nils-herrmann/clean_scirepeval huggingface dataset:
Proprioceptive localilzatilon for a quadrupedal robot on known terrain

We present a novel method for the localization of a legged robot on known terrain using only proprioceptive sensors such as joint encoders and an inertial measurement unit. In contrast to other proprioceptive pose estimation techniques, this method allows for global localization (i.e., localization with large initial uncertainty) without the use of exteroceptive sensors. This is made possible by establishing a measurement model based on the feasibility of putative poses on known terrain given observed joint angles and attitude measurements. Results are shown that demonstrate that the method performs better than dead-reckoning, and is also able to perform global localization from large initial uncertainty
Variational Low Frequency Scattering by a Rectangular Trough in a Hard Ground Plane

A set of analytical expressions are presented for the diffraction from a rectangular trough when excited by a transverse-electric polarized plane-wave. The final explicit forms are appropriate for applications requiring efficient computation, making minimal use of special functions and requiring no matrix inversions. The solution is arrived at via a quasi-static variational technique wherein the aperture field is represented by a linear combination of three simple functions suggested by the solution to Laplace's equation in the channel geometry. Comparison with an independent moment method indicates that the variational solution to the dynamic problem is highly accurate for troughs of general depth. The upper limit on the frequency corresponds to a total aperture width of one-half of a free-space wavelength.
Frank-starling control of a left ventricular assist device

A physiological control system was developed for a rotary left ventricular assist device (LVAD) in which the target pump flow rate (LVADQ) was set as a function of left atrial pressure (LAP), mimicking the Frank-Starling mechanism. The control strategy was implemented using linear PID control and was evaluated in a pulsatile mock circulation loop using a prototyped centrifugal pump by varying pulmonary vascular resistance to alter venous return. The control strategy automatically varied pump speed (2460 to 1740 to 2700 RPM) in response to a decrease and subsequent increase in venous return. In contrast, a fixed-speed pump caused a simulated ventricular suction event during low venous return and higher ventricular volumes during high venous return. The preload sensitivity was increased from 0.011 L/min/mmHg in fixed speed mode to 0.47L/min/mmHg, a value similar to that of the native healthy heart. The sensitivity varied automatically to maintain the LAP and LVADQ within a predefined zone. This control strategy requires the implantation of a pressure sensor in the left atrium and a flow sensor around the outflow cannula of the LVAD. However, appropriate pressure sensor technology is not yet commercially available and so an alternative measure of preload such as pulsatility of pump signals should be investigated.
Hybrid III-V/SOI Optoelectronic Laser Module

We report a compact laser on-chip module that is hybrid integrated with photonic circuits. Arrays of III-V MQW lasers were flip-chip bonded onto an SOI interposer along with micro-machined mirrors for optical coupling into a dedicated grating coupler array. This is a new wafer-scale opto-electronic integration platform enabling photonic links with built-in high efficiency light sources.
Assignment of fibers in tree-structured cabling plant to communication devices

This paper addresses the problem of efficiently using the fibers in the physical cabling plant to connect the pairs of communication devices. This paper formulates the structure of the physical cabling plant, the communication (physical connection) requirement, and the problem of assigning the fibers to physical connections required. Then, this paper presents algorithms that solve the fiber assignment problem. The algorithms also decide if the physical plant can satisfy the connection requirement.
Introducing SEAN: Signaling Entity for ATM networks

SEAN is freely distributed, object-oriented, extensible software for research and development in host ATM signaling. SEAN includes a complete source-level release of the host native ATM protocol stack, and implements the ATM user network interface, compliant to the ITU Q.2931 specification for point-to-point calls, the ITU Q.2971 extension for point-to-multipoint calls, and the ATM Forum extension UNI-4.0 for leaf initiated join calls, SEAN provides APIs to the programmers writing application programs that require ATM signaling. Developers can easily modify and extend SEAN, using the framework library released together. This paper describes essential parts of SEAN's architecture and guides the users and protocol developers.
Estimating manipulability of unknown obstacles for navigation in indoor environments

The challenging task of navigating in cluttered environments has been studied extensively with indoor autonomous mobile robots. However, few approaches attempt to estimate real-valued costs for manipulating said obstacles with no prior knowledge of the environment. Our approach not only estimates these costs but also models the uncertainty inherent in making such estimates. We present an algorithm that, with no prior knowledge of the environment, allows a mobile robot to determine which obstacles are movable and which are not while navigating a cluttered environment. The algorithm also applies this knowledge of manipulability to obstacles encountered in the future that are similar in appearance to ones previously seen. Using our approach, a mobile robot can act intelligently about uncertain information as well as successfully navigate initially unknown indoor environments without relying on human-provided information.
Modular MEMS-based optical cross-connect with large port-count

We describe and demonstrate a modular microelectromechanical systems (MEMS)-based optical cross-connect (OXC) architecture. The OXC port count increases modularly by adding new optical modules, and a maximum cross-connectivity of /spl sim/ 350 /spl times/ 350 can be achieved in the current design. Each optical module has 16 ports with closed-loop servo-controlled MEMS mirrors. Using a prototype OXC system, mounted in a standard telecommunications equipment bay comprising optical modules, folding mirrors, and other optical elements, we demonstrate switching times of less than 10 ms, excellent optical power stability of less than /spl plusmn/ 0.15-dB variation, and immunity to stochastic vibrations. An automatic power peak-up process is performed when the power falls below 0.5-dB off the maximum coupled power for any connection.
Low dimensional human preference tracking for motion optimization

Motion planning for high degree of freedom (DOF) robots is not an easy task, and often requires optimization in a high dimensional space. Still, a generic motion planner using a single cost function for optimization may not be optimal over a number of different tasks with various task specific constraints. In this paper, we present a motion planning system that utilizes both easy to communicate human preferences and dimensionality reduction to handle these issues. Joint trajectories with human preference costs are projected into the null space of the task space, which helps make the resulting optimization simpler and more reliable. In addition, we apply the dimensionality reduction for the optimization, which significantly lowers the computational load. The suggested controller has been successfully used in the DARPA Robotics Challenge (DRC) Finals to handle a number of manipulation tasks.
Non-negative matrix factorization of dynamic images in nuclear medicine

Recently suggested non-negative matrix factorization (NMF) seems to overcome fundamental limitations of factor analysis at least In theoretical aspect. NMF cost function uses Poisson statistics as a noise model, rather than the Gaussian statistics, and provides a simple learning rule, in contrast to the tricky optimization in factor analysis. To study the feasibility of NMF for the analysis of dynamic image sequences in nuclear medicine, NMF was applied to H/sub 2//sup 15/O dynamic myocardial PET Images acquired from dog studies, and the results were compared with those obtained by conventional factor analysis method. Using NMF we could obtain basis images corresponding to major cardiac components. Their time-activity curves showed reasonable shapes that we have been familiar with. With the assumption of proper number of factors, NMF presented good results at least similar with those by factor analysis. Our results showed that NMF would be feasible for image segmentation and factor extraction from dynamic image sequences in nuclear medicine.
Analysis of CGSDS file delivery protocol: immediate NAK mode

We analyze the immediate negative acknowledgment (NAK) mode of the consultative committee for space data systems (CCSDS) file delivery protocol for the single-hop file transfer operation. We propose a timer setting rule that minimizes the expected time taken to transfer a file under the constraint that the throughput efficiency is maximized. Then, we derive the expected file delivery time and compare it with that of the deferred NAK mode. The main contribution is the closed-form expression for evaluating the performance metrics.
Assessing Voltage Fluctuations and Lamp Flicker Using RMS Voltages

Quantifying the effect of voltage fluctuations in power systems using a flickermeter is documented in IEC 61000-4-15. Modern digital implementations of a flickermeter take continuous time voltage samples as inputs to a software processing algorithm which ultimately calculates the short-term flicker severity Pst . While this process is perfectly suited for real-time measurement applications, it is less useful for off-line applications typically encountered during planning studies or any other application where continuous time sample data are not available. Because rms values are typically produced as a result of planning calculations and cycle-be-cycle rms values are much easier to store over long time periods for later use in off-line evaluations, it is appropriate to consider the performance of the flickermeter algorithm with rms voltage inputs so that additional applications can be considered. The analytical evaluation of flickermeter performance with rms voltage inputs is presented in this paper. The conclusion is that the flickermeter algorithm performs accurately with these inputs provided that the fluctuation frequency is low, but becomes inaccurate for higher frequency fluctuations. This analytical conclusion is supported by both simulation and field test results at three sites.
Bayesian regularization and nonnegative deconvolution (BRAND) for acoustic echo cancellation

The Bayesian regularization and nonnegative deconvolution (BRAND) algorithm is used to estimate the acoustic room impulse response by incorporating prior information such as sparsity and nonnegativity about the filter coefficients. For experimental measurements with microphones and speakers with non-ideal characteristics, the overall transfer function can be decomposed into a common short FIR filter, and a long nonnegative filter representing the room response. We develop an online estimation procedure for the BRAND algorithm, along with a computationally efficient implementation. Simulations and experimental results show the robustness of the resulting algorithm for echo cancellation in the presence of large ambient noise.
Efficient Neural Codes That Minimize Lp Reconstruction Error

The efficient coding hypothesis assumes that biological sensory systems use neural codes that are optimized to best possibly represent the stimuli that occur in their environment. Most common models use information–theoretic measures, whereas alternative formulations propose incorporating downstream decoding performance. Here we provide a systematic evaluation of different optimality criteria using a parametric formulation of the efficient coding problem based on the reconstruction error of the maximum likelihood decoder. This parametric family includes both the information maximization criterion and squared decoding error as special cases. We analytically derived the optimal tuning curve of a single neuron encoding a one-dimensional stimulus with an arbitrary input distribution. We show how the result can be generalized to a class of neural populations by introducing the concept of a meta–tuning curve. The predictions of our framework are tested against previously measured characteristics of some early visual systems found in biology. We find solutions that correspond to low values of , suggesting that across different animal models, neural representations in the early visual pathways optimize similar criteria about natural stimuli that are relatively close to the information maximization criterion.
Closed-loop architecture and protocols for rapid dynamic spreading gain adaptation in CDMA networks

We present a closed-loop architecture and protocols for rapid dynamic spreading gain adaptation and fast feedback between a transmitter and a receiver communicating with each other in CDMA networks. These protocols and architecture do not require the transfer of an explicit control message indicating the change of CDMA spreading gain from transmitter to receiver. Also, with these protocols, the transmitter can change the spreading gain symbol-by-symbol as opposed to frame-by-frame, and feedback information (e.g., the fast-varying channel condition) can be exchanged almost as frequently as the symbol rate. Thus, adaptation to the time-varying channel conditions of wireless networks and/or to the rate variation of traffic can be much faster than is possible with the existing frame-by-frame approach
Online learning of visibility and appearance for object pose estimation

This paper presents an online self-supervised approach to improve the quality and relevance of input point cloud to a 3D registration algorithm. The suggested method considers the visibility of the model points and learns discriminative appearance of the object under gradual changes. It selectively reduces the amount of information to process by excluding non-visible points of the model and removing outliers from data stream, which results in better alignment between the input data and the model. Thus, by providing a good initial pose, it speeds up the iterative procedure of EM-like optimization for pose estimation (i.e., ICP) to achieve better efficiency and robustness. We compiled a new object dataset of RGBD images under camera motion with ground truth poses of the camera and the objects. We have performed experiments on this dataset and obtained promising results.
A network version of the Pump

A designer of reliable MLS networks must consider covert channels and denial of service attacks in addition to traditional network performance measures such as throughput, fairness, and reliability. 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.<<ETX>>
Search-based planning for a legged robot over rough terrain

We present a search-based planning approach for controlling a quadrupedal robot over rough terrain. Given a start and goal position, we consider the problem of generating a complete joint trajectory that will result in the legged robot successfully moving from the start to the goal. We decompose the problem into two main phases: an initial global planning phase, which results in a footstep trajectory; and an execution phase, which dynamically generates a joint trajectory to best execute the footstep trajectory. We show how R* search can be employed to generate high-quality global plans in the high-dimensional space of footstep trajectories. Results show that the global plans coupled with the joint controller result in a system robust enough to deal with a variety of terrains.
Heel and toe lifting walk controller for resource constrained humanoid robots

Common design principles for low cost humanoid robots include a low center of mass height and a large support area for increased static stability. However, such principles limit the bipedal mobility of the robot due to the kinematic constraints involved. In this paper, we present an efficient locomotion controller that utilizes automatically calculated heel and toe lift motions to overcome the kinematic constraints. This helps with uneven terrain traversal by providing additional support, and also enables a dynamic heel-strike toe-off gait with a large stride length. We demonstrate the controller in physically realistic simulations, and on the THOR-RD full-sized humanoid robot and DARwIn-OP miniature humanoid robot.
Broadband wireless network planning using evolutionary algorithms

In this paper, we present a simultaneous planning of Base Stations (BSs) and Relay Stations (RSs) with link flow for a broadband wireless network. Infrastructure costs (BS cost, RS cost and their operational costs) of a wireless network is a key factor for network service providers while planning a network. The objective of this problem is to determine a set of BSs and RSs that can serve all users and fulfill their demands at the lowest cost. This problem settings is equally important for planning networks from scratch or enhancements in existing networks. This combinatorial optimization problem is NP-hard in nature. Evolutionary Algorithms (EAs) are intelligent tools that can provide high quality solution to this type of problems. Usually, efficiency of EAs depends on the problem. The aim is to find effective EAs with minimum resources such as low computational complexity, processing time and number of fitness functions evaluations. We formulate this problem as a non-linear discrete optimization and introduce four recent EAs that are motivated by natural intelligent behaviors. The objective function of this planning problem is computationally costly, and there exist a tradeoff between resources and quality of solution. These algorithms include Biogeography-based Optimization (BBO) that is inspired by the natural migration phenomenon of species between different islands, Artificial Bee Colony (ABC) based on the intelligent behavior of honey bee swarms, Quantum-inspired Evolutionary Algorithm (QEA) from the idea of quantum computing, and Immune Quantum Evolutionary Algorithm (IQEA) motivated by both the immune theory and quantum computing. Simulation results demonstrate insights of EAs' and present tradeoff between resources and quality of solutions.
Efficient learning of stand-up motion for humanoid robots with bilateral symmetry

Standing up after falling is an essential ability for humanoid robots in order to resume their tasks without help from humans. Although many humanoid robots, especially small-size humanoid robots, have their own stand-up motions, there has not been a generalized method to automatically learn flexible stand-up motions for humanoid robots which can be applied to various fallen positions. In this research, we propose a method for learning stand-up motions for humanoid robots using Q-learning making use of their bilateral symmetry. We implemented this method on DarwIn-OP humanoid robots and learned an optimal policy in simulation. We compared the resulting stand-up motion with manually designed stand-up motions and with stand-up motions learned without considering bilateral symmetry. Both in simulation and on the real robot, the new stand-up motion was successful in most trials while other motions took longer or were not as robust.
Wireless Mesh Network Planning Using Quantum Inspired Evolutionary Algorithm

The latest increase in mobile data usage and emergence of new applications such as Multimedia Online Gaming (MMOG), mobile TV and streaming contents have motivated advances in wireless broadband systems. Recently, the Long-Term Evolution (LTE) technology, which is based on the Universal Mobile Telecommunications System (UMTS) specifications, joins WiMAX as a competitor to achieve increasing demands of the broadband wireless access. Careful deployment of such a network is required to fulfill the high data rate demands with minimal cost of infrastructure and comprehensive coverage of the subscribers. In this paper, a multi-objective network planning problem is defined as utilizing the minimum number of infrastructure sites (i.e. Base Stations or eNode B in UMTS systems) while maximum number of users in service. We proposed a Quantum Inspired Evolutionary Algorithm (QIEA) in order to achieve optimized solution for this problem. The QIEA can be viewed as a probabilistic evolutionary algorithm and thus it is plausible to expect a reasonably good performance in solving combinatorial optimization problems. In this algorithm, each individual is represented by a string of Q-bits, where a Q-bit is the probabilistic representation inspired by the qubit concept in the quantum computing. Computational experiments show that our algorithm is fairly efficient to different scenarios of the network planning problem and performs better than the Genetic Algorithm (GA).
A joint symbol detection algorithm efficient at low SNR for a Multi-Device STBC-MIMO system

In this paper, we present an algorithm for joint symbol detection (JSD) at the receiver in a Multi-Device (MD) STBC-MIMO System. We apply Cross Entropy Optimization (CEO) to JSD problem in a MD-STBC-MIMO system. We find that this algorithm has good performance especially at a low SNR region in a rank-deficient MD-STBC-MIMO system. ML detector, Sphere Decoder (SD) and Semi-definite relaxation (SDR) are computationally complex. The proposed CEO detector finds a nearly optimal solution in real time and also requires very low amount of computation as compared to the ML, SD and SDR.
An Efficient Multiple Relay Selection Scheme for Cognitive Radio Systems

In this paper, we present a low-complexity Interference Aware Multiple Relay Selection (IAMRS) scheme for cognitive radio system. The main objective in selecting multiple relays is to maximize the SNR at the destination under the constraint of acceptable interference to the primary users (PU). The computational complexity of finding an optimal IAMRS by exhaustive search grows exponentially with the number of relays. In this paper, we propose an interference-aware greedy algorithm for the relay selection problem. The proposed algorithm has low computational complexity, and its effectiveness is verified through simulation results.
Learning Dimensional Descent planning for a highly-articulated robot arm

We present an method for generating high-quality plans for a robot arm with many degrees of freedom based on Learning Dimensional Descent (LDD), a recently-developed algorithm for planning in high-dimensional spaces based on machine learning and optimization techniques. Unlike other approaches used to solve this problem, our method optimizes a well-defined objective and can be shown to generate optimal plans, in theory and practice, for a well-defined class of problems—those that possess low-dimensional cost structure. For the common case where such structure is only approximately present, LDD constitutes a powerful iterative optimization technique that makes non-homotopic path adjustments in each iteration, while still providing a guarantee of convergence to a local minimum of the objective. Experiments with a 7-DOF robot arm show that the method is able to find solutions in cluttered environments that are of a much higher quality than can be obtained with sampling-based planners and smoothing.