nils-herrmann/clean_scirepeval huggingface dataset:
Base Station and Relay Station Broadband Network Planning Using Immune Quantum Evolutionary Algorithm

In this paper, we present simultaneous planning technique for Base Stations (BSs) and Relay Stations (RSs) for a broadband wireless network while taking data flow also known as link flow into consideration. Infrastructure cost (BS cost, RS cost and their operational costs) of a wireless network proves to be a key factor for network service providers while planning a network. The objective of this study is to help determine the set of BSs and RSs that can serve all the subscribed users and fulfill their demands at the lowest cost to the utility firm. This problem setup can be used for laying new networks as well as enhancing the already existing ones. The combinatorial optimization problem at hand is NP-hard in nature. We formulate this problem as a non-linear discrete optimization problem and compare two recent Evolutionary Algorithms (EAs) in providing approximate solution to this problem. The Quantum Inspired Evolutionary Algorithm (QEA) is a probabilistic algorithm based on quantum computing with the concept of qubits and superposition of states. The Immune theory based Immune Quantum Evolutionary Algorithm (IQEA) adopts immune operator to raise the fitness and prevent deterioration during the evolutionary process. Simulation results show better performance of IQEA as compared to QEA.
Optical switch fabric design for large fabrication tolerance

Optical cross-connect switches (OXC) are key components in advanced WDM networks. Integrated optical switches have been reported using several mechanisms and materials. We have demonstrated waveguide-type thermo-optic switches which employ silica-based planar lightwave circuits. The crosstalk of OXC, due to a number of reasons, is an important parameter which influences the system performance. One of the reasons is the coupling ratio (CR) of a 3 dB coupler in a switch unit. Due to fabrication limitations, it is hard to obtain the exact CR as designed. We propose a scheme to achieve low crosstalk even with variation of CR. The design of our optical switch fabric is a strictly non-blocking matrix switch using dilated double-layer architecture.
Real-Time Adaptive VVO/CVR Topology Using Multi-Agent System and IEC 61850-Based Communication Protocol

This paper proposes a new approach for real-time and adaptive Volt/VAr optimization (VVO)/conservation voltage reduction (CVR) system using Intelligent Agents, communicating through IEC 61850 Goose Messaging Protocol. The paper also proposes new real-time adaptive VVO/CVR algorithms tailored for different service level targets and system topologies. The paper argues that each of these variations requires different Intelligent Agent Systems, data structures, and communication requirements. To test the applicability of the VVO/CVR optimization engine, a modified IEEE 34 Node system is used as case study.
Practical bipedal walking control on uneven terrain using surface learning and push recovery

Bipedal walking in human environments is made difficult by the unevenness of the terrain and by external disturbances. Most approaches to bipedal walking in such environments either rely upon a precise model of the surface or special hardware designed for uneven terrain. In this paper, we present an alternative approach to stabilize the walking of an inexpensive, commercially-available, position-controlled humanoid robot in difficult environments. We use electrically compliant swing foot dynamics and onboard sensors to estimate the inclination of the local surface, and use a online learning algorithm to learn an adaptive surface model. Perturbations due to external disturbances or model errors are rejected by a hierarchical push recovery controller, which modulates three biomechanically motivated push recovery controllers according to the current estimated state. We use a physically realistic simulation with an articulated robot model and reinforcement learning algorithm to train the push recovery controller, and implement the learned controller on a commercial DARwIn-OP small humanoid robot. Experimental results show that this combined approach enables the robot to walk over unknown, uneven surfaces without falling down.
Replication of the Frank-Starling response in a mock circulation loop

Mock circulation loops (MCLs) are used to evaluate cardiovascular devices prior to in-vivo trials; however they lack the vital autoregulatory responses that occur in humans. This study aimed to develop and implement a left and right ventricular Frank-Starling response in a MCL. A proportional controller based on ventricular end diastolic volume was used to control the driving pressure of the MCL's pneumatically operated ventricles. Ventricular pressure-volume loops and end systolic pressure-volume relationships were produced for a variety of healthy and pathological conditions and compared with human data to validate the simulated Frank-Starling response. The non-linear Frank-Starling response produced in this study successfully altered left and right ventricular contractility with changing preload and was validated with previously reported data. This improvement to an already detailed MCL has resulted in a test rig capable of further refining cardiovascular devices and reducing the number of in-vivo trials.
Achieving maximal sum-rate capacity of two-way cognitive radio with relays operating in separate bands through MO

In this paper, we study a cognitive radio network comprising a pair of sources and multiple relays operating in non-overlapping bands in the radio spectrum. We address the problem of deciding the transmission powers of the sources and relays so that the sum of the sources' transmission capacities is maximized. Due to the non-overlapping nature of the relays' signals, the problem can be represented as a constrained optimization over two variables, but such formulation results in a non-convex optimization. We transform this problem to an equivalent optimization problem over three variables for which the monotonic optimization (MO) can be employed. We discuss an algorithm that is guaranteed to terminate with an ε-optimal solution.
On static and dynamic code assignment policies in the OVSF code tree for CDMA networks

We introduce two new policies, static code assignment (SCA) and enhanced dynamic code assignment (EDCA), for assigning OVSF (orthogonal variable-spreading-factor) codes in heavily-loaded code-division multiple access systems that serve users with multiple data rates. The goal is to maximize the average throughput of the system. We present a numerical analysis based on a basic queueing model and compare the proposed techniques with the existing DCA scheme. The SCA assigns and fixes a partial set of OVSF codes in the binary code tree so that the average throughput is maximized for randomly arriving call requests. The configuration of the best partial set of OVSF codes depends on the ratios of the mixed call arrival rates. When the system becomes heavily loaded, SCA begins to outperform DCA in terms of average throughput. Another advantage of the static assignment is that it does not require any complex reassignments of codes to support a newly-arriving call. The EDCA resorts to a stationary dynamic call admission policy that accepts or rejects a newly arriving call on the basis of the state in the Markov decision process modeling call arrivals and departures. EDCA allows code reassignment to maximize the spectral efficiency. We derive an optimal EDCA policy through linear programming to solve for the optimal policy of the Markov decision process. Comparing DCA and EDCA, it is indicated that the greedy policy of DCA may not be optimal in terms of the long-term average rate of the total data served. Simulation and numerical evaluation confirms that EDCA performs better than DCA as the traffic load increases and high-rate calls become dominant.
Sensor network-based wind field estimation using deep learning

The incorporation of wind fields, or movement of clouds, significantly improves the accuracy of time-series-based solar irradiance prediction models. To resolve problems regarding the cost and accuracy of current wind field estimation methods, there are the challenges in estimating wind fields using only solar irradiance sensor networks and evaluating the performance of models. We propose a cost-effective and reliable method to estimate wind fields through the application of Deep Learning and computational geometric algorithms. Using a realistic cloud simulator, validation datasets for the proposed model were generated, accounting for various complex factors including topology of sensor placement, changing wind speed and direction, and cloud density that directly impact sensor data. Preliminary qualitative and quantitative results indicate promising potential for practical deployment as an estimation model.
Biogeography-based optimization for joint relay assignment and power allocation in cognitive radio systems

In this paper, we present a binary Biogeography-Based Optimization (BBO) algorithm-based low-complexity interference aware relay assignment scheme with power control for a cognitive radio network with one source node, multiple relays and multiple destination nodes. Optimally relay assignment using the Exhaustive Search Algorithm (ESA) has a high computational complexity that grows exponentially with the number of relays and users. We formulate the jointly relay assignment with source and relays' power allocation as a mixed integer non-linear programming problem which further reduces to an integer programming problem. We employ BBO as one of the novel Evolutionary Algorithms (EA) under the broad category of Swarm Intelligence and present a BBO-based relay assignment scheme with discrete power control at source and relays for the integer programming problem. We present a low-complex migration model for partial immigration-based BBO, and simulation results show that its performance is close to the optional ESA.
A proof that uncorrelated branch SNRs yield the lowest error rate for MRC receivers operating over Nakagami-m-fading channels

We analyze the symbol error rate of a multiple-branch predetection maximal ratio combiner for correlated Nakagami-m-fading channels. Focusing on the correlations between diversity branches, we provide a simple analytic proof, applicable to different modulation schemes, that uncorrelated branches result in the lowest symbol error probability. First, we present a proof assuming that all branches have the same fading severity m, where m can be a noninteger. We then present a proof for the case where the branches have different but, integer-valued fading severities (ms).
An efficient probabilistic surface normal estimator

An efficient surface normal estimation method is presented. The new algorithm estimates surface normal direction for each cell in a grid based on the occupancy information (both occupied and empty) of the neighboring cells. This grid representation allows user-defined sizes and scaling with the environment, not the number of measurements. Recursive and batch formulations to obtain the posterior estimate are presented, and compared. A computationally efficient implementation is derived which provides consistent and accurate estimates as measurements become available. Both simulation and experimental results are shown, demonstrating comparable estimation performance to that of using Point Cloud Library, but with significantly reduced computation time.
On surface curvature computation from level set contours

The authors consider the 3-D shape representation problem for a class of range image where the natural model of the acquired range data is in the form of level set contours (equidistance contours), as exemplified by a moire interferometry range system. They present a novel surface curvature computation scheme that directly computes the surface curvatures (the principal curvatures, Gaussian curvature, and mean curvature) from the equidistance contours without any explicit computations or implicit estimates of partial derivatives. They show how the special nature of the equidistance contours, specifically. the dense information of the surface curves in the 2-D contour plane, turns into an advantage for the computation of the surface curvatures. The approach is based on using simple geometric construction to obtain the surface normals, the normal sections, and the normal curvatures. This method is general and can be extended to any dense range image data. Computation results on both real and synthesized equidistance range contours are shown.<<ETX>>
Optimal code assignment and call admission control for OVSF-CDMA systems constrained by blocking probabilities

The tradeoff between the throughput and the blocking probability of various code assignment schemes for OVSF-CDMA (orthogonal variable spreading factor CDMA) systems is investigated, and the optimal fixed and dynamic code assignment schemes that maximize the average data throughput under a blocking probability constraint are proposed. The optimal schemes are obtained using constrained optimization through the Markov decision process and linear programming. The performance of each scheme is numerically evaluated. Numerical results demonstrate that the proposed new schemes can achieve a satisfactory balance between the average throughput and the blocking probability.
A classification of algorithms for ARMA models and ladder realizations

Applications of linear systems modeling have recently developed quite rapidly in speech modeling, seismic data processing, and other areas. Due to the diversity of these developments, there exists a plethora of methods for estimating the parameters of linear models given input-ouput data, transfer functions, or covariance functions. This paper attempts a systematic classification of existing least-squares modeling methods. Within this framework, we shall point out some recently developed algorithms that have many computational advantages over existing ones. In particular, the methods of interest will be classified according to how the input/output data is acessed and according to its type. Data can be accessed either sequentially or in blocks; the data can be either input/output signals, transfer functions, or covariance functions. Since we consider state-space, autoregressive-moving average models, and the related ladder realizations, we shall distinguish the following three classes of algorithms: Riccali or square-root type methods, recently developed "fast" algorithms, and their ladder forms. While the first class typically requires computations of O(n3) or O(n2) with n equal to the number of model parameters, the "last" forms only require operations and storage of O(n). The ladder realizations have several advantages, such as lowest complexity and their stability "by inspection" properties. In the appendices, we present an example of our new exact least-squares recursions for ladder forms, and show how to obtain stable partial minimal realizations of the joint impulse response - and covariance - matching type.
On Error-Constrained Data Throughput of Rapid Symbol Duration Adaptation with Imperfect Channel Information

This paper quantitatively analyzes the effect of noisy outdated channel estimation on symbol-by-symbol (SBS) adaptation. We consider a rudimentary yet simple duration adaptation policy, which prescribes the momentary symbol rate as a linear function of the channel estimation. We present an optimization of such a simple policy to have an error-constrained data throughput as high as possible. The results shows that even with such a rudimentary adaptation policy and with a simple channel estimation scheme, the SBS adaptation achieves significant throughput gains over a frame-by-frame (FBF) adaptation.
Performance analysis of general coherent MRC receivers in Nakagami-m fading channels

We refine the exact expressions of the average symbol error rate (SER) of several coherent, L-branched maximal-ratio-combining (MRC) receivers in a set of correlated Nakagami-m (1960) fading channels with additive white Gaussian noise (AWGN). These expressions can be applied to study the performance for M-PSK or M-QAM receivers with any number of diversity branches, with an arbitrary SNRs correlation function between branches, with any constellation size, and with any fading severity parameter m rational. In addition, we analyze the channel capacity of the M-PSK system with MRC receivers for the case of balanced branches (i.e. all branches have the same average SNR per symbol) for arbitrary SNR correlation functions. The results of this analysis can guide the design in realizing the systems that dynamically adapt modulation schemes to the time-varying channel conditions in order to maintain the data link's quality of service (QoS).
Machine learning aided prediction of family history of depression

Increased risk for psychopathology in the offspring of depressed parents has been widely known. The brain may mediate the effects of risk of familial depression on the offspring via shared genetic and environmental factors. Conventional brain imaging studies to test this mediation effects primarily use a priori knowledge to select a subset of brain imaging-derived features. Despite of the existing positive results supporting the notion of the brain as an endophenotype for familial depression, no quantitative assessment has been performed regarding to what extents the complex brain structure contains information about familial depression. To this end, here we aim to predict whether an individual has a history of familial depression. We propose a data-driven, unbiased, and rigorous machine learning approach using multimodal brain features (e.g., grey matter morphometry based on T1-weighted images and structural connectome based on probabilistic diffusion tractography) to capture the complex representations of brain structure. We implemented logistic regression (LR) with regularization, support vector machine (SVR), and graph convolutional neural network (GCN). Our models show promising cross-validated classification accuracy: 97.78% (LR), 93.67% (SVM) and 89.58% (GCN). Brain features with greatest weights in the models include brain regions previously implicated in the depression literature (e.g., emotion regulation frontal-limbic circuit) as well as new regions. Results suggest a large impact of familial depression on the brain structure and connectome. Results also highlight potentials for prediction of risk for psychopathology in a data-driven fashion using costeffective, simple, ubiquitous brain images.
Swarm based resource minimization in network coded system?

In this paper, we present swarm based Resource Minimization Scheme (RMS) for Network-Coding-based Multicast System (NCMS). The main objective of the RMS is to reduce network coding links while maintaining the desired rate of a multicast system. The resource minimization problem in NCMS is NP-Hard. In this paper, we apply the Binary Particle Swarm Optimization (BPSO) algorithm to the resource minimization problem in NCMS. The numerical results show the effectiveness of the proposed BPSO based RMS in comparison to the previously proposed algorithms.
MAX-SAT problem using evolutionary algorithms

MAX-SAT is a classic NP-hard optimization problem. Many real problems can be easily represented in, or reduced to MAX-SAT, and thus it has many applications. Finding optimum solutions of NP-hard optimization problems using limited computational resources seems infeasible in general. In particular, all known exact algorithms for MAX-SAT require worst-case exponential time, so evolutionary algorithms can be useful for finding good quality solutions in moderate time. We present the results of an experimental comparison of the performance of a number of recently proposed evolutionary algorithms for MAX-SAT. The algorithms include the Artificial Bee Colony (ABC) algorithm, Quantum Inspired Evolutionary Algorithm (QEA), Immune Quantum Evolutionary Algorithm (IQEA), Estimation of Distribution Algorithm (EDA), and randomized Monte Carlo (MC). Our experiments demonstrate that the ABC algorithm has better performance than the others. For problems with Boolean domain, such as MAX-SAT, the ABC algorithm requires specification of a suitable similarity measure. We experimentally evaluate the performance of the ABC algorithm with five different similarity measures to indicate the better choice for MAX-SAT problems.
Learning a manifold-constrained map between image sets: applications to matching and pose estimation

This paper proposes a method for matching two sets of images given a small number of training examples by exploiting the underlying structure of the image manifolds. A nonlinear map from one manifold to another is constructed by combining linear maps locally defined on the tangent spaces of the manifolds. This construction imposes strong constraints on the choice of the maps, and makes possible good generalization of correspondences between all of the image sets. This map is flexible enough to approximate an arbitrary diffeomorphism between manifolds and can serve many purposes for applications. The underlying algorithm is a non-iterative efficient procedure whose complexity mainly depends on the number of matched training examples and the dimensionality of the manifold, and not on the number of samples nor on the dimensionality of the images. Several experiments were performed to demonstrate the potential of our method in image analysis and pose estimation. The first example demonstrates how images from a rotating camera can be mapped to the underlying pose manifold. Second, computer generated images from articulating toy figures are matched using the underlying 4 dimensional manifold to generate image-driven animations. Finally, two sets of actual lip images during speech are matched by their appearance manifold. In all these cases, our algorithm is able to obtain reasonable matches between thousands of large-dimensional images, with a minimum of computation.
Room Impulse Response Estimation using Sparse Online Prediction and Absolute Loss

The need to accurately and efficiently estimate room impulse responses arises in many acoustic signal processing applications. In this work, we present a general family of algorithms which contain the conventional normalized least mean squares (NLMS) algorithm as a special case. Specific members of this family yield estimates which are robust both to different noise models and choice of parameters. We demonstrate the merits of our approach to accurately estimate sparse room impulse responses in simulations with speech signals
Ladder forms for identification and speech processing

We have recently proposed a classification of exact least-squares modeling methods. One of the most promising subset of these algorithms are based on so-called ladder form realizations. They appear in many contexts such as scattering and network theory. In addition, they have several other nice properties and advantages, such as lowest computational complexity, stability "by inspection" properties and relations to physical properties such as reflection or partial correlation coefficients. We shall present new examples of our new exact least-squares recursions for ladder forms, such as Covariance Ladder Form that is equivalent to the so-called Covariance Method or Recursive Least Squares.