The Experts below are selected from a list of 8430 Experts worldwide ranked by ideXlab platform
Patrick Hosein - One of the best experts on this subject based on the ideXlab platform.
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VoIP Capacity Model for an OFDMA downlink
IEEE Vehicular Technology Conference, 2007Co-Authors: Patrick HoseinAbstract:In traditional wireless networks, voice service is supported with synchronous power controlled channels and hence latency requirements were easily met. Fourth Generation Networks (3GPP2, 3GPP, WiMAX), are packet based and use Orthogonal Frequency Division Multiple Access (OFDMA) on the forward link. In this case voice will typically be transported using the Voice over IP (VoIP) protocol. With this approach, there is a trade-off between delay performance and user Capacity since by using queuing one can more efficiently utilize radio resources but at the expense of additional queuing delays. In this paper we provide Models for determining various performance metrics of such a system and compare the accuracy of the Models with published simulation results. These Models can be used to develop an intuitive understanding of the underlying performance as well as for performing sensitivity analysis. © 2007 IEEE.
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VoIP Capacity Model for the 1xEV-DO Uplink
2007 IEEE 66th Vehicular Technology Conference, 2007Co-Authors: Patrick HoseinAbstract:In this paper we consider the reverse traffic channel of the 1xEV-DO Revision A wireless network standard. We develop an analytic Model for determining the VoIP Capacity of this channel. This Model is then compared with published simulation results to demonstrate its accuracy. Results for the Model can be determined in real-time and the intent of this work is to be able to use such Models to dynamically adjust network parameters to more efficiently use radio resources.
J.c. Viera - One of the best experts on this subject based on the ideXlab platform.
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A variable effective Capacity Model for LiFePO4 traction batteries using computational intelligence techniques
IEEE Transactions on Industrial Electronics, 2015Co-Authors: Luciano Sánchez, Juan C. Antón, Manuela Gonzalez, Victor Garcia, Cecilio Blanco, J.c. VieraAbstract:Computational intelligence techniques are used to approximate the nonlinear operation of LiFePO4 batteries using rule-based systems. In this paper, rule-based systems are not directly fitted to data, but comprise constructive blocks in a differential equations-based dynamical Model that is numerically integrated to infer battery voltage, charge and temperature. The design methodology has been validated with three different LiFePO4 batteries and the results were found to be more accurate than those of a selection of statistical Models and state-of-the-art artificial intelligence techniques.
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A Variable Effective Capacity Model for $\hbox{LiFePO}_{4}$ Traction Batteries Using Computational Intelligence Techniques
IEEE Transactions on Industrial Electronics, 2015Co-Authors: Luciano Sánchez, Juan C. Antón, Manuela Gonzalez, Victor Garcia, Cecilio Blanco, J.c. VieraAbstract:Computational intelligence techniques are used to approximate the nonlinear operation of LiFePO4 batteries using rule-based systems. In this paper, rule-based systems are not directly fitted to data, but comprise constructive blocks in a differential-equation-based dynamical Model that is numerically integrated to infer battery voltage, charge, and temperature. The design methodology has been validated with three different LiFePO4 batteries, and the results were found to be more accurate than those of a selection of statistical Models and state-of-the-art artificial intelligence techniques.
Luciano Sánchez - One of the best experts on this subject based on the ideXlab platform.
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A variable effective Capacity Model for LiFePO4 traction batteries using computational intelligence techniques
IEEE Transactions on Industrial Electronics, 2015Co-Authors: Luciano Sánchez, Juan C. Antón, Manuela Gonzalez, Victor Garcia, Cecilio Blanco, J.c. VieraAbstract:Computational intelligence techniques are used to approximate the nonlinear operation of LiFePO4 batteries using rule-based systems. In this paper, rule-based systems are not directly fitted to data, but comprise constructive blocks in a differential equations-based dynamical Model that is numerically integrated to infer battery voltage, charge and temperature. The design methodology has been validated with three different LiFePO4 batteries and the results were found to be more accurate than those of a selection of statistical Models and state-of-the-art artificial intelligence techniques.
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A Variable Effective Capacity Model for $\hbox{LiFePO}_{4}$ Traction Batteries Using Computational Intelligence Techniques
IEEE Transactions on Industrial Electronics, 2015Co-Authors: Luciano Sánchez, Juan C. Antón, Manuela Gonzalez, Victor Garcia, Cecilio Blanco, J.c. VieraAbstract:Computational intelligence techniques are used to approximate the nonlinear operation of LiFePO4 batteries using rule-based systems. In this paper, rule-based systems are not directly fitted to data, but comprise constructive blocks in a differential-equation-based dynamical Model that is numerically integrated to infer battery voltage, charge, and temperature. The design methodology has been validated with three different LiFePO4 batteries, and the results were found to be more accurate than those of a selection of statistical Models and state-of-the-art artificial intelligence techniques.
Karen A Matthews - One of the best experts on this subject based on the ideXlab platform.
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association between socioeconomic status and metabolic syndrome in women testing the reserve Capacity Model
Health Psychology, 2008Co-Authors: Karen A Matthews, Linda C. Gallo, Katri Raikkonen, Lewis H KullerAbstract:Low socioeconomic status (SES) is associated with an elevated risk of mortality and morbidity from diverse causes (Adler, Marmot, McEwan, & Stewart, 1999). In part, SES disparities in health are because of differences in the distribution of basic resources such as health care, nutrition, and sanitary living environments (Lynch, Smith, Kaplan, & House, 2000). This focus may be particularly important to explaining poor health in groups characterized by poverty, but the impact of SES on health is not only at the poverty level and below. Rather, health disparities have a monotonic relationship with SES, so that even relatively affluent groups exhibit worse health than their higher SES counterparts. Gallo and Matthews (2003) offered the reserve Capacity Model as a framework for understanding how emotional factors in particular might contribute to SES disparities in health. The Model suggests that persons in low SES environments experience more frequent and intense harmful or potentially threatening situations and less frequent rewarding or potentially beneficial situations, relative to their higher SES counterparts. Frequent exposure to chronic and acute stressors, in turn, is thought to have a direct negative impact on emotional experiences. However, studies that account for initial differences in stress exposure suggest that at every level of stress, individuals with lower SES report more emotional distress than those with higher SES (Kessler & Cleary, 1980; McLeod & Kessler, 1990). Why might individuals in low SES environments be more reactive to stress? Our framework suggests that low SES individuals maintain a smaller bank of resources—tangible, interpersonal, and intrapersonal—to deal with stressful events compared to their higher SES counterparts. Examples of types of resources are the ability to borrow money in emergencies for tangible; having a supportive friendship network for interpersonal; and having good problem solving skills for intrapersonal. Borrowing a concept from the aging literature, we label this reserve Capacity. Low SES persons' reserve Capacity to deal with stressful environments may be inadequate for two reasons: (a) Low-SES individuals are exposed to more situations that require using their reserves; and (b) their environments prevent the development and replenishment of resources to be kept in reserve. Literature relevant to the reserve Capacity Model reveals supportive evidence for connections among components of the Model, for example, SES and stress exposure, stress exposure and negative emotions (Gallo & Matthews, 2003). However, few studies are available to directly evaluate the proposed mediational pathways, and studies that did include most pieces of the framework provided very limited evidence for the dynamic links suggested in the Model (cf. Thurston, Kubzanksy, Kawachi, & Berkman, 2006). The first study explicitly designed to test the reserve Capacity Model used ecological momentary assessment methods. Women monitored positive and negative psychosocial experiences and emotions across two days (Gallo, Bogart, Vranceanu, & Matthews, 2005). Results showed that lower SES women experienced lower perceptions of control and positive affect and more frequent social strain in their daily lives when compared with their higher SES counterparts, and that control and strain contributed to the association between SES and positive affect. Women with lower SES also had less reserve Capacity, that is, summative measures of intrapsychic and social resources, relative to those with higher SES. Reserve Capacity, in turn, was related to higher levels of social strain, lower perceived control, lower positive affect, and higher negative affect in everyday life. This suggests that individuals with lower SES may suffer a disadvantage because of direct effects of low SES on daily experiences and emotions in combination with effects related to low resources. Surprisingly, and inconsistent with the Model, SES was unrelated to ongoing negative affect. In the present paper, we evaluate the reserve Capacity Model in women using a different approach. We use trait measures of negative affect and reserve Capacity predicting an important health outcome, the metabolic syndrome. Metabolic syndrome refers to a cluster of aberrations of metabolic origin including impaired glucose and lipid metabolism, central adiposity, and hypertension. The National Cholesterol Education Program's Adult Treatment Panel III (ATP-III), the World Health Organization (WHO), and International Diabetes Foundation (IDF) have offered clinical cutoffs for defining the metabolic syndrome that vary somewhat; for example, elevated blood pressure (BP) is considered to be 130/85 or BP treatment for ATP-III and IDF versus 140/90 for WHO. Nonetheless, individuals with the metabolic syndrome are at increased risk for morbidity and mortality from cardiovascular disease (CVD) (Gami et al., 2007; Lakka et al., 2002), Type 2 diabetes (Laaksonen, Lakka, Niskanen, Kaplan, Salonen, & Lakka, 2002), and all—cause mortality (Grundy, Brewer, Cleeman, Smith Jr., & Lenfant, 2004; Grundy et al., 2005). Research devoted to testing whether psychosocial factors predict the metabolic syndrome is limited (Goldbacher & Matthews, 2007). In older adult men and women not on hormone replacement therapy, psychosocial distress predicted a composite score representing the components of the metabolic syndrome, over the course of 15 to 18 months (Vitaliano, Scanlan, Zhang, Savage, Hirsch, & Siegler, 2002). In middle-aged men and women, greater work stressors predicted the metabolic syndrome based on the ATP III definition (Chandola, Brunner, & Marmot, 2006). Cynicism also predicted a latent construct of metabolic syndrome across 3 years, in older men and women enrolled in the Swedish Adoption/Twin Study of Aging (Nelson, Palmer, & Pedersen, 2004). These studies lacked a baseline measurement of the metabolic syndrome, which precluded inferences about prospective relationships. We have previously demonstrated that among middle-aged participants of the Healthy Women Study, depressive symptoms and intense and frequent feelings of anger predicted increasing risk for the ATP III-defined metabolic syndrome over an average of 7.4 years (Raikkonen, Matthews, & Kuller, 2002) and increasing risk for ATP-III, IDF- and WHO-defined metabolic syndrome over 12 years (Raikkonen, Matthews, & Kuller, 2007). Further, reports of marital dissatisfaction, divorce, and widowhood predicted increasing risk of the ATP III-defined metabolic syndrome over approximately 12 years (Troxel, Matthews, Gallo, & Kuller, 2005). In the current report, we evaluated the association of one indicator of SES, educational attainment, and the development of the metabolic syndrome over an average of 12 years in the Healthy Women Study. We used confirmatory factor analysis to evaluate whether the concepts of reserve Capacity (optimism, social support, and self-esteem), negative emotions (anxiety, depressive symptoms, and anger), and metabolic syndrome (blood pressure, lipids, and waist circumference) fit the data. Then we used structural equation Modeling to evaluate the merits of the reserve Capacity Model for understanding the connections between SES and the development of the metabolic syndrome. Specifically, we tested the hypotheses that low SES would be connected to risk for the metabolic syndrome latent factor, in part, through connections with stressful experiences, and latent factors of reserve Capacity and negative emotion.
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socioeconomic status resources psychological experiences and emotional responses a test of the reserve Capacity Model
Journal of Personality and Social Psychology, 2005Co-Authors: Linda C. Gallo, Laura M Bogart, Anamaria Vranceanu, Karen A MatthewsAbstract:: The current study used ecological momentary assessment to test several tenets of the reserve Capacity Model (L.C. Gallo & K. A. Matthews, 2003). Women (N = 108) with varying socioeconomic status (SES) monitored positive and negative psychosocial experiences and emotions across 2 days. Measures of intrapsychic and social resources were aggregated to represent the reserve Capacity available to manage stress. Lower SES was associated with less perceived control and positive affect and more social strain. Control and strain contributed to the association between SES and positive affect. Lower SES elicited greater positive but not negative emotional reactivity to psychosocial experiences. Women with low SES had fewer resources relative to those with higher SES, and resources contributed to the association between SES and daily experiences.
Victor Garcia - One of the best experts on this subject based on the ideXlab platform.
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A variable effective Capacity Model for LiFePO4 traction batteries using computational intelligence techniques
IEEE Transactions on Industrial Electronics, 2015Co-Authors: Luciano Sánchez, Juan C. Antón, Manuela Gonzalez, Victor Garcia, Cecilio Blanco, J.c. VieraAbstract:Computational intelligence techniques are used to approximate the nonlinear operation of LiFePO4 batteries using rule-based systems. In this paper, rule-based systems are not directly fitted to data, but comprise constructive blocks in a differential equations-based dynamical Model that is numerically integrated to infer battery voltage, charge and temperature. The design methodology has been validated with three different LiFePO4 batteries and the results were found to be more accurate than those of a selection of statistical Models and state-of-the-art artificial intelligence techniques.
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A Variable Effective Capacity Model for $\hbox{LiFePO}_{4}$ Traction Batteries Using Computational Intelligence Techniques
IEEE Transactions on Industrial Electronics, 2015Co-Authors: Luciano Sánchez, Juan C. Antón, Manuela Gonzalez, Victor Garcia, Cecilio Blanco, J.c. VieraAbstract:Computational intelligence techniques are used to approximate the nonlinear operation of LiFePO4 batteries using rule-based systems. In this paper, rule-based systems are not directly fitted to data, but comprise constructive blocks in a differential-equation-based dynamical Model that is numerically integrated to infer battery voltage, charge, and temperature. The design methodology has been validated with three different LiFePO4 batteries, and the results were found to be more accurate than those of a selection of statistical Models and state-of-the-art artificial intelligence techniques.
