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- Crupi, G., et al.
(författare)
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Combined empirical and look-up table approach for non-quasi-static modelling of GaN HEMTs
- 2009
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Ingår i: 9th International Conference on Telecommunications in Modern Satellite, Cable, and Broadcasting Services, TELSIKS 2009 - Proceedings of Paper. - 9781424443833 ; , s. 40-43
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Konferensbidrag (refereegranskat)abstract
- In this paper the empirical and the look-up table approaches are combined to accurately model a gallium nitride based HEMT on silicon carbide. That solution allows to exploit the advantages of both approaches. The validity of the extracted model is verified by comparing model simulations with DC and microwave measurements.
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| 6. |
- Dancila, Dragos, et al.
(författare)
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Low Phase Noise Oscillator at 60 GHz Stabilized bya Substrate Integrated Cavity Resonator in LTCC
- 2014
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Ingår i: IEEE Microwave and Wireless Components Letters. - 1531-1309 .- 1558-1764. ; 24:12, s. 887-889
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Tidskriftsartikel (refereegranskat)abstract
- In this letter, we report a low phase noise oscillatorexhibiting state-of-the-art phase noise characteristics at 60 GHz.The oscillator is stabilized by an off-chip substrate integratedwaveguide (SIW) cavity resonator, manufactured in LTCC technology.The area on top of the cavity resonator is used to flip-chipmount the MMIC, realized in SiGe technology. Measured oscillatorsdiscussed in this paper operate at frequencies of 59.91,59.97, and 59.98 GHz. The measured phase noise at 1 MHzoffset is 115.76 dBc/Hz, 115.92 dBc/Hz and 116.41 dBc/Hz,respectively. To our knowledge, the present hybrid oscillator hasthe lowest phase noise and highest figure of merit of integratedoscillators at V-band. The simulations are in very good agreementwith the measured oscillation frequencies.
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| 8. |
- Verbeke, Mathias, et al.
(författare)
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Lazy and Eager Relational Learning Using Graph-Kernels
- 2014
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Ingår i: Statistical Language and Speech Processing. - Cham : Springer. - 9783319113975 - 9783319113968 ; , s. 171-184
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Konferensbidrag (refereegranskat)abstract
- Machine learning systems can be distinguished along two dimensions. The first is concerned with whether they deal with a feature based (propositional) or a relational representation; the second with the use of eager or lazy learning techniques. The advantage of relational learning is that it can capture structural information. We compare several machine learning techniques along these two dimensions on a binary sentence classification task (hedge cue detection). In particular, we use SVMs for eager learning, and kNN for lazy learning. Furthermore, we employ kLog, a kernel-based statistical relational learning framework as the relational framework. Within this framework we also contribute a novel lazy relational learning system. Our experiments show that relational learners are particularly good at handling long sentences, because of long distance dependencies.
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| 9. |
- Wang, Chunzhuo, et al.
(författare)
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Eating Speed Measurement Using Wrist-Worn IMU Sensors Towards Free-Living Environments
- 2024
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Ingår i: IEEE journal of biomedical and health informatics. - : IEEE. - 2168-2194 .- 2168-2208.
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Tidskriftsartikel (refereegranskat)abstract
- Eating speed is an important indicator that has been widely investigated in nutritional studies. The relationship between eating speed and several intake-related problems such as obesity, diabetes, and oral health has received increased attention from researchers. However, existing studies mainly use self-reported questionnaires to obtain participants' eating speed, where they choose options from slow, medium, and fast. Such a non-quantitative method is highly subjective and coarse at the individual level. This study integrates two classical tasks in automated food intake monitoring domain: bite detection and eating episode detection, to advance eating speed measurement in near-free-living environments automatically and objectively. Specifically, a temporal convolutional network combined with a multi-head attention module (TCN-MHA) is developed to detect bites (including eating and drinking gestures) from IMU data. The predicted bite sequences are then clustered into eating episodes. Eating speed is calculated by using the time taken to finish the eating episode to divide the number of bites. To validate the proposed approach on eating speed measurement, a 7-fold cross validation is applied to the self-collected fine-annotated full-day-I (FD-I) dataset, and a holdout experiment is conducted on the full-day-II (FD-II) dataset. The two datasets are collected from 61 participants with a total duration of 513 h, which are publicly available. Experimental results show that the proposed approach achieves a mean absolute percentage error (MAPE) of 0.110 and 0.146 in the FD-I and FD-II datasets, respectively, showcasing the feasibility of automated eating speed measurement in near-free-living environments.
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| 10. |
- Wang, Chunzhuo, et al.
(författare)
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Evaluation Metrics for Food Intake Activity Recognition Using Segment-Wise IoU
- 2024
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Konferensbidrag (refereegranskat)abstract
- AI-assisted food intake monitoring systems have drawn considerable attention from researchers. To date, various approaches have been proposed to objectively and unobtrusively detect food intake activities by utilizing novel sensors and machine learning techniques. In the development of automated food intake monitoring systems, one crucial step is to evaluate the generated results from machine learning models. In this study, we illustrate the challenge arising from the inefficiency of traditional sliding-window-based evaluation in translating results into clinical indices (i.e. number of bites). Additionally, existing evaluation metrics only focus on detection performance (count the occurrence of eating gestures); however, the segmentation performance (temporal boundary of eating gesture) is missed, which is also a clinically meaningful index. Apart from the discussion of existing evaluation methods in food intake monitoring, we introduce the segment-wise evaluation scheme using the Intersection Over Union (IoU) as threshold to assess performance. This method facilitates the evaluation of both the detection and segmentation performance of eating activities. Two public food intake datasets are used in our case study to illustrate that the segment-wise method can yield more detailed information and a more comprehensive evaluation when compared to existing metrics. The proposed evaluation scheme has the potential to be applied to other human activity recognition (HAR) cases.
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