nils-herrmann/clean_scirepeval huggingface dataset:
Rate-Energy Region of SWIPT for MIMO Broadcasting Under Nonlinear Energy Harvesting Model

This paper explores the rate-energy (R-E) region of simultaneous wireless information and power transfer for MIMO broadcasting channel under the nonlinear radio frequency energy harvesting (EH) model. The goal is to characterize the tradeoff between the maximal energy transfer versus information rate. The separated EH and information decoding (ID) receivers and the co-located EH and ID receivers scenarios are considered. For the co-located receivers scenario, both time switching (TS) and power splitting (PS) receiver architectures are investigated. Optimization problems are formulated to derive the boundaries of the R-E region s for the considered systems. As the problems are nonconvex, we first transform them into equivalent ones and derive some semi-closed-form solutions, and then design efficient algorithms to solve them. Numerical results are provided to show the R-E region s of the systems, which provide some interesting insights. It is shown that all practical circuit specifications greatly affect the system R-E region. Compared with the systems under traditional linear EH model, the ones under the nonlinear EH model achieve smaller R-E region s due to the limitations of practical circuit features and also show very different R-E tradeoff behaviors.
Computerized radiographic mass detection. II. Decision support by featured database visualization and modular neural networks

For pt.I see ibid., vol.20, no.4, p.289-301 (2001). Based on the enhanced segmentation of suspicious mass areas, further development of computer-assisted mass detection may be decomposed into three distinctive machine learning tasks: (1) construction of the featured knowledge database; (2) mapping of the classified and/or unclassified data points in the datahase; and (3) development of an intelligent user interface. A decision support system may then be constructed as a complementary machine observer that should enhance the radiologists performance in mass detection, We adopt a mathematical feature extraction procedure to construct the featured knowledge database from all the suspicious mass sites localized by the enhanced segmentation. The optimal mapping of the data points is then obtained by learning the generalized normal mixtures and decision boundaries, where a probabilistic modular neural network (PMNN) is developed to carry out both soft and hard clustering. A visual explanation of the decision making is further invented as a decision support, based on an interactive visualization hierarchy through the probabilistic principal component projections of the knowledge database and the localized optimal displays of the retrieved raw data. A prototype system is developed and pilot tested to demonstrate the applicability of this framework to mammographic mass detection.
Regularized Simultaneous Forward–Backward Greedy Algorithm for Sparse Unmixing of Hyperspectral Data

Sparse unmixing assumes that each observed signature of a hyperspectral image is a linear combination of only a few spectra (endmembers) in an available spectral library. It then estimates the fractional abundances of these endmembers in the scene. The sparse unmixing problem still remains a great difficulty due to the usually high correlation of the spectral library. Under such circumstances, this paper presents a novel algorithm termed as the regularized simultaneous forward-backward greedy algorithm (RSFoBa) for sparse unmixing of hyperspectral data. The RSFoBa has low computational complexity of getting an approximate solution for the l0 problem directly and can exploit the joint sparsity among all the pixels in the hyperspectral data. In addition, the combination of the forward greedy step and the backward greedy step makes the RSFoBa more stable and less likely to be trapped into the local optimum than the conventional greedy algorithms. Furthermore, when updating the solution in each iteration, a regularizer that enforces the spatial-contextual coherence within the hyperspectral image is considered to make the algorithm more effective. We also show that the sublibrary obtained by the RSFoBa can serve as input for any other sparse unmixing algorithms to make them more accurate and time efficient. Experimental results on both synthetic and real data demonstrate the effectiveness of the proposed algorithm.
CASE: Connectivity-Based Skeleton Extraction in Wireless Sensor Networks

Many sensor network applications are tightly cou- pled with the geometric environment where the sensor nodes are deployed. The topological skeleton extraction has shown great impact on the performance of such services as location, routing, and path planning in sensor networks. Nonetheless, current studies focus on using skeleton extraction for various applications in sensor networks. How to achieve a better skeleton extraction has not been thoroughly investigated. There are studies on skeleton extraction from the computer vision community; their centralized algorithms for continuous space, however, is not immediately applicable for the discrete and distributed sensor networks. In this paper we present CASE: a novel Connectivity-bAsed Skeleton Extraction algorithm to compute skeleton graph that is robust to noise, and accurate in preservation of the original topology. In addition, no central- ized operation is required. The skeleton graph is extracted by partitioning the boundary of the sensor network to identify the skeleton points, then generating the skeleton arcs, connecting these arcs, and finally refining the coarse skeleton graph. Our evaluation shows that CASE is able to extract a well-connected skeleton graph in the presence of significant noise and shape variations, and outperforms state-of-the-art algorithms.
A Novel Dual-Sided Fly-By Topology for 1–8 DDR With Optimized Signal Integrity by EBG Design

A novel dual-sided fly-by topology is proposed for one controller to multiple memory systems by choosing interconnected transmission line sections of alternate impedance and length. After suitable big data analysis of automatic simulation, peak distortion analysis for eye height, followed by artificial neural network optimization, the best design guideline can be deduced, which is found to be consistent to the electromagnetic bandgap (EBG) theory. The topology may greatly enhance the eye height by 20%–100% while the total length is reduced by half, compared with the conventional fly-by topology. The general design method, relevant simulation, and analysis from EBG are also provided. Measurement has been performed to validate the new design, demonstrating 53% improvement in eye height over the conventional one.
Detector Ensemble

Component-based detection methods have demonstrated their promise by integrating a set of part-detectors to deal with large appearance variations of the target. However, an essential and critical issue, i.e., how to handle the imperfectness of part-detectors in the integration, is not well addressed in the literature. This paper proposes a detector ensemble model that consists of a set of substructure-detectors, each of which is composed of several part-detectors. Two important issues are studied both in theory and in practice, (1) finding an optimal detector ensemble, and (2) detecting targets based on an ensemble. Based on some theoretical analysis, a new model selection strategy is proposed to learn an optimal detector ensemble that has a minimum number of false positives and satisfies the design requirement on the capacity of tolerating missing parts. In addition, this paper also links ensemble-based detection to the inference in Markov random field, and shows that the target detection can be done by a max-product belief propagation algorithm.
Effect of Cavity Wall Temperature and Opening Ratio on the Natural Convection Heat Loss Characteristics of a Solar Cavity Receiver

The natural convection heat loss characteristics of a solar cavity receiver have been investigated by numerical simulation method. The results show that, the natural convection heat loss, the convection heat transfer coefficient and Nusselt number increase with the increment of opening ratio. However, due to the consideration of physical property variation of air in this study, the natural convection heat loss and the convection heat transfer coefficient increases with the elevation of cavity wall temperature while Nusselt number decreases as cavity wall temperature rises. Moreover, for a receiver with small opening ratio, the cavity wall temperature has extremely subtle effect on the convection heat transfer coefficient and Nusselt number, on the contrary, the cavity wall temperature variation will lead to notable change in the natural convection heat loss characteristics when the opening ratio becomes larger.
Monitoring of focused ultrasound-induced blood-brain barrier opening in non-human primates using transcranial cavitation detection in vivo and the primate skull effect

Focused ultrasound (FUS) with microbubbles (MB) is promising for assisting the delivery of drugs across the blood-brain barrier (BBB). To assess the safety and efficacy, the monitoring using passive cavitation detection (PCD) is critical and yet the reliability of transcranial detection in large animals remained questioned. To study the primate skull effect, the PCD through the in-vitro monkey and human skulls and in the in vivo monkeys during the sonication (FUS frequency: 500 kHz) were investigated, with the use of in-house made lipid-shelled, monodisperse MB (median diameter: 4-5 μm) and a flatband hydrophone served as a passive cavitation detector. In the in vitro experiments, the MB were injected to the channel of the phantom under a degassed skull for sonication (peak negative pressure/PNP: 50-450 kPa, pulse length/PL: 0.2 ms, PRF: 10 Hz, duration: 2 s). A diagnostic B-mode imaging system was also used to monitor the cavitation. In the in vivo study, the PCD was realtime monitored during the sonication for PCD calibration (PNP: 50-700 kPa, PL: 0.2 ms and 10 ms, PRF: 2 Hz, duration: 10 s) and BBB opening (PNP: 200-600 kPa, PL: 10 ms, PRF: 2 Hz, duration: 2 min). The stable cavitation dose using harmonics (SCDh) and ultraharmonics (SCDu) and the inertial cavitation dose (ICD) were quantified. Results showed that the SCDh, SCDu, and ICD were detectable in vitro at 50 kPa and above, and the B-mode imaging showed bubble collapse at 200 kPa and above. The detection thresholds increased with the skulls in place, with the signal reduction of 15.4 dB for the monkey skull and 34.1 dB for the human skull. In the in vivo experiments, the SCDh and ICD was detectable at and above 100 kPa and 250 kPa, respectively, and the SCDu was less reliable due to spontaneous occurrence. The BBB was found to be disrupted in 250-600 kPa without edema, hemorrhage, and physiological changes were found. In conclusion, the SCDh was more detectable and reliable than the SCDu in assessing stable cavitation in vivo, and the inertial cavitation was detected at 250 kPa and may occur at lower pressures.
Problems on acousto-optic Fourier transform

The author investigated the acoustooptic Fourier transform for three and four coherent acoustic beams, recorded the patterns of Bragg-diffracted intensity distributions produced by three and four acoustic beams, respectively, and studied the pattern for the phase difference of pi between three acoustic beams. He extends the concept of the diffraction of a single equivalent slit to that of N equivalent slits, proposes an arranged mode of N equivalent slits in the acoustooptic medium, and obtains expressions for the spacing between equivalent slits.<<ETX>>
Learning Fine-Grained Image Similarity with Deep Ranking

Learning fine-grained image similarity is a challenging task. It needs to capture between-class and within-class image differences. This paper proposes a deep ranking model that employs deep learning techniques to learn similarity metric directly from images. It has higher learning capability than models based on hand-crafted features. A novel multiscale network structure has been developed to describe the images effectively. An efficient triplet sampling algorithm is also proposed to learn the model with distributed asynchronized stochastic gradient. Extensive experiments show that the proposed algorithm outperforms models based on hand-crafted visual features and deep classification models.
Research on the E-Commerce-oriented solution of reengineering and operating of business model of specialized market in China: With the case of SJQ.cn

This paper systematically analyzed specialized market's demands and business models of E-Commerce, made a research on the construction of E-Commerce platform for specialized market, and presented the general system structure. On this basis, a modular and integrate function structure was proposed by structural system design. Finally we used a specific case to provide a feasible solution for the development of E-Commerce platform for specialized market.
A goal programming approach to Group Decision Making with incomplete linguistic preference relations

In order to solve Group Decision Making (GDM) problems where the preference relations are provided by decision makers with incomplete linguistic labels, this paper develops a GDM approach on the basis of multiple objective optimization and goal programming methods. The proposed approach first analyzes the incomplete linguistic preference relation and then builds the corresponding optimization model. Subsequently, a programming model is developed to minimize the inconsistency among the decision makers' opinions. By solving the programming model, the ranking of alternatives or selection of the most desirable alternative can be obtained according to the optimization result. A numerical example with incomplete linguistic preference relations is examined to illustrate and show the applicability of the proposed approach.
Towards a Unified Compositional Model for Visual Pattern Modeling

Compositional models represent visual patterns as hierarchies of meaningful and reusable parts. They are attractive to vision modeling due to their ability to decompose complex patterns into simpler ones and resolve the lowlevel ambiguities in high-level image interpretations. However, current compositional models separate structure and part discovery from parameter estimation, which generally leads to suboptimal learning and fitting of the model. Moreover, the commonly adopted latent structural learning is not scalable for deep architectures. To address these difficult issues for compositional models, this paper quests for a unified framework for compositional pattern modeling, inference and learning. Represented by And-or graphs (AOGs), it jointly models the compositional structure, parts, features, and composition/sub-configuration relationships. We show that the inference algorithm of the proposed framework is equivalent to a feed-forward network. Thus, all the parameters can be learned efficiently via the highlyscalable back-propagation (BP) in an end-to-end fashion. We validate the model via the task of handwritten digit recognition. By visualizing the processes of bottom-up composition and top-down parsing, we show that our model is fully interpretable, being able to learn the hierarchical compositions from visual primitives to visual patterns at increasingly higher levels. We apply this new compositional model to natural scene character recognition and generic object detection. Experimental results have demonstrated its effectiveness.
A Channel Awareness Vehicle Detector

State-of-the-art traffic-flow-detection technology includes the use of a looped vehicle detector, an infrared detector, an image detector, and a microwave detector. However, there are limitations that are associated with each detector. In this paper, we propose a channel awareness vehicle detector that can overcome these limitations. It employs only one pair of transmitter-receiver antennas to simultaneously perform the multilane and multivehicle identifications. By using the characteristics of channel variations, the proposed vehicle detector can determine the vehicle location, speed, and type. Our measurement results demonstrate its capabilities.
A two-phase switching hybrid supply modulator for polar transmitters with 9% efficiency improvement

Emerging polar transmitters for highly efficient and linear power amplifiers (PAs) demand for high-efficiency, high-bandwidth and low-ripple supply modulators. In , a linear regulator is used; however, its efficiency is low at low power levels. A switched-mode power supply (SMPS) is used in ; but the high switching loss due to high switching frequency (necessary for high bandwidth) limits the maximum efficiency to ∼76%. A hybrid amplifier (HA) topology combining both linear amplifier (LA) and switching amplifier (SA) is used in some recent work [3, 4]. In this topology, a high-bandwidth LA replicates the input envelope voltage Vin at its output Vo; while the high-efficiency SA supplies most of the load current within its bandwidth. Yet, the tracking bandwidth is finite in ; and the LA in  needs to supply most of the high-frequency load current due to the exceptionally large inductor (20µH) used for realizing small output ripple; and therefore, limiting the dynamic efficiency. Figure 10.1.1 shows the proposed wideband HA featuring two-phase switching (2PHSW) for reducing ripple and improving static efficiency, and a feedforward bandpass filter (FF-BBF) for enhancing dynamic efficiency.
Robust Inversion-Based 2-DOF Control Design for Output Tracking: Piezoelectric-Actuator Example

In this paper, a novel robust inversion-based 2-DOF control approach for output tracking is proposed. Inversion-based feedforward control techniques have been successfully implemented in various applications. Usually, to account for adverse effects such as dynamics variations and disturbances, the inverse feedforward control is applied by augmenting it with a feedback control. However, such effects have not been directly addressed in existing system-inversion methods, and the integration of the feedback control with the inversion-based feedforward control is performed in an ad hoc manner, which may not lead to an optimal complement of the inversion-based feedforward control with the feedback control. The contribution of this paper is the development of the following: 1) a robust system-inversion approach to directly account for and then minimize the dynamics-uncertainty effect when finding the inversion-based feedforward controller, and 2) a systematic integration (of such a feedforward controller) with a robust feedback controller. The proposed robust inversion method achieves a guaranteed tracking performance of the feedforward control for bounded dynamics uncertainties. Then, the quantified bound of the feedforward control tracking error is utilized in designing an H infin robust feedback controller to complement the feedforward control. Based on the concept of Bode's integral, it is shown that the feedback bandwidth can be improved from that obtained by using feedback alone. We illustrated the proposed approach by implementing it in experiments on a piezotube actuator of an atomic force microscope for precision positioning.
Gabor-based Orthogonal Locality Sensitive Discriminant Analysis for face recognition

An innovative Gabor-based Orthogonal Locality Sensitive Discriminant Analysis for face recognition is presented in this paper. This algorithm is based on a combination of Gabor wavelets representation of face images and a new Orthogonal Locality Sensitive Discriminant Analysis for face recognition. In this paper, a Gabor filter is first designed to extract the features from the whole face images, and then a new Orthogonal Locality Sensitive Discriminant Analysis, which is proposed to preserve the local geometrical structure by computing the mutually orthogonal basis functions iteratively, is used to subject these feature vectors onto locality subspace projection. Experiments based on the ORL face database demonstrate the effectiveness and efficiency of the new method. Results show that our new algorithm is robust to changes in illumination and facial expressions and poses. And it outperforms the other popular approaches reported in the literature and achieves a much higher accurate recognition rate.
Resonant frequency of microstrip antenna with multi-dielectric superstrates

In aerospace and other applications, microstrip antennas may be covered with multi-dielectric superstrates for protection against heat and environment hazards. During flight or in severe weather conditions, an additional superstrate may be naturally formed on its cover. In the design of such structures, the accurate determination of its resonant frequency is important due to the inherent narrow bandwidth of microstrip antennas. However, works reported are mostly limited in the case with one dielectric superstrate. An accurate computation for the resonant frequency of a microstrip antenna with multi-dieiectric superstrates is presented by means of the spectral domain immittance (SDI) approach and by improvements in numerical evaluations of integrals. Some useful conclusions have also been achieved.<<ETX>>
Visualization and analysis of email networks

This paper presents various methods for visualization and analysis of email networks; visualization on the surface of a sphere to reveal communication patterns between different groups, a hierarchical drawing displaying the centrality analysis of nodes to emphasize important nodes, a 2.5D visualization for temporal email networks to analyze the evolution of email relationships changing over time, and an ambient display for finding social circles derived from the email network. Each method was evaluated with various data sets from a research organization. We also extended our method for visual analysis of an email virus network.
Iterative Control Approach to Compensate for Both the Hysteresis and the Dynamics Effects of Piezo Actuators

In this brief, the compensation for both the nonlinear hysteresis and the vibrational dynamics effects of piezo actuators is studied. Piezo actuators are the enabling device in many applications such as atomic force microscopy (AFM) to provide nano- to atomic-levels precision positioning. During high-speed, large-range positioning, however, large positioning errors can be generated due to the combined hysteresis and dynamics effects of piezo actuators, making it challenging to achieve precision positioning. The main contribution of this brief is the use of an inversion-based iterative control (IIC) technique to compensate for both the hysteresis and vibrational dynamics effects of piezo actuators. The convergence of the IIC algorithm is investigated by capturing the input-output behavior of piezo actuators with a cascade model consisting of a rate-independent hysteresis at the input followed by the dynamics part of the system. The size of the hysteresis and the vibrational dynamics variations that can be compensated for (by using the IIC method) is quantified. The IIC approach is illustrated through experiments on a piezotube actuator used for positioning on an AFM system. Experimental results show that high-speed, large-range precision positioning can be achieved by using the proposed IIC technique. Furthermore, the proposed IIC algorithm is also applied to experimentally validate the cascade model and the rate-independence of the hysteresis effect of the piezo actuator.