HyperSurfaces are a merge of structurally reconfigurable metasurfaces whose electromagnetic properties can be changed via a software interface, using an embedded miniaturized network of controllers, thus enabling nove...
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HyperSurfaces are a merge of structurally reconfigurable metasurfaces whose electromagnetic properties can be changed via a software interface, using an embedded miniaturized network of controllers, thus enabling novel capabilities in wireless communications. Resource constraints associated with the development of a hardware testbed of this breakthrough technology necessitate network controller architectures different from traditional regular Network-on-Chip architectures. The Manhattan-like topology chosen to realize the controller network in the testbed under development is irregular, with restricted local path selection options, operating in an asynchronous fashion. These characteristics render traditional fault-tolerant routing mechanisms inadequate. In this paper, we present work in progress towards the development of fault-tolerant routing mechanisms for the chosen architecture. We present two XY-based approaches which have been developed aiming to offer reliable data delivery in the presence of faults. The first approach aims to avoid loops while the second one attempts to maximize the success delivery probabilities. Their effectiveness is demonstrated via simulations conducted on a custom developed simulator.
Objectives The aim of this paper is to investigate whether a smartphone-based system can be used to quantify dexterity in Parkinson's disease (PD). More specifically, the aim was to develop data-driven methods to ...
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Objectives The aim of this paper is to investigate whether a smartphone-based system can be used to quantify dexterity in Parkinson's disease (PD). More specifically, the aim was to develop data-driven methods to quantify and characterize dexterity in PD. Methods Nineteen advanced PD patients and 22 healthy controls participated in a clinical trial in Uppsala, Sweden. The subjects were asked to perform tapping and spiral drawing tests using a smartphone. Patients performed the tests before, and at pre-specified time points after they received 150% of their usual levodopa morning dose. Patients were video recorded and their motor symptoms were assessed by three movement disorder specialists using three Unified PD Rating Scale (UPDRS) motor items from part III, the dyskinesia scoring and the treatment response scale (TRS). The raw tapping and spiral data were processed and analyzed with time series analysis techniques to extract 37 spatiotemporal features. For each of the five scales, separate machine learning models were built and tested by using principal components of the features as predictors and mean ratings of the three specialists as target variables. Results There were weak to moderate correlations between smartphone-based scores and mean ratings of UPDRS item #23 (0.52;finger tapping), UPDRS #25 (0.47;rapid alternating movements of hands), UPDRS #31 (0.57;body bradykinesia and hypokinesia), sum of the three UPDRS items (0.46), dyskinesia (0.64), and TRS (0.59). When assessing the test-retest reliability of the scores it was found that, in general, the clinical scores had better test-retest reliability than the smartphone-based scores. Only the smartphone-based predicted scores on the TRS and dyskinesia scales had good repeatability with intra-class correlation coefficients of 0.51 and 0.84, respectively. Clinician-based scores had higher effect sizes than smartphone-based scores indicating a better responsiveness in detecting changes in relation to treatment
In this paper, a low cost e-health platform is evaluated concerning the precision of the employed sensors. The sensors used by the developed e-health platform are not medically certified but are compared against.medic...
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Preclinical diffusion MRI (dMRI) has proven value in methods development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. While dMRI enables in vivo non-invasive cha...
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Humans use signs, e.g., sentences in a spoken language, for communication and thought. Hence, symbol systems like language are crucial for our communication with other agents and adaptation to our real-world environme...
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A various number of graph clustering algorithms have been proposed and applied in real-world applications such as network analysis, bio-informatics, social computing, and etc. However, existing algorithms usually focu...
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A various number of graph clustering algorithms have been proposed and applied in real-world applications such as network analysis, bio-informatics, social computing, and etc. However, existing algorithms usually focus on optimizing specified quality measures at the global network level, without carefully considering the destruction of local structures which could be informative and significant in practice. In this paper, we propose a novel clustering algorithm for undirected graphs based on a new structure similarity measure which is computed in a recursive procedure. Our method can provide robust and high-quality clustering results, while preserving informative local structures in the original graph. Rigorous experiments conducted on a variety of benchmark and protein datasets show that our algorithm consistently outperforms existing algorithms.
A keyphrase (a multi-word unit) in a document denotes one or multiple keywords capturing a main topic of the underlying document. Finding good keyphrases of a document can quickly summarize knowledge for efficient dec...
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A keyphrase (a multi-word unit) in a document denotes one or multiple keywords capturing a main topic of the underlying document. Finding good keyphrases of a document can quickly summarize knowledge for efficient decision making and benefit domains involving intensive text information. To date, existing keyphrase extraction methods cannot be customized to each specific document, mainly because their patterns used to form paraphrases are too restrictive and may not capture flexible keyword relationships inside the text. In this paper, we propose a sequential pattern mining based document-specific keyphrase extraction method. Our key innovation is to use wildcards (or gap constraints) to help extract sequential patterns, so the flexible wildcard constraints within a pattern can capture semantic relationships between words, and the system will have full flexibility to discover different types of sequential patterns as candidates for keyphrase extraction. To achieve the goal, we regard each single document as a sequential dataset, and propose an efficient algorithm to mine sequential patterns with wildcard and one-off conditions that allows important keyphrases to be captured during the mining process. For each extracted keyphrase candidate, we use some statistical pattern features to characterize it, and further collect all keyphrases from the document to form a training set. A supervised learning classifier is trained to identify keyphrases from a test document. Because our pattern mining and pattern characterization processes are customized to each single document, keyphases extracted from our method are highly specific for each document. Experimental results demonstrate that the proposed sequential pattern mining method outperforms existing pattern mining methods in both runtime performance and completeness. Comparisons on keyphrase benchmark datasets also confirm that the proposed document-specific keyphrase extraction method is effective in improving the quality
Rotor Switches are optical switches that provide cyclic-shift permutations with a fixed round-robin schedule. They have been shown to offer high throughput while significantly reducing control complexity, making them ...
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ISBN:
(纸本)9781538693933
Rotor Switches are optical switches that provide cyclic-shift permutations with a fixed round-robin schedule. They have been shown to offer high throughput while significantly reducing control complexity, making them a promising candidate for use in optical data center interconnects. We successfully address two critical challenges impeding the adoption of such circuit switched data center interconnects: 1) switch scalability, and 2) support for simultaneous 2-way connectivity to enable using low-latency protocols.
The growing average age of the urban population, with an increasing number of 65+ years old citizens, is calling for the cities to provide global services specifically geared to elderly people. In this context, collec...
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The growing average age of the urban population, with an increasing number of 65+ years old citizens, is calling for the cities to provide global services specifically geared to elderly people. In this context, collecting data from the elderly's environment and his/her habits and making them available in a structured way to third parties for analysis, is the first step towards the realization of innovative user-centric services. This paper is focused on a performance analysis of three main blocks of an IoT-aware monitoring system: (i) data capturing in home and in the city, (ii) data store and management in the Cloud and, (iii) data analytics. Critical points in the system architecture have been highlighted trying also to define potential solutions able to overcome them. The analyzed system architecture is used by the H2020 City4Age project to help geriatricians in identifying the onset of Mild Cognitive Impairment (MCI) and frailty conditions.
Machine learning (ML) algorithms have made a tremendous impact in the field of medical imaging. While medical imaging datasets have been growing in size, a challenge for supervised ML algorithms that is frequently men...
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