides the way forward to fabricate accurate rugged structures. Multi-level deep reactive ion etching can be used to replace traditional machining methods achieving smaller feature size, improved surface finish and greater freedom in circuit layout. Photonic Bandgap waveguides enable three dimensional arrangements of active devices antennae and filters, and removes the requirement for metallisation of adjoining surfaces. This paper describes some of the state of the art terahertz circuit design and realisation using these techniques.
Application and evaluation of texture-adaptive skin detection in TV image enhancement

This paper evaluates a case study where a previously reported texture-adaptive skin detection algorithm is applied for TV image enhancement. A color-only skin detector of an existing high-end TV chip is extended with a texture feature, enabling exclusion of skin-colored textured areas. We report the performance in terms of detection result, and in terms of image quality in a cascade of three image enhancement functions. In terms of detection score, at 80% true positive rate, the false positive rate of the texture-adaptive skin detector is 29% lower than that of the color-only skin detector, forming a clear improvement. With respect to its application in enhancement, we assess the enhancement quality by measuring the RMS error of the enhancement output compared to an optimally enhanced image based on ground-truth skin areas. When using the texture-adaptive skin detector, the enhancement RMS error is 44% lower than the RMS error when using the color-only skin detector, thereby confirming the applicability of the proposal. Subjective evaluation indicates that the proposed algorithm is better suitable for mid/high-frequency boosting applications like sharpness enhancement, and less suitable for enhancements that operate on low frequencies like color correction functions 1.
Support Vector Regression and Radial Basis Function Neural Networks Applied to Semi-quantitative Prediction of Rhubarbs

Methods for building near-infrared spectrometry (NIRS) calibration models and for predicting active constituents of rhubarb samples using principal components analysis (PCA) and support vector regression (SVR) were investigated. Principal component analysis was used to reduce the number of spectral variables. Radial basis function neural networks (RBFNNs), ridge regression RBFNNs (RRRBFNNs), and SVR were used to model and predict four classes of active constituents (anthraquinones, anthraquinone glucosides, stilbene glucosides, tannins and tannin derivatives) in rhubarb using the principal component scores of the first-derivative spectra. The results show that the prediction accuracy by SVR is better than the accuracy obtained from the RBFNNs and RRRBFNNs. Therefore, SVR is a promising method for semiquantitative prediction of active constituents in Chinese herbal medicine.
Boosting descriptors condensed from video sequences for place recognition

We investigate the task of efficiently training classifiers to build a robust place recognition system. We advocate an approach which involves densely capturing the facades of buildings and landmarks with video recordings to greedily accumulate as much visual information as possible. Our contributions include (1) a preprocessing step to effectively exploit the temporal continuity intrinsic in the video sequences to dramatically increase training efficiency, (2) training sparse classifiers discriminatively with the resulting data using the AdaBoost principle for place recognition, and (3) methods to speed up recognition using scaled kd-trees and to perform geometric validation on the results. Compared to straightforwardly applying scene recognition methods, our method not only allows a much faster training phase, the resulting classifiers are also more accurate. The sparsity of the classifiers also ensures good potential for recognition at high frame rates. We show extensive experimental results to validate our claims.
Learning Similarity Matching in Multimedia Content-Based Retrieval

Many multimedia content-based retrieval systems allow query formulation with the user setting the relative importance of features (e.g., color, texture, shape, etc.) to mimic the user's perception of similarity. However, the systems do not modify their similarity matching functions, which are defined during the system development. We present a neural network-based learning algorithm for adapting the similarity matching function toward the user's query preference based on his/her relevance feedback. The relevance feedback is given as ranking errors (misranks) between the retrieved and desired lists of multimedia objects. The algorithm is demonstrated for facial image retrieval using the NIST Mugshot Identification Database with encouraging results.
Feature analysis in slit-lamp image for nuclear cataract diagnosis

Nuclear cataract is the most common type of age-related cataract and it is clinically diagnosed using slit-lamp images. Objective measurement of the features in slit-lamp image is investigated using a computerized software system. The correlation between the features and the nuclear cataract grades is analyzed. Experimental results show that intensity of sulcus, color in the lens and nucleus region, intensity of nucleus, and color in posterior reflex region are the key features for grading nuclear cataract. This study of feature analysis can benefit clinical cataract diagnosis and clinical research. The feature analysis can also be utilized to investigate the performance of different graders and be employed in training of new graders.
Propagating Image-Level Part Statistics to Enhance Object Detection

The bag-of-words approach has become increasingly attractive in the fields of object category recognition and scene classification, witnessed by some successful applications [5, 7, 11]. Its basic idea is to quantize an image using visual terms and exploit the image-level statistics for classification. However, the previous work still lacks the capability of modeling the spatial dependency and the correspondence between patches and object parts. Moreover, quantization always deteriorates the descriptive power of the patch feature. This paper proposes the hidden maximum entropy (HME) approach for modeling the object category. Each object is modeled by the parts, each having a Gaussian distribution. The spatial dependency and image-level statistics of parts are modeled through the maximum entropy approach. The model is learned by an EM-IIS (expectation maximum embedded with improved iterative scaling) algorithm. Our experiments on the Caltech 101 dataset show that the relative reduction of equal error rate of 23.5 % and relative improvement of AUC (area under ROC) of 22.0 % are obtained when comparing the HME based system with the ME based baseline system.
A Bayesian approach integrating regional and global features for image semantic learning

In content-based image retrieval, the “semantic gap” between visual image features and user semantics makes it hard to predict abstract image categories from low-level features. We present a hybrid system that integrates global features (G-features) and region features (R-features) for predicting image semantics. As an intermediary between image features and categories, we introduce the notion of mid-level concepts, which enables us to predict an image's category in three steps. First, a G-prediction system uses G-features to predict the probability of each category for an image. Simultaneously, a R-prediction system analyzes R-features to identify the probabilities of mid-level concepts in that image. Finally, our hybrid H-prediction system based on a Bayesian network reconciles the predictions from both R-prediction and G-prediction to produce the final classifications. Results of experimental validations show that this hybrid system outperforms both G-prediction and R-prediction significantly.
Neuro-Retinal Optic Cup Detection in Glaucoma Diagnosis

Glaucoma is the one of the two major causes of blindness, which can be diagnosed through measurement of neuro-retinal optic cup-to-disc ratio (CDR). Automatic calculation of optic cup boundary is challenging due to the interweavement of blood vessels with the surrounding tissues around the cup. A multimodality fusion approach for neuroretinal cup detection improves the accuracy of the boundary estimation. The algorithm’s effectiveness is demonstrated on 71 manually segmented retina fundus images collected from Singapore Eye Research Institute. By comparing our automatic cup height measurement to ground truth, we found that our method accurately detected neuro-retinal cup height for 69 images, achieved 97.2% accuracy. The evaluation was based on a criterion that is more stringent than the clinically acceptable interor intra-observer variability. This further leads to a large clinical evaluation of the algorithm involving 15 thousand patients from Australia and Singapore. Keywordsglaucoma; neuro-retinal optic cup; computer-aideddiagnosis; ellipse fitting; convex hull
An ensemble classifier learning approach to ROC optimization

An ensemble learning framework is proposed to optimize the receiver operating characteristic (ROC) curve corresponding to a given classifier. The proposed ensemble maximal figure-of-merit (E-MFoM) learning framework meets four key requirements desirable for ROC optimization, namely: (1) each classifier in the ensemble can be learned with any specified performance metric for any given classifier design; (2) such a classifier is discriminative in nature and attempts to optimize a particular operating point on the ROC curve of the classifier; (3) an ensemble approximation to the overall behavior of the ROC curve can be established by sampling a set of operating points; and (4) ensemble decision rules can be formulated by grouping these sampled classifiers with a uniform scoring function. We evaluate the proposed framework using 3 testing databases, the Reuters and two UCI sets. Our experimental results clearly show that E-MFoM learning outperforms the state-of-the-art algorithms using Wilcoxon-Mann-Whitney rank statistics