OpenRaiser/Intern-Atlas huggingface dataset:
A modified sequence domain impedance definition and its equivalence to the dq-domain impedance definition for the stability analysis of AC power electronic systems
Venue: arXiv cs.SY 2016
Year: 2016

Representations of AC power systems by frequency dependent impedance equivalents is an emerging technique in the dynamic analysis of power systems including power electronic converters. The technique has been applied for decades in DC-power systems, and it was recently adopted to map the impedances in AC systems. Most of the work on AC systems can be categorized in two approaches. One is the analysis of the system in the \textit{dq}-domain, whereas the other applies harmonic linearization in the phase domain through symmetric components. Impedance models based on analytical calculations, numerical simulation and experimental studies have been previously developed and verified in both domains independently. The authors of previous studies discuss the advantages and disadvantages of each domain separately, but neither a rigorous comparison nor an attempt to bridge them has been conducted. The present paper attempts to close this gap by deriving the mathematical formulation that shows the equivalence between the \textit{dq}-domain and the sequence domain impedances. A modified form of the sequence domain impedance matrix is proposed, and with this definition the stability estimates obtained with the Generalized Nyquist Criterion (GNC) become equivalent in both domains. The second contribution of the paper is the definition of a \textit{Mirror Frequency Decoupled} (MFD) system. The analysis of MFD systems is less complex than that of non-MFD systems because the positive and negative sequences are decoupled. This paper shows that if a system is incorrectly assumed to be MFD, this will lead to an erroneous or ambiguous estimation of the equivalent impedance.
Weaker consistency notions for all the CSPs of bounded width
Venue: arXiv cs.CC 2016
Year: 2016

The characterization of all the Constraint Satisfaction Problems of bounded width, proposed by Feder and Vardi [SICOMP'98], was confirmed in [Bulatov'09] and independently in [FOCS'09, JACM'14]. Both proofs are based on the (2,3)-consistency (using Prague consistency in [FOCS'09], directly in [Bulatov'09]) which is costly to verify. We introduce a new consistency notion, Singleton Linear Arc Consistency (SLAC), and show that it solves the same family of problems. SLAC is weaker than Singleton Arc Consistency (SAC) and thus the result answers the question from [JLC'13] by showing that SAC solves all the problems of bounded width. At the same time the problem of verifying weaker consistency (even SAC) offers significant computational advantages over the problem of verifying (2,3)-consistency which improves the algorithms solving the CSPs of bounded width.
Power Side Channels in Security ICs: Hardware Countermeasures
Venue: arXiv cs.CR 2016
Year: 2016

Power side-channel attacks are a very effective cryptanalysis technique that can infer secret keys of security ICs by monitoring the power consumption. Since the emergence of practical attacks in the late 90s, they have been a major threat to many cryptographic-equipped devices including smart cards, encrypted FPGA designs, and mobile phones. Designers and manufacturers of cryptographic devices have in response developed various countermeasures for protection. Attacking methods have also evolved to counteract resistant implementations. This paper reviews foundational power analysis attack techniques and examines a variety of hardware design mitigations. The aim is to highlight exposed vulnerabilities in hardware-based countermeasures for future more secure implementations.
An Effective Limited Feedback Scheme for FD-MIMO Based on Noncoherent Detection and Kronecker Product Codebook
Venue: arXiv cs.IT 2016
Year: 2016

The low complexity quantization of channel state information (CSI) and the utilization of vertical freedom of three dimension (3D) channels are two critical issues in the limited feedback design of the \emph{full dimension multi-input-multi-output} (FD-MIMO) systems. In this paper, we propose an effective limited feedback scheme. We first employ Kronecker product based codebook (KPC) to explore the vertical freedom of 3D channels, extending the limited feedback from two dimension (2D) to 3D. Fruthermore, we use noncoherent sequence detection (NCSD) to quantify the CSI which includes both the vertical and horizontal channel information. This quantization method exploits the duality between codebook searching and NCSD to transform the CSI quntization on KPC to two parallel NCSD. The complexity is reduced from exponential to linear with the number of antennas. We also show the proposed scheme does not affect the diversity gain of the system by demonstrating the full diversity order. Monte Carlo simulation results show that the proposed scheme provides at least 1.2dB coding gain compared with traditional 2D limited feedback schemes. Moreover, the proposed scheme outperforms other FD/3D CSI quantization schemes by 0.8dB coding gain with moderate complexity when the channel is highly spatially correlated.
Real-time Quasi-Optimal Trajectory Planning for Autonomous Underwater Docking
Venue: arXiv cs.RO 2016
Year: 2016

In this paper, a real-time quasi-optimal trajectory planning scheme is employed to guide an autonomous underwater vehicle (AUV) safely into a funnel-shape stationary docking station. By taking advantage of the direct method of calculus of variation and inverse dynamics optimization, the proposed trajectory planner provides a computationally efficient framework for autonomous underwater docking in a 3D cluttered undersea environment. Vehicular constraints, such as constraints on AUV states and actuators; boundary conditions, including initial and final vehicle poses; and environmental constraints, for instance no-fly zones and current disturbances, are all modelled and considered in the problem formulation. The performance of the proposed planner algorithm is analyzed through simulation studies. To show the reliability and robustness of the method in dealing with uncertainty, Monte Carlo runs and statistical analysis are carried out. The results of the simulations indicate that the proposed planner is well suited for real-time implementation in a dynamic and uncertain environment.
Performance of Multilevel Flash Memories with Different Binary Labelings: A Multi-User Perspective
Venue: arXiv cs.IT 2016
Year: 2016

In this work, we study the performance of different decoding schemes for multilevel flash memories where each page in every block is encoded independently. We focus on the multi-level cell (MLC) flash memory, which is modeled as a two-user multiple access channel suffering from asymmetric noise. The uniform rate regions and sum rates of Treating Interference as Noise (TIN) decoding and Successive Cancelation (SC) decoding are investigated for a Program/Erase (P/E) cycling model and a data retention model. We examine the effect of different binary labelings of the cell levels, as well as the impact of further quantization of the memory output (i.e., additional read thresholds). Finally, we extend our analysis to the three-level cell (TLC) flash memory.
Automatic Identification of Retinal Arteries and Veins in Fundus Images using Local Binary Patterns
Venue: arXiv cs.CV 2016
Year: 2016

Artery and vein (AV) classification of retinal images is a key to necessary tasks, such as automated measurement of arteriolar-to-venular diameter ratio (AVR). This paper comprehensively reviews the state-of-the art in AV classification methods. To improve on previous methods, a new Local Bi- nary Pattern-based method (LBP) is proposed. Beside its simplicity, LBP is robust against low contrast and low quality fundus images; and it helps the process by including additional AV texture and shape information. Experimental results compare the performance of the new method with the state-of-the art; and also methods with different feature extraction and classification schemas.
Delay Analysis and Optimization in Cache-enabled Multi-Cell Cooperative Networks
Venue: arXiv cs.IT 2016
Year: 2016

Caching at the base stations (BSs) has been widely adopted to reduce the delivery delay and alleviate the backhaul traffic between BSs and the core network. In this paper, we consider a collaborative content caching scheme among BSs in cache-enabled multi-cell cooperative networks, where the requested contents can be obtained from the associated BS, the other collaborative BSs or the core network. Novelly, we model the stochastic request traffic and derive a closed form expression for the average delay per request based on multi-class processor sharing queuing theory. We then formulate a cooperative caching optimization problem of minimizing the average delay under the finite cache size constraint at BSs and show it to be at least NP-complete. Furthermore, we prove it equivalent to the maximization of a monotone submodular function subject to matroid constraints, allowing us to adopt the common greedy algorithm with 1/2 performance guarantee. A heuristic greedy caching strategy is also developed, achieving a better performance than the conventional greedy solution. Simulation results verify the accuracy of the analytical results and demonstrate the performance gains obtained by our proposed caching scheme.
Fast Simulation of Probabilistic Boolean Networks (Technical Report)
Venue: arXiv cs.CE 2016
Year: 2016

Probabilistic Boolean networks (PBNs) is an important mathematical framework widely used for modelling and analysing biological systems. PBNs are suited for modelling large biological systems, which more and more often arise in systems biology. However, the large system size poses a~significant challenge to the analysis of PBNs, in particular, to the crucial analysis of their steady-state behaviour. Numerical methods for performing steady-state analyses suffer from the state-space explosion problem, which makes the utilisation of statistical methods the only viable approach. However, such methods require long simulations of PBNs, rendering the simulation speed a crucial efficiency factor. For large PBNs and high estimation precision requirements, a slow simulation speed becomes an obstacle. In this paper, we propose a structure-based method for fast simulation of PBNs. This method first performs a network reduction operation and then divides nodes into groups for parallel simulation. Experimental results show that our method can lead to an approximately 10 times speedup for computing steady-state probabilities of a real-life biological network.
Random Access in Uplink Massive MIMO Systems: How to exploit asynchronicity and excess antennas
Venue: arXiv cs.IT 2016
Year: 2016

Massive MIMO systems, where the base stations are equipped with hundreds of antennas, are an attractive way to handle the rapid growth of data traffic. As the number of users increases, the initial access and handover in contemporary networks will be flooded by user collisions. In this work, we propose a random access procedure that resolves collisions and also performs timing, channel, and power estimation by simply utilizing the large number of antennas envisioned in massive MIMO systems and the inherent timing misalignments of uplink signals during network access and handover. Numerical results are used to validate the performance of the proposed solution under different settings. It turns out that the proposed solution can detect all collisions with a probability higher than 90%, at the same time providing reliable timing and channel estimates. Moreover, numerical results demonstrate that it is robust to overloaded situations.
Lossless Linear Analog Compression
Venue: arXiv cs.IT 2016
Year: 2016

We establish the fundamental limits of lossless linear analog compression by considering the recovery of random vectors ${\boldsymbol{\mathsf{x}}}\in{\mathbb R}^m$ from the noiseless linear measurements ${\boldsymbol{\mathsf{y}}}=\boldsymbol{A}{\boldsymbol{\mathsf{x}}}$ with measurement matrix $\boldsymbol{A}\in{\mathbb R}^{n\times m}$. Specifically, for a random vector ${\boldsymbol{\mathsf{x}}}\in{\mathbb R}^m$ of arbitrary distribution we show that ${\boldsymbol{\mathsf{x}}}$ can be recovered with zero error probability from $n>\inf\underline{\operatorname{dim}}_\mathrm{MB}(U)$ linear measurements, where $\underline{\operatorname{dim}}_\mathrm{MB}(\cdot)$ denotes the lower modified Minkowski dimension and the infimum is over all sets $U\subseteq{\mathbb R}^{m}$ with $\mathbb{P}[{\boldsymbol{\mathsf{x}}}\in U]=1$. This achievability statement holds for Lebesgue almost all measurement matrices $\boldsymbol{A}$. We then show that $s$-rectifiable random vectors---a stochastic generalization of $s$-sparse vectors---can be recovered with zero error probability from $n>s$ linear measurements. From classical compressed sensing theory we would expect $n\geq s$ to be necessary for successful recovery of ${\boldsymbol{\mathsf{x}}}$. Surprisingly, certain classes of $s$-rectifiable random vectors can be recovered from fewer than $s$ measurements. Imposing an additional regularity condition on the distribution of $s$-rectifiable random vectors ${\boldsymbol{\mathsf{x}}}$, we do get the expected converse result of $s$ measurements being necessary. The resulting class of random vectors appears to be new and will be referred to as $s$-analytic random vectors.
Distributed Frequency Control in Power Grids Under Limited Communication
Venue: arXiv cs.SY 2016
Year: 2016

In this paper, we analyze the impact of communication failures on the performance of optimal distributed frequency control. We consider a consensus-based control scheme, and show that it does not converge to the optimal solution when the communication network is disconnected. We propose a new control scheme that uses the dynamics of power grid to replicate the information not received from the communication network, and prove that it achieves the optimal solution under any single communication link failure. In addition, we show that this control improves cost under multiple communication link failures. Next, we analyze the impact of discrete-time communication on the performance of distributed frequency control. In particular, we will show that the convergence time increases as the time interval between two messages increases. We propose a new algorithm that uses the dynamics of the power grid, and show through simulation that it improves the convergence time of the control scheme significantly.
Personalized Risk Scoring for Critical Care Patients using Mixtures of Gaussian Process Experts
Venue: arXiv cs.LG 2016
Year: 2016

We develop a personalized real time risk scoring algorithm that provides timely and granular assessments for the clinical acuity of ward patients based on their (temporal) lab tests and vital signs. Heterogeneity of the patients population is captured via a hierarchical latent class model. The proposed algorithm aims to discover the number of latent classes in the patients population, and train a mixture of Gaussian Process (GP) experts, where each expert models the physiological data streams associated with a specific class. Self-taught transfer learning is used to transfer the knowledge of latent classes learned from the domain of clinically stable patients to the domain of clinically deteriorating patients. For new patients, the posterior beliefs of all GP experts about the patient's clinical status given her physiological data stream are computed, and a personalized risk score is evaluated as a weighted average of those beliefs, where the weights are learned from the patient's hospital admission information. Experiments on a heterogeneous cohort of 6,313 patients admitted to Ronald Regan UCLA medical center show that our risk score outperforms the currently deployed risk scores, such as MEWS and Rothman scores.
Hierarchical Bayesian Noise Inference for Robust Real-time Probabilistic Object Classification
Venue: arXiv cs.CV 2016
Year: 2016

Robust environment perception is essential for decision-making on robots operating in complex domains. Principled treatment of uncertainty sources in a robot's observation model is necessary for accurate mapping and object detection. This is important not only for low-level observations (e.g., accelerometer data), but for high-level observations such as semantic object labels as well. This paper presents an approach for filtering sequences of object classification probabilities using online modeling of the noise characteristics of the classifier outputs. A hierarchical Bayesian approach is used to model per-class noise distributions, while simultaneously allowing sharing of high-level noise characteristics between classes. The proposed filtering scheme, called Hierarchical Bayesian Noise Inference (HBNI), is shown to outperform classification accuracy of existing methods. The paper also presents real-time filtered classification hardware experiments running fully onboard a moving quadrotor, where the proposed approach is demonstrated to work in a challenging domain where noise-agnostic filtering fails.
Structural Synthesis for GXW Specifications
Venue: arXiv cs.LO 2016
Year: 2016

We define the GXW fragment of linear temporal logic (LTL) as the basis for synthesizing embedded control software for safety-critical applications. Since GXW includes the use of a weak-until operator we are able to specify a number of diverse programmable logic control (PLC) problems, which we have compiled from industrial training sets. For GXW controller specifications, we develop a novel approach for synthesizing a set of synchronously communicating actor-based controllers. This synthesis algorithm proceeds by means of recursing over the structure of GXW specifications, and generates a set of dedicated and synchronously communicating sub-controllers according to the formula structure. In a subsequent step, 2QBF constraint solving identifies and tries to resolve potential conflicts between individual GXW specifications. This structural approach to GXW synthesis supports traceability between requirements and the generated control code as mandated by certification regimes for safety-critical software. Synthesis for GXW specifications is in PSPACE compared to 2EXPTIME-completeness of full-fledged LTL synthesis. Indeed our experimental results suggest that GXW synthesis scales well to industrial-sized control synthesis problems with 20 input and output ports and beyond.
A Generic Method for Automatic Ground Truth Generation of Camera-captured Documents
Venue: arXiv cs.CV 2016
Year: 2016

The contribution of this paper is fourfold. The first contribution is a novel, generic method for automatic ground truth generation of camera-captured document images (books, magazines, articles, invoices, etc.). It enables us to build large-scale (i.e., millions of images) labeled camera-captured/scanned documents datasets, without any human intervention. The method is generic, language independent and can be used for generation of labeled documents datasets (both scanned and cameracaptured) in any cursive and non-cursive language, e.g., English, Russian, Arabic, Urdu, etc. To assess the effectiveness of the presented method, two different datasets in English and Russian are generated using the presented method. Evaluation of samples from the two datasets shows that 99:98% of the images were correctly labeled. The second contribution is a large dataset (called C3Wi) of camera-captured characters and words images, comprising 1 million word images (10 million character images), captured in a real camera-based acquisition. This dataset can be used for training as well as testing of character recognition systems on camera-captured documents. The third contribution is a novel method for the recognition of cameracaptured document images. The proposed method is based on Long Short-Term Memory and outperforms the state-of-the-art methods for camera based OCRs. As a fourth contribution, various benchmark tests are performed to uncover the behavior of commercial (ABBYY), open source (Tesseract), and the presented camera-based OCR using the presented C3Wi dataset. Evaluation results reveal that the existing OCRs, which already get very high accuracies on scanned documents, have limited performance on camera-captured document images; where ABBYY has an accuracy of 75%, Tesseract an accuracy of 50.22%, while the presented character recognition system has an accuracy of 95.10%.
Successor-Invariant First-Order Logic on Graphs with Excluded Topological Subgraphs
Venue: arXiv cs.LO 2016
Year: 2016

We show that the model-checking problem for successor-invariant first-order logic is fixed-parameter tractable on graphs with excluded topological subgraphs when parameterised by both the size of the input formula and the size of the exluded topological subgraph. Furthermore, we show that model-checking for order-invariant first-order logic is tractable on coloured posets of bounded width, parameterised by both the size of the input formula and the width of the poset. Our result for successor-invariant FO extends previous results for this logic on planar graphs (Engelmann et al., LICS 2012) and graphs with excluded minors (Eickmeyer et al., LICS 2013), further narrowing the gap between what is known for FO and what is known for successor-invariant FO. The proof uses Grohe and Marx's structure theorem for graphs with excluded topological subgraphs. For order-invariant FO we show that Gajarský et al.'s recent result for FO carries over to order-invariant FO.
Info-Clustering: A Mathematical Theory for Data Clustering
Venue: arXiv cs.IT 2016
Year: 2016

We formulate an info-clustering paradigm based on a multivariate information measure, called multivariate mutual information, that naturally extends Shannon's mutual information between two random variables to the multivariate case involving more than two random variables. With proper model reductions, we show that the paradigm can be applied to study the human genome and connectome in a more meaningful way than the conventional algorithmic approach. Not only can info-clustering provide justifications and refinements to some existing techniques, but it also inspires new computationally feasible solutions.
Hierarchical Modeling of Multidimensional Data in Regularly Decomposed Spaces: Applications in Image Analysis
Venue: arXiv cs.CV 2016
Year: 2016

This last document is showing the gradual introduction of hierarchical modeling techniques in image analysis. The first chapter is dealing with the first works carried out in the field of industrial applications of pattern recognition. The second chapter is focusing on the usage of these techniques in satellite imagery and on the development of a satellite data archiving system in the aim of using it in digital geography. The third chapter is about face recognition based on planar image analysis and about the recognition of partially hidden patterns. The present publication is ending with the description of a future system of self-descriptive coding of still or moving pictures in relation with the current video coding standards. As in the previous documents, it will be found in annex algorithms targeted on image analysis according two complementary approaches: - boundary-based approach for the industrial applications of artificial vision; - region-based approach for satellite image analysis.