nils-herrmann/clean_scirepeval huggingface dataset:
Turbo detection with recursive EM channel estimation

A novel semi-blind joint channel parameters and data estimator is proposed in this paper. Recursive expectation-maximization (REM) algorithm is naturally embedded in the framework of turbo detector to perform iterative estimation and detection. The REM recursive times needed for converging is much decreased in such framework and it is applicable to time-variant/invariant channel. Simulation results show that the proposed detector works well with rough initial channel estimate obtained with very short training sequence, which improves the spectrum efficiency.
Construction, Testing and Operation of a 1 MJ HTS Magnet at a 10.5 kV Superconducting Power Substation

An HTS magnet was designed for the 1 MJ/0.5 MVA SMES with Bi2223/Ag tapes. According to the design, it consists of 44 double pancakes with an inductance of 6.28 H, and the rated operating current is 565 A, the maximum magnetic field is up to 5.72 T. In this paper, the insulation of the tapes and the magnet, the fabrication and tests of the 44 double pancakes, the joints among double pancakes and the assemblage of the magnet are described in detail. And then, the fundamental performances testing of the magnet were carried out at liquid nitrogen and helium, respectively. With zero helium vaporization, the 1 MJ HTS magnet was dip in liquid helium and cooled with four G-M cryocoolers. After the whole 1 MJ/0.5 MVA SMES were field installed and tested, it had been put into operation since Feb. 16 of 2011 at a 10.5 kV superconducting power substation locating in Baiyin, Gansu Province of China.
Electromigration immortality of purely intermetallic micro -bump for 3D integration

The progress of three-dimensional integrated circuit (3D IC) micro-bump joining technology has led to an increased volume fraction of intermetallics (IMC) in the post reflow joints, to an extent that a solder micro-bump may consist almost entirely of IMCs. Therefore, the current carrying capability and electromigration (EM) life time of the purely IMC micro-joint needs to be understood as functions of stressing conditions and degradation mechanisms. Superior EM performance and robustness of IMC joints is demonstrated with no resistance fluctuation under ultra-high stressing condition for over 9000 hrs while solder micro-bumps led to an open failure within 500 hrs. At least an order of magnitude greater current carrying capability of IMC micro-joint compared with solder micro-joint is observed experimentally. The observed degradation mechanism is void formation within Al trace rather than damage inside IMC joint. IMC joint is not the EM reliability bottle neck of the test circuit.
A research for application of HTSC to high magnetic field at 4.2 K

Some high-temperature oxide superconductors (HTSCs) exhibit their best electromagnetic transport property at 4.2 K, which can be applied to produce high magnetic fields. The adiabatic stability of Bi-2212 at 4.2 K is analyzed. A configuration of the composite Bi-2212 conductors which can be used in a high magnetic field magnet is suggested. The stability performance of bi-2212 silver-sheathed tape was studied experimentally. By using composite conductors, a 10 T HTSC coil which can be inserted into a NbTi-Nb/sub 3/Sn magnet of 15 T was designed, and the possibility of the application of HTSC to 30-40 T superconducting magnets is discussed.<<ETX>>
Quantitative robustness analysis of discrete-time singularly perturbed systems

The robust stability of feedback designs for discrete-time, singularly perturbed systems is discussed. The developed stability measures complement the quantitative information for unmodeled fast modes that cannot be obtained by the usual qualitative analyses. The approach is able to provide an explicit, computable bound on the magnitude of the neglected dynamics that could be tolerated by the nominal stabilizing control design.<<ETX>>
A study on performance of three levels quality management system and the satisfaction of implementing construction quality of public construction in Taiwan

It has been nearly a decade for the quality management of construction applied by Public construction in the country, the virtue or vice for implementation quality of public construction is one of the important indicators for national economic development and overall construction, the impact on the quality of national life is very deep. This study mainly aggregated the evolution of ISO 9000 quality control system, the difference between the practice of implementing management of construction quality by many countries and three-level quality control of our country, it is induced by the national power in our country and develop our three-level quality control system and audit mechanism based on IOS system to develop three-level quality control system and implement the works by legislation, our study also explored its enhancement and relevance with engineering quality of public construction that are intervened by such system and national power. And focuses on the analysis and discussion of customer satisfaction from the building construction quality of public construction delivery method. First of all, five factor facets that could impact satisfaction were established, from which, 37 evaluation factors of quality satisfaction of construction engineering are created. Besides, the construction engineers are taken as target of this study, 210 questionnaires were issued by a survey project, and 188 effective questionnaires were collected, and they had been arranged the satisfaction degree of implementation of each construction process of engineering project of public construction. Finally, the average t test is applied to verify hypothesis. The result shows that five facets would affect the satisfaction of owner according to the analysis of this study, and the implementation quality of construction engineering of domestic public construction failed to achieve the expectation of owner, the items are: "Completes the work on times, "Immediate treatment for deviation and complains during work", "Engineering personnel are capable to complete work-loading in peak time" "Fluent contact and communication channel", "Excellent implementation experience and skills.
Flight path planning for mini rotor UAVs

Optimized path planning algorithm design is an important function of unmanned aerial vehicles (UAVs). Since the path obtained by the (rapidly exploring random tree) RRT is not necessarily optimal, excessive waypoints need to be refined. Heading of the quadrotor UAV is considered here as a moving cost of the improved A-star algorithm. Since the secondary path generated by the improved A-star algorithm is piecewise linear and may exhibit rugged curvature, it's necessary to refine the flight path making it to be applicable for quadrotor UAV flight. The proposed path planning method has been well verified via a variety of scenarios.
Regulating nonlinear properties of lipid-coated microbubbles using polymer network scaffolds for ultrasound drug delivery applications

Stable and inertial cavitation of microbubbles can enhance drug delivery efficiency, but inertial cavitation poses a very high risk of mechanical damage in vivo. Stiff polymershelled microbubbles may exhibit weaker inertial cavitation but also weaker stable cavitation. We developed lipid-polymer composite microbubbles (LP-MBs) that exhibit lower inertial cavitation activity without compromising their performance to exhibit stable cavitation. The regulation of these nonlinear properties, making them more preferable for ultrasound drug delivery. Polymer materials including monomers (butyl methacrylate) and crosslinkers (ethylene glycol dimethacrylate) were incorporated into the inner shell of C3F8 MBs stablized by DSPC and DSPEPEG2000, followed by a polymerization process for 2 h under increased temperature. LP-MBs had a mean size of ~1.8 μm. The integrity of the polymer network was observed under negative-stain TEM even after removal of the lipids. Although no significant differences in the resonance frequencies of 10-15 MHz and stable cavitation activity were observed due to the polymerization process, the inertial cavitation dose was decreased by up to 30% under sonication above 1000 kPa. The polymer network may have served as a flexible scaffold to stabilize the lipids and impede inertial collapse of LP-MBs, rendering them safer for in vivo use. Research is underway to validate the in vivo feasibility (e.g., blood brain barrier disruption or gene delivery).
Pulsed high-intensity focused ultrasound enhances the relative permeability of the blood–tumor barrier in a glioma-bearing rat model

The use of pulsed high-intensity focused ultrasound (HIFU) with an ultrasound contrast agent (UCA) has been shown to disrupt the blood-brain barrier (BBB) noninvasively and reversibly in the targeted regions. This study evaluated the relative permeability of the blood-tumor barrier (BTB) after sonication by pulsed HIFU. Entry into the brain of chemotherapeutic agents is impeded by the BBB even though the permeability of this barrier may be partially reduced in the presence of a brain tumor. F98 glioma-bearing rats were injected intravenously with Evans blue (EB) with or without BTB disruption induced by pulsed HIFU. Sonication was applied at an ultrasound frequency of 1 MHz with a 5% duty cycle, and a repetition frequency of 1 Hz. The accumulation of EB in brain tumor and the tumor-to-contralateral brain ratio of EB were highest after pulsed HIFU exposure. Sonication followed by EB injection showed a tumor-to-contralateral brain ratio in the target tumors which was about 2 times that of the control tumors. This research demonstrates that pulsed HIFU enhances the relative permeability of the BTB in glioma- bearing rats. The results of this pilot study support the idea that further evaluation of other treatment strategies, such as HIFU exposure in addition to combined chemotherapy or repeated pulsed HIFU exposure to increase delivery of drugs into brain tumors, might be useful.
Scribble-a-Secret: Similarity-based password authentication using sketches

This paper presents a sketch-based password authentication system called Scribble-a-Secret as a graphical password scheme in which free-form drawings are used as a means to authenticate users. Unlike existing schemes, this approach requires no input of graphical passwords in particular sequences of strokes. Moreover, the system allows for a modicum of variation when users recreate their passwords. Our technique uses edge orientations extracted from sketch images to discern one user from another. Our experiments show that our recognition technique is robust for recognizing sketches while differentiating from others with both a false acceptance rate and false rejection rate of less than 1%.
SOLD: Sub-optimal low-rank decomposition for efficient video segmentation

This paper investigates how to perform robust and efficient unsupervised video segmentation while suppressing the effects of data noises and/or corruptions. We propose a general algorithm, called Sub-Optimal Low-rank Decomposition (SOLD), which pursues the low-rank representation for video segmentation. Given the supervoxels affinity matrix of an observed video sequence, SOLD seeks a sub-optimal solution by making the matrix rank explicitly determined. In particular, the affinity matrix with the rank fixed can be decomposed into two sub-matrices of low rank, and then we iteratively optimize them with closed-form solutions. Moreover, we incorporate a discriminative replication prior into our framework based on the obervation that small-size video patterns tend to recur frequently within the same object. The video can be segmented into several spatio-temporal regions by applying the Normalized-Cut (NCut) algorithm with the solved low-rank representation. To process the streaming videos, we apply our algorithm sequentially over a batch of frames over time, in which we also develop several temporal consistent constraints improving the robustness. Extensive experiments on the public benchmarks demonstrate superior performance of our framework over other state-of-the-art approaches.
Iris Recognition: A Method to Increase the Robustness to Noisy Imaging Environments through the Selection of the Higher Discriminating Features

Continuous efforts have been made in searching for robust and effective iris coding methods, since Daugman's pioneering work on iris recognition was published. However, due to lack of robustness, the error rates of iris recognition systems significantly increase when images contain large portions of noise (reflections and iris obstructions), resultant from less constrained imaging conditions. Current iris encoding and matching proposals do not take into account the specific lighting conditions of the imaging environment, decreasing their adaptability to such dynamics conditions. In this paper we propose a method that, through a learning stage, takes into account the typical noisy regions propitiated by the imaging environment to select the higher discriminating features. Our experiments were performed on two well known iris image databases (CASIA and UBIRIS) and show a significant decrease of the error rates in the recognition of iris images corrupted by noise.
An Effective Wireless Charger Deployment Method for Complete Coverage in Wireless Chargeable Sensor Networks

Wireless charger deployment is an important problem in Wireless Chargeable Sensor Networks (WCSNs). In this research, we focus on how to deploy the chargers effectively to maximize the lifetime of the network. This research proposed a method that can find the minimal number of chargers to cover the sensor nodes in the WCSNs and this problem is also called the minimum dominating set problem. The charging power of the charger is taken into considerations in designing the charger deployment. Unlike previous studies, the deployment of a mixture of directional and omnidirectional chargers is considered. The simulations show that the proposed MTCD (Mixed-Type Charger Deployment) performs better than random deployment and less chargers are used in WCSNs.
Interpretable Structure-Evolving LSTM

This paper develops a general framework for learning interpretable data representation via Long Short-Term Memory (LSTM) recurrent neural networks over hierarchal graph structures. Instead of learning LSTM models over the pre-fixed structures, we propose to further learn the intermediate interpretable multi-level graph structures in a progressive and stochastic way from data during the LSTM network optimization. We thus call this model the structure-evolving LSTM. In particular, starting with an initial element-level graph representation where each node is a small data element, the structure-evolving LSTM gradually evolves the multi-level graph representations by stochastically merging the graph nodes with high compatibilities along the stacked LSTM layers. In each LSTM layer, we estimate the compatibility of two connected nodes from their corresponding LSTM gate outputs, which is used to generate a merging probability. The candidate graph structures are accordingly generated where the nodes are grouped into cliques with their merging probabilities. We then produce the new graph structure with a Metropolis-Hasting algorithm, which alleviates the risk of getting stuck in local optimums by stochastic sampling with an acceptance probability. Once a graph structure is accepted, a higher-level graph is then constructed by taking the partitioned cliques as its nodes. During the evolving process, representation becomes more abstracted in higher-levels where redundant information is filtered out, allowing more efficient propagation of long-range data dependencies. We evaluate the effectiveness of structure-evolving LSTM in the application of semantic object parsing and demonstrate its advantage over state-of-the-art LSTM models on standard benchmarks.
Enhanced Output Power of GaN-Based LEDs With Nano-Patterned Sapphire Substrates

GaN-based light-emitting diodes (LEDs) with an emitting wavelength of 450 nm were grown on nano-patterned sapphire substrates (NPSS) fabricated by nanosphere lithography. The crystalline quality of the epitaxial film could be improved by using the NPSS technique. The output power of LED grown on NPSS was 1.3 and 1.11 times higher than those of LEDs grown on conventional and patterned sapphire substrates at the injection current of 20 mA, respectively. The enhancement in output power could be contributed to the efficiently scattering by NPSS. But some voids formed at the GaN/NPSS interface cause a thermal dissipation problem of NPSS LED operated at high injection current.
Egocentric activity recognition by leveraging multiple mid-level representations

Existing approaches for egocentric activity recognition mainly rely on a single modality (e.g., detecting interacting objects) to infer the activity category. However, due to the inconsistency between camera angle and subject's visual field, important objects may be partially occluded or missing in the video frames. Moreover, where the objects are and how we interact with the objects are usually ignored in prior works. To resolve these difficulties, we propose multiple mid-level representations (e.g., objects manipulated by a user, background context, and motion patterns of hands) to compensate the insufficiency of a single modality, and jointly consider what, where, and how a subject is interacting with. To evaluate the method, we introduce a new and challenging egocentric activity dataset (ADL+) that contains video and wrist-worn accelerometer data of people performing daily-life activities. Our approach significantly outperforms the state-of-the-art method on the public ADL dataset (i.e., 36.8% to 46.7%) and our ADL+ dataset (i.e., 32.1 % to 60.0%) in terms of classification accuracy. In addition, we also conduct a series of analyses to explore relative merits of each modality to egocentric activity recognition.
Recognizing Focal Liver Lesions in CEUS With Dynamically Trained Latent Structured Models

This work investigates how to automatically classify Focal Liver Lesions (FLLs) into three specific benign or malignant types in Contrast-Enhanced Ultrasound (CEUS) videos, and aims at providing a computational framework to assist clinicians in FLL diagnosis. The main challenge for this task is that FLLs in CEUS videos often show diverse enhancement patterns at different temporal phases. To handle these diverse patterns, we propose a novel structured model, which detects a number of discriminative Regions of Interest (ROIs) for the FLL and recognize the FLL based on these ROIs. Our model incorporates an ensemble of local classifiers in the attempt to identify different enhancement patterns of ROIs, and in particular, we make the model reconfigurable by introducing switch variables to adaptively select appropriate classifiers during inference. We formulate the model learning as a non-convex optimization problem, and present a principled optimization method to solve it in a dynamic manner: the latent structures (e.g. the selections of local classifiers, and the sizes and locations of ROIs) are iteratively determined along with the parameter learning. Given the updated model parameters in each step, the data-driven inference is also proposed to efficiently determine the latent structures by using the sequential pruning and dynamic programming method. In the experiments, we demonstrate superior performances over the state-of-the-art approaches. We also release hundreds of CEUS FLLs videos used to quantitatively evaluate this work, which to the best of our knowledge forms the largest dataset in the literature. Please find more information at “http://vision.sysu.edu.cn/projects/fllrecog/”.
System design and performance evaluation for power domain non-orthogonal multiple access

To meet the 5G requirements on spectral efficiency and connection density, non-orthogonal multiple access (NMA) is becoming an important candidate technology. This paper focuses on power-domain NMA (PNMA) due to the relatively low design complexity and standardization impact. In PNMA, the data of more than one user equipment (UE) are superposed with different transmission power setting at the transmitter, and are separated by utilizing interference cancellation at the receiver. The theoretical performance of PNMA has been shown in some previous research. In this paper, we gives our proposals on PNMA practical system design aspects including base station (BS) scheduling, UE receiver implementation and network signaling, aiming to seek for good trade-off between overall performance and complexity. System-level simulations validate that compared to orthogonal multiple access (OMA), PNMA with our proposed scheduling algorithm can bring 14.16% and 23.88% gain in cell average spectral efficiency (SE) and cell edge SE respectively, as well as improve the number of connected UEs by 55%.
Individual identification based on chaotic electrocardiogram signals during muscular exercise

An electrocardiogram (ECG) records changes in the electric potential of cardiac cells using a noninvasive method. Previous studies have shown that each person's cardiac signal possesses unique characteristics. Thus, researchers have attempted to use ECG signals for personal identification. However, most studies verify results using ECG signals taken from databases which are obtained from subjects under the condition of rest. Therefore, the extraction and analysis of a subject's ECG typically occurs in the resting state. This study presents experiments that involve recording ECG information after the heart rate of the subjects was increased through exercise. This study adopts the root mean square value, nonlinear Lyapunov exponent, and correlation dimension to analyse ECG data, and uses a support vector machine (SVM) to classify and identify the best combination and the most appropriate kernel function of a SVM. Results show that the successful recognition rate exceeds 80% when using the nonlinear SVM with a polynomial kernel function. This study confirms the existence of unique ECG features in each person. Even in the condition of exercise, chaotic theory can be used to extract specific biological characteristics, confirming the feasibility of using ECG signals for biometric verification.
Development of HTS AC Power Transmission Cables

Along with the rapid growth of the national economy in China, the electric utility is faced with an ever rising demand for electricity and the problem to solve large capacity power transmission. Under the support of the High Technology Research & Development Program of China, a 1.5 kA/10.5 kV 10-m long 3-phases HTS transmission power cable and a 75-m long, 3-phase, 10.5 kV/1.5 kA HTS power cable have been developed successfully by the Institute of Electrical Engineering, Chinese Academy of Sciences. The 75-m long HTS power cable operates now at a local live distribution grid of 6.6 kV to supply electric power for Changtong's factory in Baiyin, Gansu Province. The cable operates stably and reliably over 7000 hours since December 2004.
Learning-based heart rate detection from remote photoplethysmography features

Remote photoplethysmography (rPPG) enables measuring heart rate from recorded skin color variations with consumer cameras. Recent research has aimed to improve the signal strength of color variations caused by heart beat by using independent component analysis (ICA) technique or analyzing chrominance-based model. In this paper, we argue for treating this emerging problem in a novel aspect - proposing a learning-based framework to accommodate multiple and temporal feature and yielding significant and robust improvement. Using support vector regression (SVR) on published chrominance-based feature improves the root mean square error (RMSE) from 22.7 to 7.31 as well as correlation coefficient (CC) from 0.30 to 0.77. With proposed novel multiple feature fusion and multiple segment fusion techniques, we achieved the best estimation result with RMSE 5.48 and CC 0.88. Meanwhile, the proposed framework can be extended to other promising features.