Query: A statistical model-based voice activity detection

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Speech Enhancement Using a-Minimum Mean-Square Error Short-Time Spectral Amplitude Estimator <=SEP=> Absstroct-This paper focuses on the class of speech enhancement systems which capitalize on the major importance of the short-time spectral amplitude (STSA) of the speech signal in its perception. A system which utilizes a minimum mean-square error (MMSE) STSA estimator is proposed and then compared with other widely used systems which are based on Wiener filtering and the " spectral subtraction " algorithm. In this paper we derive the MMSE STSA estimator, based on modeling speech and noise spectral components as statistically independent Gaussian random variables. We analyze the performance of the proposed STSA estimator and compare it with a STSA estimator derived from the Wiener estimator. We also examine the MMSE STSA estimator under uncertainty of signal presence in the noisy observations. In constructing the enhanced signal, the MMSE STSA estimator is combined with the complex exponential of the noisy phase. It is shown here that the latter is the MMSE estimator of the complex exponential of the original phase, which does not affect the STSA estimation. The proposed approach results in a significant reduction of the noise, and provides enhanced speech with colorless residual noise. The complexity of the proposed algorithm is approximately that of other systems in the discussed class.

Elimination of the musical noise phenomenon with the Ephraim and Malah noise suppressor <=SEP=> This paper presents a study of the noise suppression technique proposed by Ephraim and Malah. This technique has been used recently for the restoration of degraded audio recordings because it is free of the frequently encountered ‘musical noise’ artifact. It is demonstrated how this artifact is actually eliminated without bringing distortion to the recorded signal even if the noise is only poorly stationary.

Suppression of acoustic noise in speech using spectral subtraction <=SEP=> A new technique for the modelling of perceptual systems called formal modelling is developed. This technique begins with qualitative observations about the perceptual system, the so-called perceptual symmetries, to obtain through mathematical analysis certain model structures which may then be calibrated by experiment. The analysis proceeds in two different ways depending upon the choice of linear or nonlinear models. For the linear case, the analysis proceeds through the methods of unitary representation theory. It begins with a unitary group representation on the image space and produces what we have called the fundamental structure theorem. For the nonlinear case, the analysis makes essential use of infinite-dimensional manifold theory. It ber ins with a Lie group action on an image manifold and produces the fundamental structure formula. These techniques will be used to study the brightness perception mechanism of the human visual system. Several visual groups are defined and their corresponding structures for visual system models are obtained. A new transform called the Mandala transform will be deduced from a certain visual group and its implications for image processing will be discussed. Several new phenomena of brightness perception will be presented. New facts about the Mach band illusion along with new adaptation phenomena will be presented. Also a new visual illusion will be presented. A visual model based on the above techniques will be presented. It will also be shown how use of statistical estimation theory can be made in the study of contrast adaptation.

Speech enhancement using a minimum mean-square error log-spectral amplitude estimator <=SEP=> 

Enhancement and bandwidth compression of noisy speech <=SEP=> Over the past several years there has been considerable attention focused on the problem of enhancement and bandwidth compression of speech degraded by additive background noise. This interest is motivated by several factors including a broad set of important applications, the apparent lack of robustness in current speech-compression systems and the development of several potentially promising and practical solutions. One objective of this paper is to provide an overview of the variety of techniques that have been proposed for enhancement and bandwidth compression of speech degraded by additive background noise. A second objective is to suggest a unifying framework in terms of which the relationships between these systems is more visible and which hopefully provides a structure which will suggest fruitful directions for further research.

Narrative Schema Stability in News Text <=SEP=> We investigate the stability of narrative schemas (Chambers & Jurafsky, 2009) automatically induced from a news corpus, representing recurring narratives in a corpus. If such techniques produce meaningful results, we should expect that small changes to the corpus will result in only small changes to the induced schemas. We describe experiments involving successive ablation of a corpus and cross-validation at each stage of ablation, on schemas generated by three different techniques over a general news corpus and topically-specific subcorpora. We also develop a method for evaluating the similarity between sets of narrative schemas, and thus the stability of the schema induction algorithms. This stability analysis affirms the heterogeneous/homogeneous document category hypothesis first presented in Simonson & Davis (2016), whose technique is problematically limited. Additionally, increased ablation leads to increasing stability, so the smaller the remaining corpus, the more stable schema generation appears to be. We surmise that as a corpus grows larger, novel and more varied narratives continue to appear and stability declines, though at some point this decline levels off as new additions to the corpus consist essentially of “more of the same.”

Content Centric Networking in tactical and emergency MANETs <=SEP=> Reliable and secure content distribution in a disruptive environment is a critical challenge due to high mobile and lossy channels. Traditional IP networking and wireless protocols tend to perform poorly. In this paper, we propose Content Centric Networking (CCN) for emergency wireless ad hoc environments. CCN is a novel communication architecture capable to access and retrieve content by name. This new approach achieves scalability, security, and efficient network resource management in large scale disaster recovery and battlefield networks. Existing Internet CCN schemes cannot be directly applied to wireless mobile ad hoc networks due to different environments and specific limitations. Thus, we must extend the CCN architecture by introducing features and requirements especially designed for disruptive networks. We prove feasibility and performance gain of the new design via implementation and experimentation.

Tunable VHF Miniaturized Helical Filters <=SEP=> In this paper, a miniaturization technique and a tuning concept that enable the realization of highly miniaturized and frequency reconfigurable helical filters are reported for the first time. The proposed filter design is based on tunable helical resonators with an internal volume of only 8.6 cm 3 and variable capacitive loading. A second-order bandpass filter was built and measured as a proof-of-concept demonstrator. The helical filter is manufactured with a CNC process and tuning is achieved through piezoelectrically actuated tuners, which are bonded over the helix apex. A measured center frequency tuning between 257-365 MHz was realized with an insertion loss between 0.3-3.2 dB including connector losses. The measured filter bandwidth varied between 6%-7.7%. Tunable resonators were also manufactured so as to extract the unloaded quality factor. It was measured between 392-639 for a center frequency tuning of 2.5:1 (156.5-395 MHz).

Blind Speech Separation and Enhancement With GCC-NMF <=SEP=> We present a blind source separation algorithm named GCC-NMF that combines unsupervised dictionary learning via non-negative matrix factorization NMF with spatial localization via the generalized cross correlation GCC method. Dictionary learning is performed on the mixture signal, with separation subsequently achieved by grouping dictionary atoms, at each point in time, according to their spatial origins. The resulting source separation algorithm is simple yet flexible, requiring no prior knowledge or information. Separation quality is evaluated for three tasks using stereo recordings from the publicly available SiSEC signal separation evaluation campaign: 3 and 4 concurrent speakers in reverberant environments, speech mixed with real-world background noise, and noisy recordings of a moving speaker. Performance is quantified using perceptually motivated and SNR-based measures with the PEASS and BSS Eval toolkits, respectively. We evaluate the effects of model parameters on separation quality, and compare our approach with other unsupervised and semi-supervised speech separation and enhancement approaches. We show that GCC-NMF is a flexible source separation algorithm, outperforming task-specific approaches in each of the three settings, including both blind as well as several informed approaches that require prior knowledge or information.

Anomaly detection approach using hybrid algorithm of data mining technique <=SEP=> The excessive use of the communication networks, rising of Internet of Things leads to increases the vulnerability to the important and secret information. advance attacking techniques and number of attackers are increasing radically. Intrusion is one of the main threats to the internet. Hence security issues had been big problem, so that various techniques and approaches have been presented to address the limitations of intrusion detection system such as low accuracy, high false alarm rate, and time consuming. This paper proposes a hybrid machine learning technique for network intrusion detection based on combination of K-means clustering and Sequential Minimal Optimization (SMO) classification. It introduces hybrid approach that able to reduce the rate of false positive alarm, false negative alarm rate, to improve the detection rate and detect zero-day attackers. The NSL-KDD dataset has been used in the proposed technique.. The classification has been performed by using Sequential Minimal Optimization. After training and testing the proposed hybrid machine learning technique, the results have shown that the proposed technique (K-mean + SMO) has achieved a positive detection rate of (94.48%) and reduce the false alarm rate to (1.2%) and achieved accuracy of (97.3695%).

A Deep Network with Visual Text Composition Behavior <=SEP=> While natural languages are compositional, how state-of-the-art neural models achieve compositionality is still unclear. We propose a deep network, which not only achieves competitive accuracy for text classification, but also exhibits compositional behavior. That is, while creating hierarchical representations of a piece of text, such as a sentence, the lower layers of the network distribute their layer-specific attention weights to individual words. In contrast, the higher layers compose meaningful phrases and clauses, whose lengths increase as the networks get deeper until fully composing the sentence.

Tamperproof transmission of fingerprints using visual cryptography schemes <=SEP=> Visual Cryptography and biometrics have been identified as the two most important aspects of digital security. In this paper, we propose a method for the preparation and secure transmission of fingerprint images using visual cryptography scheme. Visual cryptography (VC) is a kind of secret image sharing scheme that uses the human visual system to perform the decryption computations. A visual cryptography scheme (VCS) allows confidential messages to be encrypted into k-out-of-n secret sharing schemes. Whenever the number of participants from the group (n) is larger than or equal to the predetermined threshold value (k), the confidential message can be obtained by these participants. VCS is interesting because decryption can be done with no prior knowledge of cryptography and can be performed without any cryptographic computations. In this paper, the fingerprint image is broken up into n pieces, which individually yield no information about the image. These pieces of the images, called shares or shadows, may then be distributed among a group of n participants/dealers. By combining any k (k ≤ n) of these shares, the original fingerprint image can be recovered, but combining less than k of them will not reveal any information about the image. The scheme is perfectly secure and very easy to implement. To decode the encrypted information, i.e., to get the original information back, the shares are stacked and the secret image pops out. The only drawback of the VCS is the loss in contrast of reconstructed image. The proposed method for the reconstruction of fingerprint image is based on XNOR operation. This will enable one to obtain back perfect fingerprint image.

Planning With Agents : An Efficient Approach Using Hierarchical Dynamic Decision Networks <=SEP=> To be useful in solving real world problems, agents need to be able to act in environments in which it may not be possible to be completely aware of the current state and where actions do not always work as planned. Additional complexity is added to the problem when one considers groups of agents working together. By casting the agent planning problem as a partially observable Markov decision problem (POMDP), optimal policies can be generated for partially observable and stochastic environments. Exact solutions, however, are notoriously difficult to find for problems of a realistic nature. We introduce a hierarchical decision network-based planning algorithm that can generate high quality plans during execution while demonstrating significant time savings. We also discuss how this approach is particularly applicable to planning in a multiagent environment as compared to other POMDP-based planning algorithms. We present experimental results comparing our algorithm with results obtained by current POMDP and hierarchical POMDP (HPOMDP) methods.

Balanced-to-Unbalanced Bandpass Filters and the Antenna Application <=SEP=> Novel balanced-to-unbalanced (balun) bandpass filters are presented and carefully examined. By properly converting a symmetric four-port balanced-to-balanced bandpass filter to a three-port device, with two ports forming a balanced output and one port remaining unbalanced, a novel three-port balun bandpass filter with both functions of balun and bandpass filter may be realized. Specifically, a balun bandpass filter is implemented with its center frequency at 0.99 GHz and a 3-dB fractional bandwidth (Bomega) of 10.1%. The implemented balun bandpass filter presents an excellent in-band balance performance with common-mode rejection ratio (Rcm) better than 41 dB (the corresponding amplitude and phase imbalances are within 0.003deg and 1deg) over the pass- band. In this study, a wideband balun bandpass filter is also implemented so as to improve the wideband performance of the quasi- Yagi antenna. Being integrated with the proposed wideband balun bandpass filter, the implemented quasi-Yagi antenna achieves a bandwidth of 48% for IS11 | < -10 dB, with front-to-back ratio better than 13 dB, cross-polarization smaller than -20 dB, and peak gain of 3-5 dBi within the wide operating bandwidth.

Active defence through deceptive IPS <=SEP=> Modern security mechanisms such as Unified Threat Management (UTM), Next-Generation Firewalls and Security Information and Event Management (SIEM) have become more sophisticated over recent years, promising advanced security features and immediate mitigation of the most advanced threats. While this appears promising, in practice even this cutting-edge technology often fails to protect modern organisations as they are being targeted by attacks that were previously unknown to the security industry. Most security mechanisms are based on a database of previously known attack artefacts (signatures) and they will fail on slightly modified or new attacks. The need for threat intelligence is in complete contrast with the way current security solutions are responding to the threats they identify, as they immediately block them without attempting to acquire any further information. In this report, we present and evaluate a security mechanism that operates as an intrusion prevention system which uses honeypots to deceive an attacker, prevent a security breach and which allows the potential acquisition of intelligence on each intrusion attempt. a aThis article is published online by Computer Weekly as part of the 2017 Royal Holloway information security thesis series http://www.computerweekly.com/ehandbook/Active-defence-through-deceptive-IPS. It is based on an MSc dissertation written as part of the MSc in Information Security at the ISG, Royal Holloway, University of London. The full thesis is published on the ISG’s website at https://www.royalholloway.ac.uk/isg/.

Gait Generation With Smooth Transition Using CPG-Based Locomotion Control for Hexapod Walking Robot <=SEP=> This paper presents a locomotion control method based on central pattern generator (CPG) for hexapod walking robot to achieve gait generation with smooth transition. By deriving an analytical limit cycle approximation of the Van der Pol oscillator, a simple diffusive coupling scheme is proposed to construct a ring-shape CPG network with phase-locked behavior. The stability of the proposed network is proved using synchronization analysis with guaranteed uniform ultimate boundedness of the synchronous errors. In contrast to conventional numerical methods in tuning parameters of the CPG network, our method provides an explicit result that could analytically determine the network parameters to yield the rhythmic waveforms with prescribed frequency, amplitude, and phase relationship among neurons. Employing the proposed network to govern the swing/stance phase according to the profile of the resulting CPG signals, a locomotion control strategy for the hexapod robot is further developed to manipulate the leg movements during the gait cycle. By simply adjusting the phase lags of the CPG network, the proposed control strategy is capable of generating stable walking gaits for hexapod robots and achieving smooth transition among the generated gaits. The simulation and experimental results have demonstrated the effectiveness of the proposed locomotion control method.

Microbial Fuel Cells for Wastewater Treatment <=SEP=> 1.1 Screening The influent is strained to remove all large objects carried in the sewage stream. This is most commonly performed with an automated mechanically-raked bar screen in modern plants serving large populations, whilst in smaller or less modern plants a manually-cleaned screen may be used. The raking action of a mechanical bar screen is typically paced according to the accumulation on the bar screens and/or flow rate. The solids are collected and later disposed of in landfill or incinerated. Bar screens or mesh screens of varying sizes may be used to optimise solids removal, so as to trap and remove the floating matter, such as pieces of cloth, paper, wood, kitchen refuse, etc. These floating materials will choke pipes or adversely affect the working of the pumps if not removed. They should be placed before the grit chambers. However, if the quality of grit is not of much importance, as in the case of landfilling etc., screens may even be placed after the grit chambers. They may sometimes be accommodated in the body of the grit chambers themselves.

Detecting hot topics from Twitter: A multiview approach <=SEP=> Twitter is widely used all over the world, and a huge number of hot topics are generated by Twitter users in real time. These topics are able to reflect almost every aspect of people’s daily lives. Therefore, the detection of topics in Twitter can be used in many real applications, such as monitoring public opinion, hot product recommendation and incidence detection. However, the performance of traditional topic detection methods is still far from perfect largely owing to the tweets’ features, such as their limited length and arbitrary abbreviations. To address these problems, we propose a novel framework (MVTD) for Twitter topic detection using multiview clustering, which can integrate multirelations among tweets, such as semantic relations, social tag relations and temporal relations. We also propose some methods for measuring relations among tweets. In particular, to better measure the semantic similarity of tweets, we propose a new document similarity measure based on a suffix tree (STVSM). In addition, a new keyword extraction method based on a suffix tree is proposed. Experiments on real datasets show that the performance of MVTD is much better than that of a single view, and it is useful for detecting topics from Twitter.

Job insecurity and organizational citizenship behavior: exploring curvilinear and moderated relationships. <=SEP=> This article examined a curvilinear relationship between job insecurity and organizational citizenship behavior (OCB). Drawing from social exchange theory and research on personal control, we developed and tested an explanation for employees' reactions to job insecurity based on their conceptualization of their social exchange relationship with the organization at different levels of job insecurity. Using data from 244 Chinese employees and 102 supervisory ratings of OCB, we found support for a U-shaped relationship between job insecurity and OCB. Moreover, 2 factors--psychological capital and subordinate-supervisor guanxi--moderated the curvilinear relationship, such that the curvilinear relationship is more pronounced among those with lower psychological capital or less positive subordinate-supervisor guanxi.

A study on the road accidents using data investigation and visualization in Los Baños, Laguna, Philippines <=SEP=> Road safety is one of the most vital and crucial part of every countries daily economic growth. It gives so much impact in public health specially in the Philippines. Securing its safety will be a big help to a particular country's economic growth. The main objective of this paper is to analyze road accident's data to discover hidden pattern that can be used as precautionary measure to at least lessen the accident that occur yearly in Los Baños, Laguna, Philippines. Predicting algorithms such as Decision Tree, Naïve Bayes and Rule induction were used to identify factors affecting accident in Los Baños, Laguna. Using these three classifier the following are the results obtained by the researchers; for Decision Tree 92.84% accuracy occurred with 0.797 kappa while in Naïve Bayes 91.50% accuracy was generated with 0.741 kappa and 92.50% accuracy for Rule Induction and 0.783 kappa was produced. The researchers discovered that the place where an accident happened don't have significant correlation on the fatality of the victim. On the other hand, the researchers also found that the time and day play a vital role on the fatality or severity of victims of road accident particularly car collision.

Feature reduction and selection for EMG signal classification <=SEP=> 0957-4174/$ see front matter 2012 Elsevier Ltd. A doi:10.1016/j.eswa.2012.01.102 ⇑ Corresponding author. Tel.: +66 74 55 8831; fax: E-mail address: angkoon.p@hotmail.com (A. Phiny Feature extraction is a significant method to extract the useful information which is hidden in surface electromyography (EMG) signal and to remove the unwanted part and interferences. To be successful in classification of the EMG signal, selection of a feature vector ought to be carefully considered. However, numerous studies of the EMG signal classification have used a feature set that have contained a number of redundant features. In this study, most complete and up-to-date thirty-seven time domain and frequency domain features have been proposed to be studied their properties. The results, which were verified by scatter plot of features, statistical analysis and classifier, indicated that most time domain features are superfluity and redundancy. They can be grouped according to mathematical property and information into four main types: energy and complexity, frequency, prediction model, and time-dependence. On the other hand, all frequency domain features are calculated based on statistical parameters of EMG power spectral density. Its performance in class separability viewpoint is not suitable for EMG recognition system. Recommendation of features to avoid the usage of redundant features for classifier in EMG signal classification applications is also proposed in this study. 2012 Elsevier Ltd. All rights reserved.

An Introduction to the Theory of Mechanism Design <=SEP=> 1 The authors will donate all payments that they receive from the publisher for this book to Amnesty International.

Semantic exploitation of implicit patent information <=SEP=> In recent years patents have become increasingly important for businesses to protect their intellectual capital and as a valuable source of information. Patent information is, however, not employed to its full potential and the interpretation of structured and unstructured patent information in large volumes remains a challenge. We address this by proposing an integrated interdisciplinary approach that uses natural language processing and machine learning techniques to formalize multilingual patent information in an ontology. The ontology further contains patent and domain specific knowledge, which allows for aligning patents with technological fields of interest and other business-related artifacts. Our empirical evaluation shows that for categorizing patents according to well-known technological fields of interest, the approach achieves high accuracy with selected feature sets compared to related work focussing on monolingual patents. We further show that combining OWL RL reasoning with SPARQL querying over the patent knowledge base allows for answering complex business queries and illustrate this with real-world use cases from the automotive domain.

Dynamic Pricing in Spatial Crowdsourcing: A Matching-Based Approach <=SEP=> In spatial crowdsourcing, requesters submit their task-related locations and increase the demand of a local area. The platform prices these tasks and assigns spatial workers to serve if the prices are accepted by requesters. There exist mature pricing strategies which specialize in tackling the imbalance between supply and demand in a local market. However, in global optimization, the platform should consider the mobility of workers; that is, any single worker can be the potential supply for several areas, while it can only be the true supply of one area when assigned by the platform. The hardness lies in the uncertainty of the true supply of each area, hence the existing pricing strategies do not work. In the paper, we formally define this <u>G</u>lobal <u>D</u>ynamic <u>P</u>ricing(GDP) problem in spatial crowdsourcing. And since the objective is concerned with how the platform matches the supply to areas, we let the matching algorithm guide us how to price. We propose a <u>MA</u>tching-based <u>P</u>ricing <u>S</u>trategy (MAPS) with guaranteed bound. Extensive experiments conducted on the synthetic and real datasets demonstrate the effectiveness of MAPS.

Mechanics and energetics of human locomotion on sand. <=SEP=> Moving about in nature often involves walking or running on a soft yielding substratum such as sand, which has a profound effect on the mechanics and energetics of locomotion. Force platform and cinematographic analyses were used to determine the mechanical work performed by human subjects during walking and running on sand and on a hard surface. Oxygen consumption was used to determine the energetic cost of walking and running under the same conditions. Walking on sand requires 1.6-2.5 times more mechanical work than does walking on a hard surface at the same speed. In contrast, running on sand requires only 1.15 times more mechanical work than does running on a hard surface at the same speed. Walking on sand requires 2.1-2.7 times more energy expenditure than does walking on a hard surface at the same speed; while running on sand requires 1.6 times more energy expenditure than does running on a hard surface. The increase in energy cost is due primarily to two effects: the mechanical work done on the sand, and a decrease in the efficiency of positive work done by the muscles and tendons.

Open Set Learning with Counterfactual Images <=SEP=> In open set recognition, a classifier must label instances of known classes while detecting instances of unknown classes not encountered during training. To detect unknown classes while still generalizing to new instances of existing classes, we introduce a dataset augmentation technique that we call counterfactual image generation. Our approach, based on generative adversarial networks, generates examples that are close to training set examples yet do not belong to any training category. By augmenting training with examples generated by this optimization, we can reformulate open set recognition as classification with one additional class, which includes the set of novel and unknown examples. Our approach outperforms existing open set recognition algorithms on a selection of image classification tasks.

Obstacle Detection on Railway Track by Fusing Radar and Image Sensor <=SEP=> Novel technology to recognize the situation in distant place is necessary to develop a railway safety monitoring system by which human being having fallen onto the tracks from a platform and obstacles in the level crossing can be detected. In this research, we propose a method for detecting a stationary or moving obstacle by the technology which employs the super resolution radar techniques, the image recognition techniques, and the technology to fuse these techniques. Our method is designed for detecting obstacles such as cars, bicycles, and human on the track in the range up to hundreds of meters ahead by using sensors mounted on a train. In super resolution radar techniques, novel stepped multiple frequency radar is confirmed to provide the expected high resolution performance by experimental study using software defined radar. We designed the parameters of the stepped multiple frequency radar. The signal processing processor which employs A/Ds, D/As of high-sampling rate and the highest performance FPGAs has been designed and developed. In image recognition techniques, the main algorithm which detects a pair of rails is based on information about rail edges and intensity gradients. As the obstacle exists in the vicinity of rails, all the detection processes can be limited to the region in the vicinity of rails. As an example, we detect an obstacle by estimating image boundaries which enclose a group of feature points exactly on the obstacle. In order to determine the group of feature points, feature points of the whole image are detected at each frames and tracked over image sequence. For robustness estimation of boundary, we use radar so as to detect the obstacle’s rough position in an image region where an obstacle would exist; the motion segmentation technique is applied to the tracks that is located in the region. Note that an obstacle's position detected by radar is remarkably rough because radar has low transverse resolution. Nevertheless, the position detected by radar contributes to rapid and robust estimation of satisfactory image boundaries. To allow for monitoring in large distances, a prototype for an onboard pan/tilt camera platform with directional control was designed and manufactured as required in a railway situation. To detect obstacles in real time, we have built a high-performance test machine with a GPU and an image processing environment.

The potential distribution measurement on the stress grading system of high-voltage rotating machines by using pockels effect <=SEP=> A surface potential measuring system was developed by utilizing Pockels sensor for measuring the potential distribution on the stress grading (SG) system of form wound motor coils. A Bi4Ge3O12 (BGO) Pockels crystal is put close to a measuring object and the capacitively-induced voltage on the end surface of the BGO crystal is measured by using Pockels effect of the crystal itself. The spatial resolution of the measuring system is 5 mm, and the frequency response reaches up to GHz-range. With this system, the surface potential distributions on a model bar coil were measured. According to the measuring results, it is shown that the newly developed potential measuring system here is very useful for evaluating the reliability of the SG system of motors.

Polarization-agile antennas <=SEP=> A polarization-agile antenna is a type of antenna the polarization state of which can be changed dynamically, i.e., it can have either linear (vertical or horizontal) or circular polarization (left hand or right hand), depending on the requirements of its specific application. This special characteristic offers great flexibility for antenna systems, because a single antenna could be used to satisfy the different requirements of several systems. This paper gives a comprehensive review of polarization-agile antennas. The basics of polarization in antennas and polarization-agile antennas are explained. Various examples of recent developments are then provided, and the antenna configurations and design principles are illustrated and discussed. Finally, the future development of polarization-agile antennas for wireless systems is discussed

Hesitation Disfluencies in Spontaneous Speech: The Meaning of um <=SEP=> Human speech is peppered with ums and uhs, among other signs of hesitation in the planning process. But are these so-called fillers (or filled pauses) intentionally uttered by speakers, or are they side-effects of difficulties in the planning process? And how do listeners respond to them? In the present paper, we review evidence concerning the production and comprehension of fillers such as um and uh, in an attempt to determine whether they can be said to be ‘words’ with ‘meanings’ that are understood by listeners. We conclude that, whereas listeners are highly sensitive to hesitation disfluencies in speech, there is little evidence to suggest that they are intentionally produced, or should be considered to be words in the conventional sense. When humans communicate, their messages are conveyed by more than just the words they use. They can, for example, use gesture to indicate what a phrase such as ‘this one’ refers to, or change their tone of voice to show that the assertion that they ‘love linguistics’ is not to be taken at face value. As well as these (presumably deliberate) additional components of communication, the words of unprepared spoken language are likely to be accompanied by a range of unintentional errors. If, for example, a speaker exchanges the onsets of two words, by perhaps saying ‘darn bore’ when intending to say ‘barn door’, it is highly unlikely that the exchange is intentional, and the occurrence of accidental speech errors like this may inform us about the nature of speech planning (e.g. Hartsuiker et al. 2005). Between these extremes of intentionality are disfluencies, or the false starts, repetitions, and hesitations that accompany the words that speakers plan and utter. Averaging across several studies, Fox Tree (1995) estimated that approximately 6% of words uttered are, or are affected by, some form of disfluency (see also Bortfeld et al. 2001). These disfluencies may not always be accidental: It has been argued that some types of disfluency should be counted among the tools the speaker has for communicating to the listener, alongside things such as tone of voice (others, such as false starts, may be due to speakers editing their own speech). Chief among the potentially communicative disfluencies are the so-called fillers, such as um 590 Martin Corley and Oliver W. Stewart © 2008 The Authors Language and Linguistics Compass 2/4 (2008): 589–602, 10.1111/j.1749-818x.2008.00068.x Journal Compilation © 2008 Blackwell Publishing Ltd and uh, which (together with prolongations and pauses) mark a hesitation on the part of the speaker. In this paper, we investigate the role played by hesitation in human communication, with a particular focus on fillers and the communicative goals they may serve. Producing Hesitation Disfluencies Hesitation phenomena such as fillers are most likely to occur at the beginning of an utterance or phrase, presumably as a consequence of the greater demand on planning processes at these junctures (Maclay and Osgood 1959; Beattie 1979; Barr 2001). The view that cognitive load is an important predictor of disfluency is supported by the fact that disfluencies are found to occur more often before longer utterances (Oviatt 1995; Shriberg 1996), and when the topic is unfamiliar (Bortfeld et al. 2001; Merlo and Mansur 2004). Cognitive load is also implicated when we look at hesitations on a word-by-word basis. Investigations of where disfluencies such as fillers occur throughout utterances have established that they are more likely to occur before content words (Maclay and Osgood 1959), such as low-frequency color names (Levelt 1983). However, Beattie and Butterworth (1979) came to a different conclusion when they investigated the distributional properties of disfluencies across a set of recordings of two-person conversations. They showed that both low-frequency content words and those rated as contextually improbable were likely to be preceded by hesitations such as fillers; when frequency was held constant, contextual probability still predicted disfluency. Rather than attributing disfluency to cognitive load, Beattie and Butterworth suggested that speakers might be aware of an element of choice in selecting words with low contextual probability, and were more likely to be disfluent for this reason. Choice was also implicated in a study by Schachter et al. (1991) in which lectures in the natural sciences, social sciences, and humanities were recorded and analyzed for numbers of fillers per minute. Disfluency differed between topics, with the natural sciences resulting in the least and the humanities in the most frequent use of fillers. However, when the lecturers were interviewed on general topics, their rates of disfluency did not differ. Schachter et al. attributed the differences in lecture disfluency rates to the fact that there were fewer linguistic options in the sciences, causing lecturers to hesitate less as they selected appropriate terms. They later corroborated their claims by measuring vocabulary size in lectures, learned articles, and topic-related journalism (Schachter et al. 1994), showing that there were indeed fewer terms used in the sciences. So far, we have been speaking about cognitive load and choice as if they were different, but it is of course the case that a higher number of options to choose from could result in an increased cognitive load. In a detailed experimental investigation, Oomen and Postma (2001) manipulated speech rate, using a task modified from Levelt (1983; see also Martin