Emerging flexible and stretchable devices open up novel and attractive applications beyond traditional rigid wearable devices. Since the small and flexible form-factor severely limits the battery ...capacity, energy harvesting (EH) stands out as a critical enabler of new devices. Despite increasing interest in recent years, the capacity of wearable energy harvesting remains unknown. Prior work analyzes the power generated by a single and typically rigid transducer. This choice limits the EH potential and undermines physical flexibility. Moreover, current results do not translate to total harvested energy over a given period, which is crucial from a developer perspective. In contrast, this paper explores the daily energy harvesting potential of combining flexible light and motion energy harvesters. It first presents a multi-modal energy harvesting system design whose inputs are flexible photo-voltaic cells and piezoelectric patches. We measure the generated power under various light intensity and gait speeds. Finally, we construct daily energy harvesting patterns of 9593 users by integrating our measurements with the activity data from the American Time Use Survey. Our results show that the proposed system can harvest on average 0. 6mAh @ 3. 6V per day.
Sentiment analysis (SA) is a continuing field of research that lies at the intersection of many fields such as data mining, natural language processing and machine learning. It is concerned with the ...automatic extraction of opinions conveyed in a certain text. Due to its vast applications, many studies have been conducted in the area of SA especially on English texts, while other languages such as Arabic received less attention. This survey presents a comprehensive overview of the works done so far on Arabic SA (ASA). The survey groups published papers based on the SA-related problems they address and tries to identify the gaps in the current literature laying foundation for future studies in this field.
This paper gives a general overview of hidden Markov model (HMM)-based speech synthesis, which has recently been demonstrated to be very effective in synthesizing speech. The main advantage of this ...approach is its flexibility in changing speaker identities, emotions, and speaking styles. This paper also discusses the relation between the HMM-based approach and the more conventional unit-selection approach that has dominated over the last decades. Finally, advanced techniques for future developments are described.
Robust Text Detection in Natural Scene Images Yin, Xu-Cheng; Yin, Xuwang; Huang, Kaizhu ...
IEEE transactions on pattern analysis and machine intelligence,
05/2014, Letnik:
36, Številka:
5
Journal Article
Recenzirano
Odprti dostop
Text detection in natural scene images is an important prerequisite for many content-based image analysis tasks. In this paper, we propose an accurate and robust method for detecting texts in natural ...scene images. A fast and effective pruning algorithm is designed to extract Maximally Stable Extremal Regions (MSERs) as character candidates using the strategy of minimizing regularized variations. Character candidates are grouped into text candidates by the single-link clustering algorithm, where distance weights and clustering threshold are learned automatically by a novel self-training distance metric learning algorithm. The posterior probabilities of text candidates corresponding to non-text are estimated with a character classifier; text candidates with high non-text probabilities are eliminated and texts are identified with a text classifier. The proposed system is evaluated on the ICDAR 2011 Robust Reading Competition database; the f-measure is over 76%, much better than the state-of-the-art performance of 71%. Experiments on multilingual, street view, multi-orientation and even born-digital databases also demonstrate the effectiveness of the proposed method.
Methods of automatic extraction and identification of properties, quantities and units of measurement from texts are considered. The developed ontology of properties and units of measurement includes ...both fundamental connections of properties and units of measurement and connections that show the formation of concepts. The technology of property identification is based on fact that semantically significant text elements extracted within the boundaries of one or several sentences forming the semantic neighborhood of the property are correlated with the corresponding components of the ontology, which allows to restore the missing semantic fragments or identify discrepancies in the designations. The results of experimental studies of the effectiveness of the developed tools are presented.
Global Position Systems and other navigation systems that collect spatial data through an array of sensors carried on by people and distributed in space have changed the way we navigate complex ...environments, such as cities. However, indoor navigation without reliable GPS signals relies on wall-mounted antennas, WiFi, or quantum sensors. Despite the gains of such technologies, underlying these navigation systems is the dismissal of the human wayfinding ability based on visual recognition of spatial features. In this paper, we propose a robust and parsimonious approach using Deep Convolutional Neural Network (DCNN) to recognize and interpret interior space. DCNN has achieved incredible success in object and scene recognition. In this study we design and train a DCNN to classify a pre-zoning indoor space, and from a single phone photo to recognize the learned space features, with no need of additional assistive technology. We collect more than 600,000 images inside MIT campus buildings to train our DCNN model, and achieved 97.9% accuracy in validation dataset and 81.7% accuracy in test dataset based on spatial-scale fixed model. Furthermore, the recognition accuracy and spatial resolution can be potentially improved through multiscale classification model. We identify the discriminative image regions through Class Activating Mapping (CAM) technique, to observe the model's behavior in how to recognize space and interpret it in an abstract way. By evaluating the results with misclassification matrix, we investigate the visual spatial feature of interior space by looking into its visual similarity and visual distinctiveness, giving insights into interior design and human indoor perception and wayfinding research. The contribution of this paper is threefold. First, we propose a robust and parsimonious approach for indoor navigation using DCNN. Second, we demonstrate that DCNN also has a potential capability in space feature learning and recognition, even under severe appearance changes. Third, we introduce a DCNN based approach to look into the visual similarity and visual distinctiveness of interior space.
Handwritten signatures are biometric traits at the center of debate in the scientific community. Over the last 40 years, the interest in signature studies has grown steadily, having as its main ...reference the application of automatic signature verification, as previously published reviews in 1989, 2000, and 2008 bear witness. Ever since, and over the last 10 years, the application of handwritten signature technology has strongly evolved and much research has focused on the possibility of applying systems based on handwritten signature analysis and processing to a multitude of new fields. After several years of haphazard growth of this research area, it is time to assess its current developments for their applicability in order to draw a structured way forward. This perspective reports a systematic review of the last 10 years of the literature on handwritten signatures with respect to the new scenario, focusing on the most promising domains of research and trying to elicit possible future research directions in this subject.
•Participants read multiple documents with or without media multitasking.•Participants summarized main ideas of paragraphs or reread paragraphs.•Media multitasking negatively affected processing and ...comprehension.•Main idea summarization mitigated effects of multitasking on comprehension.
Media multitasking refers to simultaneous engagement in two activities, or the act of switching between multiple activities, of which at least one is a media activity. Based on this definition, we had 134 Norwegian undergraduates read four partly conflicting documents on sun exposure and health on a computer in order to write a report on the issue, with half of the participants (randomly assigned) receiving and reading short, authentic social media messages on a smartphone while reading the documents, and the other half reading the documents without being sent any such messages. Further, we manipulated what participants did after reading each document paragraph, with half of the participants (randomly assigned) briefly summarizing the main idea of each paragraph in writing, and the other half just rereading each paragraph. Participants’ integrative processing (i.e., cross-text elaboration strategies) were assessed with a task-specific self-report measure immediately after reading all four documents, and their comprehension of the documents was assessed by analyzing their written reports in terms of their ability to elaborate and integrate information within and across the perspectives discussed in the documents. Results indicated that social media multitasking on a smartphone disturbed both the integrative processing and the integrated understanding of the documents, with main idea summarization mitigating or counteracting these negative effects of multitasking. However, when controlling for working memory, reading comprehension skills, and prior knowledge, integrative processing was not found to mediate the effect of multitasking on integrated understanding of the documents. Limitations of the present study and directions for future research are discussed.
The purpose of this study was to test a hypothesized model that specified direct and indirect linkages between the individual difference variables of epistemic beliefs, need for cognition, individual ...interest, and prior knowledge, the processing variables of effort, deeper-level strategies, and situational interest, and multiple-text comprehension. Using a path analysis approach with a sample of 279 Norwegian upper secondary school students, results indicated that students' effort and deeper-level strategies predicted their multiple-text comprehension, with the individual difference variables indirectly affecting multiple-text comprehension through their influence on effortful, adaptive multiple-text processing. In addition, students' prior knowledge about the topic of the texts seemed to affect their multiple-text comprehension directly as well as indirectly. Both theoretical and educational implications of the results are discussed.
•Path analysis revealed direct and indirect effects on multiple-text comprehension.•Effort and deeper-level strategies had direct effects on comprehension.•Epistemic beliefs, need for cognition, interest and knowledge had indirect effects.
Les dernières années, les services d'archives ont entrepris de vastes campagnes de numérisation, dans le but de préserver les fonds documentaires. Ces documents scannés sont alors disponibles sous ...forme d'images, matrices de pixels. Notre objectif est de reconnaître automatiquement le contenu de ces images pour en extraire de l'information interprétée. C’est ce que l’on appelle l’analyse automatique d’images de documents.
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