nils-herrmann/clean_scirepeval huggingface dataset:
A double-sided stator type axial self-bearing motor development for total artificial heart

This paper proposed a self-bearing motor that has tilt control function and clarified its fundamental characteristics as a maglev motor and maglev pump. The rotor has eight permanent magnets on each side (sixteen PMs in total). The two stators have twelve poles each with two types of concentrated windings. One is for motoring and axial control; the other is for tilt control. The theoretical analysis clarified operating principle and showed that the proposed motor could control translational motion, inclinational and rotational movement independently. An experimental setup was fabricated, and the control performance of the self-bearing motor was also investigated. The fabricated prototype achieved a stable magnetically levitated rotation. In addition, the proposed axial self-bearing motor fabricated into a prototype of the maglev pump to investigate pump characteristics for a total artificial heart applications.
Capability evaluation and validation of FC chip scale package structure

The requirement of Chip Scale Package (CSP) is growing popular in current 3C industries due to the increasing needs of handheld devices and energy saving. Flip-Chip Chip Scale Package (FCCSP) structure is then designed to meet the small form factor as well as high electrical performance requirements with cost efficiency. The purpose of this study is to evaluate the performance of different kinds of FCCSP structures as FCCSP-A (molding compound with underfill), FCCSP-B (only underfill) and FCCSP-C (only molding compound) structure. Firstly the package warpage performance is compared by using Finite Element Method (FEM). Actual warpage measurements of these three structures are also conducted by the use of shadow moiré methodology for validation. Secondly the die corner stress is compared for the evaluation of package reliability. Thermal performance is also compared and finally the investigation of the solder joint reliability performance by drop test.
Comparison of two optimization techniques for the estimation of complex permittivities of multilayered structures using waveguide measurements

In this paper, two separate techniques, i.e., sequential quadratic programming (SQP) and a genetic algorithm (GA), were used to estimate the complex permittivity of each layer in a multilayer composite structure. The relative performance of the algorithms was characterized by applying each algorithm to one of three different error functions. Computer generated S-parameter data sets were initially used in order to establish the achievable accuracy of each algorithm. Based on these data sets and S-parameter measurements of single and multilayer samples obtained using a standard X-band waveguide procedure, the GA was determined to be the more robust algorithm in terms of minimizing rms error of measured/generated and formulated S-parameters. The GA was found to perform exceptionally well for all cases considered, whereas SQP, although a more computationally efficient method, was somewhat limited for two error function choices due to local minima trapping.
A Network Pump

A designer of reliable multi-level secure (MLS) networks must consider covert channels and denial of service attacks in addition to traditional network performance measures such as throughput, fairness, and reliability. In this paper we show how to extend the NRL data Pump to a certain MLS network architecture in order to balance the requirements of congestion control, fairness, good performance, and reliability against those of minimal threats from covert channels and denial of service attacks. We back up our claims with simulation results.
Multiplicative Updates for Nonnegative Quadratic Programming

Many problems in neural computation and statistical learning involve optimizations with nonnegativity constraints. In this article, we study convex problems in quadratic programming where the optimization is confined to an axis-aligned region in the nonnegative orthant. For these problems, we derive multiplicative updates that improve the value of the objective function at each iteration and converge monotonically to the global minimum. The updates have a simple closed form and do not involve any heuristics or free parameters that must be tuned to ensure convergence. Despite their simplicity, they differ strikingly in form from other multiplicative updates used in machine learning. We provide complete proofs of convergence for these updates and describe their application to problems in signal processing and pattern recognition.
Hilbert space array methods for finite rank process modeling and ladder form realizations

We present a Hilbert space array approach for deriving fast estimation and adaptive signal processing algorithms. These are obtained using, as a basic tool, projection operators and orthonormalizations, via the Gram-Schmidt procedure. The resultant algorithms are recursive in time and order, and are realized in ladder forms. The importance of this array method is that it can be applied to a large variety of signal processing problems. This paper deals with the application of our array approach to the class of fimte (shift) rank processes.
From Reactive to Cognitive Agents: Extending Reinforcement Learning to Generate Symbolic Knowledge Bases

A new methodology for knowledge-based agents to learn from interactions with their environment is presented in this paper. This approach combines Reinforcement Learning and Knowledge-Based Systems. A Q-Learning algorithm obtains the optimal policy, which is automatically coded into a symbolic rule base, using first-order logic as knowledge representation formalism. The knowledge base was embedded in an omnidirectional mobile robot, making it able to navigate autonomously in unpredictable environments with obstacles using the same knowledge base. Additionally, a method of space abstraction based in human reasoning was formalized to reduce the number of complex environment states and to accelerate the learning. The experimental results of autonomous navigation executed by the real robot are also presented here.
EDA-based joint power, subcarrier allocation and relay assignment scheme for multiuser relaying in OFDMA-based cognitive radio systems

In this paper, we present an interference and channel conditions aware multiple relay assignment and subcarrier allocation scheme for OFDMA based cognitive radio systems employing cooperative transmission using Decode and Forward (DF) technique. We focus on the problem of assigning the relays, the relay powers and allocating the subcarriers to the destination nodes using the sum capacity of the cognitive radio system as the objective function to be maximized under the constraint of maximum acceptable levels of interference for the primary users (PU). The computational complexity of this assignment using exhaustive search algorithm grows exponentially with the number of relays, subcarriers and receivers. Thus we propose Estimation of Distribution Algorithm (EDA) for this assignment problem. EDA is a probabilistic evolutionary algorithm which updates its population at each iteration on the basis of the probability densities obtained from the population of superior candidates evaluated and chosen at the previous iteration. EDA employed with our allocation scheme has low computational complexity and its performance is comparable to the Upper Bound of the solution as shown by various simulation results.
A CAC scheme with code and interference limits on the forward link in CDMA cellular network

Future CDMA cellular networks are expected to carry a wide variety of data traffic. Traffic characteristics of data users can be characterized by burstiness and asymmetricity. Data traffic such as in Internet web browsing applications show asymmetric characteristics that cause a relatively heavy load on the forward link. Bursty data can drive the system into a situation wherein available CDMA codes are depleted even when there is enough margin of signal-to-interference ratio (SIR) to accept a new call. Thus, we propose a new call admission control (CAC) scheme on the CDMA forward link that reserves both some codes and interference margin for handoff calls. With the proposed CAC scheme, we construct a Markovian model for voice and data calls (two classes) in progress. The proposed scheme is compared with the conventional scheme that reserves interference margin only. Numerical results show that the proposed scheme is more efficient in accommodating calls under a given set of constraints.
Some Compartmentalized Secure Task Assignment Models for Distributed Systems

This paper formulates abstract problems of assigning subtasks to agents (processors) in a distributed system with a goal that they can perform its global task efficiently. The paper models the distributed system with a graph that describes the communication capabilities of the constituting agents. This graph is referred to as the "organizational graph.” In addition, the desired task-performing activity is modeled with another graph describing the required communications. Then, a few variants of the task assignment problem are formulated with potentially conflicting objectives (or constraints) of load balancing and communication costs. For some of these variants this paper provides efficient algorithms that solve the assignment problem. Some problems are proven NP-complete, and some others are left open.
Fluid Dynamic Models for Bhattacharyya-Based Discriminant Analysis

Classical discriminant analysis attempts to discover a low-dimensional subspace where class label information is maximally preserved under projection. Canonical methods for estimating the subspace optimize an information-theoretic criterion that measures the separation between the class-conditional distributions. Unfortunately, direct optimization of the information-theoretic criteria is generally non-convex and intractable in high-dimensional spaces. In this work, we propose a novel, tractable algorithm for discriminant analysis that considers the class-conditional densities as interacting fluids in the high-dimensional embedding space. We use the Bhattacharyya criterion as a potential function that generates forces between the interacting fluids, and derive a computationally tractable method for finding the low-dimensional subspace that optimally constrains the resulting fluid flow. We show that this model properly reduces to the optimal solution for homoscedastic data as well as for heteroscedastic Gaussian distributions with equal means. We also extend this model to discover optimal filters for discriminating Gaussian processes and provide experimental results and comparisons on a number of datasets.
Scalable real-time object recognition and segmentation via cascaded, discriminative Markov random fields

We present a method for real-time simultaneous object recognition and segmentation based on cascaded discriminative Markov random fields. A Markov random field models coupling between the labels of adjacent image regions. The MRF affinities are learned as linear functions of image features in a structured max-margin framework that admits a solution via convex optimization. In contrast to other known MRF/CRF-based approaches, our method classifies in real-time and has computational complexity that scales only logarithmically in the number of object classes. We accomplish this by applying a cascade of binary MRF-classifiers in a way similar to error-correcting output coding for general multiclass learning problems. Inference in this model is exact and can be performed very efficiently using graph cuts. Experimental results are shown that demonstrate a marked improvement in classification accuracy over purely local methods.
New opportunities in digital imaging systems

The widespread adoption of the Internet as a new paradigm for delivering client/server imaging applications has created exciting opportunities for digital imaging. This paper presents several examples of how Hewlett-Packard is building collaborative relationships with industry and universities to help meet the challenge. To encourage industries to collaborate on development of open industry standards, Hewlett-Packard has worked with industries to develop new image file formats and to contribute to imaging standardization activities. To encourage interdisciplinary collaborations in imaging research and education, Hewlett-Packard is supporting the development of university curricula that combine the disciplines of psychology, neuroscience, computer science and electrical engineering into a coherent engineering curriculum. To stimulate the development of new image processing methods, Hewlett-Packard Laboratories is investigating new approaches to image processing, such as Occam filtering and geometry driven partial differential equations for imaging.
Online learning of low dimensional strategies for high-level push recovery in bipedal humanoid robots

Bipedal humanoid robots will fall under unforeseen perturbations without active stabilization. Humans use dynamic full body behaviors in response to perturbations, and recent bipedal robot controllers for balancing are based upon human biomechanical responses. However these controllers rely on simplified physical models and accurate state information, making them less effective on physical robots in uncertain environments. In our previous work, we have proposed a hierarchical control architecture that learns from repeated trials to switch between low-level biomechanically-motivated strategies in response to perturbations. However in practice, it is hard to learn a complex strategy from limited number of trials available with physical robots. In this work, we focus on the very problem of efficiently learning the high-level push recovery strategy, using simulated models of the robot with different levels of abstraction, and finally the physical robot. From the state trajectory information generated using different models and a physical robot, we find a common low dimensional strategy for high level push recovery, which can be effectively learned in an online fashion from a small number of experimental trials on a physical robot. This learning approach is evaluated in physics-based simulations as well as on a small humanoid robot. Our results demonstrate how well this method stabilizes the robot during walking and whole body manipulation tasks.
Capacity analysis of forward link with deterministic power model in CDMA systems with adaptive antenna array and soft handoff

In this paper, we analyze the capacity of the forward link for CDMA systems with adaptive antenna arrays, considering soft handoffs. We take it into account that a mobile near a cell boundary can receive from two or more base stations and use MRC. For the capacity analysis we divide a cell into two regions: the region in which the soft handoff occurs and the one in which the handoff does not occur. Then, we compute the maximum number of users a base station can support under the constraint of acceptable outage probability. We assume that there is no limitation on the base station's transmission power, so a cell's capacity is limited by the interference between signals intended for different users in the same cell and in different cells.
A VLSI speech analysis chip set based on square root normalized ladder forms

A VLSI (very large scale integration) chip set is presented that is based on the square root normalized ladder recursions developed by Lee and Morf . It is shown that the equations are amenable to implementation using the so-called CORDIC (Coordinate Rotation Digital Computer) algorithms (see eq. ) because the time and order updates are easily representable as orthogonal transformations or rotations [7, 12]. An integrated implementation which exploits the concurrency of the ladder recursions together with possible hardware, speed and area tradeoffs are presented. The general applicability of the chip set to other signal processing tasks is demonstrated by showing that the discrete Fourier transform (DFT) is naturally suited to the architecture.
A novel innovation based time domain pitch detector

We present a novel innovations based time-domain pitch detection technique for speech-like signals using one of our recursive least-squares ladder algorithms. The basic assumption is that the speech driving process consists of an approximately Gaussian part (unvoiced) and a jump part (voiced). The pitch pulse positions located by processing the innovations alone are known not to be very accurate due to phase-distortions, effects of zeros and inaccurate model parameter estimates. In our ladder form linear prediction recursions, a log-likelihood function is recursively computed (on-line) for each speech sample. The derivative of this log-likelihood function becomes a sensitive measure of extreme outliers of the speech waveforms, i.e., samples that very likely do not fit the Gaussian statistics. When combined with the innovations of our ladder algorithms a good statistic is obtained for locating the pitch pulses by thresholding. This pitch detection scheme has the advantage that all the necessary variables are already computed in the modeling ladder recursions and therefore is suited for fast on-line or even hardware (e.g., VLSI) implementations.
Systems perspectives on optically-assisted RF signal processing using silicon photonics

This talk will discuss opportunities for photonics to contribute to signal processing applications in general, and RF signal processing in particular. Key issues associated with embedded photonic links, important analog RF functions, and key component and systems requirements will be addressed. Progress in silicon photonics and associated systems technologies, along with basic proof of concept demonstrations will be presented. Specific topics include reconfigurable silicon photonic filters, operation of such filters in coherent RF photonic links, precision photonic control, link linearization methods, and the utility of optical amplifiers.
Effects of leaky bucket parameters on the average queueing delay: worst case analysis

Studies a single server queueing system with deterministic service time in which arrivals are regulated by the leaky-bucket control. The worst traffic of arrivals passing the leaky-bucket regulation is discussed. The performance measure considered is queueing delay averaged over all packets. The worst traffic is characterized as the repetition of the following three phases: bulky arrival with bulk size related to the bucket size, arrival at every token generation for a specified length of interval, and then no arrival till the token bucket is full. The average queueing delay for the worst traffic, i.e. tight performance bound, is expressed in explicit form as a function of leaky bucket parameters (bucket size and token generation rate). The authors expect that this function will provide insights into the relationship between leaky bucket parameters and the corresponding network resources allocated.<<ETX>>
Electrical analyses of TSV-RDL-bump of interposers for high-speed 3D IC integration

As the progress of the packaging technology for the electronic consuming devices, the customer demands more and more. From the trend of the development on electronic devices, it shows that these demands require for more functions or higher density of devices within a limited space. By the capabilities of the 3D-IC technology, it could support such a design with multi-purposes including a smaller size, the high-speed and multi-functions. There are many approaches and technologies to make the 3D-IC. Amount of them, the stacking with Through-Silicon-Via (TSV), plays a very important role. It shortens the path of the circuit in a device. And hence, this device may support a faster operation. In this study, we analyze the different designs based on two TSV technologies, the Cu-filled and coaxial-type TSVs. By using the simulation approach, we evaluate the performances of these proposed designs. And, the results in our study should have the benefits for designing the interposer substrates which are used for developing the 3D-IC.
Generative Local Metric Learning for Nearest Neighbor Classification

We consider the problem of learning a local metric in order to enhance the performance of nearest neighbor classification. Conventional metric learning methods attempt to separate data distributions in a purely discriminative manner; here we show how to take advantage of information from parametric generative models. We focus on the bias in the information-theoretic error arising from finite sampling effects, and find an appropriate local metric that maximally reduces the bias based upon knowledge from generative models. As a byproduct, the asymptotic theoretical analysis in this work relates metric learning to dimensionality reduction from a novel perspective, which was not understood from previous discriminative approaches. Empirical experiments show that this learned local metric enhances the discriminative nearest neighbor performance on various datasets using simple class conditional generative models such as a Gaussian.
Avrora: scalable sensor network simulation with precise timing

Simulation can be an important step in the development of software for wireless sensor networks and has been the subject of intense research in the past decade. While most previous efforts in simulating wireless sensor networks have focused on protocol-level issues utilizing models of the software implementation, a significant challenge remains in precisely measuring time-dependent properties such as radio channel utilization. One promising approach, first demonstrated by ATEMU, is to simulate the behavior of sensor network programs at the machine code level with cycle-accuracy, but poor performance has so far limited its scalability. In this paper we present Avrora, a cycle-accurate instruction-level sensor network simulator which scales to networks of up to 10,000 nodes and performs as much as 20 times faster than previous simulators with equivalent accuracy, handling as many as 25 nodes in real-time. We show how an event queue can enable efficient instruction-level simulation of microcontroller programs and allow the hidden parallelism in finegrained sensor network simulations to be extracted, once two core synchronization problems are identified and solved. Avrora's ability to measure detailed time-critical phenomena can shed new light on design Issues for large-scale sensor networks.
Enhanced discrimination among UXO-like targets using extremely low-frequency magnetic fields

We show that the ability to discriminate among unexploded ordnance (UXO) targets is enhanced by extending the measurement range of our continuous-wave (CW) electromagnetic induction (EMI) system down to extremely low frequencies (ELFs). In particular, we demonstrate that for a typical UXO target set, when data in the range 30 Hz to 24 kHz are extended downward toward 1-Hz discrimination performance improves by a factor of 1.5. Measurements and numerical simulations show that very low frequency magnetic fields penetrate deep into the target, enabling one to discriminate between thick- and thin-walled ferrous cylinders that are otherwise visually identical. Data acquisition at extremely low frequencies is a time-consuming process especially if data averaging is required to achieve acceptable signal-to-noise ratio. Therefore, in practice, discrimination improvement using ELF is probably best carried out using a "cued" approach. In a "cued" approach, target locations would be marked using another detection technique (e.g., a simple total-field magnetometer), and the low-frequency CW EMI system would be deployed to these known target locations for classification.