Metamaterials based on effective media can be used to produce a number of unusual physical properties (for example, negative refraction and invisibility cloaking) because they can be tailored with effective medium parameters that do not occur in nature. Recently, the use of coding metamaterials has been suggested for the control of electromagnetic waves through the design of coding sequences using digital elements ‘0’ and ‘1,' which possess opposite phase responses. Here we propose the concept of an anisotropic coding metamaterial in which the coding behaviors in different directions are dependent on the polarization status of the electromagnetic waves. We experimentally demonstrate an ultrathin and flexible polarization-controlled anisotropic coding metasurface that functions in the terahertz regime using specially designed coding elements. By encoding the elements with elaborately designed coding sequences (both 1-bit and 2-bit sequences), the x- and y-polarized waves can be anomalously reflected or independently diffused in three dimensions. The simulated far-field scattering patterns and near-field distributions are presented to illustrate the dual-functional performance of the encoded metasurface, and the results are consistent with the measured results. We further demonstrate the ability of the anisotropic coding metasurfaces to generate a beam splitter and realize simultaneous anomalous reflections and polarization conversions, thus providing powerful control of differently polarized electromagnetic waves. The proposed method enables versatile beam behaviors under orthogonal polarizations using a single metasurface and has the potential for use in the development of interesting terahertz devices.

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Abstract-Insects are intimately connected to human life and wellbeing, in both positive and negative senses. While it is estimated that insects pollinate at least two-thirds of the all food consumed by humans, malaria, a disease transmitted by the female mosquito of the Anopheles genus, kills approximately one million people per year. Due to the importance of insects to humans, researchers have developed an arsenal of mechanical, chemical, biological and educational tools to help mitigate insects harmful effects, and to enhance their beneficial effects. However, the efficiency of such tools depends on knowing the time and location of migrations/infestations/population as early as possible.Insect detection and counting is typically performed by means of traps, usually "sticky traps", which are regularly collected and manually analyzed. The main problem is that this procedure is expensive in terms of materials and human time, and creates a lag between the time the trap is placed and inspected. This lag may only be a week, but in the case of say, mosquitoes or sand flies, this can be more than half their adult life span.We are developing an inexpensive optical sensor that uses a laser beam to detect, count and ultimately classify flying insects from distance. Our objective is to use classification techniques to provide accurate real-time counts of disease vectors down to the species/sex level. This information can be used by public health workers, government and non-government organizations to plan the optimal intervention strategies in the face of limited resources.In this work, we present some preliminary results of our research, conducted with three insect species. We show that using our simple sensor we can accurately classify these species using their wing-beat frequency as feature. We further discuss how we can augment the sensor with other sources of information in order to scale our ideas to classify a larger number of species.

Monitoring animals by the sounds they produce is an important and challenging task, whether the application is outdoors in a natural habitat, or in the controlled environment of a laboratory setting.In the former case, the density and diversity of animal sounds can act as a measure of biodiversity. In the latter case, researchers often create control and treatment groups of animals, expose them to different interventions, and test for different outcomes. One possible manifestation of different outcomes may be changes in the bioacoustics of the animals.With such a plethora of important applications, there have been significant efforts to build bioacoustic classification tools. However, we argue that most current tools are severely limited. They often require the careful tuning of many parameters (and thus huge amounts of training data), are either too computationally expensive for deployment in resource-limited sensors, specialized for a very small group of species, or are simply not accurate enough to be useful.In this work we introduce a novel bioacoustic recognition/classification framework that mitigates or solves all of the above problems. We propose to classify animal sounds in the visual space, by treating the texture of their sonograms as an acoustic fingerprint using a recently introduced parameter-free texture measure as a distance measure. We further show that by searching for the most representative acoustic fingerprint, we can significantly outperform other techniques in terms of speed and accuracy.

Abstract-Time series classification has been an active area of research in the data mining community for over a decade, and significant progress has been made in the tractability and accuracy of learning. However, virtually all work assumes a one-time training session in which labeled examples of all the concepts to be learned are provided. This assumption may be valid in a handful of situations, but it does not hold in most medical and scientific applications where we initially may have only the vaguest understanding of what concepts can be learned. Based on this observation, we propose a never-ending learning framework for time series in which an agent examines an unbounded stream of data and occasionally asks a teacher (which may be a human or an algorithm) for a label. We demonstrate the utility of our ideas with experiments that consider real-world problems in domains as diverse as medicine, entomology, wildlife monitoring, and human behavior analyses.

Time series classification has been an active area of research in the data mining community for over a decade, and significant progress has been made in the tractability and accuracy of learning. However, virtually all work assumes a one-time training session in which labeled examples of all the concepts to be learned are provided. This assumption may be valid in a handful of situations, but it does not hold in most medical and scientific applications where we initially may have only the vaguest understanding of what concepts can be learned. Based on this observation, we propose a never-ending learning framework for time series in which an agent examines an unbounded stream of data and occasionally asks a teacher (which may be a human or an algorithm) for a label. We demonstrate the utility of our ideas with experiments in domains as diverse as medicine, entomology, wildlife monitoring, and human behavior analyses.

No abstract

Many algorithms for data mining or indexing time series data do not operate directly on the raw data, but instead they use alternative representations that include transforms, quantization, approximation, and multi-resolution abstractions. Choosing the best representation and abstraction level for a given task/dataset is arguably the most critical step in time series data mining. In this work, we investigate the problem of discovering the natural intrinsic representation model, dimensionality and alphabet cardinality of a time series. The ability to automatically discover these intrinsic features has implications beyond selecting the best parameters for particular algorithms, as characterizing data in such a manner is useful in its own right and an important subroutine in algorithms for classification, clustering and outlier discovery. We will frame 123Discover the intrinsic cardinality and dimensionality of time series 359 the discovery of these intrinsic features in the Minimal Description Length framework. Extensive empirical tests show that our method is simpler, more general and more accurate than previous methods, and has the important advantage of being essentially parameter-free.