Cardiovascular disease (CVD) risk assessment is an important instrument to enhance the clinical decision in the daily practice as well as to improve the preventive health care promoting the transfer from the hospital ...Cardiovascular disease (CVD) risk assessment is an important instrument to enhance the clinical decision in the daily practice as well as to improve the preventive health care promoting the transfer from the hospital to patient’s home. Due to its importance, clinical guidelines recommend the use of risk scores to predict the risk of a cardiovascular disease event. Therefore, there are several well known risk assessment tools, unfortunately they present some limitations.This work addresses this problem with two different methodologies:1) combination of risk assessment tools based on fusion of Bayesian classifiers complemented with genetic algorithm optimization;2) personalization of risk assessment through the creation of groups of patients that maximize the performance of each risk assessment tool. This last approach is implemented based on subtractive clustering applied to a reduced-dimension space.Both methodologies were developed to short-term CVD risk prediction for patients with Acute Coronary Syndromes without ST segment eleva-tion (ACS-NSTEMI). Two different real patients’ datasets were considered to validate the developed strategies:1) Santa Cruz Hospital, Portugal, N=460 patients;2)LeiriaPombal Hospital Centre, Portugal, N=99 patients.This work improved the performance in relation to current risk assessment tools reaching maximum values of sensitivity, specificity and geometric mean of, respectively, 80.0%, 82.9%, 81.5%. Besides this enhancement, the proposed methodologies allow the incorporation of new risk factors, deal with missing risk factors and avoid the selection of a single tool to be applied in the daily clinical practice. In spite of these achievements, the CVD risk assessment (patient stratification) should be improved. The incorporation of new risk factors recognized as clinically significant, namely parameters derived from heart rate variability (HRV), is introduced in this work. HRV is a strong and independent predictor of mortality in patients following acute myocardial infarction. The impact of HRV parameters in the characterization of coronary artery disease (CAD) patients will be conducted during hospitalization of these patients in the Leiria-Pombal Hospital Centre (LPHC).展开更多
Accurate classification of fruit varieties in processing factories and during post-harvesting applications is a challenge that has been widely studied.This paper presents a novel approach to automatic fruit identifica...Accurate classification of fruit varieties in processing factories and during post-harvesting applications is a challenge that has been widely studied.This paper presents a novel approach to automatic fruit identification applied to three common varieties of oranges(Citrus sinensis L.),namely Bam,Payvandi and Thomson.A total of 300 color images were used for the experiments,100 samples for each orange variety,which are publicly available.After segmentation,263 parameters,including texture,color and shape features,were extracted from each sample using image processing.Among them,the 6 most effective features were automatically selected by using a hybrid approach consisting of an artificial neural network and particle swarm optimization algorithm(ANN-PSO).Then,three different classifiers were applied and compared:hybrid artificial neural network–artificial bee colony(ANN-ABC);hybrid artificial neural network–harmony search(ANN-HS);and k-nearest neighbors(kNN).The experimental results show that the hybrid approaches outperform the results of kNN.The average correct classification rate of ANN-HS was 94.28%,while ANN-ABS achieved 96.70%accuracy with the available data,contrasting with the 70.9%baseline accuracy of kNN.Thus,this new proposed methodology provides a fast and accurate way to classify multiple fruits varieties,which can be easily implemented in processing factories.The main contribution of this work is that the method can be directly adapted to other use cases,since the selection of the optimal features and the configuration of the neural network are performed automatically using metaheuristic algorithms.展开更多
Synchronization in parallel programs is a major performance bottleneck in multiprocessor systems. Shared data is protected by locks and a lot of time is spent on the competition arising at the lock hand-off. In order ...Synchronization in parallel programs is a major performance bottleneck in multiprocessor systems. Shared data is protected by locks and a lot of time is spent on the competition arising at the lock hand-off. In order to be serialized, requests to the same cache line can either be bounced （NACKed） or buffered in the coherence controller. In this paper, we focus mainly on systems whose coherence controllers buffer requests. In a lock hand-off, a burst of requests to the same line arrive at the coherence controller. During lock hand-off only the requests from the winning processor contribute to progress of the computation, since the winning processor is the only one that will advance the work. This key observation leads us to propose a hardware mechanism we call request bypassing, which allows requests from the winning processor to bypass the requests buffered in the coherence controller keeping the lock line. We present an inexpensive implementation of request bypassing that reduces the time spent on all the execution phases of a critical section （acquiring the lock, accessing shared data, and releasing the lock） and which, as a consequence, speeds up the whole parallel computation. This mechanism requires neither compiler or programmer support nor ISA or coherence protocol changes. By simulating a 32-processor system, we show that using request bypassing does not degrade but rather improves performance in three applications with low synchronization rates, while in those having a large amount of synchronization activity （the remaining four）, we see reductions in execution time and in lock stall time ranging from 14% to 39% and from 52% to 7170, respectively. We compare request bypassing with a previously proposed technique called read combining and with a system that bounces requests, observing a significantly lower execution time with the bypassing scheme. Finally, we analyze the sensitivity of our results to some key hardware and software parameters.展开更多
文摘Cardiovascular disease (CVD) risk assessment is an important instrument to enhance the clinical decision in the daily practice as well as to improve the preventive health care promoting the transfer from the hospital to patient’s home. Due to its importance, clinical guidelines recommend the use of risk scores to predict the risk of a cardiovascular disease event. Therefore, there are several well known risk assessment tools, unfortunately they present some limitations.This work addresses this problem with two different methodologies:1) combination of risk assessment tools based on fusion of Bayesian classifiers complemented with genetic algorithm optimization;2) personalization of risk assessment through the creation of groups of patients that maximize the performance of each risk assessment tool. This last approach is implemented based on subtractive clustering applied to a reduced-dimension space.Both methodologies were developed to short-term CVD risk prediction for patients with Acute Coronary Syndromes without ST segment eleva-tion (ACS-NSTEMI). Two different real patients’ datasets were considered to validate the developed strategies:1) Santa Cruz Hospital, Portugal, N=460 patients;2)LeiriaPombal Hospital Centre, Portugal, N=99 patients.This work improved the performance in relation to current risk assessment tools reaching maximum values of sensitivity, specificity and geometric mean of, respectively, 80.0%, 82.9%, 81.5%. Besides this enhancement, the proposed methodologies allow the incorporation of new risk factors, deal with missing risk factors and avoid the selection of a single tool to be applied in the daily clinical practice. In spite of these achievements, the CVD risk assessment (patient stratification) should be improved. The incorporation of new risk factors recognized as clinically significant, namely parameters derived from heart rate variability (HRV), is introduced in this work. HRV is a strong and independent predictor of mortality in patients following acute myocardial infarction. The impact of HRV parameters in the characterization of coronary artery disease (CAD) patients will be conducted during hospitalization of these patients in the Leiria-Pombal Hospital Centre (LPHC).
基金This work was partly supported by the Spanish MINECO,as well as European Commission FEDER funds,under grant TIN2015-66972-C5-3-R.
文摘Accurate classification of fruit varieties in processing factories and during post-harvesting applications is a challenge that has been widely studied.This paper presents a novel approach to automatic fruit identification applied to three common varieties of oranges(Citrus sinensis L.),namely Bam,Payvandi and Thomson.A total of 300 color images were used for the experiments,100 samples for each orange variety,which are publicly available.After segmentation,263 parameters,including texture,color and shape features,were extracted from each sample using image processing.Among them,the 6 most effective features were automatically selected by using a hybrid approach consisting of an artificial neural network and particle swarm optimization algorithm(ANN-PSO).Then,three different classifiers were applied and compared:hybrid artificial neural network–artificial bee colony(ANN-ABC);hybrid artificial neural network–harmony search(ANN-HS);and k-nearest neighbors(kNN).The experimental results show that the hybrid approaches outperform the results of kNN.The average correct classification rate of ANN-HS was 94.28%,while ANN-ABS achieved 96.70%accuracy with the available data,contrasting with the 70.9%baseline accuracy of kNN.Thus,this new proposed methodology provides a fast and accurate way to classify multiple fruits varieties,which can be easily implemented in processing factories.The main contribution of this work is that the method can be directly adapted to other use cases,since the selection of the optimal features and the configuration of the neural network are performed automatically using metaheuristic algorithms.
基金supported in part by Spanish Government and European ERDF under Grant Nos. TIN2007-66423, TIN2010-21291-C02-01 and TIN2007-60625gaZ:T48 research group (Arag'on Government and European ESF)+1 种基金Consolider CSD2007-00050 (Spanish Government)HiPEAC-2 NoE (European FP7/ICT 217068)
文摘Synchronization in parallel programs is a major performance bottleneck in multiprocessor systems. Shared data is protected by locks and a lot of time is spent on the competition arising at the lock hand-off. In order to be serialized, requests to the same cache line can either be bounced （NACKed） or buffered in the coherence controller. In this paper, we focus mainly on systems whose coherence controllers buffer requests. In a lock hand-off, a burst of requests to the same line arrive at the coherence controller. During lock hand-off only the requests from the winning processor contribute to progress of the computation, since the winning processor is the only one that will advance the work. This key observation leads us to propose a hardware mechanism we call request bypassing, which allows requests from the winning processor to bypass the requests buffered in the coherence controller keeping the lock line. We present an inexpensive implementation of request bypassing that reduces the time spent on all the execution phases of a critical section （acquiring the lock, accessing shared data, and releasing the lock） and which, as a consequence, speeds up the whole parallel computation. This mechanism requires neither compiler or programmer support nor ISA or coherence protocol changes. By simulating a 32-processor system, we show that using request bypassing does not degrade but rather improves performance in three applications with low synchronization rates, while in those having a large amount of synchronization activity （the remaining four）, we see reductions in execution time and in lock stall time ranging from 14% to 39% and from 52% to 7170, respectively. We compare request bypassing with a previously proposed technique called read combining and with a system that bounces requests, observing a significantly lower execution time with the bypassing scheme. Finally, we analyze the sensitivity of our results to some key hardware and software parameters.
