General queuing model for optimal seamless delivery of payload processing in multi-core processors

This is a pre-print of an article published in The Journal of Supercomputing. The final authenticated version is available online at: https://doi.org/10.1007/s11227-017-2109-4.Recent developments in unmanned aerial systems (UAS) provide new opportunities in remote sensing application. In contrast to satellite and conventional (manned) aerial tasks, UAS flights can be operated in a very short period of time. UAS can also be more specifically focused toward a given task such as crop reconnaissance or electric line tower inspection. For some applications, the delivery time of the remote sensing results is crucial. The current three-phase procedure of data acquisition, data downloading and data processing, performed sequentially in time, represents a drawback that reduces the benefits of using unmanned aerial systems. In this paper, we present a parallel processing strategy, based on queuing theory, in which the data processing phase is performed on board in parallel with data acquisition. The unmanned aerial system payload has been enlarged with low-cost, lightweight, multi-core boards to facilitate remote sensing data processing during flight. The storage of the raw sensing data is also done for possible further analysis; however, the ultimate decision support information can be seamless delivered to the customer upon landing. Furthermore, text alarms and limited imagery can also be provided during flight.Peer ReviewedPostprint (author's final draft

Define design thinking

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Doing research on the effectiveness of psychotherapy and psychotherapy training: a person-centered/experiential perspective

In this article, we present a framework for selecting instruments for evaluating psychotherapy and psychotherapy training from a person-centered and experiential psychotherapy (PCEP) perspective. The protocol is divided into eight therapy measurement domains, consisting of four research themes (therapy outcome, therapy process, client predictors, training outcome) and two levels (general/pan-theoretical concepts vs. treatment specific/PCEP-oriented concepts). This research protocol provides recommendations about what to measure, encouraging collaboration across different training sites, while still allowing flexibility for individual centers. Minimum and systematic case study data collection designs are described: Minimum designs are appropriate for use in private practice settings with one's own clients; systematic case-study designs can be used for student case-presentation requirements or for publication. The framework and research protocols described are part of an emerging international research project involving private and public training centers in several countries

Cheetah Experimental Platform Web 1.0: Cleaning Pupillary Data

Recently, researchers started using cognitive load in various settings, e.g., educational psychology, cognitive load theory, or human-computer interaction. Cognitive load characterizes a tasks' demand on the limited information processing capacity of the brain. The widespread adoption of eye-tracking devices led to increased attention for objectively measuring cognitive load via pupil dilation. However, this approach requires a standardized data processing routine to reliably measure cognitive load. This technical report presents CEP-Web, an open source platform to providing state of the art data processing routines for cleaning pupillary data combined with a graphical user interface, enabling the management of studies and subjects. Future developments will include the support for analyzing the cleaned data as well as support for Task-Evoked Pupillary Response (TEPR) studies

Music Similarity Estimation

Music is a complicated form of communication, where creators and culture communicate and expose their individuality. After music digitalization took place, recommendation systems and other online services have become indispensable in the field of Music Information Retrieval (MIR). To build these systems and recommend the right choice of song to the user, classification of songs is required. In this paper, we propose an approach for finding similarity between music based on mid-level attributes like pitch, midi value corresponding to pitch, interval, contour and duration and applying text based classification techniques. Our system predicts jazz, metal and ragtime for western music. The experiment to predict the genre of music is conducted based on 450 music files and maximum accuracy achieved is 95.8% across different n-grams. We have also analyzed the Indian classical Carnatic music and are classifying them based on its raga. Our system predicts Sankarabharam, Mohanam and Sindhubhairavi ragas. The experiment to predict the raga of the song is conducted based on 95 music files and the maximum accuracy achieved is 90.3% across different n-grams. Performance evaluation is done by using the accuracy score of scikit-learn