Tracking recurrent concepts using context

The problem of recurring concepts in data stream classification is a special case of concept drift where concepts may reappear. Although several existing methods are able to learn in the presence of concept drift, few consider contextual information when tracking recurring concepts. Nevertheless, in many real-world scenarios context information is available and can be exploited to improve existing approaches in the detection or even anticipation of recurring concepts. In this work, we propose the extension of existing approaches to deal with the problem of recurring concepts by reusing previously learned decision models in situations where concepts reappear. The different underlying concepts are identified using an existing drift detection method, based on the error-rate of the learning process. A method to associate context information and learned decision models is proposed to improve the adaptation to recurring concepts. The method also addresses the challenge of retrieving the most appropriate concept for a particular context. Finally, to deal with situations of memory scarcity, an intelligent strategy to discard models is proposed. The experiments conducted so far, using synthetic and real datasets, show promising results and make it possible to analyze the trade-off between the accuracy gains and the learned models storage cost.

Atmospheric attenuation at 100 and 300 GHz estimated with radiosonde data

The study of atmospheric propagation impairments at submillimeter and THz frequencies is becoming increasingly relevant due to the strong effects caused by the composition of the troposphere and the phenomena occurring in it. The present paper is devoted to the estimation of total attenuation at 100 GHz and 300 GHz under non-rainy scenarios. With this purpose, 4 years of meteorological data from Madrid have been collected, including radiosoundings from Madrid-Barajas Airport and co-site SYNOP observations. This volume of data has been analyzed with the aim of also introducing a detection method of rain conditions, which cannot be easily identified in radiosounding profiles. Finally, the method has been used to discard several probable events which would be responsible of scattering conditions and, hence, yearly CDFs of total attenuation have been obtained. It is expected that the statistics would be closest to the ones obtained by experimental techniques under similar atmospheric conditions.

New analysis and estimations of atmospheric attenuation at 100 GHz using meteorological data in Madrid

Atmospheric propagation at frequencies within the THz domain are deeply affected by the influence of the composition and phenomena of the troposphere. This paper is focused on the estimation of first order statistics of total attenuation under non-rainy conditions at 100 GHz. With this purpose, a yearly meteorological database from Madrid, including radiosoundings, SYNOP observations and co-site rain gauge, have been used in order to calculate attenuation due to atmospheric gases and clouds, as well as to introduce and evaluate a rain detection method. This method allows to filter out rain events and refine the statistics of total attenuation under the scenarios under study. It is expected that the behavior of the statistics would be closest to the ones obtained by experimental techniques under similar conditions.

Multiobjective structural damage identification in uncertain environments

Evolutionary algorithms are suitable to solve damage identification problems in a multiobjective context. However, the performance of these methods can deteriorate quickly with increasing noise intensities originating numerous uncertainties. In this paper, a statistic structural damage detection method formulated in a multiobjective context is proposed. The statistic analysis is implemented to take into account the uncertainties existing in the structural model and measured structural modal parameters. The presented method is verified by a number of simulated damage scenarios. The effects of noise and damage levels on damage detection are investigated.

Damage Assessment of Structures with Uncertainty

Evolutionary algorithms are suitable to solve damage identification problems in a multiobjective context. However, the performance of these methods can deteriorate quickly with increasing noise intensities originating numerous uncertainties. In this work, a statistic structural damage detection method formulated in a multiobjective context is proposed, taking into account the uncertainties existing. The presented method is verified by a number of simulated damage scenarios. The effects of noise on damage detection are investigated.

Statistical Multi-Objective Structural Damage Identification based on Dynamic Parameters

Evolutionary algorithms are suitable to solve damage identification problems in a multi-objective context. However, the performance of these methods can deteriorate quickly with increasing noise intensities originating numerous uncertainties. In this paper, a statistic structural damage detection method formulated in a multi-objective context is proposed. The statistic analysis is implemented to take into account the uncertainties existing in the structural model and measured structural modal parameters. The presented method is verified by a number of simulated damage scenarios. The effects of noise and damage levels on damage detection are investigated.

Imaging of H217O distribution in the brain of a live rat by using proton-detected 17O MRI

Imaging of H217O has a number of important applications. Mapping the distribution of H217O produced by oxidative metabolism of 17O-enriched oxygen gas may lead to a new method of metabolic functional imaging; regional cerebral blood flow also can be measured by measuring the H217O distribution after the injection of 17O-enriched physiological saline solution. Previous studies have proposed a method for indirect detection of 17O. The method is based on the shortening of the proton T2 in H217O solutions, caused by the residual 17O-1H scalar coupling and transferred to the bulk water via fast chemical exchange. It has been shown that the proton T2 of H217O solutions can be restored to that of H216O by irradiating the resonance frequency of the 17O nucleus. The indirect 17O image thus is obtained by taking the difference between two T2-weighted spin-echo images: one acquired after irradiation of the 17O resonance and one acquired without irradiation. It also has been established that, at relatively low concentrations of H217O, the indirect method yields an image that quantitatively reflects the H217O distribution in the sample. The method is referred to as PRIMO (proton imaging of oxygen). In this work, we show in vivo proton images of the H217O distribution in a rat brain after an i.v. injection of H217O-enriched physiological saline solution. Implementing the indirect detection method in an echo-planar imaging sequence enabled obtaining H217O images with good spatial and temporal resolution of few seconds.

Noninvasive measurement of gene expression in skeletal muscle

We have developed a noninvasive detection method for expression of viral-mediated gene transfer. A recombinant adenovirus was constructed by using the gene for arginine kinase (AK), which is the invertebrate correlate to the vertebrate ATP-buffering enzyme, creatine kinase. Gene expression was noninvasively monitored using 31P-magnetic resonance spectroscopy (31P-MRS). The product of the AK enzyme, phosphoarginine (PArg), served as an MRS-visible reporter of AK expression. The recombinant adenovirus coding for arginine kinase (rAdCMVAK) was injected into the right hindlimbs of neonatal mice. Two weeks after injection of rAdCMVAK, a unique 31P-MRS resonance was observed. It was observable in all rAdCMVAK injected hindlimbs and was not present in the contralateral control or the vehicle injected limb. PArg and phosphocreatine (PCr) concentrations were calculated to be 11.6 ± 0.90 and 13.6 ± 1.1 mM respectively in rAdCMVAK injected limbs. AK activity was demonstrated in vivo by monitoring the decreases in PArg and ATP resonances during prolonged ischemia. After 1 h of ischemia intracellular pH was 6.73 ± 0.06, PCr/ATP was decreased by 77 ± 8%, whereas PArg/ATP was decreased by 50 ± 15% of basal levels. PArg and PCr returned to basal levels within 5 min of the restoration of blood flow. AK activity persisted for at least 8 mo after injection, indicating that adenoviral-mediated gene transfer can produce stable expression for long periods of time. Therefore, the cDNA encoding AK provides a useful reporter gene that allows noninvasive and repeated monitoring of gene expression after viral mediated gene transfer to muscle.

Towards Closed-loop Deep Brain Stimulation: Behavior Recognition from Human STN

Deep brain stimulation (DBS) provides significant therapeutic benefit for movement disorders such as Parkinson’s disease (PD). Current DBS devices lack real-time feedback (thus are open loop) and stimulation parameters are adjusted during scheduled visits with a clinician. A closed-loop DBS system may reduce power consumption and side effects by adjusting stimulation parameters based on patient’s behavior. Thus behavior detection is a major step in designing such systems. Various physiological signals can be used to recognize the behaviors. Subthalamic Nucleus (STN) Local field Potential (LFP) is a great candidate signal for the neural feedback, because it can be recorded from the stimulation lead and does not require additional sensors. This thesis proposes novel detection and classification techniques for behavior recognition based on deep brain LFP. Behavior detection from such signals is the vital step in developing the next generation of closed-loop DBS devices. LFP recordings from 13 subjects are utilized in this study to design and evaluate our method. Recordings were performed during the surgery and the subjects were asked to perform various behavioral tasks. Various techniques are used understand how the behaviors modulate the STN. One method studies the time-frequency patterns in the STN LFP during the tasks. Another method measures the temporal inter-hemispheric connectivity of the STN as well as the connectivity between STN and Pre-frontal Cortex (PFC). Experimental results demonstrate that different behaviors create different m odulation patterns in STN and it’s connectivity. We use these patterns as features to classify behaviors. A method for single trial recognition of the patient’s current task is proposed. This method uses wavelet coefficients as features and support vector machine (SVM) as the classifier for recognition of a selection of behaviors: speech, motor, and random. The proposed method is 82.4% accurate for the binary classification and 73.2% for classifying three tasks. As the next step, a practical behavior detection method which asynchronously detects behaviors is proposed. This method does not use any priori knowledge of behavior onsets and is capable of asynchronously detect the finger movements of PD patients. Our study indicates that there is a motor-modulated inter-hemispheric connectivity between LFP signals recorded bilaterally from STN. We utilize a non-linear regression method to measure this inter-hemispheric connectivity and to detect the finger movements. Our experimental results using STN LFP recorded from eight patients with PD demonstrate this is a promising approach for behavior detection and developing novel closed-loop DBS systems.

Rapid Scan Electron Paramagnetic Resonance (EPR) and Digital EPR Development

Rapid scan electron paramagnetic resonance (EPR) was developed in the Eaton laboratory at the University of Denver. Applications of rapid scan to wider spectra, such as for immobilized nitroxides, spin-labeled proteins, irradiated tooth and fingernail samples were demonstrated in this dissertation. The scan width has been increased from 55 G to 160 G. The signal to noise (S/N) improvement for slowly tumbling spin-labeled protein samples that is provided by rapid scan EPR will be highly advantageous for biophysical studies. With substantial improvement in S/N by rapid scan, the dose estimation for irradiated tooth enamels became more reliable than the traditional continuous wave (CW) EPR. An alternate approach of rapid scan, called field-stepped direct detection EPR, was developed to reconstruct wider EPR signals. A Mn2+ containing crystal was measured by field-stepped direct detection EPR, which had a spectrum more than 6000 G wide. Since the field-stepped direct detection extends the advantages of rapid scan to much wider scan ranges, this methodology has a great potential to replace the traditional CW EPR. With recent advances in digital electronics, a digital rapid scan spectrometer was built based on an arbitrary waveform generator (AWG), which can excite spins and detect EPR signals with a fully digital system. A near-baseband detection method was used to acquire the in-phase and quadrature signals in one physical channel. The signal was analyzed digitally to generate ideally orthogonal quadrature signals. A multiharmonic algorithm was developed that employed harmonics of the modulation frequencies acquired in the spectrometer transient mode. It was applied for signals with complicated lineshapes, and can simplify the selection of modulation amplitude. A digital saturation recovery system based on an AWG was built at X-band (9.6 GHz). To demonstrate performance of the system, the spin-lattice relaxation time of a fused quartz rod was measured at room temperature with fully digital excitation and detection.

Bayesian models for finding and grouping junctions

In this paper, we propose two Bayesian methods for detecting and grouping junctions. Our junction detection method evolves from the Kona approach, and it is based on a competitive greedy procedure inspired in the region competition method. Then, junction grouping is accomplished by finding connecting paths between pairs of junctions. Path searching is performed by applying a Bayesian A* algorithm that has been recently proposed. Both methods are efficient and robust, and they are tested with synthetic and real images.

3D Visual Data-Driven Spatiotemporal Deformations for Non-Rigid Object Grasping Using Robot Hands

Sensing techniques are important for solving problems of uncertainty inherent to intelligent grasping tasks. The main goal here is to present a visual sensing system based on range imaging technology for robot manipulation of non-rigid objects. Our proposal provides a suitable visual perception system of complex grasping tasks to support a robot controller when other sensor systems, such as tactile and force, are not able to obtain useful data relevant to the grasping manipulation task. In particular, a new visual approach based on RGBD data was implemented to help a robot controller carry out intelligent manipulation tasks with flexible objects. The proposed method supervises the interaction between the grasped object and the robot hand in order to avoid poor contact between the fingertips and an object when there is neither force nor pressure data. This new approach is also used to measure changes to the shape of an object’s surfaces and so allows us to find deformations caused by inappropriate pressure being applied by the hand’s fingers. Test was carried out for grasping tasks involving several flexible household objects with a multi-fingered robot hand working in real time. Our approach generates pulses from the deformation detection method and sends an event message to the robot controller when surface deformation is detected. In comparison with other methods, the obtained results reveal that our visual pipeline does not use deformations models of objects and materials, as well as the approach works well both planar and 3D household objects in real time. In addition, our method does not depend on the pose of the robot hand because the location of the reference system is computed from a recognition process of a pattern located place at the robot forearm. The presented experiments demonstrate that the proposed method accomplishes a good monitoring of grasping task with several objects and different grasping configurations in indoor environments.

Biogenic opal estimation of deep-sea sediment cores from the Scotia Sea

We present biogenic opal flux records from two deep-sea sites in the Scotia Sea (MD07-3133 and MD07-3134) at decadal-scale resolution, covering the last glacial cycle. Besides conventional and time-consuming biogenic opal measuring methods, we introduce new biogenic opal estimation methods derived from sediment colour b*, wet bulk density, Si/Ti-count ratio, and Fourier transform infrared spectroscopy (FTIRS). All methods capture the biogenic opal amplitude, however, FTIRS - a novel method for marine sediment - yields the most reliable results. 230Th normalization data show strong differences in sediment focusing with intensified sediment focusing during glacial times. At MD07-3134 230Th normalized biogenic opal fluxes vary between 0.2 and 2.5 g/cm2/kyr. Our biogenic opal flux records indicate bioproductivity changes in the Southern Ocean, strongly influenced by sea ice distribution and also summer sea surface temperature changes. South of the Antarctic Polar Front, lowest bioproductivity occurred during the Last Glacial Maximum when upwelling of mid-depth water was reduced and sea ice cover intensified. Around 17 ka, bioproductivity increased abruptly, corresponding to rising atmospheric CO2 contents and decreasing seasonal sea ice coverage.

High throughput analysis of endogenous glutamate release using a fluorescence plate reader

Recent discoveries of different modes of exocytosis and a plethora of molecules involved in neurotransmitter release has resulted in demand for more rapid and efficient methods for monitoring endogenous glutamate release from various tissue sources. In this article, we describe a high throughput microplate version of the enzyme-linked fluorescence detection method for the measurement of released glutamate, which utilises glutamate dehydrogenase, and the reduction of NADP to NADPH. Previous versions of this method rely upon cuvette-based fluorimeters for detection that are limited by large sample volumes and small numbers of samples that can be measured simultaneously. Comparison between the two methods shows that the microplate assay has comparable performance to the cuvette-based assay but has the capacity to analyse many times more samples in a given run. This increased capacity provides improved experimental design opportunities, higher experimental throughput and better comparison between experimental conditions. (c) 2005 Elsevier B.V. All rights reserved.