Improving PLDA speaker verification with limited development data

This paper analyses the probabilistic linear discriminant analysis (PLDA) speaker verification approach with limited development data. This paper investigates the use of the median as the central tendency of a speaker’s i-vector representation, and the effectiveness of weighted discriminative techniques on the performance of state-of-the-art length-normalised Gaussian PLDA (GPLDA) speaker verification systems. The analysis within shows that the median (using a median fisher discriminator (MFD)) provides a better representation of a speaker when the number of representative i-vectors available during development is reduced, and that further, usage of the pair-wise weighting approach in weighted LDA and weighted MFD provides further improvement in limited development conditions. Best performance is obtained using a weighted MFD approach, which shows over 10% improvement in EER over the baseline GPLDA system on mismatched and interview-interview conditions.

Blast response of reinforced concrete pile using fully coupled computer simulation techniques

Pile foundations transfer loads from superstructures to stronger sub soil. Their strength and stability can hence affect structural safety. This paper treats the response of reinforced concrete pile in saturated sand to a buried explosion. Fully coupled computer simulation techniques are used together with five different material models. Influence of reinforcement on pile response is investigated and important safety parameters of horizontal deformations and tensile stresses in the pile are evaluated. Results indicate that adequate longitudinal reinforcement and proper detailing of transverse reinforcement can reduce pile damage. Present findings can serve as a benchmark reference for future analysis and design.

Resilient Navigation through Probabilistic Modality Reconfiguration

This paper proposes an approach to achieve resilient navigation for indoor mobile robots. Resilient navigation seeks to mitigate the impact of control, localisation, or map errors on the safety of the platform while enforcing the robot’s ability to achieve its goal. We show that resilience to unpredictable errors can be achieved by combining the benefits of independent and complementary algorithmic approaches to navigation, or modalities, each tuned to a particular type of environment or situation. In this paper, the modalities comprise a path planning method and a reactive motion strategy. While the robot navigates, a Hidden Markov Model continually estimates the most appropriate modality based on two types of information: context (information known a priori) and monitoring (evaluating unpredictable aspects of the current situation). The robot then uses the recommended modality, switching between one and another dynamically. Experimental validation with a SegwayRMP- based platform in an office environment shows that our approach enables failure mitigation while maintaining the safety of the platform. The robot is shown to reach its goal in the presence of: 1) unpredicted control errors, 2) unexpected map errors and 3) a large injected localisation fault.

Selection and monitoring of navigation modes for an autonomous rover

Considering the wide spectrum of situations that it may encounter, a robot navigating autonomously in outdoor environments needs to be endowed with several operating modes, for robustness and efficiency reasons. Indeed, the terrain it has to traverse may be composed of flat or rough areas, low cohesive soils such as sand dunes, concrete road etc. . .Traversing these various kinds of environment calls for different navigation and/or locomotion functionalities, especially if the robot is endowed with different locomotion abilities, such as the robots WorkPartner, Hylos [4], Nomad or the Marsokhod rovers. Numerous rover navigation techniques have been proposed, each of them being suited to a particular environment context (e.g. path following, obstacle avoidance in more or less cluttered environments, rough terrain traverses...). However, seldom contributions in the literature tackle the problem of selecting autonomously the most suited mode [3]. Most of the existing work is indeed devoted to the passive analysis of a single navigation mode, as in [2]. Fault detection is of course essential: one can imagine that a proper monitoring of the Mars Exploration Rover Opportunity could have avoided the rover to be stuck during several weeks in a dune, by detecting non-nominal behavior of some parameters. But the ability to recover the anticipated problem by switching to a better suited navigation mode would bring higher autonomy abilities, and therefore a better overall efficiency. We propose here a probabilistic framework to achieve this, which fuses environment related and robot related information in order to actively control the rover operations.

A probabilistic framework to monitor a multi-mode outdoor robot

This paper presents an approach to autonomously monitor the behavior of a robot endowed with several navigation and locomotion modes, adapted to the terrain to traverse. The mode selection process is done in two steps: the best suited mode is firstly selected on the basis of initial information or a qualitative map built on-line by the robot. Then, the motions of the robot are monitored by various processes that update mode transition probabilities in a Markov system. The paper focuses on this latter selection process: the overall approach is depicted, and preliminary experimental results are presented

Towards training-free appearance-based localization : probabilistic models for whole-image descriptors

Whole image descriptors have recently been shown to be remarkably robust to perceptual change especially compared to local features. However, whole-image-based localization systems typically rely on heuristic methods for determining appropriate matching thresholds in a particular environment. These environment-specific tuning requirements and the lack of a meaningful interpretation of these arbitrary thresholds limits the general applicability of these systems. In this paper we present a Bayesian model of probability for whole-image descriptors that can be seamlessly integrated into localization systems designed for probabilistic visual input. We demonstrate this method using CAT-Graph, an appearance-based visual localization system originally designed for a FAB-MAP-style probabilistic input. We show that using whole-image descriptors as visual input extends CAT-Graph’s functionality to environments that experience a greater amount of perceptual change. We also present a method of estimating whole-image probability models in an online manner, removing the need for a prior training phase. We show that this online, automated training method can perform comparably to pre-trained, manually tuned local descriptor methods.

Transforming morning to afternoon using linear regression techniques

Visual localization in outdoor environments is often hampered by the natural variation in appearance caused by such things as weather phenomena, diurnal fluctuations in lighting, and seasonal changes. Such changes are global across an environment and, in the case of global light changes and seasonal variation, the change in appearance occurs in a regular, cyclic manner. Visual localization could be greatly improved if it were possible to predict the appearance of a particular location at a particular time, based on the appearance of the location in the past and knowledge of the nature of appearance change over time. In this paper, we investigate whether global appearance changes in an environment can be learned sufficiently to improve visual localization performance. We use time of day as a test case, and generate transformations between morning and afternoon using sample images from a training set. We demonstrate the learned transformation can be generalized from training data and show the resulting visual localization on a test set is improved relative to raw image comparison. The improvement in localization remains when the area is revisited several weeks later.

Orientation robust transmit polarization diversity techniques

Polarisation diversity is a technique to improve the quality of mobile communications, but its reliability is suboptimal because it depends on the mobile channel and the antenna orientations at both ends of the mobile link. A method to optimise the reliability is established by minimising the dependency on antenna orientations. While the mobile base station can have fixed antenna orientation, the mobile terminal is typically a handheld device with random orientations. This means orientation invariance needs to be established at the receiver in the downlink, and at the transmitter in the uplink. This research presents separate solutions for both cases, and is based on the transmission of an elliptically polarised signal synthesised from the channel statistics. Complete receiver orientation invariance is achieved in the downlink. Effects of the transmitter orientation are minimised in the uplink.

Identification of abundant alkyl ether glycerophospholipids in the human lens by tandem mass spectrometry techniques

Previous studies have shown that the human lens contains glycerophospholipids with ether linkages. These lipids differ from conventional glycerophospholipids in that the sn-1 substituent is attached to the glycerol backbone via an 1-O-alkyl or an 1-O-alk-1'-enyl ether rather than an ester bond. The present investigation employed a combination of collision-induced dissociation (CID) and ozone-induced dissociation (OzID) to unambiguously distinguish such 1-O-alkyl and 1-O-alk-1'-enyl ethers. Using these methodologies the human lens was found to contain several abundant 1-O-alkyl glycerophos-phoethanolamines, including GPEtn(16:0e/9Z-18:1), GPEtn(11Z-18:1e/9Z-18:1), and GPEtn(18:0e/9Z-18:1), as well as a related series of unusual 1-O-alkyl glycerophosphoserines, including GPSer(16:0e/9Z-18:1), GPSer(11Z-18:1e/9Z-18:1), GPSer(18:0e/9Z-18:1) that to our knowledge have not previously been observed in human tissue. Isomeric 1-O-alk-1'-enyl ethers were absent or in low abundance. Examination of the double bond position within the phospholipids using OzID revealed that several positional isomers were present, including sites of unsaturation at the n-9, n-7, and even n-5 positions. Tandem CID/OzID experiments revealed a preference for double bonds in the n-7 position of 1-O-ether linked chains, while n-9 double bonds predominated in the ester-linked fatty acids [e.g., GPEtn(11Z-18:1e/9Z-18:1) and GPSer(11Z-18:1e/9Z-18:1)]. Different combinations of these double bond positional isomers within chains at the sn-1 and sn-2 positions point to a remarkable molecular diversity of ether-lipids within the human lens.

Critical analysis of spam prevention techniques

E-mail spam has remained a scourge and menacing nuisance for users, internet and network service operators and providers, in spite of the anti-spam techniques available; and spammers are relentlessly circumventing these anti-spam techniques embedded or installed in form of software products on both client and server sides of both fixed and mobile devices to their advantage. This continuous evasion degrades the capabilities of these anti-spam techniques as none of them provides a comprehensive reliable solution to the problem posed by spam and spammers. Major problem for instance arises when these anti-spam techniques misjudge or misclassify legitimate emails as spam (false positive); or fail to deliver or block spam on the SMTP server (false negative); and the spam passes-on to the receiver, and yet this server from where it originates does not notice or even have an auto alert service to indicate that the spam it was designed to prevent has slipped and moved on to the receiver’s SMTP server; and the receiver’s SMTP server still fail to stop the spam from reaching user’s device and with no auto alert mechanism to inform itself of this inability; thus causing a staggering cost in loss of time, effort and finance. This paper takes a comparative literature overview of some of these anti-spam techniques, especially the filtering technological endorsements designed to prevent spam, their merits and demerits to entrench their capability enhancements, as well as evaluative analytical recommendations that will be subject to further research.

Binary image steganographic techniques classification based on multi-class steganalysis

In this paper, we propose a new multi-class steganalysis for binary image. The proposed method can identify the type of steganographic technique used by examining on the given binary image. In addition, our proposed method is also capable of differentiating an image with hidden message from the one without hidden message. In order to do that, we will extract some features from the binary image. The feature extraction method used is a combination of the method extended from our previous work and some new methods proposed in this paper. Based on the extracted feature sets, we construct our multi-class steganalysis from the SVM classifier. We also present the empirical works to demonstrate that the proposed method can effectively identify five different types of steganography.

Non-formal techniques for early assessment of design ideas for services

Designing systems for multiple stakeholders requires frequent collaboration with multiple stakeholders from the start. In many cases at least some stakeholders lack a professional habit of formal modeling. We report observations from student design teams as well as two case studies, respectively of a prototype for supporting creative communication to design objects, and of stakeholder-involvement in early design. In all observations and case studies we found that non-formal techniques supported strong collaboration resulting in deep understanding of early design ideas, of their value and of the feasibility of solutions.

Fault detection of slow speed rolling element bearing with noise removal techniques

Rolling Element Bearings (REBs) are vital components in rotating machineries for providing rotating motion. In slow speed rotating machines, bearings are normally subjected to heavy static loads and a catastrophic failure can cause enormous disruption to production and human safety. Due to its low operating speed the impact energy generated by the rotating elements on the defective components is not sufficient to produce a detectable vibration response. This is further aggravated by the inability of general measuring instruments to detect and process the weak signals at the initiation of the defect accurately. Furthermore, the weak signals are often corrupted by background noise. This is a serious problem faced by maintenance engineers today and the inability to detect an incipient failure of the machine can significantly increases the risk of functional failure and costly downtime. This paper presents the application of noise removal techniques for enhancing the detection capability for slow speed REB condition monitoring. Blind deconvolution (BD) and adaptive line enhancer (ALE) are compared to evaluate their performance in enhancing the source signal with consequential removal of background noise. In the experimental study, incipient defects were seeded on a number of roller bearings and the signals were acquired using acoustic emission (AE) sensor. Kurtosis and modified peak ratio (mPR) were used to determine the detectability of signal corrupted by noise.

Computational aspects of probabilistic assessment of UAS robust autonomy

As the level of autonomy in Unmanned Aircraft Systems (UAS) increases, there is an imperative need for developing methods to assess robust autonomy. This paper focuses on the computations that lead to a set of measures of robust autonomy. These measures are the probabilities that selected performance indices related to the mission requirements and airframe capabilities remain within regions of acceptable performance.

The chef as designer : classifying the techniques that chefs use in creating innovative dishes

This qualitative study explores the methods that chefs use to create innovative marketable product and compares these findings to other design tools. This study is based on a series of interviews with locally recognised chefs in Minnesota and observations of them in their kitchens in order to understand the details of how they conceive and develop dishes from preliminary concept to final plating and user consumption. This paper focuses on idea generation and discusses two key findings: first, the variety of idea generation techniques presented by the chefs can be classified into the creativity tool SCAMPER (substitute, combine, adapt, modify/magnify, put to other use, eliminate, reverse/rearrange); second, chefs evoke the theory of MAYA or Most Advanced Yet Acceptable when innovating new dishes, which implies making novel changes while remaining relatable to the consumer. Other reoccurring topics in the interview discussion of food innovation include play, surprise, and humour.