Query: Applying LTE-D2D to Support V2V Communication Using Local Geographic Knowledge

Relevant passages:
LTE for vehicular networking: a survey <=SEP=> A wide variety of applications for road safety and traffic efficiency are intended to answer the urgent call for smarter, greener, and safer mobility. Although IEEE 802.11p is considered the de facto standard for on-the-road communications, stakeholders have recently started to investigate the usability of LTE to support vehicular applications. In this article, related work and running standardization activities are scanned and critically discussed; strengths and weaknesses of LTE as an enabling technology for vehicular communications are analyzed; and open issues and critical design choices are highlighted to serve as guidelines for future research in this hot topic.

3GPP LTE Versus IEEE 802.11p/WAVE: Which Technology is Able to Support Cooperative Vehicular Safety Applications? <=SEP=> The concept of vehicular ad-hoc networks enables the design of emergent automotive safety applications, which are based on the awareness among vehicles. Recently, a suite of 802.11p/WAVE protocols aimed at supporting car-to-car communications was approved by IEEE. Existing cellular infrastructure and, above all 3GPP LTE, is being considered as another communication technology appropriate for vehicular applications. This letter provides a theoretical framework which compares the basic patterns of both the technologies in the context of safety-of-life vehicular scenarios. We present mathematical models for the evaluation of the considered protocols in terms of successful beacon delivery probability.

A Survey on Device-to-Device Communication in Cellular Networks <=SEP=> Device-to-device (D2D) communications was initially proposed in cellular networks as a new paradigm for enhancing network performance. The emergence of new applications such as content distribution and location-aware advertisement introduced new user cases for D2D communications in cellular networks. The initial studies showed that D2D communications has advantages such as increased spectral efficiency and reduced communication delay. However, this communication mode introduces complications in terms of interference control overhead and protocols that are still open research problems. The feasibility of D2D communications in Long-Term Evolution Advanced is being studied by academia, industry, and standardization bodies. To date, there are more than 100 papers available on D2D communications in cellular networks, but there is no survey on this field. In this paper, we provide a taxonomy based on the D2D communicating spectrum and review the available literature extensively under the proposed taxonomy. Moreover, we provide new insights into the over-explored and under-explored areas that lead us to identify open research problems of D2D communications in cellular networks.

D2D for Intelligent Transportation Systems: A Feasibility Study <=SEP=> Intelligent transportation systems (ITS) are becoming a crucial component of our society, whereas reliable and efficient vehicular communications consist of a key enabler of a well-functioning ITS. To meet a wide variety of ITS application needs, vehicular-to-vehicular and vehicular-to-infrastructure communications have to be jointly considered, configured, and optimized. The effective and efficient coexistence and cooperation of the two give rise to a dynamic spectrum management problem. One recently emerged and rapidly adopted solution of a similar problem in cellular networks is the so-termed device-to-device (D2D) communications. Its potential in the vehicular scenarios with unique challenges, however, has not been thoroughly investigated to date. In this paper, we for the first time carry out a feasibility study of D2D for ITS based on both the features of D2D and the nature of vehicular networks. In addition to demonstrating the promising potential of this technology, we will also propose novel remedies necessary to make D2D technology practical as well as beneficial for ITS.

Vehicular communication systems: Enabling technologies, applications, and future outlook on intelligent transportation <=SEP=> Numerous technologies have been deployed to assist and manage transportation. But recent concerted efforts in academia and industry point to a paradigm shift in intelligent transportation systems. Vehicles will carry computing and communication platforms, and will have enhanced sensing capabilities. They will enable new versatile systems that enhance transportation safety and efficiency and will provide infotainment. This article surveys the state-of-the-art approaches, solutions, and technologies across a broad range of projects for vehicular communication systems.

Inferring Painting Style with Multi-Task Dictionary Learning <=SEP=> Recent advances in imaging and multimedia technologies have paved the way for automatic analysis of visual art. Despite notable attempts, extracting relevant patterns from paintings is still a challenging task. Different painters, born in different periods and places, have been influenced by different schools of arts. However, each individual artist also has a unique signature, which is hard to detect with algorithms and objective features. In this paper we propose a novel dictionary learning approach to automatically uncover the artistic style from paintings. Specifically, we present a multi-task learning algorithm to learn a style-specific dictionary representation. Intuitively, our approach, by automatically decoupling style-specific and artist-specific patterns, is expected to be more accurate for retrieval and recognition tasks than generic methods. To demonstrate the effectiveness of our approach, we introduce the DART dataset, containing more than 1.5K images of paintings representative of different styles. Our extensive experimental evaluation shows that our approach significantly outperforms state-of-the-art methods.

Eigenvector-centrality - a node-centrality? <=SEP=> Networks of social relations can be represented by graphs and socioor adjacency-matrices and their structure can be analyzed using different concepts, one of them called centrality. We will provide a new formalization of a “node-centrality” which leads to some properties a measure of centrality has to satisfy. These properties allow to test given measures, for example measures based on degree, closeness, betweenness or Bonacich’s eigenvector-centrality. It turns out that it depends on normalization whether eigenvector-centrality does satisfy the expected properties or not. © 2000 Elsevier Science B.V. All rights reserved.

Image Based Camera Localization: an Overview <=SEP=> Virtual reality, augmented reality, robotics, and autonomous driving, have recently attracted much attention from both academic and industrial communities, in which image-based camera localization is a key task. However, there has not been a complete review on image-based camera localization. It is urgent to map this topic to enable individuals enter the field quickly. In this paper, an overview of image-based camera localization is presented. A new and complete classification of image-based camera localization approaches is provided and the related techniques are introduced. Trends for future development are also discussed. This will be useful not only to researchers, but also to engineers and other individuals interested in this field.

The peculiarities of knowledge management processes in SMEs: the case of Singapore <=SEP=> Purpose – The objectives of this study are two-fold. The first is to examine the peculiarities of KM processes that are unique in SMEs from three perspectives, namely knowledge creation, knowledge sharing and knowledge reuse. Secondly, to identify enablers and impediments of these KM processes that influence the competitiveness of SMEs. Design/methodology/approach – The study adopted a case study approach involving 21 participants comprising management staff and front-line employees from four Singaporean SMEs. Findings – The SME owner, rather than the employees, was found to be the key source and creator of knowledge and the sole driver of the KM processes. In SMEs, knowledge creation takes the form of innovative customized solutions to meet customers’ needs; knowledge sharing occurs through cross functionality, overlapping roles, and facilitated by close physical proximity in open workspaces; and knowledge reuse is often made tacitly, where common knowledge is prevalently embedded within the KM processes of SMEs. The enablers of knowledge creation process rested largely on the owner’s innovativeness, creativity and ability to acquire knowledge of the industry. Knowledge sharing processes are enabled by the awareness of roles, mutual respect and the level of trust among employees within the SME while knowledge reuse is fostered by close proximity of employees and the willingness and openness of the owner to impart his knowledge. The lack of the above enablement factors mentioned will hinder these KM processes. Research limitations/implications – The study is limited by the fact that data was collected from four SMEs in Singapore. Furthermore, only a small sample of staff from these SMEs was interviewed. Hence the findings need to be interpreted in light of such a scope. Practical implications – For SMEs, this research provides perspectives on the factors influencing KM processes, in particular, the importance of the owners’ knowledge and leadership, the flexibility and adaptability of the organization, and open culture to enable the capitalization of its knowledge assets to survive and stay competitive. For practitioners, this paper reinforces the importance of the management owners’ innovativeness, initiatives and support, and the level of social interaction and high level of trusts among employees in the SMEs to as enablers to effective KM processes in SMEs. Originality/value – To deepen on-going knowledge management research on SMEs, this paper provides insights and rich context to the distinctness of KM processes in SMEs.

An Analytical Solution to the Stance Dynamics of Passive Spring-Loaded Inverted Pendulum with Damping <=SEP=> The Spring-Loaded Inverted Pendulum (SLIP) model has been established both as a very accurate descriptive tool as well as a good basis for the design and control of running robots. In particular, approximate analytic solutions to the otherwise nonintegrable dynamics of this model provide principled ways in which gait controllers can be built, yielding invaluable insight into their stability properties. However, most existing work on the SLIP model completely disregards the effects of damping, which often cannot be neglected for physical robot platforms. In this paper, we introduce a new approximate analytical solution to the dynamics of this system that also takes into account viscous damping in the leg. We compare both the predictive performance of our approximation as well as the tracking performance of an associated deadbeat gait controller to similar existing methods in the literature and show that it significantly outperforms them in the presence of damping in the leg.

Smart foot device for women safety <=SEP=> In this paper, an attempt has been made to develop a smart device that can assist women when they feel unsafe. This smart device will be clipped to the footwear of the user and can be triggered discreetly. On tapping one foot behind the other four times, an alert is sent via Bluetooth Low Energy communication to an application on the victim's phone, programmed to generate a message seeking help with the location of the device attached. The results obtained were analysed using Naïve Bayes classifier and this low cost device showed an overall accuracy of 97.5%.

Informal social communication. <=SEP=> 

Stuttering in relation to anxiety, temperament, and personality: review and analysis with focus on causality. <=SEP=> UNLABELLED
Anxiety and emotional reactions have a central role in many theories of stuttering, for example that persons who stutter would tend to have an emotionally sensitive temperament. The possible relation between stuttering and certain traits of temperament or personality were reviewed and analyzed, with focus on temporal relations (i.e., what comes first). It was consistently found that preschool children who stutter (as a group) do not show any tendencies toward elevated temperamental traits of shyness or social anxiety compared with children who do not stutter. Significant group differences were, however, repeatedly reported for traits associated with inattention and hyperactivity/impulsivity, which is likely to reflect a subgroup of children who stutter. Available data is not consistent with the proposal that the risk for persistent stuttering is increased by an emotionally reactive temperament in children who stutter. Speech-related social anxiety develops in many cases of stuttering, before adulthood. Reduction of social anxiety in adults who stutter does not in itself appear to result in significant improvement of speech fluency. Studies have not revealed any relation between the severity of the motor symptoms of stuttering and temperamental traits. It is proposed that situational variability of stuttering, related to social complexity, is an effect of interference from social cognition and not directly from the emotions of social anxiety. In summary, the studies in this review provide strong evidence that persons who stutter are not characterized by constitutional traits of anxiety or similar constructs.


EDUCATIONAL OBJECTIVES
This paper provides a review and analysis of studies of anxiety, temperament, and personality, organized with the objective to clarify cause and effect relations. Readers will be able to (a) understand the importance of effect size and distribution of data for interpretation of group differences; (b) understand the role of temporal relations for interpretation of cause and effect; (c) discuss the results of studies of anxiety, temperament and personality in relation to stuttering; and (d) discuss situational variations of stuttering and the possible role of social cognition.

Feature Based Sentiment Analysis for Service Reviews <=SEP=> Sentiment Analysis deals with the analysis of emotions, opinions and facts in the sentences which are expressed by the people. It allows us to track attitudes and feelings of the people by analyzing blogs, comments, reviews and tweets about all the aspects. The development of Internet has strong influence in all types of industries like tourism, healthcare and any business. The availability of Internet has changed the way of accessing the information and sharing their experience among users. Social media provide this information and these comments are trusted by other users. This paper recognizes the use and impact of social media on healthcare industry by analyzing the users’ feelings expressed in the form of free text, thereby gives the quality indicators of services or features related with them. In this paper, a sentiment classifier model using improved Term Frequency Inverse Document Frequency (TFIDF) method and linear regression model has been proposed to classify online reviews, tweets or customer feedback for various features. The model involves the process of gathering online user reviews about hospitals and analyzes those reviews in terms of sentiments expressed. Information Extraction process filters irrelevant reviews, extracts sentimental words of features identified and quantifies the sentiment of features using sentiment dictionary. Emotionally expressed positive or negative words are assigned weights using the classification prescribed in the dictionary. The sentiment analysis on tweets/reviews is done for various features using Natural Language Processing (NLP) and Information Retrieval (IR) techniques. The proposed linear regression model using the senti-score predicts the star rating of the feature of service. The statistical results show that improved TF-IDF method gives better accuracy when compared with TF and TF-IDF methods, used for representing the text. The senti-score obtained as a result of text analysis (user feedback) on features gives not only the opinion summarization but also the comparative results on various features of different competitors. This information can be used by business to focus on the low scored features so as to improve their business and ensure a very high level of user satisfaction.

A quad 25Gb/s 270mW TIA in 0.13µm BiCMOS with <0.15dB crosstalk penalty <=SEP=> The push for 100Gb/s optical transport and beyond necessitates electronic components at higher speed and integration level in order to drive down cost, complexity and size of transceivers [1-2]. This requires parallel multi-channel optical transceivers each operating at 25Gb/s and beyond. Due to variations in the output power of transmitters and in some cases different optical paths the parallel receivers have to operate at different input optical power levels. This trend places increasing strain to the acceptable inter-channel crosstalk in integrated multi-channel receivers [3]. Minimizing this cross-talk penalty when all channels are operational is becoming increasingly important in ultra-high throughput optical links.

Learning from Multiple Partially Observed Views - an Application to Multilingual Text Categorization <=SEP=> We address the problem of learning classifiers when observat ions have multiple views, some of which may not be observed for all examples. We a ssume the existence of view generating functions which may complete t he missing views in an approximate way. This situation corresponds for examp le to learning text classifiers from multilingual collections where documents are not available in all languages. In that case, Machine Translation (MT) systems m ay be used to translate each document in the missing languages. We derive a gene ralization error bound for classifiers learned on examples with multiple arti ficially created views. Our result uncovers a trade-off between the size of the train ing set, the number of views, and the quality of the view generating functions. A s a consequence, we identify situations where it is more interesting to use mu ltiple views for learning instead of classical single view learning. An extension of this framework is a natural way to leverage unlabeled multi-view data in semisupervised learning. Experimental results on a subset of the Reuters RCV1/RCV2 co llecti ns support our findings by showing that additional views obtained from M T may significantly improve the classification performance in the cases identifi d by our trade-off.

Bilateral Back-Projection for Single Image Super Resolution <=SEP=> In this paper, a novel algorithm for single image super resolution is proposed. Back-projection [1] can minimize the reconstruction error with an efficient iterative procedure. Although it can produce visually appealing result, this method suffers from the chessboard effect and ringing effect, especially along strong edges. The underlining reason is that there is no edge guidance in the error correction process. Bilateral filtering can achieve edge-preserving image smoothing by adding the extra information from the feature domain. The basic idea is to do the smoothing on the pixels which are nearby both in space domain and in feature domain. The proposed bilateral back-projection algorithm strives to integrate the bilateral filtering into the back-projection method. In our approach, the back-projection process can be guided by the edge information to avoid across-edge smoothing, thus the chessboard effect and ringing effect along image edges are removed. Promising results can be obtained by the proposed bilateral back-projection method efficiently.

Prediction of air temperature using Multi-layer perceptrons with Levenberg-Marquardt training algorithm <=SEP=> related to the prediction of the air temperature in the region of Meknes in Morocco. Depending on weather parameters, such as atmospheric pressure (Pr), humidity (H), visibility (Vis), wind speed (V), dew point (Tr) and the precipitation (P). The database used contains a history of meteorological parameters covering 3288 days, from 2004 to 2012. This multilayer perceptron type of neuron (MLP) was used to approximate the relationship between these parameters with minimum square error. For a better air temperature prediction, we developed a stochastic neural model. The mean square error (MSE) and the correlation coefficient (R) were used to evaluate the performance of the developed models. The study of these statistical indicators demonstrated that the prediction of the air temperature was powerful with the Levenberg-Marquard algorithm, with architecture [610-1], the Tansig function in the hidden layer and the Purelin function in the output layer.

FiberMesh: designing freeform surfaces with 3D curves <=SEP=> This paper presents a system for designing freeform surfaces with a collection of 3D curves. The user first creates a rough 3D model by using a sketching interface. Unlike previous sketching systems, the user-drawn strokes stay on the model surface and serve as handles for controlling the geometry. The user can add, remove, and deform these control curves easily, as if working with a 2D line drawing. The curves can have arbitrary topology; they need not be connected to each other. For a given set of curves, the system automatically constructs a smooth surface embedding by applying functional optimization. Our system provides real-time algorithms for both control curve deformation and the subsequent surface optimization. We show that one can create sophisticated models using this system, which have not yet been seen in previous sketching or functional optimization systems.

A framework and theory for cyber security assessments <=SEP=> Information system security risk, defined as the product of the monetary losses associated with security incidents and the probability that they occur, is a suitable decision criterion when considering different information system architectures. This paper describes how probabilistic relational models can be used to specify architecture metamodels so that security risk can be inferred from metamodel-instantiations. A probabilistic relational model contains classes, attributes, and class-relationships. It can be used to specify architectural metamodels similar to class diagrams in the Unified Modeling Language. In addition, a probabilistic relational model makes it possible to associate a probabilistic dependency model to the attributes of classes in the architectural metamodel. This paper proposes a set of abstract classes that can be used to create probabilistic relational models so that they enable inference of security risk from instantiated architecture models. If an architecture metamodel is created by specializing the abstract classes proposed in this paper, the instantiations of the metamodel will generate a probabilistic dependency model that can be used to calculate the security risk associated with these instantiations. The abstract classes make it possible to derive the dependency model and calculate security risk from an instance model that only specifies assets and their relationships to each other. Hence, the person instantiating the architecture metamodel is not required to assess complex security attributes to quantify security risk using the instance model. Paper A: A probabilistic relational model for security risk analysis

An interface for targeted collection of common sense knowledge using a mixture model <=SEP=> We present a game-based interface for acquiring common sense knowledge. In addition to being interactive and entertaining, our interface guides the knowledge acquisition process to learn about the most salient characteristics of a particular concept. We use statistical classification methods to discover the most informative characteristics in the Open Mind Common Sense knowledge base, and use these characteristics to play a game of 20 Questions with the user. Our interface also allows users to enter knowledge more quickly than a more traditional knowledge-acquisition interface. An evaluation showed that users enjoyed the game and that it increased the speed of knowledge acquisition.

DISCO: Distributed multi-domain SDN controllers <=SEP=> Software-Defined Networking (SDN) is now envisioned for Wide Area Networks (WAN) and constrained overlay networks. Such networks require a resilient, scalable and easily extensible SDN control plane. In this paper, we propose DISCO, an extensible DIstributed SDN COntrol plane able to cope with the distributed and heterogeneous nature of modern overlay networks. A DISCO controller manages its own network domain and communicates with other controllers to provide end-to-end network services. This east-west communication is based on a lightweight and highly manageable control channel. We implemented DISCO on top of the Floodlight OpenFlow controller and the AMQP protocol and we evaluated it through an inter-domain topology disruption use case.

Folksonomies-Cooperative Classification and Communication Through Shared Metadata <=SEP=> This paper examines user-generated metadata as implemented and applied in two web services designed to share and organize digital media to better understand grassroots classification. Metadata data about data allows systems to collocate related information, and helps users find relevant information. The creation of metadata has generally been approached in two ways: professional creation and author creation. In libraries and other organizations, creating metadata, primarily in the form of catalog records, has traditionally been the domain of dedicated professionals working with complex, detailed rule sets and vocabularies. The primary problem with this approach is scalability and its impracticality for the vast amounts of content being produced and used, especially on the World Wide Web. The apparatus and tools built around professional cataloging systems are generally too complicated for anyone without specialized training and knowledge. A second approach is for metadata to be created by authors. The movement towards creator described documents was heralded by SGML, the WWW, and the Dublin Core Metadata Initiative. There are problems with this approach as well often due to inadequate or inaccurate description, or outright deception. This paper examines a third approach: user-created metadata, where users of the documents and media create metadata for their own individual use that is also shared throughout a community. 1 The Creation of Metadata: Professionals, Content Creators, Users Metadata is often characterized as “data about data.” Metadata is information, often highly structured, about documents, books, articles, photographs, or other items that is designed to support specific functions. These functions are usually

Hemihyperplasia-multiple lipomatosis syndrome (HHML): a challenge in spinal care. <=SEP=> A 15-year-old girl developed a progressive paraparesis over a period of six months, secondary to spinal cord compression by a lipomatous mass and anomalies of the vertebral column. Clinically, a right hemihyperplasia affecting the trunk and lower limb was evident, as well as a right convex lumbar scoliosis. CT and MRI demonstrated severe spinal cord compression resulting from intraspinal lipomatosis, overgrowth of right facet joints (T8 to L5), and kyphoscoliosis. Surgical decompression was undertaken. A lumbar scoliosis of 48 degrees was partially corrected by means of dual-rod instrumentation. The neurological deficit improved significantly, and ambulation was progressively restored. The patient carried the diagnosis of Proteus syndrome for several years, but reevaluation of clinical features prompted the diagnosis of Hemihyperplasia Multiple Lipomatosis syndrome (HHML). This rare sporadic disorder is often confused with Proteus syndrome. As in Proteus syndrome, spinal cord compression in patients with HHML can result from lipomatous infiltration and/or significant spinal abnormalities including kyphoscoliosis and overgrowth. HHML and Proteus syndrome are discussed and compared with special emphasis on spinal and orthopaedic pathologies.

Reverse Word Order Models <=SEP=> In this work, we study the impact of the word order decoding direction for statistical machine translation (SMT). Both phrase-based and hierarchical phrasebased SMT systems are investigated by reversing the word order of the source and/or target language and comparing the translation results with the normal direction. Analysis are done on several components such as alignment model, language model and phrase table to see which of them accounts for the differences generated by various translation directions. Furthermore, we propose to use system combination, alignment combinations and phrase table combinations to take benefit from systems trained with different translation directions. Experimental results show improvements of up to 1.7 points in BLEU and 3.1 points in TER compared to the normal direction systems for the NTCIR9 Japanese-English and Chinese-English tasks.

Groove Radio: A Bayesian Hierarchical Model for Personalized Playlist Generation <=SEP=> This paper describes an algorithm designed for Microsoft's Groove music service, which serves millions of users world wide. We consider the problem of automatically generating personalized music playlists based on queries containing a ``seed'' artist and the listener's user ID. Playlist generation may be informed by a number of information sources including: user specific listening patterns, domain knowledge encoded in a taxonomy, acoustic features of audio tracks, and overall popularity of tracks and artists. The importance assigned to each of these information sources may vary depending on the specific combination of user and seed~artist.
 The paper presents a method based on a variational Bayes solution for learning the parameters of a model containing a four-level hierarchy of global preferences, genres, sub-genres and artists. The proposed model further incorporates a personalization component for user-specific preferences. Empirical evaluations on both proprietary and public datasets demonstrate the effectiveness of the algorithm and showcase the contribution of each of its components.

Communication-optimal parallel algorithm for strassen's matrix multiplication <=SEP=> Parallel matrix multiplication is one of the most studied fundamental problems in distributed and high performance computing. We obtain a new parallel algorithm that is based on Strassen's fast matrix multiplication and minimizes communication. The algorithm outperforms all known parallel matrix multiplication algorithms, classical and Strassen-based, both asymptotically and in practice. A critical bottleneck in parallelizing Strassen's algorithm is the communication between the processors. Ballard, Demmel, Holtz, and Schwartz (SPAA '11) prove lower bounds on these communication costs, using expansion properties of the underlying computation graph. Our algorithm matches these lower bounds, and so is communication-optimal. It exhibits perfect strong scaling within the maximum possible range.
 Benchmarking our implementation on a Cray XT4, we obtain speedups over classical and Strassen-based algorithms ranging from 24% to 184% for a fixed matrix dimension n=94080, where the number of processors ranges from 49 to 7203.
 Our parallelization approach generalizes to other fast matrix multiplication algorithms.

Road scene analysis for determination of road traffic density <=SEP=> Road traffic density has always been a concern in large cities around the world, and many approaches were developed to assist in solving congestions related to slow traffic flow. This work proposes a congestion rate estimation approach that relies on real-time video scenes of road traffic, and was implemented and evaluated on eight different hotspots covering 33 different urban roads. The approach relies on road scene morphology for estimation of vehicles average speed along with measuring the overall video scenes randomness acting as a frame texture analysis indicator. Experimental results shows the feasibility of the proposed approach in reliably estimating traffic density and in providing an early warning to drivers on road conditions, thereby mitigating the negative effect of slow traffic flow on their daily lives.

Recurrent Continuous Translation Models <=SEP=> We introduce a class of probabilistic continuous translation models called Recurrent Continuous Translation Models that are purely based on continuous representations for words, phrases and sentences and do not rely on alignments or phrasal translation units. The models have a generation and a conditioning aspect. The generation of the translation is modelled with a target Recurrent Language Model, whereas the conditioning on the source sentence is modelled with a Convolutional Sentence Model. Through various experiments, we show first that our models obtain a perplexity with respect to gold translations that is > 43% lower than that of stateof-the-art alignment-based translation models. Secondly, we show that they are remarkably sensitive to the word order, syntax, and meaning of the source sentence despite lacking alignments. Finally we show that they match a state-of-the-art system when rescoring n-best lists of translations.

The principles of software QRS detection <=SEP=> The QRS complex is the most striking waveform within the electrocardiogram (ECG). Since it reflects the electrical activity within the heart during the ventricular contraction, the time of its occurrence as well as its shape provide much information about the current state of the heart. Due to its characteristic shape it serves as the basis for the automated determination of the heart rate, as an entry point for classification schemes of the cardiac cycle, and often it is also used in ECG data compression algorithms. In that sense, QRS detection provides the fundamentals for almost all automated ECG analysis algorithms. Software QRS detection has been a research topic for more than 30 years. The evolution of these algorithms clearly reflects the great advances in computer technology. Within the last decade many new approaches to QRS detection have been proposed; for example, algorithms from the field of artificial neural networks genetic algorithms wavelet transforms, filter banks as well as heuristic methods mostly based on nonlinear transforms. The authors provide an overview of these recent developments as well as of formerly proposed algorithms.