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检索条件"机构=Lab. for Systems Dynamics and Signal Processing"

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DYNAMIC 3D PET RECONSTRUCTION FOR KINETIC ANALYSIS USING PATCH-BASED LOW-RANK PENALTY

DYNAMIC 3D PET RECONSTRUCTION FOR KINETIC ANALYSIS USING PAT...

引用

IEEE Nuclear Science Symposium and Medical Imaging Conference

作者： K. S. Kim Y. D. Son Z. H. Cho J. B. Ra J. C. Ye Bio Imaging & Signal Processing Lab. Dept. of Bio& Brain Engineering KAIST Neuroscience Research Institute Gachon University of Medicine and Science Image Systems Lab. Electrical Engineering KAIST

ISBN: (纸本)9781467320283

Dynamic positron emission tomography (PET) is widely used to identify metabolism over time. However, conventional reconstruction algorithm provides a noisy reconstruction due to the lack of photon counts in each frame. Therefore, the main goal of this paper is to develop a novel spatio-temporal regularization approach that exploits inherent similarities within intra- and inter- frames. One of the main contributions of this paper is to demonstrate that such correlations can be exploited using a low rank constraint of overlapping similarity blocks. The resulting optimization framework is, however, non-smooth and non Lipschitz due to the low-rank penalty terms and Poisson log-likelihood. Therefore, we propose a novel globally convergent optimization method using the concave-convex procedure (CCCP) by exploiting Legendre-Fenchel *** confirm that the proposed algorithm can provide significantly improved image quality.

关键词： CCCP PET LOW-RANK PENALTY

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On connection between the convolutive and ordinary nonnegative matrix factorizations

On connection between the convolutive and ordinary nonnegati...

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10th International Conference on Latent Variable Analysis and signal Separation, LVA/ICA 2012

作者： Phan, Anh Huy Cichocki, Andrzej Tichavský, Petr Koldovský, Zbyněk Lab. for Advanced Brain Signal Processing Brain Science Institute - RIKEN Japan Institute of Information Theory and Automation Prague Czech Republic Faculty of Mechatronics Informatics and Interdisciplinary Studies Technical University of Liberec Studentská 2 461 17 Liberec Czech Republic EE Dept. Warsaw University of Technology Systems Research Institute Poland

ISBN: (纸本)9783642285509

A connection between the convolutive nonnegative matrix factorization (NMF) and the conventional NMF has been established. As a result, we can convey arbitrary alternating update rules for NMF to update rules for CNMF. In order to illustrate the novel derivation method, a multiplicative algorithm and a new ALS algorithm for CNMF are derived. The experiments confirm validity and high performance of our method and of the proposed algorithm. © 2012 Springer-Verlag.

关键词： Matrix algebra

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BodyANT: Miniature wireless sensors for naturalistic monitoring of daily activity 4

BodyANT: Miniature wireless sensors for naturalistic monitor...

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4th International ICST Conference on Body Area Networks, BODYNETS 2009

作者： Kusserow, Martin Amft, Oliver Tröster, Gerhard Wearable Computing Lab. ETH Zurich Switzerland Signal Processing Systems TU Eindhoven Netherlands

ISBN: (纸本)9789639799417

In this paper we present a novel wireless sensor node design, called BodyANT. BodyANT has a miniature outline of 20×10×3mm and reaches almost 5 days of battery operation when continuously transmitting acceleration data at 32 Hz. We evaluate the performance of BodyANT in an extensive set of lab.ratory and naturalistic experiments over multiple days. Our sensor node implementation showed robust performance for up to eight simultaneously operating nodes. We deployed three BodyANT nodes in a study of daily activities in combination with two further devices, a heart rate monitor, and a GPS receiver and analysed the network performance over multiple recording days. The data loss per sensor node in the experiment was 3.3% on average. We present and discuss detailed results on data losses per user activity. Our results demonstrate the feasibility of using BodyANT for long-term activity monitoring and recognition applications. Copyright 2009 ICST.

关键词： Sensor nodes

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Rejection of irrelevant human actions in real-time hidden markov model based recognition systems for wearable computers 11

Rejection of irrelevant human actions in real-time hidden ma...

引用

2nd Wireless Health Conference, WH'11

作者： Mannil, Jerry Bidmeshki, Mohammad-Mahdi Jafari, Roozbeh Embedded Systems and Signal Processing Lab. The University of Texas at Dallas Richardson TX 75080-3021 United States

ISBN: (纸本)9781450309820

Hidden Markov Model (HMM) is a well established technique for detecting patterns in a stream of observations. It performs well when the observation sequence does not contain unseen patterns that were not part of the training set. In an unconstrained environment, the observation sequence might contain new patterns that the HMM model is not familiar with. In such cases, HMM will match the test pattern to some trained pattern, which is most similar to the test pattern. This increases the false positives in the system. In this paper, we are describing a threshold based technique to detect such irrelevant patterns in a continuous stream of observations, and classify them as unwanted or bad patterns. The novelty of our approach is that it allows early detection of unwanted patterns. Test patterns are validated on a fixed length substring of observation sequence, rather than on the whole observation sequence corresponding to the test pattern. The substrings are validated based on its similarity with a valid pattern using a threshold value. This reduces the latency of detection of unwanted movement, and makes the detection process independent of duration of the various pattern classes. We evaluated this technique in the context of body sensor networks based human action recognition, and have achieved about 93 percent accuracy in detecting unwanted actions, while maintaining a 94 percent accuracy of recognizing valid actions. Copyright 2011 ACM.

关键词： Hidden Markov models

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Lightweight power aware and scalab.e movement monitoring for wearable computers: A mining and recognition technique at the fingertip of sensors 11

Lightweight power aware and scalable movement monitoring for...

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2nd Wireless Health Conference, WH'11

作者： Loseu, Vitali Mannil, Jerry Jafari, Roozbeh Embedded Systems and Signal Processing Lab. University of Texas at Dallas Richardson TX 75080-3021 United States

ISBN: (纸本)9781450309820

Activity monitoring using Body Sensor Networks(BSN) has gained much attention from the scientific community due to its recreational and medical applications. Suggested techniques for activity monitoring face two major problem. First, systems have to be trained for the individual subjects due to the heterogeneity of the BSN data. While most solutions can address this problem on a small data set, they have no mechanics for automatic scaling of the solution as the data set increases. Second, the battery limitations of the BSN severely limit the maximum deployment time for the continuous monitoring. This problem is often solved by shifting some processing to the local sensor nodes to avoid a very heavy communication cost. However, little work has been done to optimize the sensing and processing cost of the action recognition. In this paper, we propose an action recognition approach based on the BSN repository. We show how the information of a large repository can be automatically used to customize the processing on sensor nodes based on a limited and automated training process. We also investigate the power cost of such a repository mining approach on the sensor nodes based on our implementation. To assess the power requirement, we define an energy model for data sensing and processing. We demonstrate the relationship between the activity recognition precision and the power consumption of the system during continuous action monitoring. We demonstrate the energy effectiveness of our approach with a classification accuracy constraint based on limited data repository. Copyright 2011 ACM.

关键词： Body sensor networks

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Sport training using body sensor networks: A statistical approach to measure wrist rotation for golf swing 4

Sport training using body sensor networks: A statistical app...

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4th International ICST Conference on Body Area Networks, BODYNETS 2009

作者： Ghasemzadeh, Hassan Loseu, Vitali Guenterberg, Eric Jafari, Roozbeh Embedded Systems and Signal Processing Lab. Department of Electrical Engineering University of Texas at Dallas RichardsonTX75080 United States

ISBN: (纸本)9789639799417

Athletes in any sports can greatly benefit from feedback systems for improving the quality of their training. In this paper, we present a golf swing training system which incorporates wearable motion sensors to obtain inertial information and provide feedback on the quality of movements. The sensors are placed on a golf club and athlete's body at positions which capture the unique movements of a golf swing. We introduce a quantitative model which takes into consideration signal processing techniques on the collected data and quantifies the correctness of the performed actions. We evaluate the effectiveness of our framework on data obtained from four subjects and discuss ongoing research. © 2008 ICST.

关键词： Body sensor networks

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Towards wearable sensing-based assessment of fluid intake

Towards wearable sensing-based assessment of fluid intake

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2010 8th IEEE International Conference on Pervasive Computing and Communications Workshops, PERCOM Workshops 2010

作者： Amft, Oliver Bannach, David Pirkl, Gerald Kreil, Matthias Lukowicz, Paul Signal Processing Systems TU Eindhoven Netherlands Embedded Systems Lab. Uni Passau Germany

ISBN: (纸本)9781424466054

Fluid intake is an important information for many health and assisted living applications. At the same time it is inherently difficult to monitor. Existing reliable solutions require augmented drinking containers, which severely limits the applicability of such systems. In this paper we investigate two key components of an unobtrusive, wearable solution that is independent of a particular drinking container or environment. We first describe a system for spotting individual instances of drinking (lifting a container to the mouth and taking a single sip) in a continuous stream of data from a wrist-worn acceleration sensor. We show that drinking motion can be detected across different drinking containers (glass, cup, large beer mug, bottle) on a large dataset (560 drinking motion instances from six users, embedded in 5.84 hours of complex natural activities). An average performance of 84% recall at 94% precision was achieved for the drinking motion spotting. Based on the events derived from drinking event spotting, we show how additional information can be obtained. Specifically, we demonstrate the recognition of container types and fluid level from upper body postures during drinking events. Nine containers and three container fluid levels were evaluated to recognize container type and fluid amounts with three users. Recognition rate for container type was 75%, and for fluid level 72%. © 2010 IEEE.

关键词： Bottles

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Adaptive activity spotting based on event rates

Adaptive activity spotting based on event rates

引用

IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing

作者： Amft, Oliver Signal Processing Systems TU Eindhoven Netherlands Wearable Computing Lab. ETH Zurich Switzerland

ISBN: (纸本)9780769540498

To date many activity spotting approaches are static: once the system is trained and deployed it does not change anymore. There are substantial shortcomings of this approach, specifically spotting performance is hampered when patterns or sensor noise level changes. In this work an unsupervised sensitivity adaptation mechanism is proposed for activity event spotting based on expected activity event rates. The expected event rate for activity spotting was derived from the generalisation metric used in information retrieval. To illustrate generalisation effects and depict relations of spotting performance and event rate, different event rates were simulated and their precision-recall spotting performance analysed. Subsequently, the sensitivity adaptation concept is presented and evaluated. For this purpose two large datasets from personal healthcare applications were considered to explore benefits and limitations of this adaptation approach: recognition of drinking motions from inertial sensors and chewing strokes from sound. Results showed up to 28% spotting performance increase for event rate adapted operation, confirming performance benefits for sensitivity adaptation. The approach will be most applicable in situations, where estimated event rate statistics show low variance and long monitoring durations allow effective sensitivity adaptations. © 2010 IEEE.

关键词： activtiy spotting adaptive recognition pattern event detection unsupervised recogntition Metrorrhagia inertia sensor information retrieval

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Collab.rative real-time speaker identification for wearable systems

Collaborative real-time speaker identification for wearable ...

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8th IEEE International Conference on Pervasive Computing and Communications, PerCom 2010

作者： Rossi, Mirco Amft, Oliver Kusserow, Martin Tröster, Gerhard Wearable Computing Lab. ETH Zurich Zurich Switzerland Signal Processing Systems TU Eindhoven Eindhoven Netherlands

ISBN: (纸本)9781424453290

We present an unsupervised speaker identification system for personal annotations of conversations and meetings. The system dynamically learns new speakers and recognizes already known speakers using one audio channel and speech-independent modeling. Multiple personal systems could collab.rate in robust unsupervised speaker identification and online learning. The system was optimized for real-time operation on a DSP system that can be worn during daily activities. The system was evaluated on the freely availab.e 24-speaker Augmented Multiparty Interaction dataset. For 5s recognition time, the system achieves 81% recognition rate. Collab.ration between four identification systems resulted in a performance increase of up to 17%, however even two collab.rating systems yield an performance improvement. A prototypical wearable DSP implementation could continuously operate for more than 8 hours from a 4.1 Ah battery. ©2009 IEEE.

关键词： Online systems

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Stabilization of new generation solid-state dual-frequency laser at 1.5 μm for optical distribution of high purity microwave signals

Stabilization of new generation solid-state dual-frequency l...

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2010 IEEE International Topical Meeting on Microwave Photonics, MWP 2010

作者： Pillet, Gregoire Morvan, Loïc Dolfi, Daniel Schiellein, Julien Merlet, Thomas Optics and Signal Processing Lab. Thales Research and Technology - France Palaiseau France Thales Air Systems Limours France

We report on a compact and ruggedized dual-frequency laser at 1.5 μm delivering widely tunable (0-13 GHz) optically-carried microwave signals with a high stability (phase and amplitude noise below -120 dBc/Hz @10 kHz).

ISBN: (纸本)9781424478255

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