nils-herrmann/clean_scirepeval huggingface dataset:
A high linearity active-RC BPF with 70MHz center frequency and 22MHz bandwidth in 65nm CMOS

A 5th-order Butterworth active-RC band-pass filter (BPF) in 65nm CMOS is presented. By utilizing the integrator frequency compensation technique (IFC), the power consumption can be significantly saved. The embedded operational amplifier (op-amp) achieves high Gain-bandwidth product (GBW) by using the Feed-forward compensation (FFC) and anti-pole-splitting techniques. The simulations show that the presented filter could support 22MHz bandwidth, with a center frequency of 70MHz. It achieves a maximum in-band IIP3 of +32dBm over various process corners, with a power consumption of 48.4 mW.
Locality Versus Globality: Query-Driven Localized Linear Models for Facial Image Computing

Conventional subspace learning or recent feature extraction methods consider globality as the key criterion to design discriminative algorithms for image classification. We demonstrate in this paper that applying the local manner in sample space, feature space, and learning space via linear subspace learning can sufficiently boost the discriminating power, as measured by discriminating power coefficient (DPC). The proposed solution achieves good classification accuracy gains and shows computationally efficient. Particularly, we approximate the global nonlinearity through a multimodal localized piecewise subspace learning framework, in which three locality criteria can work individually or jointly for any new subspace learning algorithm design. It turns out that most existing subspace learning methods can be unified in such a common framework embodying either the global or local learning manner. On the other hand, we address the problem of numerical difficulty in the large-size pattern classification case, where many local variations cannot be adequately handled by a single global model. By localizing the modeling, the classification error rate estimation is also localized and thus it appears to be more robust and flexible for the model selection among different model candidates. As a new algorithm design based on the proposed framework, the query-driven locally adaptive (QDLA) mixture-of-experts model for robust face recognition and head pose estimation is presented. Experiments demonstrate the local approach to be effective, robust, and fast for large size, multiclass, and multivariance data sets.
Mining actionlet ensemble for action recognition with depth cameras

Human action recognition is an important yet challenging task. The recently developed commodity depth sensors open up new possibilities of dealing with this problem but also present some unique challenges. The depth maps captured by the depth cameras are very noisy and the 3D positions of the tracked joints may be completely wrong if serious occlusions occur, which increases the intra-class variations in the actions. In this paper, an actionlet ensemble model is learnt to represent each action and to capture the intra-class variance. In addition, novel features that are suitable for depth data are proposed. They are robust to noise, invariant to translational and temporal misalignments, and capable of characterizing both the human motion and the human-object interactions. The proposed approach is evaluated on two challenging action recognition datasets captured by commodity depth cameras, and another dataset captured by a MoCap system. The experimental evaluations show that the proposed approach achieves superior performance to the state of the art algorithms.
Time-efficient broadcasting in cognitive radio networks

In this paper, we study the delay minimization problem for wireless data broadcasting in cognitive radio networks (CRN). In CRN, each secondary user (SU) has a set of available channels to communicate with each other but there may not be a common available channel set among all the SUs. Therefore, a simple way to shorten the broadcasting delay among them is to select a minimal set of available channels which cover all SUs in the network. However, this scheme may incur a fairness problem in resource sharing among SUs and also lower the system resource utilization. To tackle this problem, we assign channels to SUs for data broadcasting service in downlink sub-frames by jointly considering broadcast delay minimization and fair resource sharing. We formulate this problem as a nonlinear integer programming problem, and show that it is NP-hard by applying a reduction from the 3-dimensional matching problem. We then propose an enumerative algorithm to find an optimal solution and design a greedy-based polynomial-time heuristic algorithm to reduce the computational complexity. The simulation results show that our enumerative algorithm not only effectively reduces the broadcasting delay but also ensures the fairness among SUs in resource sharing. We also show that the performance gap between our enumerative algorithm and our heuristic algorithm is small.
An experimental study on the apparent stiffness of size-isolated microbubbles used for blood-brain barrier opening applications

In order to fully assess contrast-enhanced acoustic bioeffects in diagnostic and therapeutic applications, the mechanical properties of microbubbles need to be taken into account. In the current study, direct measurements of the microbubble apparent stiffness were performed using atomic force microscopy by applying nanoscale compressions (up to 25 nN/s) on size-isolated, phospholipid-coated microbubbles (diameters between 4-6 and 6-8 μm). The apparent stiffness was found to lie between 4 and 22 mN/m and to decrease exponentially with microbubble size within the diameter range investigated. No cantilever spring constant effect was found on the measured stiffness. The Young's modulus of the sizeisolated microbubbles used in our study ranged between 0.4 and 2 MPa. Microstructures on the surface of the microbubbles were found to influence the overall microbubble elasticity. Our results indicated that more detailed theoretical models are needed to account for the size-dependent microbubble mechanical properties in order to accurately predict their acoustic behavior. The findings provided useful insights to control cavitation-induced drug and gene delivery and could be used as part of the framework in studies on the shear stresses induced on the blood vessel walls by the oscillating microbubbles.
Design and similarity evaluation on humanoid facial expression

This paper introduces the similarity evaluation on humanoid facial expression. For the purpose of understanding easily, firstly, the process of designing humanoid robot's facial expressions is explored in brief. Then, a similarity function is proposed to evaluate the similarity of the robot facial motion compared with the human being quantitatively based on both the kinematics and dynamics factors, also including how to select the key value of parameter in similarity function. Finally, the effectiveness of the similarity function is confirmed by an experiment of facial expressions based on BHR-4 robot.
Case-based teaching for the course of Visual Basic Program Design

Program Design is the first course to understand the kernel theory and the programming thought for the freshmen. If they can study well, they will have a good foundation to study later, keep interest to the computer programming. Otherwise they maybe lose interesting to study programming even be afraid of it. In order to improve the effective of teaching, keep students more interest to this course, a new method of case-based teaching is introduced in this paper. By analyzing the process of case teaching, the article talks about some problems in teaching organization and practice on computer, supplies some methods of improvement. The idea and characteristics of the case teaching method are introduced, the feasibility of the case teaching in Visual Basic programming is analyzed, and the organizing and implementing process of the case teaching activity is also demonstrated in virtue of a specific ATM teaching case. Guided with appropriate teaching theory, the teaching activity can be more intentional, and the expectant teaching effect can be gained more easily.
Heteroscedastic Max–Min Distance Analysis for Dimensionality Reduction

Max–min distance analysis (MMDA) performs dimensionality reduction by maximizing the minimum pairwise distance between classes in the latent subspace under the homoscedastic assumption, which can address the class separation problem caused by the Fisher criterion but is incapable of tackling heteroscedastic data properly. In this paper, we propose two heteroscedastic MMDA (HMMDA) methods to employ the differences of class covariances. Whitened HMMDA extends MMDA by utilizing the Chernoff distance as the separability measure between classes in the whitened space. Orthogonal HMMDA (OHMMDA) incorporates the maximization of the minimal pairwise Chernoff distance and the minimization of class compactness into a trace quotient formulation with an orthogonal constraint of the transformation, which can be solved by bisection search. Two variants of OHMMDA further encode the margin information by using only neighboring samples to construct the intra-class and inter-class scatters. Experiments on several UCI datasets and two face databases demonstrate the effectiveness of the HMMDA methods.
A low dropout voltage regulator with programmable output

In this paper, we present a low dropout voltage regulator (LDO) which can be programmed to generate four output voltages (3.3 V, 2.5 V, 1.8 V, and 0 V) by the external control signals. Between the error amplifier and the power transistor, we place a simple buffer so that the power supply rejection (PSR) of LDO can be improved. The design specification of the maximum load current is 100mA. The proposed LDO is designed by the 0.35µm CMOS 2P4M process technology and simulated by HSPICE. Simulation results show that the maximum transient-output variation (overshoot voltage) is about 50 mV with full-load step change of 100 mA and the PSR is larger than 60dB for different output voltages. Therefore, the proposed LDO can be applied for power management systems in system-on-chip (SOC), where the output voltage can be changed by control logic to optimize the power consumption of supplied device.
Mining Compositional Features From GPS and Visual Cues for Event Recognition in Photo Collections

As digital cameras with Global Positioning System (GPS) capability become available and people geotag their photos using other means, it is of great interest to annotate semantic events (e.g., hiking, skiing, party) characterized by a collection of geotagged photos with timestamps and GPS information at the capture. We address this emerging event classification problem by mining informative features derived from image contents and spatio-temporal traces of GPS coordinates that characterize the underlying movement patterns of various event types, both based on the entire collection as opposed to individual photos. Considering that events are better described by the co-occurrence of objects and scenes, we bundle primitive features such as color and texture histograms or GPS features to form the discriminative compositional feature. A data mining method is proposed to efficiently discover discriminative compositional features of small classification errors. A theoretical analysis is also presented to guide the selection of the data mining parameters. Upon compositional feature mining, we perform the multiclass AdaBoost to further integrate the mined compositional features. Finally, the GPS and visual modalities are united through a confidence-based fusion. Based on a dataset of more than 3000 geotagged images, experimental results have shown the synergy of all of the components in our proposed approach to event classification.
High performance 110 nm InGaAs/InP DHBTs in dry-etched in-situ refractory emitter contact technology

We report a 110 nm InP/In<inf>0.53</inf>Ga<inf>0.47</inf>As/InP double heterojunction bipolar transistor (DHBT) demonstrating a simultaneous f<inf>t</inf>/f<inf>max</inf> of 465/660 GHz and operating at power densities in excess of 50 mW/µm<sup>2</sup>. To our knowledge this is the smallest junction width reported for a III–V DHBT. The narrow 110 nm emitter junction permits the devices to be biased simultaneously at high voltages and high current densities (J<inf>e</inf>) with peak RF performance at 41 mW/µm<sup>2</sup> (J<inf>e</inf> = 23.6 mA/µm<sup>2</sup>, V<inf>ce</inf> = 1.75 V). Devices incorporate low contact resistance, refractory, in-situ Mo emitter contact to a highly doped, regrown InGaAs cap. A low stress, sputter deposited, refractory, dry-etched W/Ti<inf>0.1</inf>W<inf>0.9</inf> emitter metal process was developed demonstrating both high emitter yield and scalability to sub-100 nm junctions. Previously reported dry etch processes involving Ti/Ti<inf>0.1</inf>W<inf>0.9</inf> metals could not be scaled below 180 nm junction widths due to high metal stress resulting in very low emitter yield [1, 2]. The emitter metal contacts reported here are 100 nm wide and the emitter-base junction width is 110 nm. On-wafer Through-Reflect-Line (TRL) calibration structures were used to measure the RF performance of devices from 140 – 180 GHz.
Mining And-Or Graphs for Graph Matching and Object Discovery

This paper reformulates the theory of graph mining on the technical basis of graph matching, and extends its scope of applications to computer vision. Given a set of attributed relational graphs (ARGs), we propose to use a hierarchical And-Or Graph (AoG) to model the pattern of maximal-size common subgraphs embedded in the ARGs, and we develop a general method to mine the AoG model from the unlabeled ARGs. This method provides a general solution to the problem of mining hierarchical models from unannotated visual data without exhaustive search of objects. We apply our method to RGB/RGB-D images and videos to demonstrate its generality and the wide range of applicability. The code will be available at https://sites.google.com/site/quanshizhang/mining-and-or-graphs.
CACH: Cycle-Adjustable Channel hopping for control channel establishment in cognitive radio networks

Establishing control channels in a cognitive radio network (CRN) is an important and challenging problem. To cope with the problem of control channel saturation and the problem of channel blocking by primary users, channel hopping (CH) schemes are commonly used in the literature for control channel establishment in CRNs. There are three metrics that are widely used for evaluating the performance of CH schemes: (i) degree of overlapping (the number of distinct rendezvous channels), (ii) worst case time-to-rendezvous (TTR), and (iii) system load. In this paper, we focus on the symmetric and synchronous setting and propose a novel Cycle-Adjustable Channel Hopping (CACH) scheme that outperforms several existing CH schemes, including SSCH and QCH, in terms of the three metrics. The key idea of CACH is to create an additional layer of logical channels on the top of physical channels so that the cycle of channel hopping sequences can be adjusted to optimize system performance. The mathematic tools for our scheme are based on the operations in Galois fields that are more general than the prime number modular arithmetic used in SSCH. We show that CACH is much more general than SSCH and it can achieve the maximum degree of overlapping while allowing the worst case TTR to be adjustable. It is also much better than QCH in terms of reducing system load while keeping the same degree of overlapping and the same worst case TTR. Our simulation results show that CACH outperforms several existing schemes in many other aspects, including throughput, and robustness to the disturbance of PUs.
Development of a RF-Driven Neutron Generator for Associated Particle Imaging

We present recent work in the development of a compact, radio frequency (RF) D-T neutron generator with a maximum neutron yield of 108 n/s for the application of associated particle imaging (API) used in explosive and contraband detection. API makes use of alpha particles produced in conjunction with 14 MeV neutrons in the D-T reaction to locate the neutron interaction and reduce background noise. To achieve high spatial resolution in API, a beam diameter of les1 mm on target is desired. For portable neutron generators used in API, the ion source and target cannot be water-cooled and the power deposited on the target must be low. By increasing the atomic ion fraction, the ion beam can be used more efficiently to generate neutrons, resulting in a lower beam power requirement and an increased lifetime of the alpha detector inside the acceleration column. The RF ion source produces very high atomic ion fractions (> 90%) compared to traditional Penning ion sources that produce less than 10% of atomic ions. Experimental measurements of the ion source plasma parameters including ion current density, atomic ion fraction, ignition and operating pressures, will be presented along with a discussion on the ion optics and engineering challenges.
Inference of multiple subspaces from high-dimensional data and application to multibody grouping

Multibody grouping is a representative of applying sub-space constraints in computer vision tasks. Under linear projection models, feature points of multibody reside in multiple subspaces. We formulate the problem of multi-body grouping as multiple subspace inference from high- dimensional data space. The theoretical value and practical advantage of this formulation come from the relaxation of the motion independency assumption, which has to be enforced in most factorization, based methods. In the proposed method, an oriented-frame (OF), which is a multi-dimensional coordinate frame, is associated with each data point indicating the point's preferred subspace structure. Then, a similarity measurement of these OFs is introduced and a novel mechanism is devised for conveying the information of the inherent subspace structure among the data points. In contrast to the existing factorization-based algorithms that cannot find correct segmentation of correlated motions such as articulated motion, the proposed method can robustly handle motion segmentation of both independent and correlated cases. Results on controlled and real experiments show the effectiveness of the proposed sub-space inference method.
A Fragile Software Watermarking for Tamper-Proof

A fragile software watermarking scheme for integrity verification of software is proposed in this paper. The algorithm uses the idea of semantic-preserving code substitution for embedding the watermark. With the system, the generated watermark relates closely to the content of software, and the scheme has highly sensitive to different types of attacks. Furthermore, both watermark generating controlled by a key and watermark embedding position under the control of another key enhance the security of watermarking scheme. The scheme not only can effectively detect tampering, but also has the ability to identify the type of tampering clearly.
The design of interactive negotiation agent on the Web

Because of the growth of Internet commerce, individual online transactions will grow rapidly. So in this paper we propose an interactive negotiation agent system on the Internet to help buyers make decisions. We use multi-attribute utility theory as the basic decision making strategy and the theorem is useful for the multicriteria decision. We stated the user goal that can be divided into several independent goals and the sub goals can be negotiated with sellers in parallel. On the other hand the user cannot handle the complex process individually; we take advantage of the agent technology as the major system developed.
Robot speech learning via entropy guided LVQ and memory association

The goal of this project is to teach a computer-robot system to understand human speech through natural human-computer interaction. To achieve this goal, we develop an interactive and incremental learning algorithm based on entropy-guided learning vector quantisation (LVQ) and memory association. Supported by this algorithm, the robot has the potential to learn unlimited sounds progressively. Experimental results of a multilingual short-speech learning task are given after the presentation of the learning system. Further investigation of this learning system will include human-computer interactions that involve more modalities, and applications that use the proposed idea to train home appliances.
Application of precision magnetic measurements for control of the Duke storage ring

The 1 GeV Duke FEL storage ring is dedicated to drive UV and VUV free electron laser devices. Specifics of this ring include the use of combined function magnets: quadrupole-sextupole and dipole-sextupole. The use of combined function magnets is necessitated by the close spacing of magnetic elements. A discussion on the measurement procedures of these magnets is included in this paper, as well as the data analysis used to create a viable control system for the combined function magnets. The design of the Duke storage ring was driven by the requirement to use existing hardware manufactured for the original ring design at Stanford. The original design called for the use of 'nose' and 'dimple' endpieces attached to the dipoles as the main fixed strength sextupole source. In addition, adjustable sextupole magnets were placed in an 18 cm gap between dipoles and arc quadrupoles. In that design the distance between magnetic poles of dipoles, quadrupoles, and sextupoles was less than 2 cm. It was no surprise that this design uncovered major asymmetric saturation of dipoles and sextupoles. Saturation would cause intolerable orbit distortion and high order nonlinearities in the magnetic field. To solve these problems we decided to: a) replace odd asymmetric endpieces with smooth and symmetric ones; b) remove discrete sextupoles which were saturated anyway; c) create necessary sextupole moments in the quadrupoles using asymmetric excitation; d) place fixed strength sextupole shims in the center of the dipoles.
Natural convection heat loss estimation of solar cavity receiver by incorporating a modified aperture ratio

On the basis of the consideration of air property variation with temperature, a three-dimensional (3-D) numerical investigation has been performed to attain insight into the cavity aspect ratio on the natural convection heat loss of a cavity receiver for a high-temperature solar dish system. Temperature and velocity contours as well as the variation curves have shown that the expansion of convection zone together with the augmentation of velocity magnitude is mainly responsible for the increase in natural convection heat loss with decreasing cavity aspect ratio, that is, shallower cavity. Moreover, the dependency of natural convection heat loss on the cavity aspect ratio is related to the tilt angle. Also, a modified definition of aperture ratio has been introduced, aiming to reflect the combined impact of the cavity aspect ratio and aperture size. Finally, two correlations of Nusselt number have been proposed by incorporating the cavity aspect ratio and the newly defined modified aperture ratio, respectively.