In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

The triboelectric effect is ubiquitous in our everyday life and results from two different materials coming into contact. It is generally regarded as a negative effect in industry given that the electrostatic charges induced from it can lead to ignition, dust explosions, dielectric breakdown, electronic damage, etc. From an energy point of view, those electrostatic charges constitute a capacitive energy device when the two triboelectric surfaces are separated, which led to the invention of early electrostatic generators such as the “friction machine” and Van de Graaff generator.[1] As the world is marching into the era of the internet of things (IoTs) and artificial intelligence, the most vital development for hardware is a multifunctional array of sensing systems, which forms the foundation of the fourth industrial revolution toward an intelligent world. Given the need for mobility of these multitudes of sensors, the success of the IoTs calls for distributed energy sources, which can be provided by solar, thermal, wind, and mechanical triggering/vibrations. The triboelectric nanogenerator (TENG) for mechanical energy harvesting developed by Z.L. Wang’s group is one of the best choices for this energy for the new era, since triboelectrification is a universal and ubiquitous effect with an abundant choice of materials. The development of self-powered active sensors enabled by TENGs is revolutionary compared to externally powered passive sensors, similar to the advance from wired to wireless communication. In this paper, the fundamental theory, experiments, and applications of TENGs are reviewed as a foundation of the energy for the new era with four major application fields: micro/nano power sources, self-powered sensors, large-scale blue energy, and direct high-voltage power sources. A roadmap is proposed for the research and commercialization of TENG in the next 10 years.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/aenm.201802906

Triboelectric Nanogenerator: A Foundation of the Energy for the New Era

We describe methods for learning dictionaries that are appropriate for the representation of given classes of signals and multisensor data. We further show that dimensionality reduction based on dictionary representation can be extended to address specific tasks such as data analy sis or classification when the learning includes a class separability criteria in the objective function. The benefits of dictionary learning clearly show that a proper understanding of causes underlying the sensed world is key to task-specific representation of relevant information in high-dimensional data sets.

Dictionary Learning

Inference and Disputed Authorship: The Federalist

Dependence on computers to store and process sensitive information has made it necessary to secure them from intruders. A behavioral biometric such as keystroke dynamics which makes use of the typing cadence of an individual can be used to strengthen existing security techniques effectively and cheaply. Due to the ballistic (semi-autonomous) nature of the typing behavior it is difficult to impersonate, making it useful as abiometric. Therefore in this paper, we provide a basic background of the psychological basis behind the use of keystroke dynamics. We also discuss the data acquisition methods, approaches and the performance of the methods used by researchers on standard computer keyboards. In this survey, we find that the use and acceptance of this biometric could be increased by development of standardized databases, assignment of nomenclature for features, development of common data interchange formats, establishment of protocols for evaluating methods, and resolution of privacy issues.

Biometric Authentication and Identification using Keystroke Dynamics: A Survey

Finger surface as a biometric identifier

In the recent past, deep learning methods have demonstrated remarkable success for supervised learning tasks in multiple domains including computer vision, natural language processing, and speech processing. In this article, we investigate the impact of deep learning in the field of biometrics, given its success in other domains. Since biometrics deals with identifying people by using their characteristics, it primarily involves supervised learning and can leverage the success of deep learning in other related domains. In this article, we survey 100 different approaches that explore deep learning for recognizing individuals using various biometric modalities. We find that most deep learning research in biometrics has been focused on face and speaker recognition. Based on inferences from these approaches, we discuss how deep learning methods can benefit the field of biometrics and the potential gaps that deep learning approaches need to address for real-world biometric applications.

Deep Learning for Biometrics: A Survey

Human recognition based on the iris biometric is severely impacted when encountering non-ideal images of the eye characterized by occluded irises, motion and spatial blur, poor contrast, and illumination artifacts. This paper discusses the use of the periocular region surrounding the iris, along with the iris texture patterns, in order to improve the overall recognition performance in such images. Periocular texture is extracted from a small, fixed region of the skin surrounding the eye. Experiments on the images extracted from the Near Infra-Red (NIR) face videos of the Multi Biometric Grand Challenge (MBGC) dataset demonstrate that valuable information is contained in the periocular region and it can be fused with the iris texture to improve the overall identification accuracy in non-ideal situations.

/pdf/on-the-fusion-of-periocular-and-iris-biometrics-in-non-ideal-2rep6dm27i.pdf

On the Fusion of Periocular and Iris Biometrics in Non-ideal Imagery

We evaluate the utility of the periocular region appearance cues for biometric identification. Even though periocular region is considered to be a highly discriminative part of a face, its utility as an independent modality or as a soft biometric is still an open ended question. It is our goal to establish a performance metric for the periocular region features so that their potential use in conjunction with iris or face can be evaluated. In this approach, we employ the local appearance based feature representation, where the image is divided into spatially salient patches, and histograms of texture and color are computed for each patch. The images are matched by computing the distance between the corresponding feature representations using various distance metrics. We report recognition results on images captured in the visible and near-infrared (NIR) spectrum. For the color periocular region data consisting of about 410 subjects and the NIR images of 85 subjects, we obtain the Rank-1 recognition rate of 91% and 87% respectively. Furthermore, we also demonstrate that recognition performance of the periocular region images is comparable to that of face.

Damon L. Woodard

Papers

Biometric Authentication and Identification using Keystroke Dynamics: A Survey

Finger surface as a biometric identifier

Deep Learning for Biometrics: A Survey

On the Fusion of Periocular and Iris Biometrics in Non-ideal Imagery

Periocular region appearance cues for biometric identification