A residential PHEV load coordination mechanism with renewable sources in smart grids A high penetration of Plug-in Hybrid Electric Vehicles (PHEVs) can lead to stress and overload in distribution systems when uncoordinated charging is considered. Thus, coordination mechanisms to manage PHEV charging during peak periods are needed. This paper evaluates the impact of PHEVs in the residential distribution grid and proposes a coordination mechanism based on heuristic rules as a load shaping tool. The impact of the mechanism on user convenience is also studied, and the benefits of having a renewable generation system is examined. Results show that our coordination mechanism is able to prevent the overloading of the distribution transformer. Further, it has been found that the performance of the renewable system (e.g., renewable fraction and renewable cost ratio) depend on many factors such as weather, region, equipment specification, and user preference. Achieving a more efficient use of energy in residential grids has become of increasing importance for economic, environmental and energy conservation reasons. This has encouraged moving from a traditional electricity grid to a smart grid with Demand Response (DR) capabilities. Smart grids can support, based on a reliable communication infrastructure, the integration of renewable generation systems and variable pricing schemes to manage loads in the distribution system more efficiently [1] . Several efforts are currently underway in Europe, the US and Asia to deploy smart grids.. The increasing price of conventional fuel and oil, as well as environmental concerns, are also encouraging the adoption of Plug-in Hybrid Electric Vehicles (PHEVs) [2] [3] . PHEVs represent a new significant type of load on distribution systems. The charging process of these vehicles can demand a great amount of energy during long periods depending on the battery specifications. Thus, a high penetration of PHEVs in the vehicle market could severely impact the distribution grids causing overloads in the distribution transformers. This is particularly true when considering the scenario in which multiple PHEVs are charging during peak hours. To address this challenge, distribution systems need to be adapted to support the new load by implementing coordination mechanisms that allow them to manage PHEV charging efficiently.. In this paper we evaluate the impact of charging multiple PHEVs in residential distribution networks with a smart grid infrastructure. A coordination mechanism to manage PHEV charging is proposed based on heuristic rules and taking into account user priority in charging. The performance of the proposed mechanism is evaluated, as well as the impact it has on user convenience. The study also presents a model of a smart grid system integrating renewable generation capabilities and including realistic data of significant factors such as: electricity pricing, appliances activation time, driving habits, solar radiation and wind speed during the year, feed-in tariff, etc. Without coordination, a high penetration of PHEVs in the vehicle market can lead to stress in the residential distribution systems, particularly when several vehicles are plugged into the grid at peakevening hours. To address this challenge, the distribution system needs to be adapted to support the new loads. The evolution of traditional electricity grids to smart grids will allow implementation of coordination mechanisms that control PHEV charging in order to prevent distribution transformer from being overloaded.. In this paper, a coordination mechanism has been introduced to manage PHEV charging by shifting the charging time of some vehicles from peak hours to off-peak hours. It has been shown that the mechanism is able to prevent the load profile to exceed the transformer rated power even for a worse case scenario. The impact of the coordination mechanism on user convenience has been considered in terms of the time a user needs to wait for his/her vehicle to be fully charged. It has been found that the maximum waiting time can be covered during night hours, and PHEV battery will be ready by next morning.. The energy efficiency and cost efficiency of the renewable system depend on many factors, e.g. weather, region, equipment specification, user preference, etc. Efficiencies are higher in those seasons with higher In this section we briefly discuss a number of relevant issues. So far a lot papers in the literature (including the current paper) have discussed the technical challenges and solutions for PHEV integration into the grid. However, a large scale adoption of electric vehicles also very much depends on various economic factors. In [24] , we present an economic analysis of electric vehicles (EVs) based on comparisons with conventional vehicles to determine the economic and environmental benefits offered by EVs from a user's perspective. Our findings show that EVs can help to reduce energy cost and emissions. However, the retail cost of EVs still needs to decrease for EVs to become economically attractive for users.. We would like to point out that PHEV charging coordination can be formulated as different optimization problems according to different objective functions, e.g., cost minimization, or load leveling.. In contrast to the centralized solution in this paper, there are also distributed ones such as those based on game theory. The main focus of this paper, however, is to show that it is possible to design a simple and effective mechanism that avoids overloading the transformer and in the meantime satisfies user preference. In the literature, several works have been found regarding managing PHEV battery charging and evaluating the impact of PHEV penetration in distribution networks.. In [4] , the authors evaluate the impact of charging multiple PHEVs on residential distribution networks with different charging strategies. Their results reveal that a high penetration of PHEVs will create new load peaks that in some cases may overload the distribution transformer. In another work [5] , the same authors introduce a load shaping tool based on DR strategy to mitigate the impact of EV in the distribution grid. Their strategy takes into account customer preferences, comfort level and load priority. Their proposed shaping tool is able to manage the total demand, preventing the transformer from being overloaded. However, in these works the system studied is limited to a residential transformer serving three homes and does not consider renewable generation systems deployed in user premises.. In [6] a comprehensive approach for evaluating the impact of different levels of PHEV penetration on distribution networks is proposed. Their results show that a high penetration of PHEVs leads to an increasing investment cost and energy losses in the distribution network. In [7] , PHEV charging is treated as a demand side management (DSM) problem and two solutions have been proposed: a multi-agent systems (MAS) solution and an optimal quadratic programming (QP) scheduler solution. While the QP scheduler is able to optimally flatten peak loads and sufficiently charge PHEVs, it is not scalable. The MAS solution is shown to be scalable and adaptable to incomplete and unpredictable information. The authors of [8] have shown that we can take advantage of PHEV charging to mitigate the intermittency effect of renewable energy generation. A decision tool has been developed for the coordination of PHEV charging.. The integration of renewable systems in the management of PHEV batteries in smart grids is addressed by a number of papers, e.g. [9] [10]. In [9] the authors propose a control mechanism based on a cyber-physical power infrastructure and heuristic rules to coordinate the use of renewable energy and PHEV battery charging. Their model only considers solar energy source. A similar work is reported in [11] , in which an enhanced model of a distribution system is adopted to include additional renewable sources. Also, the authors evaluate the effect failures in the communication infrastructure have on the survivability of the distribution system. Finally, there is a rich literature on demand response and demand side management [12] [13] [14] . For example, the authors of [12] present a demand dispatch algorithm for smart charging of PHEVs.. The contributions of this work, in comparison with some of the related works examined, can be summarized as follows:. 1. We present a more realistic model of a residential distribution grid with renewable sources of energy. This includes realistic modeling of seasonal weather conditions, activation of appliances, and PHEV driving habits and plug-in time.. 2. We propose an efficient load coordination mechanism, based on heuristics, to control the PHEV battery charging process avoiding the overload of distribution transformers. The proposed mechanism considers user preference in the charging process, management of the energy provided by renewables, and flexible policy definitions to prioritize the vehicle charging.

O O B O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Quantitative Analysis of Pulmonary Emphysema Using Local Binary Patterns Abstract-We aim at improving quantitative measures of emphysema in computed tomography (CT) images of the lungs. Current standard measures, such as the relative area of emphysema (RA), rely on a single intensity threshold on individual pixels, thus ignoring any interrelations between pixels. Texture analysis allows for a much richer representation that also takes the local structure around pixels into account. This paper presents a texture classification-based system for emphysema quantification in CT images. Measures of emphysema severity are obtained by fusing pixel posterior probabilities output by a classifier. Local binary patterns (LBP) are used as texture features, and joint LBP and intensity histograms are used for characterizing regions of interest (ROIs). Classification is then performed using a nearest neighbor classifier with a histogram dissimilarity measure as distance. A 95.2% classification accuracy was achieved on a set of 168 manually annotated ROIs, comprising the three classes: normal tissue, centrilobular emphysema, and paraseptal emphysema. The measured emphysema severity was in good agreement with a pulmonary function test (PFT) achieving correlation coefficients of up to = 0 79 in 39 subjects. The results were compared to RA and to a Gaussian filter bank, and the texture-based measures correlated significantly better with PFT than did RA. C HRONIC obstructive pulmonary disease (COPD) is a growing health problem worldwide. In the United States alone, it is the fourth leading cause of morbidity and mortality, and it is estimated to become the fifth most burdening disease worldwide by 2020 [1] . COPD is a chronic lung disease characterized by limitation of airflow. It comprises two components: small airway disease and emphysema, which is characterized by gradual loss of lung tissue. Detection and quantification of emphysema is important, since it is thought to be the main cause of shortness of breath and disability in COPD. The primary diagnostic tool for COPD is spirometry by which various pulmonary function tests (PFTs) are performed [1] . However, PFTs have a low sensitivity to emphysema and are not capable of detecting early stages of COPD [2] . Another diagnostic tool that is gaining more and more attention is computed tomography (CT) imaging. CT is a sensitive method for diagnosing emphysema, assessing its severity, and determining its subtype, and both visual and quantitative CT assessment are closely correlated with the pathological extent of emphysema [3] .. In this study, we focus on the assessment of emphysema in CT images. Emphysema lesions, or bullae, are visible in CT images as areas of abnormally low attenuation values close to that of air. In CT, emphysema can be classified into three subtypes, or patterns, and we will adopt the naming and definitions used in Webb et al. [4] . These subtypes are the following: centrilobular emphysema (CLE), defined as multiple small low-attenuation areas; paraseptal emphysema (PSE), defined as multiple low-attenuation areas in a single layer along the pleura often surrounded by interlobular septa that is visible as thin white walls; and panlobular emphysema (PLE), defined as a low-attenuation lung with fewer and smaller pulmonary vessels. Examples of CLE and PSE, as well as normal tissue (NT), are shown in Fig. 1 .. Common computerized approaches to emphysema quantification in CT are based on the histogram of CT attenuation values, and different quantitative measures of the degree of emphysema can be derived from this histogram. The most common measure is the relative area of emphysema (RA), also referred to as emphysema index or density mask [3] , which measures the relative amount of lung parenchyma pixels that have attenuation values below a certain threshold. Usually, thresholds in the range to Hounsfield units (HU) are used. Measures based on the attenuation histogram disregard the information present in the morphology of the emphysema subtypes such as shape and size distribution of bullae. This was exemplified in a recent clinical study that reported discrepancies between visual scoring and RA for assessing the craniocaudal distribution of the three emphysema subtypes [5] .. One way to objectively characterize the emphysema morphology is to describe the local image structure using texture analysis techniques [6] , [7] . Uppaluri et al. introduced the idea of classifying emphysema in lung CT images using texture features [8] . Several authors followed this idea and classified regions of interest (ROIs) of various lung disease patterns using different texture features, mostly measures on gray-level cooccurrence matrices (GLCM), gray-level run-length matrices (GLRLM), and on the attenuation histogram, and different classifiers [9] - [18] . Other examples of texture features used in the lung tissue classification literature are: the gray-level difference method [17] , [18] ; discrete wavelet frame decomposition using third-order B-splines [15] ; convolving with partial derivatives of the Gaussian and the Laplacian of the Gaussian [12] , [13] ; gradient magnitude [16] ; and fractal dimension [8] , [9] , [14] . In some cases, shape, or geometric, measures are also included in conjunction with the texture features [10] , [13] , [16] . Most studies use a mix of rotation invariant and rotation variant texture features, whereas the texture features used in this study are solely rotation invariant.. Most of the studies on lung texture classification have one or several explicit emphysema classes [8] - [11] , [14] - [18] . Multiple emphysema classes are defined by subdividing according to disease severity [14] , [16] or emphysema morphology [11] , [17] , [18] . Chabat et al. discriminate between CLE and PLE [11] , whereas Prasad et al. distinguish between different stages of emphysema, ranging from diffuse to bullous emphysema [17] , [18] . The study described in this paper has two emphysema classes defined based on morphology, namely, CLE and PSE. PLE is not considered since only 2 out of 39 subjects had PLE as leading pattern in the data used in the experiments. The data come from a population of (ex-)smokers, and PLE is known to be more prevalent in subjects with -antitrypsin deficiency than in subjects with smoking-related COPD [2] .. A trained classifier can be used for quantification by classifying all pixels in the lung field. In [9] , [10] , [13] , [14] , [16] - [18] , the full lung is classified either by labeling complete ROIs [9] , [13] , [14] or by labeling individual pixels [10] , [16] - [18] . Xu et al. report the percentage of different disease patterns present in a few subjects, but these quantitative measures are not evaluated further [14] . Park et al. quantify emphysema by a weighted sum of relative emphysema class areas [16] , and it is to our knowledge the only emphysema based quantitative study on a group of subjects in the lung CT texture analysis literature. This paper proposes two new ideas in the area of lung texture analysis in CT images. The specific application is emphysema quantification, but the ideas are also applicable to other lung disease patterns. Preliminary versions of the study presented here appeared in [19] and [20] .. The first idea is to use local binary patterns (LBPs) originally formulated by Ojala et al. [21] as lung texture features. LBP unify structural and statistical information by a histogram of LBP codes that correspond to microstructures in the image at different scales. LBP have shown promising results in various applications in computer vision and have successfully been applied in a small number of other medical image analysis tasks, e.g., in mammographic mass detection [22] and magnetic resonance image analysis of the brain [23] . In [19] , we showed that histogram dissimilarity measures between LBP feature histograms in a nearest neighbor classifier [24] can discriminate between emphysematous tissue and NT.. The second idea is to fuse the posterior probabilities obtained from a classification of all pixels in the lung field into quantitative measures of emphysema severity. Texture-based classification allows for quantification of different emphysema subtypes, which may be important in phenotyping emphysema for increased understanding of COPD. Furthermore, texture features may be less influenced by inspiration level and noise compared to, e.g., RA, which uses intensity in single pixels. In [20] , we showed that this approach agrees well with the outcome of PFTs, achieving a significant correlation. Two fusion schemes are considered in this paper; mean class posterior (MCP) and relative class area (RCA). The second fusion scheme, RCA, is related to the fusion scheme in [16] that uses a weighted sum of RCAs. The difference is that we consider each RCA individually.. The proposed system is evaluated in two ways; ROI classification and emphysema quantification on subject level. A data set comprising 2-D high-resolution CT (HRCT) slices with manually annotated ROIs is used for these purposes. The LBP features are compared to two other sets of features, one based on a Gaussian filter bank (GFB) and one comprising measures on GLCM, GLRLM, and the attenuation histogram.

O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O B I O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O B I O O O O O O O O O O O O B I O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Free-Running 1550 nm VCSEL for 10.7 Gb/s Transmission in 99.7 km PON Abstract-We present a cooler-less, free-running 1550 nm vertical cavity surface emitting laser (VCSEL) directly modulated at 10.7 Gb/s. We also report on error-free transmission through 40 km of standard single-mode optical fiber, achieved without the use of dispersion-mitigation or mid-span amplification. Inverse-dispersion fiber was utilized to realize a dispersion-matched 99.7 km optical access uplink supporting error-free transmission with 27 dB loss margin. These results indicate the feasibility of implementing cooler-less long-wavelength VCSEL devices in long-reach optical access networks.Index Terms-Passive optical network; Vertical cavity surface emitting laser. S ustained increase in demand for broadband data services has motivated the expansion of modern data communications networks [1] . Additionally, bandwidth limitations of traditional copper-based or wireless data transmission schemes have encouraged the deployment of broadband optical transmission technologies in the access networking environment. Sustainable deployment of optical access networks requires reliable, inexpensive and energy-efficient broadband optical sources [2, 3] ; this has encouraged the development of low-cost laser sources for directly modulated optical links. Vertical cavity surface emitting laser (VCSEL) technology is a potential candidate for realizing low-cost broadband signaling sources. Un-cooled VCSEL units have been demonstrated supporting data rates of 10 Gb/s [4] [5] [6] and 20 Gb/s [7] ; cooled VCSEL sources operating at 38 Gb/s [8] have also been presented. Injection locking has also been used to extend the transmission range achieved with directly modulated VCSEL devices. VCSEL sources have been operated at 10 Gb/s and demonstrated at 40 km [9] ; longer range transmission has also been reported [6, 10] with more sophisticated control of the chirp of the locked laser source.. We extend on our previous investigations [11] [12] [13] of the performance of a cooler-less 1550 nm VCSEL directly modulated at 10.7 Gb/s. We report on error-free transmission of the optical signal thus obtained over 40 km of standard single-mode fiber (SMF), without the use of any dispersion-mitigation techniques. For uncompensated transmission over 35 km and 40 km, we obtained error-free results with a pseudorandom binary sequence (PRBS) of length 2 7 − 1 bits, with a receiver power level of −19 dBm; this is similar to the sensitivity levels reported by injection-locked systems operating at 10 Gb/s [9] . We also successfully transmitted the 10 Gb/s optical signal over an un-amplified 99.7 km dispersion-matched link, in which a field-deployable inverse-dispersion fiber (IDF) was used for dispersion compensation; no other dispersion-mitigation technique was used for this analysis. IDF has a dispersion profile which is the inverse [14, 15] of G.652 standard SMF. We obtained error-free data transmission with a bit error rate (BER) below 10 −9 . To assess the influence of pattern-length dependencies, the data signal was generated using PRBSs of varying lengths. Error-free transmission was also achieved over the dispersion-matched 99.7 km uplink when a PRBS of length 2 31 −1 bits was used. Our results show the feasibility of long-wavelength VCSELs in the deployment of enhanced range passive optical network (PON) systems, and also highlight the applicability of IDF in extending the range of broadband optical networks.. The remainder of this paper is structured as follows: we present the characterization of the VCSEL in Section II, and present the description of the PON uplink evaluation in Section III. Significant system results are presented in Section IV; concluding remarks are presented in Section V. We have presented results on the characterization of a novel VCSEL device that can be directly modulated at 10.7 Gb/s, and the output signal can be transmitted error-free over 40 km of uncompensated SMF with a receiver sensitivity of −29 dBm when a PRBS of length 2 7 − 1 is used. Field-deployable IDF fiber was used to implement an extended range 99.7 km PON uplink; error-free transmission was also achieved over this link. Less than 3 dB penalty was observed when a PRBS of length 2 31 −1 bits was used over the same length transmission span, which is consistent with reported assessments of optical transmission over negative-dispersion fiber.

O O O O O O O O O O O O O O O O O O O B I I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Approximate decoding for network coded inter-dependent data In this paper, we consider decoding of loss tolerant data encoded by network coding and transmitted over error-prone networks. Intermediate network nodes typically perform the random linear network coding in a Galois field and a Gaussian elimination is used for decoding process in the terminal nodes. In such settings, conventional decoding approaches can unfortunately not reconstruct any encoded data unless they receive at least as many coded packets as the original number of packets. In this paper, we rather propose to exploit the incomplete data at a receiver without major modifications to the conventional decoding architecture. We study the problem of approximate decoding for inter-dependent sources where the difference between source vectors is characterized by a unimodal distribution. We propose a mode-based algorithm for approximate decoding, where the mode of the source data distribution is used to reconstruct source data. We further improve the mode-based approximate decoding algorithm by using additional short information that is referred to as position similarity information (PSI). We analytically study the impact of PSI size on the approximate decoding performance and show that the optimal size of PSI can be determined based on performance requirements of applications. The proposed approach has been tested in an illustrative example of data collection in sensor networks. The simulation results confirm the benefits of approximate decoding for inter-dependent sources and further show that 93.3% of decoding errors are eliminated when the approximate decoding uses appropriate PSI. In this paper, we consider the transmission of network coded inter-dependent sources in error prone networks. In order to solve the all-or-nothing problem of network coding approaches, we propose a solution that approximately reconstructs the source data when the number of received data packets is not sufficient for perfect recovery without major change in the conventional decoding architecture. Unlike prior works, we consider a generalized source inter-dependency characterized by unimodal shapes. Given the information about source distributions, we show that the performance of the proposed approximate decoding algorithm is improved by using the mode of the distributions (SI). We further improve the performance of the proposed approximate decoding algorithms by deploying additional information called PSI. We evaluate the proposed approaches in an illustrative example of sensor network and the simulation results confirm that approximate decoding along with minimal side information leads to effective source reconstruction. In this section, we study prior work related with the proposed approaches. Several regularization techniques [28] can be deployed for overcoming the all-or-nothing problem of network coding in finite fields. For example, the pseudo-inverse of underdetermined coefficient matrix can be used for decoding. However, it is generally known that this type of regularization techniques may result in unreasonable approximations [28] . Alternatively, Tikhonov regularization [29] can improve the decoding performance by slightly modifying the standard least square approach. However, this technique cannot be easily deployed in practice because it requires to determine additional optimization parameters. Sparsity assumptions can also be used for regularized decoding in underdetermined systems in cases where a model of the signal of interest is known a priori [30] . However, all of these regularization techniques have been designed and developed in the field of real numbers, but not in finite fields that are used in network coding approaches. Hence, they may show significant performance degradation if they are blindly deployed in the proposed framework, as they cannot consider several properties (e.g., cyclic properties) of finite field operations. Underdetermined systems can also be solved approximately based on the maximum likelihood estimation (MLE) techniques (see e.g., Part II of [31] ) or based on mixed integer linear programming [32] . However, these techniques require effective data models and may typically involve large computational complexity.. Unlike the approaches mentioned above, several approaches have been proposed to overcome the all-ornothing problem of network coding in finite fields (see e.g., [33, 32] ). In [33] , a loss-tolerant protocol for broadcasting, named by Dragoncast, based on network coding is proposed. In [32] , a coding scheme that jointly considers both network coding and multiple description coding is proposed. The problem of data reconstruction is formulated as a mixed integer quadratic programming and robustness against missing packets is achieved. Our prior work [14] propose an approximate decoding approach based on simple matches of the most similar data between neighbor sources. The information on the inter-dependency of the sources has been exploited in order to improve the performance of data reconstruction. In [34] , network coding for two arbitrary inter-dependent sources is studied in a generic network and an upper bound on the probability of decoding errors is found as a function of the network parameters such as the number of links upstream of a receiver or network topologies. Several practical aspects of joint source and network coding are investigated in [35] . However, the proposed approach is sub-optimal and provides a solution only for two inter-dependent sources that are transmitted over binary symmetric channels. The design of optimal network codes under joint distributed source and network coding framework is studied in a sensor network [36] . The goal is to find the optimal tradeoff between compression efficiency and network robustness. However, the complexity of the proposed solution in [36] grows exponentially with the network size. In [23] , an iterative decoding algorithm for source reconstruction is proposed for inter-dependent sources following a belief propagation approach that incorporates inter-dependence characteristics. However, none of prior works solve the all-or-nothing problem of network coding in finite fields with low-complexity, while the algorithm proposed in this paper can be implemented with lowcomplexity and it is compatible with well-known Gaussian elimination methods. Several extensions of [14] can be found in [15] and [16] for deterministic sources with SI and PSI, respectively. In [17] , approximate decoding algorithm for sources that have bell-shaped, symmetric and unimodal distribution is proposed. Unlike these prior works, we propose more generalized framework that can cover a large range of source types. Moreover, the proposed approximate decoding solution can exploit both SI and PSI such that the approximate decoding performance can be significantly improved.. Since additional information is transmitted [37] in the proposed scenario, the proposed approach can be considered as an index coding problem [38] . However, the index coding is generally considered in broadcasting scenarios (i.e., a single server wishes to communicate with several clients) and the side information in the index coding contains the source information [39] . Hence, the amount of additional information required for the proposed approach is significantly smaller than that for index coding.

B I O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Distributed call admission control for a heterogeneous PCS network Abstract-Personal communication service (PCS) networks offer mobile users multimedia applications with different quality-ofservice (QoS) and bandwidth requirements. This paper proposes a distributed call admission algorithm to provide QoS guarantees for multimedia traffic carried in a heterogeneous PCS network, where the parameters (e.g., number of channels, new and hand-off call arrival rates) of cells can be varied. The algorithm is based on the moveable boundary scheme that dynamically adjusts the number of channels for different types of traffic. With the moveable boundary scheme, the bandwidth can be utilized efficiently while satisfying the QoS requirements for different types of traffic. In addition, by taking the effect of hand-off traffic into account, we develop an analytical model for the network using the proposed algorithm. Using our model, the impact of hot-spot traffic caused by hand-offs can be effectively analyzed. The analytical model is validated by simulation. I N personal communication service (PCS) [1] , [2] , [3] networks, the service area is divided into cells and users communicate via wireless links to base stations in the cells. PCS networks are expected to support multimedia applications such as voice, data, and images, etc., with different quality-of-service (QoS) and bandwidth requirements. The bandwidth in a PCS network is not only expensive but inherently limited. Thus, it is essential to design PCS networks that furnish effective and dynamic allocation of the bandwidth to satisfy different service demands. In integrated voice/data PCS networks, several proposed bandwidth allocation schemes [4] , [5] , [6] , [7] attempt to achieve improved bandwidth utilization while providing QoS guarantees with respect to voice-call blocking probability, data loss probability, and mean data delay. Generally, these schemes are based on a moveable boundary strategy with or without reservation for voice or data traffic. Although these schemes were evaluated extensively, the effect of hand-off traffic [1] , [2] , [3] , [8] due to the movement of portables was not taken into account.. As a portable moves from one cell to another during a call, a hand-off to the new cell is required to maintain acceptable service quality. When the cell size of a PCS network is relatively small [1] , [2] , [3] , the hand-off traffic has an important effect on the network performance. In addition, if no channel is available in the new cell, the handoff call is blocked. Due to a hand-off blocking, an ongoing call is forcefully terminated. However, from the point of view of mobile users, forced termination of an ongoing call is less desirable than blocking a new call. As a result, it needs to design an efficient call handling scheme in a base station to reduce the hand-off blocking probability (or the forced termination probability) for different classes of traffic in addition to allocating bandwidth efficiently.. The problem of call handling has received considerable attention in the literature [9] , [10] , [11] , [12] , [13] , [14] , [15] , [16] . In [9] , [10] , [11] , a fixed number of channels at each base station are reserved for hand-off calls. Generally, these schemes are based on cutoff priority, with or without a buffer for new or hand-off calls, and do not adapt to changes in the hand-off traffic. Unlike [9] , [10] , [11] , Yu and Leung [12] proposed a dynamic guard channel scheme which adapts the number of guard channels in each base station according to the estimate of the hand-off call arrival rate. Similarly, by taking the effect of hand-offs into account, Naghshineh and Schwartz [13] proposed a distributed call admission methodology to limit the hand-off blocking probability. In [9] , [10] , [11] , [12] , [13] , only a single traffic class was considered. Yang and Geraniotis [14] derived admission control policies for integrated voice/ data traffic in packet radio networks. They did not consider the changes in the network loading conditions. Unlike [14] , Naghshineh and Acampora [15] introduced an adaptive call admission mechanism in which the admission threshold for different classes of traffic is updated periodically to adapt to changing traffic conditions. Similarly, Oliveira et al. [16] proposed an adaptive bandwidth reservation scheme which adjusts the amount of reserved bandwidth based on the current network loading conditions. All of these works [9] , [10] , [11] , [12] , [13] , [14] , [15] , [16] assumed a homogeneous PCS network where all cells have the same number of channels and experience the same new and hand-off call arrival rates. Therefore, the impact of hot-spot traffic caused by hand-offs cannot be effectively analyzed.. This paper proposes a distributed call admission algorithm for a heterogeneous PCS network, in which the parameters (e.g., number of channels, new and hand-off call arrival rates) of cells can be varied, supporting two types of traffic: real-time (RT) and non-real-time (NRT). The algorithm is based on the moveable boundary scheme that dynamically adjusts the number of channels for RT and NRT traffic. In addition, by taking the effect of hand-off traffic into account, we develop an analytical model for the network using the proposed algorithm.. With the moveable boundary scheme, the available bandwidth (i.e., radio channels) of each cell is partitioned into three classes, namely, designated RT channels, designated NRT channels, and shared channels. Designated RT and NRT channels are dedicated for RT and NRT transmissions, respectively. The shared channels can be used by either type of traffic. The boundary between classes is dynamically moved such that the bandwidth can be utilized efficiently while satisfying the QoS requirements for both types of traffic.. As mentioned above, previous call handling studies [9] , [10] , [11] , [12] , [13] , [14] , [15] , [16] assumed a homogeneous PCS network where all cells have the same number of channels and experience the same new and hand-off call arrival rates. Our model accommodates the heterogeneity of a PCS network by relaxing the restrictions in those previously proposed models. With our model, the parameters (e.g., number of channels, new and hand-off call arrival rates) of cells can be varied and, hence, the impact of hot-spot traffic caused by hand-offs can be effectively analyzed. In addition, an iterative algorithm based on our model is proposed to determine the hand-off traffic and compute the network performance. Simulation experiments are conducted to validate the accuracy of the analytical model.. The paper is organized as follows: Section 2 describes the call admission algorithm for a heterogeneous PCS network and presents the analytical model for the network using this algorithm. In Section 3, extensive performance results are presented. In addition, the accuracy of analytical results is confirmed by simulation results. Finally, Section 4 concludes the paper. This paper proposed a distributed call admission algorithm for a heterogeneous PCS network supporting two classes of traffic. In addition, we presented an analytical model for the network using the proposed algorithm. With our model, the network performance based on the movable-boundary scheme and the impact of hot-spot traffic in a heterogeneous PCS network can be effectively analyzed. According to the numerical results, we observed the following:. . The network performance of hot cells is significantly impaired by hot-spot traffic. . In the cold cell, the new-call and hand-off blocking probabilities of both classes of traffic and the average waiting time of NRT traffic decrease as RT (hand-off rate of RT portable) or NRT (hand-off rate of NRT portable) increases. . In the hot cell, the new-call and hand-off blocking probabilities of both classes of traffic increase as RT increases. . In the hot cell, the new-call blocking probability of NRT traffic increases as NRT increases, while the hand-off blocking probability of NRT traffic decreases as NRT increases under smaller mobility of NRT portable and increases as NRT increases under larger mobility of NRT portable. . In the hot cell, the average waiting time of NRT traffic increases as RT increases and decreases as NRT increases. Jan-Ming Ho received the PhD degree in electrical engineering and computer science from Northwestern University in 1989. He received the BS in electrical engineering from National Cheng Kung University in 1978 and the MS degree from the Institute of Electronics of National Chiao Tung University, in 1980. He joined the Institute of Information Science, Academia Sinica, Taiwan, Republic of China, as an associate research fellow in 1989 and was promoted to research fellow in 1994. His research interests target the integration of theoretical and application-oriented research, including environment for management and presentation of digital archive, management, retrieval, and classification of web documents, continuous video streaming and distribution, video conferencing, real-time operating systems with applications to continuous media systems, computational geometry, combinatorial optimization, VLSI design algorithms, and implementation and testing of VLSI algorithms on real designs. He is an associate editor of the IEEE Transactions on Multimedia.

O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Link prediction and classification in social networks and its application in healthcare and systems biology Abstract Prediction is one of the most attractive aspects in data mining. Link prediction has recently attracted the attention of many researchers as an effective technique to be used in graph based models in general and in particular for social network analysis due to the recent popularity of the field. Link prediction helps to understand associations between nodes in social communities. Existing link prediction-related approaches described in the literature are limited to predict links that are anticipated to exist in the future. To the best of our knowledge, none of the previous works in this area has explored the prediction of links that could disappear in the future. We argue that the latter set of links are important to know about; they are at least equally important as and do complement the positive link prediction process in order to plan better for the future. In this paper, we propose a link prediction model which is capable of predicting both links that might exist and links that may disappear in the future. The model has been successfully applied in two different though very related domains, namely health care and gene expression networks. The former application concentrates on physicians and their interactions while the second application covers genes and their interactions. We have tested our model using different classifiers and the reported results are encouraging. Finally, we compare our approach with the internal links approach and we reached the conclusion that our approach performs very well in both bipartite and non-bipartite graphs. The social network model has received considerable attention in the research community. It has been successfully adapted to investigate different applications such as terrorist networks (Ressler 2006) , online communities (Crandall et al. 2008) , and calling communities (Kianmehr and Alhajj 2009), among others. We argue that the model is powerful enough and can be used in analyzing any domain that could be represented in a network structure. Some other works have focused on link prediction issues. For example, the work described in Liu and Lu (2010) introduced a link prediction model based on the similarity of the nodes. This is important in applications that consider the similarity of nodes such as gender, age, etc. They proposed two similarity indices based on random walk. Other works apply link prediction in friendship applications where a person can be recommended to be a friend of another person based on mutual friends. The work described in Gilbert and Karahalios (2009) applied link prediction techniques to predict tie strength in social media. Shortcomings of these works are described as follows:. • limitation to undirected networks where multiple or self-link directions are not allowed, • links are predicted based on one time period and not compared with how the prediction will be changed in different time periods using the same nodes, • predictions are limited to the addition of the links and ignore the removal of other links, • predictions are not studied based on the effect of node removal.. To the best of our knowledge, most of the previous works have concentrated on predicting links that might exist in the future (positive link prediction); there is no research effort described in the literature for predicting links that may disappear in the future (negative link prediction), though it is very important in determining the loss of relationships; and this may lead to weaken the network structure. In addition, no one has yet tackled the link prediction problem within the scope of gene expression networks and the health care domain which are considered as two of the most important domains for humans. One previous work related to our research has been done by Hasan et al. (2006) to study link prediction as a supervised learning task in the authorship domain. They identified a set of features that are key to this domain. With the help of these features, they were able to propose a classification model to predict a set of authors who will work together in the future. Allali et al. (2011) has introduced a new approach in link prediction called internal links and weighted projection to predict links in bipartite graphs. This approach performs well in some social networks to predict links that will appear in the future but does not predict which link will be lost. We give more details about how this approach works later as we compare this approach to our new link prediction model. Several existing methods in mining gene expression data are described in the literature. Lee et al. (2006) introduced the fuzzy element in dealing with financial features. Gao et al. (2009) extended the fuzzy prediction method using the combination of rough and fuzzy sets in deriving association rules. Traditional approaches such as using support vector machines and others are discussed in Cao and Tay (2003) .. Our proposed model will be tested on the gene expression networks and health care domains where we identify a set of features that we believe are powerful to build an accurate classification model for the link prediction purpose.

B I O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O


A Phenomenological Study on Threshold Improvement via Spatial Coupling SUMMARY Kudekar et al. proved an interesting result in low-density parity-check (LDPC) convolutional codes: The belief-propagation (BP) threshold is boosted to the maximum-a-posteriori (MAP) threshold by spatial coupling. Furthermore, the authors showed that the BP threshold for code-division multiple-access (CDMA) systems is improved up to the optimal one via spatial coupling. In this letter, a phenomenological model for elucidating the essence of these phenomenon, called threshold improvement, is proposed. The main result implies that threshold improvement occurs for spatially-coupled general graphical models. Low-density parity-check (LDPC) convolutional codes have been shown to outperform conventional LDPC block codes when iterative decoders based on belief propagation (BP) are used [1, 2] . An LDPC convolutional code is constructed as a one-dimensionally coupled chain of LDPC block codes. As an explanation of this interesting result, it has been shown theoretically [3, 4] and numerically [5] that the BP threshold of an LDPC convolutional code is boosted to the maximum-a-posteriori (MAP) one of the corresponding LDPC block code. This phenomenon is called "threshold saturation" via spatial coupling [3, 4] .. Recently, we showed that the threshold saturation also occurs in spatially-coupled code-division multiple-access (CDMA) systems: The BP threshold for sparsely-spread CDMA systems is boosted to the optimal one via spatial coupling [6, 7] . Since it is unclear whether the BP threshold for any graphical model can be improved to the optimal one, the term "threshold improvement" via spatial coupling is used in this letter, instead of threshold saturation. It is believed that threshold improvement is universal, i.e., the performance of the BP algorithm can be improved by coupling general graphical models spatially. This conjecture has been verified for several graphical models [8, 9] . The purpose of this letter is to present a phenomenological study that supports the universality of threshold improvement. Threshold improvement is a static property of spatiallycoupled graphical models, rather than dynamical properties. It is well-known that BP can calculate the MAP solution exactly if the factor graph defining the BP is a tree [10] . Furthermore, if the BP algorithm for a general factor graph converges, the BP fixed-points correspond to the stationary points of the so-called Bethe free energy for the factor graph [11] , while the MAP solution corresponds to the global minimizer of the true free energy. Roughly speaking, the Bethe free energy is obtained by locally approximating the original factor graph by trees. These results allow us to characterize the static properties of the BP algorithm by stationary solutions to a dynamical system that has a potential energy function whose fixed-points coincide with those for the Bethe free energy, which is analogous to the density evolution (DE) equation for LDPC codes. In this letter, we restrict graphical models to such a class of graphical models that BP algorithms converge asymptotically.. As a phenomenological model for elucidating threshold improvement, we propose a spatially-coupled dynamical system with multiple stable solutions. This letter is organized as follows: In Section 2 the BP algorithm is characterized via a dynamical system with multiple stable solutions after presenting a motivating example for regular LDPC codes. In Section 3 a spatially-coupled dynamical system with multiple stable solutions is defined. Furthermore, an intuitive understanding of threshold improvement is presented. Section 4 presents the main result of this letter.

O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


A model for revocation forecasting in public-key infrastructures Abstract One of the hardest tasks of a certification infrastructure is to manage revocation. This process consists in collecting and making the revocation status of certificates available to users. Research on this topic has focused on the trade-offs that different revocation mechanisms offer. Much less effort has been conducted to understand and model real-world revocation processes. For this reason, in this paper, we present a novel analysis of real-world collected revocation data and we propose a revocation prediction model. The model uses an autoregressive integrated moving average model. Our prediction model enables certification authorities to forecast the number of revoked certificates in short term. In a public-key infrastructure (PKI), digital certificates are the means of accurately and reliably distributing public keys to users needing to encrypt messages or verify digital signatures. Certificates are signed by certification authorities (CAs), and they are issued with a planned lifetime, which is defined through a validity start time and an explicit expiration date. Once issued, a certificate becomes valid when its validity start time is reached, and it is considered valid until its expiration date. However, various circumstances may cause a certificate to become invalid prior to the expiration of the validity period. Such circumstances include requiring to change the name of the subject of a certificate, the compromise or suspected compromise of the private key associated with the certificate and a change in association between subject and CA, for example, when an employee terminates employment with an organization. Thus, the PKI has to collect and distribute information about revoked certificates.. C. Gañán (B) · J. Mata-Díaz · J. L. Muñoz · O. Esparza · J. Alins Departament Enginyeria Telemàtica, Universitat Politècnica de Catalunya, 1-3 Jordi Girona, C3, 08034 Barcelona, Spain e-mail: carlos.ganan@entel.upc.edu There are several mechanisms to manage revocation information. Currently, deployed PKIs rely mostly on certificate revocation lists (CRLs) for handling certificate revocation [1] . A CRL is a list identifying revoked certificates, which is signed by a CA and made available at a public distribution point. The CRL has a validity period, and updated versions of the CRL are published before the previous CRL's validity period expires. Although CRL is the most widely used mechanism to distribute certificate status information (CSI), there have been proposed other mechanisms to make revocation distribution more efficient (see Sect. 2.1 for further details).. The work presented in this paper is motivated by the fact that despite there are many works in the literature that propose and evaluate different mechanisms for distributing revocation data like [2] [3] [4] [5] [6] [7] , little work has been done for analyzing the revocation process itself. For instance, many of the previous studies consider very simplistic assumptions about the revocation process like that the percentage of revoked certificates remains always constant. Only recently, we can find works like [8] [9] [10] that carry out statistical studies about the revocation process using data available from real CAs. These studies can be considered a first step toward understanding the revocation process. However, they essentially just analyze the probability distribution of the revocation process. In [11] , authors analyzed empirical revocation data to measure the degree of self-similarity. However, this analysis is not useful to predict the number of revoked certificates as the fractal pattern of the revocation process has a marginal impact on future revocation events. In this paper, we go a step further and we use time series analysis to build a model and a predictor for revocation. Using this novel way of exploring the revocation data, we are able to obtain a model that predicts extremely well the daily number of revoked certificates.. We build a revocation model which is exclusively based on temporal information, namely the revocation dates. Using this information and the Box-Jenkins methodology [12] , we are able to build a model and a predictor for revocation. More precisely, we are able to show that an autoregressive integrated moving average (ARIMA) model provides objective and accurate predictions of the daily number of revoked certificates. To find the number of significant coefficients and their corresponding values we used Z-transforms and a vision of the problem based on digital filters. This greatly simplified the overall process of finding the parameters of the model. The resulting model exploits autocorrelations, captures the dependencies within the revocation data and simplifies these complex relationships to linear dependencies.. Regarding the data used to build the model, we have gathered CRLs from the main CAs, i.e., Comodo, GeoTrust, GoDaddy, Thawte, Verisign and Globalsign. Using these CAs, we build a time series that captures the daily evolution of the number of revoked certificates. Although the collected revocation data only belong to the leading CAs, the model is expected to be valid for other certification providers because of the huge amount of certificates analyzed and because our model captures the general pattern of the temporal correlation that exists among the revocation events. This pattern is expected to be the same for other CAs because the theoretical reasons to revoke certificates (and therefore the temporal correlation) do not essentially differ, no matter which certificate provider you use. In this way, we validate the accuracy of the model when using other CAs such as the US Social Security Administration and the Catalan Certification Agency. As a final remark, we would like to mention that our model might be interesting for several reasons: on one hand, it provides some insights about the revocation process, like showing that the process is highly autocorrelated, and on the other hand, it allows CAs to make predictions about certificate revocation. This might be useful in some real scenarios (see Sect. 7).. The rest of this paper is organized as follows. Section 2 briefly reviews the required background. Section 3 presents a preliminary analysis of the revocation codes. In Sect. 4, we discuss the methodology we used to collect and analyze the revocation data. In Sect. 5, we identify the best ARIMA model that fits the revocation events. Next, in Sect. 6, we present the predictor from the previous ARIMA model. In Sect. 7, we discuss some possible practical applications of the revocation forecasting model. Finally, we conclude in Sect. 8. We have analyzed real empirical data to derive a model, which allows to forecast the number of daily revocations in the near future. Our research represents a step toward linking empirical observations to mathematical models in description of the complex issue of certificate revocation.. This paper has proposed an ARIMA model for short-range forecasting in a CSI distribution system. The ARIMA model completely considers the dynamic process of data series and the autocorrelation of residuals to achieve precise forecasting of the number of revoked certificates. We used the Box-Jenkins methodology as a framework for our modeling procedure, and we built the best ARIMA model possible for the available data. The model exhibits great accuracy for short time scales.. Although the collected revocation data only belong to the leading CAs, the model is expected to be valid for other certification providers because of the huge amount of certificates analyzed and because our model captures the general pattern of the temporal correlation that exists among the revocation events. This pattern is expected to be the same for other CAs because the theoretical reasons to revoke certificates (and therefore the temporal correlation) do not essentially differ, no matter which certificate provider you use.

O O O B O O O O O O O O O O O O B O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O B O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Target prediction utilising negative bioactivity data covering large chemical space Background: In silico analyses are increasingly being used to support mode-of-action investigations; however many such approaches do not utilise the large amounts of inactive data held in chemogenomic repositories. The objective of this work is concerned with the integration of such bioactivity data in the target prediction of orphan compounds to produce the probability of activity and inactivity for a range of targets. To this end, a novel human bioactivity data set was constructed through the assimilation of over 195 million bioactivity data points deposited in the ChEMBL and PubChem repositories, and the subsequent application of a sphere-exclusion selection algorithm to oversample presumed inactive compounds.Results: A Bernoulli Naïve Bayes algorithm was trained using the data and evaluated using fivefold cross-validation, achieving a mean recall and precision of 67.7 and 63.8 % for active compounds and 99.6 and 99.7 % for inactive compounds, respectively. We show the performances of the models are considerably influenced by the underlying intraclass training similarity, the size of a given class of compounds, and the degree of additional oversampling. The method was also validated using compounds extracted from WOMBAT producing average precision-recall AUC and BEDROC scores of 0.56 and 0.85, respectively. Inactive data points used for this test are based on presumed inactivity, producing an approximated indication of the true extrapolative ability of the models. A distance-based applicability domain analysis was also conducted; indicating an average Tanimoto Coefficient distance of 0.3 or greater between a test and training set can be used to give a global measure of confidence in model predictions. A final comparison to a method trained solely on active data from ChEMBL performed with precision-recall AUC and BEDROC scores of 0.45 and 0.76.The inclusion of inactive data for model training produces models with superior AUC and improved early recognition capabilities, although the results from internal and external validation of the models show differing performance between the breadth of models. The realised target prediction protocol is available at https://github. com/lhm30/PIDGIN. We have implemented a method for the extraction of inactive compounds from the PubChem repository. The application of a sphere exclusion algorithm enabled the oversampling of additional inactive compounds for targets with insufficient number of inactive compounds.. The realised target prediction protocol has been packaged and available for download at https://github.com/ lhm30/PIDGIN.. The results from the internal and external validation of the tool show differing performance between the breadth of models. Recall and precision performance is influenced significantly on the underlying intraclass similarity and the number of compounds for a target. During internal validation, sphere exclusion models perform better in comparison to PubChem inactive classes, due to the dissimilarity requirement between the active compounds and SE inactive compounds.. The external performance of the models showed a considerable drop in precision, which may be exaggerated due to the absence of inactivity information held within the WOMBAT data set. Applications of target-specific thresholds exhibited a trade-off between recall and precision and indicate that different metrics for thresholds should be used for different applications of the target prediction tool. The precision threshold gleans the highest F-Score performance, producing a close balance between the precision and recall (0.5 and 0.48) for the classes. In comparison, the recall threshold can be applied to generate thresholds when predictions require high recall without the concern for the cost of sacrificing precision.. A distance-based analysis of the applicability domain (AD) for WOMBAT compounds showed that the reliability of the predictions from the models improved with the increasing similarity between the training and WOM-BAT test set. The AD analysis indicated that an average Tc cut-off distance between test and train of 0.3 could be incorporated into the predictions of the classes in the future, to give insight into the degree of confidence for the probabilities generated by the model.. A comparison between the inactivity-inclusive models and the activity-only based approach showed the benefit in including negative bioactivity data when building the target prediction models with statistical significance. The ability to take into account the features that contribute and counteract bioactivity, combined with the ability to create individual target models, results in superior precision, recall, precision-recall AUC and BEDROC values when compared to models trained solely on activity data. A principle challenge faced by the information gleaned from phenotypic screening is that many of the assessed compounds remain orphan ligands, as the respective mode-of-action (MOA) remains undetermined in the first instance [1] . Consequently, the subsequent identification of the modulated targets for active compounds, known as 'target deconvolution' , is required [2] . Various biochemical affinity purification methods provide direct approaches to discover target proteins binding small molecules of interest for this purpose [3] [4] [5] [6] . Although these elucidation experiments can explicitly determine compound-target interactions, such procedures are costly and time-consuming [4, 7, 8] . These procedures require large amounts of protein extract and stringent experimental conditions, while many techniques have also been deemed best suited for situations where a high affinity ligand binds to a protein [1, 9] . Other difficulties involve the challenge of preparing and immobilizing affinity reagents (targets) that still retain their cellular activity (i.e. ensuring that proteins still interact with the small molecule while it is bound to the solid surface) [10] . Such caveats are responsible for the increased interest in novel computational target deconvolution strategies for drug discovery [11] .. In silico protein target prediction is a well-established computational technique that offers an alternative avenue to infer target-ligand interactions by utilizing known bioactivity information [12] . These methods have played an important role in the field of efficacy prediction and the prediction of toxicity [13] [14] [15] [16] [17] . Such approaches are designed to predict targets for orphan compounds early in the drug development phase, with the predictions forming the base of an experimental confirmation afterwards. Both structure-based and ligand-based methods exist for the prediction of protein targets for small molecule ligands [12, [18] [19] [20] [21] [22] . The former methods generally describe approaches that exploit the structural information of the protein, combined with scoring functions, in an attempt to predict ligand-target pairs [23] [24] [25] . Ligand-based methods investigate an identified structure-activity relationship (SAR) space, using similarity searching on large numbers of annotated protein-ligand pairs obtained from chemogenomic databases [26] [27] [28] [29] . Such predictive models are founded upon the principle of chemical similarity, relying on the relevant similarities of compound features from targets which are likely to be responsible for binding activity [30, 31] . The focus of this work is concerned with improving current ligand-based methods for target prediction.. Similarity searching for ligand-based target prediction is considered the simplest form of in silico target prediction and has long been established within the literature [12, 17, 22, 27, [32] [33] [34] [35] [36] [37] [38] . In these models, predictions are based on the principle of molecular similarity to identified bioactive compounds from chemogenomic databases [31, 35] . The simplistic nature of these methods means that they are only able to consider the structure of a molecule as a whole, which significantly hinders the predictive power of these models. Building on this theme, data-mining algorithms are capable of considering multiple combinations of compound fragments by applying pattern recognition techniques. They have gained traction in target prediction due to their demonstrated ability to more efficiently extrapolate predictions [12] , and are less time consuming when compared to other approaches such as molecular docking [20] . One of the earliest and most widely used examples of data-mining target elucidation is the continuously curated and expanded Prediction of Activity Spectra for Substances (PASS) software [21] , which was assimilated from the bioactivites of more than 270,000 compound-ligand pairs. The authors trained the model on multilevel neighbourhoods of atoms (MNA) descriptors, producing predictions based on Bayesian estimates of probabilities.. The Naïve Bayes (NB) classifiers are a popular family of algorithms used for the prediction of bioactivity for compounds [39] [40] [41] [42] . These methods offer a quick training and prediction times and are relatively insensitive to noise [43] . Nidhi et al. [40] , employed a multi-class Naïve Bayes classification algorithm trained on a data set comprised of over 960 target proteins extracted from the 'World Of Molecular BioAcTivity' (WOMBAT) database [44] . Another target prediction algorithm developed by Koutsoukas et al. [39] , is able to predict structure activity relationships (SARs) for orphan compounds using either a Laplacian-modified Naïve Bayes classifier or a Parzen-Rosenblatt Window (PRW) learner. The algorithm was trained on more than 155,000 ligand-protein pairs from the ChEMBL14 database [45] , encompassing 894 different human protein targets. After benchmarking experiments [39] , it was found that the PRW learner outperformed the Naïve Bayes algorithm overall, achieving a recall and precision of 66.6 and 63.3 %, respectively. Support Vector Machines (SVMs) have also been employed for task of target prediction. These models have utilised smaller amounts of data in comparison to NB models, mostly due to the computational expense of the SVM algorithm. Examples of SVM methods include a model obtaining more than 80 % sensitivity consistently for five different protein targets [42] ; however the limitation of this implementation to five molecular targets only allows for a very narrow view of compound activity. Nigsch et al. [28] . investigated the use of a Winnow algorithm for target prediction and compared the learner to the performance of a Naïve Bayes model, comprising a set of 20 targets extracted from WOMBAT. The comparison of the overall performance of the two algorithms did not yield significant differences, but the overall findings supported the view that ensembled machine-learning models could be used to yield superior predictions.. Ligand-based predictions provide a basis for further analysis when attempting to rationalise mechanism-ofaction. Liggi et al. [46] , extended the predictions from a PRW target prediction algorithm, by annotating enriched targets implicated with a cytotoxic phenotype, using associated pathways from the GO [47, 48] and KEGG [49, 50] pathway databases. The annotation of enriched targets with pathways gave improved insight into understanding the MOA of the phenotype, underlining the signalling pathways involved in cytotoxicity. Other examples include the combination of target prediction and classification tree techniques for rationalising phenotypic readouts from a rat model for hypnotics [51] . The authors derived interpretable decision trees for the observed phenotypes and inferred the combination of modulated targets which contribute towards good sleeping patterns. A review of the known mechanisms of hypnotics suggested that the results were consistent with current literature in many cases.. Such publications illustrate the real-world application of predictions obtained from in silico target prediction methods for both on-target and off-target bioactivity predictions [14] . Although most of these methods produce a probability of activity for an orphan compound against a given target, the approaches mentioned here do not utilise inactive bioactivity data [39] . The objective of this work is hence concerned with the construction of an in silico target prediction approach that is able to consider both the probability for activity and inactivity of orphan compounds against a range of biological targets, thus, giving a more holistic perspective of chemical space for factors that contribute and counteract bioactivity.. The realized target prediction tool is validated both through cross-validation and an external validation set. Five-fold cross validation was employed to evaluate the model and was also used to generate class-specific activity thresholds, based on the optimum cut-off value for a range of different metrics. The performance of the tool when applied to test data extracted from the WOMBAT database is also evaluated. This provides insight into the models ability to extrapolate predictions to a real-world setting. Finally, a comparison to a model generated solely from activity data was also conducted to evaluate the value of the incorporation of inactivity data in the models.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Voltage Setup Problem for Embedded Systems With Multiple Voltages Abstract-We formulate the following voltage setup problem: how many levels and at which values should voltages be implemented on the system to achieve the maximum energy saving by dynamic voltage scaling (DVS)? This problem challenges whether DVS technique's full potential in energy saving can be reached on multiple voltage systems. In this paper, 1) we derive analytical solutions for dual-voltage system; 2) we develop efficient numerical methods for the general case where analytical solutions do not exist; 3) we demonstrate how to apply our proposed algorithms in system design; and 4) our experimental results suggest that, interestingly, multiple voltage systems with proper voltage setup can be very close to DVS technique's full potential in energy saving. Energy consumption has become a major design issue for modern embedded systems, especially battery-operated portable devices. The aggressive push for low-power design has prompted the International Technology Roadmap for Semiconductors to predict that the future system will feature multiple supply voltages (V dd ) and multiple threshold voltages (V th ) on the same chip. This enables the dynamic voltage scaling (DVS), an energy reduction technique that varies the clock frequency and supply voltage according to workload at run time. The highest energy efficiency is achieved when voltage can be varied arbitrarily [1] , [2] . However, physical constraints of CMOS circuit limit the applicability of having voltage varying continuously. Instead, it is more practical to make multiple discrete voltages simultaneously available for the system [3] - [5] .. Most existing work on multiple voltage DVS systems assumes that the voltage setup, which includes the number of voltage levels and the voltage value at each level, is given a priori and focuses on developing voltage scheduling algorithms to minimize the system's energy consumption [6] - [8] . However, the multiple voltage DVS system's energy consumption depends on not only the scheduler but also the voltage setup. Raje and Sarrafzadeh [12] used dual-voltage (5.0 and 3.0 V) and three-voltage (5.0, 3.0, and 2.4 V) to demonstrate their multiple voltage scheduling algorithm for data flow graph. Chang and Pedram [11] presented a dynamic programming algorithm for the more general cases and use four-voltage (5.0, 3.3, 2.4, and 1.5 V) in their simulation. Chen and Sarrafzadeh [9] studied the dual-voltage system at gate level, where 5.0 V was used as the high voltage and different voltages from 2.0 to 4.2 V were used as the low voltage. Qu and Potkonjak [7] gave analytical solutions on how to build energy-efficient communication pipelines under latency constraints by voltage scaling and packet fragmentation. Dhar and Maksimovic [10] considered the design of finite impulse response filters with 2N + 1 voltages for power minimization, where N is the order of the filter.. In this paper, we formulate and provide practical solutions to the system level voltage setup problem that seeks the most energy efficient voltage setting for the multiple voltage DVS system design. This work is a novel extension under the DVS research framework. We show that our methods can be used to guide system design and simulation results reveal that the three-or four-voltage system can actually be (almost) as energy efficient as the ideal system that varies voltage arbitrarily. We consider the voltage setup problem for application specific multiple voltage DVS system design. The problem seeks to determine the number of voltage levels and the voltage at each level to minimize the average energy consumption for a given set of applications. We give optimal solutions in analytic form for the dual-voltage system and develop two heuristics (an iterative approach and an approximation method) for the general case. The hardware overhead to supply multiple voltages, once obtained, can be conveniently integrated into our techniques to solve the voltage setup problem. We apply our methods to the designs of an ad hoc application specific system and the MPEG video encoder. Simulation results show the correctness and efficiency of our approaches. We also observe that multiple voltage system, if the voltage levels are set properly, can indeed achieve energy reduction very close to the full potential by DVS.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O