nils-herrmann/clean_scirepeval huggingface dataset:
Estimation of distribution algorithm for scheduling in uplink multiuser wireless communication system

In this paper, we present a real-time low-complexity user scheduling scheme for uplink multiuser multiple input multiple output (MIMO) wireless communication systems. We apply Estimation of Distribution Algorithm (EDA) for user scheduling problem. The computational complexity of exhaustive search for user scheduling problem grows exponentially with the number of users. The proposed EDA has low computational complexity and can find a near-optimum solution in real time. In addition to applying the general EDA to user scheduling, we also present a specific improvement to EDA, which increases the likelihood of getting the optimum solution by generating cyclic shifted initial population.
Overview of the large digital arrays of the space fence radar

Space Fence, currently under construction on Kwajalein Atoll, will revolutionize Space Situational Awareness (SSA) using advanced phased array radar technology in two of the largest phased arrays to ever be constructed. Space Fence will provide catalog completeness, accuracy and timeliness with vastly improved performance in Low Earth Orbit (LEO) and capability to support missions in Geosynchronous Earth Orbit (GEO). Attaining detection and tracking performance within the large coverage volume necessitated developing advanced technologies including long-pulse high-duty factor Gallium Nitride (GaN) transmit modules, low-cost dual-polarized Radio Frequency Integrated Circuit (RFIC) receivers, and element-level digital beam sforming across 86,000 receive elements. These technologies have been matured to Technology Readiness Level (TRL) 7 and Manufacturing Readiness Level (MRL) 7 based on end-to-end scaled prototypes. This paper will summarize these technologies, the design choices that led to their selection, and their application to the Space Fence phased arrays.
Performance benefits of uplink packet relay protocols for cellular-like systems: quantitative analysis

Relaying can replace one transmission over a long distance with several transmissions over shorter distances, and the total transmission power of mobile stations thus can be lower in the case of relaying. As such, the relaying concept and protocols in a cellular environment have been much discussed in the literature recently. We mathematically analyze the idea of uplink relaying schemes, using a simple model that represents their essential functions. In order to simplify our performance analysis, we assume that the originator of a packet sets the maximum number of hops to the base station to be 2. Our results indicate that the performance benefits of relaying are significant
Resource allocation in bidirectional cooperative cognitive radio networks using swarm intelligence

In this work we consider an OFDMA-based two-way cognitive relay network that comprises multiple source-destination pairs and multiple relays. The relays assist communication between the source-destination pairs, and different relays transmit on orthogonal subcarriers. The relays employ amplify-and-forward relaying. For this network, we formulate a sum capacity maximization problem to determine the subcarrier assignment and power allocation to the relays. The optimization problem is formulated as a mixed integer nonlinear programming (MINLP). An intuitive way to obtain the optimal solution of MINLP is to exhaustively try all the combination of the discrete variables and solve the resulting non linear optimization problem. However, this approach is computationally intractable. Therefore, we use particle swarm optimization (PSO) to solve the MINLP. The proposed algorithm has low computational complexity, and we verify its effectiveness through simulation results.
Active stabilization of a humanoid robot for real-time imitation of a human operator

Imitating the motion of a human operator is an intuitive and efficient way to make humanoid robots perform complex, human-like behaviors. With the help of recently introduced affordable and real-time depth sensors, the real time imitation of human behavior has become more feasible. However, due to their small footprint and high center of mass, humanoid robots are not inherently stable. The momentum generated by dynamic upper body movements can induce instabilities that are often large enough to make the robot fall down. In this work, we describe a motion controller for a humanoid robot where the upper body is controlled in real time to imitate a human teacher, and the lower body is reactively stabilized based on the current measured state of the robot. Instead of relying on the accuracy of robot dynamics, we use biomechanically motivated push recovery controllers to stabilize the robot against unknown perturbations that include possible impacts. We demonstrate our approach experimentally on a small humanoid robot platform.
Two-Term Ritz–Galerkin Solution for the Low Frequency Scattering by a Rectangular Trough in a Soft Ground Plane

An analytic form is derived for the diffracted field that originates when a transverse-magnetic plane-wave excites a rectangular trough cut in a ground plane. The low-frequency analysis is valid for trough apertures that are less than half a free-space wavelength in width. Trough depth is arbitrary. The quasi-static nature of the long wave problem is exploited by approximating the aperture field as a complex superposition of decoupled symmetric and antisymmetric solutions to Laplace's equation in the vicinity of the aperture, subject to Dirichlet boundary conditions. Geometrical contributions to the final algebraic expressions for the two aperture coefficients arise from two sources: A rapidly convergent series governs the aperture interaction with the trough modes and the effect of the half-space is embodied in a small number of terms of low powers in kappaalpha and factors of In kappaalpha.
Hilbert space array methods for finite rank process estimation and ladder realizations for adaptive signal processing

We present a Hilbert space array approach for deriving fast estimation and adaptive signal processing algorithms, that are recursive in time and order. Ladder (or lattice) forms turn out to be the natural realizations of these algorithms. From a stochastic point of view, the natural class of processes associated with these techniques include stationary but also nonstationary processes of finite (displacement) rank, also referred to as shift-low-rank or alpha-stationary processes. They are encountered in adaptive signal processing, speech modeling and encoding, digital communication, radar and sonar, high-resolution spectral estimation, distance measures etc. The use of projections and orthonormalizations, e.g. via Gram Schmidt procedures, induces real and complex rotations as basic operations, resulting in magnitude normalized variables and numerically stable computations.
Proof of a modified Dijkstra's algorithm for computing shortest bundle delay in networks with deterministically time-varying links

This paper presents and proves the correctness of a modified Dijkstra's algorithm that computes the shortest-delay path in networks with deterministically time-varying links. Examples of such networks include interplanetary, orbital (e.g., low Earth orbit) satellite, and sensor networks. This paper presents and proves the correctness of a modified Dijkstra's algorithm that computes the shortest-delay path in networks with deterministically time-varying links. Examples of such networks include interplanetary, orbital (e.g., low earth orbit) satellite, and sensor networks
Quantum Inspired Evolutionary algorithm for joint user selection and power allocation for uplink cognitive MIMO systems

In this paper, we consider the uplink communication in a network of cognitive radio nodes. The transmitting nodes and the receiver are equipped with multiple antennas and MIMO processing abilities. For this network, we study the problem of interference-aware joint secondary user (SU) selection/scheduling and power control (JSUS-QPC). The main objective of the JSUS-QPC is to maximize the sum-rate capacity of the cognitive MIMO uplink communication system under the constraint that the interference to the primary users (PU) is below a specified level. We formulate this optimization problem as nonlinear integer programming problem. The computational complexity of finding an optimal solution to the JSUS-QPC problem by exhaustive search grows exponentially with the number of users and power levels. Therefore, we apply a Quantum Inspired Evolutionary algorithm (QIEA) to determine the suboptimal solution to the JSUS-QPC problem. The proposed scheme has low computational complexity and its results are comparable to the optimal exhaustive search algorithm.
Solving the MAX-SAT problem by binary enhanced fireworks algorithm

In this paper, we present a binary enhanced firework algorithm (BEFWA) for optimization problems with a search space of binary vectors, and we test this new algorithm for the MAX-SAT problem. The EFWA algorithm is a relatively recent development in swarm intelligence (SI) for continuous optimization, and the explosion amplitude operator in EFWA does not fit for searching a good solution in a discrete binary space. The original ABC algorithm is also not suitable for searching through a binary space, but its adaptation, the discrete ABC (DisABC), for a binary space was recently presented. In the present paper, we employ the similarity-measure-based differential expression from DisABC to design the binary EFWA algorithm to operate in binary space. The MAX-SAT is a well-known modelling framework for various computationally challenging problems, and thus it has many applications. However, the MAX-SAT problem has been proven to be NP-hard. Existing results indicate that evolutionary algorithms (EAs) can be useful for finding good-quality solutions without excessive computational resources. Our experimental results demonstrate that the binary EFWA can be a better choice over DisABC and Genetic Algorithm (GA) for various classes of MAX-SAT instances.
On-wafer probing measurement and simulation modeling correlation of spiral inductors for integrative passive device application

Foundry process manufacture cost will increase with device size decrease from 28nm to 22nm. Wafer area will be focused on active device implementation. IC designer will want to implement passive device using package process not foundry process because cost concern. In this paper, we use Agilent ADS Momentum and EMpro to design and simulate spiral inductors and use our process capability to implement these inductors. From on-wafer probing measurement results, we can extract equivalent device model and relative parameters. This equivalent device model can be used in 2turns, 3turns…etc inductors. Finally, we will provide inductors' library for circuit designers during IC design stage. Designers can design circuits using foundry process to implement active devices and using packaging process to implement passive devices.
Interference Aware Relay Assignment Schemes for Multiuser Cognitive Radio Systems

In this paper, we present two low-complexity interference aware multiple relay assignment schemes for cognitive radio systems. The main objective in assigning multiple relays is to maximize the sum capacity of the cognitive radio system under the constraint of acceptable interference to the primary users (PU). The computational complexity of finding an optimal assignment by exhaustive search grows exponentially with the number of relays and users. The proposed schemes have low computational complexity, and their effectiveness is verified through simulation results.
Resource Allocation Techniques in Cooperative Cognitive Radio Networks

In the past decade, cognitive radio and cooperative communication techniques have been proposed in the literature for efficiently utilizing the radio resources. Cognitive radio is an emerging technology intended to enhance the utilization of the radio frequency spectrum. The cooperative communication system, with the same total power and bandwidth of legacy wireless communication systems, can increase the data rate of the future wireless communication system. A combination of cognitive radio with cooperative communication can further improve the future wireless network performance. Efficient resource allocation in cooperative cognitive radio network (CRN) is essential in order to meet the challenges of future wireless networks. In this article, a survey of resource allocation in cooperative CRN is presented. We discuss the taxonomy of objectives and protocols used in the literature for resource allocation in cooperative CRN. This paper also highlights the use of power control, cooperation types, network configurations and decision types used in cooperative CRN. Finally, directions for future research are outlined.
Blind Sparse-Nonnegative (BSN) Channel Identification for Acoustic Time-Difference-of-Arrival Estimation

Estimating time-difference-of-arrival (TDOA) remains a challenging task when acoustic environments are reverberant and noisy. Blind channel identification approaches for TDOA estimation explicitly model multipath reflections and have been demonstrated to be effective in dealing with reverberation. Unfortunately, existing blind channel identification algorithms are sensitive to ambient noise. This paper shows how to resolve the noise sensitivity issue by exploiting prior knowledge about an acoustic room impulse response (RIR), namely, an acoustic RIR can be modeled by a sparse-nonnegative FIR filter. This paper shows how to formulate a single-input two-output blind channel identification into a least square convex optimization, and how to incorporate the sparsity and nonnegativity priors so that the resulting optimization remains convex and can be solved efficiently. The proposed blind sparse-nonnegative (BSN) channel identification approach for TDOA estimation is not only robust to reverberation, but also robust to ambient noise, as demonstrated by simulations and experiments in real acoustic environments.
Dynamic heel-strike toe-off walking controller for full-size modular humanoid robots

General purpose modular actuators have greatly lowered the difficulty of building and maintaining high degree of freedom humanoid robots. In addition to widely adopted miniature modular humanoid robots, full-size humanoid robots can now be built using such modular actuators. To overcome torque and control bandwidth limitations of modular actuators, such robots tend to have a design principle of relatively short leg length and large foot size. However, the kinematic constraints from such design limit the bipedal mobility of the robot. In this paper, we present an efficient walking controller that utilizes a moving Zero Moment Point (ZMP) trajectory, automatically calculated heel and toe lift motions and a reference foot tilt trajectory to generate a dynamic heel-strike toe-off gait with large stride lengths while overcoming the kinematic constraints. We demonstrate the suggested controller in physically realistic simulations, and on the THOR-RD full-sized humanoid robot.
Bayesian L1-Norm Sparse Learning

We propose a Bayesian framework for learning the optimal regularization parameter in the L1-norm penalized least-mean-square (LMS) problem, also known as LASSO (R. Tibshirani, 1996) or basis pursuit (S.S. Chen et al., 1998). The setting of the regularization parameter is critical for deriving a correct solution. In most existing methods, the scalar regularization parameter is often determined in a heuristic manner; in contrast, our approach infers the optimal regularization setting under a Bayesian framework. Furthermore, Bayesian inference enables an independent regularization scheme where each coefficient (or weight) is associated with an independent regularization parameter. Simulations illustrate the improvement using our method in discovering sparse structure from noisy data
Package characterization of UTAC's Grid Array package (GQFN) and performance comparison over standard laminate packages

The increasing demand and requirements for package with smaller footprint, higher dense I/O counts and better performance at lower cost are one of the key challenges faced by semiconductor manufacturing companies. As one of the strategies for sustainable competitive advantages and to address the issue, UTAC introduced a new generation of leadframe package design called Grid Array QFN (GQFN), where base material is leadframe but allows traces to be routed through etching process, hence providing higher I/Os that to date have required a two or four layers of laminate base packages. GQFN also has much smaller package form factor with reduction of package size up to 60%, shorter wire length with lower parasitic values and the feasibility to include a die attach paddle whilst having array of solder balls simultaneously. GQFN technology allows flip chip, stacked die, multi-chip module, passive integration and to meet the demand in leadless lead-frame applications. As part of package characterization, this package is subjected to comprehensive package analysis study using simulation and experimental data. In order to demonstrate the advantages and benefits of the package, this study projects the performance of GQFN to laminates package as a benchmark. Analysis methodologies and results of thermal, electrical and mechanical performance study will be discussed in detailed. From the comparative analysis study conducted, the overall thermal and electrical performance of GQFN is better than laminate packages. The package also has good results to meet the test requirement according to the IPC/JEDEC standards showing that the mechanical performance of GQFN is comparable to laminate packages.
Dynamic quality of service mapping framework for relative service differentiation-aware media streaming

We investigate the source domain's scheme of dynamically and intelligently pre-marking the streaming media packets before entering a DS-domain providing a relative differentiated service. The pre-marking scheme takes into account the packet content, the price limit for the transport of streaming media, and the traffic conditioning agreement. In this scheme, the sender cooperates with what we call media gateway (MG), which is a special node on the boundaries of the DS-domain. MG is responsible for meeting the traffic conditioning agreement while fully utilizing the received differentiated services for the quality of the streaming media. Our proposed QoS control scheme includes the following components: 1) the relative priority based indexing and categorization of streaming video content at the sender and 2) the dynamic feedforward QoS mapping of categorized index to DS grade at the proposed MG. With the proposed scheme, enhanced quality of streaming media applications is demonstrated.
Monotonic Optimization for Power Assignment in Two-Way Cognitive Radio Networks with Shared-Band Amplify-and-Forward Relays

In this work, we consider a cognitive radio network comprising a pair of sources and multiple relays exercising the amplify-and-forward cooperation in a shared frequency band. For such a network, we consider the problem of determining transmit powers of the sources and relays so that the lesser of the achievable error-free data rates from the two sources is maximized. The formulated problem is a non-convex optimization problem and does not have a structure to guarantee the quality of a solution. We present the transformation of this problem to an equivalent optimization problem for which the monotonic optimization methods can be applied. We establish that the polyblock outer approximation algorithm for this monotonic optimization terminates with an ε-optimal solution after a finite number of iterations.
A low complexity evolutionary algorithm for multi-user MIMO detection

Employing the Multi-Device Multi Input Multi Output (MIMO) communication scheme improves the wireless communication via diversity methods and spacial multiplexing. In this paper, we apply Biogeography-Based Optimization Algorithm (BBO) for Joint Symbol Detection (JSD) at the receiver station in a Multi-Device Space-Time Block Coded (STBC) MIMO system. Exhaustive search for finding an optimal detection - Maximum Likelihood (ML) detection - has a computational complexity that increases exponentially with the number of wireless devices, transmit antennas per wireless device, and the number of bits per symbols (complex modulation schemes e.g. MPSK or M-QAM), which yields to higher complexity and cost. BBO is a new population-based, biogeography inspired global optimization algorithm that mainly uses the biogeography-based migration operator to share the information among solutions. We present a low-complex migration model for partial immigration-based BBO, and apply it to JSD to find a nearly optimal solution in real time with much less computational complexity. The results of multiple independent simulation runs of the algorithms indicate a good performance/complexity trade-off compared with other well-known previously proposed algorithms for JSD, such as the near-optimal Sphere Decoding (SD), Genetic Algorithm (GA), Minimum Mean Square Error (MMSE), Vertical Bell Laboratories Layered Space-Time (V-BLAST) and Semi-Definite Relaxation (SDR). The effectiveness of BBO is verified through these simulation results.
Haemodynamic modeling of the cardiovascular system using mock circulation loops to test cardiovascular devices

Comprehensive testing and evaluation of cardiovascular device function and performance is required prior to clinical implementation. Initial proof of concept investigations are conducted within in-vitro mock circulation loops, before proof of principle is demonstrated via in-vivo animal testing. To facilitate the rapid transition of cardiovascular devices through this development period, a testing apparatus was developed that closely models the natural human cardiovascular system haemodynamics. This mock circulation system accurately replicates cardiac function, coupled to systemic and pulmonary circulations. The physiological response produced by a number of clinical cardiovascular conditions can be actively controlled by variable parameters such as vascular resistance, arterial/venous compliance, ventricle contractility, heart rate, and heart /vascular volumes, while anatomical variations such as valve regurgitation and septal defects can be included. Auto-regulation of these parameters was attempted to reproduce the Frank-Starling mechanism, baroreceptor reflex, skeletal muscle pump, and postural changes. Steady state validation of loop performance was achieved by replicating the progression of a patient's clinical haemodynamics from heart failure, through VAD support, to heart transplantation. The system has been used to evaluate pulsatile and non-pulsatile ventricular assist devices, counter pulsation devices, non-invasive cardiac output monitors and cardiovascular stents. The interaction of these devices with the cardiovascular system was also investigated with regards to physiological control strategies and cannula placement. The system is a valuable tool for the accelerated progression of cardiovascular device development.
System Order Estimation of ARMA Models by Ladder Canonical Forms

Conventional methods for estimating the model order in system indentification, e.g., the Akaike information criterion, usually involve the maximization of some likelihood functions. In modeling autoregressive moving-average (ARMA) processes by ladder canonical forms, the ARMA process in embedded into an autoregressive (AR) ladder form and the parameters are estimated by a class of efficient least-squares algorithms that has very fast and strong convergence. Since the likelihood functions are functions of the innovations and partial correlations, which the ladder algorithms recursively compute, the embedded ARMA ladder form becomes the "natural" canonical form for the realization and computation of likelihood functions and for solving the problem of system order identification. The basic algorithm of this approach is described in this paper.