In the development of predictive models, the problem of missing data is a critical issue that traditionally requires a two-step analysis. Data scientists analyze the patterns of missing values, select variables, impute missing values on the basis of domain knowledge, and then train a model. Models typically have their input sizes hardcoded, and have limitations in handling data with high missing rates or changes in available variables. We propose an attention-based neural network combined with a novel real number representation, which requires little work on manually selecting variables, and in which missing data can be overlooked, making imputation unnecessary. In this proposed model, data analysis can be one step, omitting the first step of imputing missing values. The study included data on 32,709 intensive care unit (ICU) admissions and 60 healthcare variables from the Medical Information Mart for Intensive Care (MIMIC)-IV. The proposed algorithm yielded an area under the receiver operating characteristic curve (AUC) of 0.842 (95% CIs: 0.828–0.856) when predicting prolonged length of stay in the ICU, outperforming current approaches using imputation methods. The proposed algorithm can be applied to a range of problems in data science, as it addresses the issue of incomplete data with automatic variable selection.

The importance of effective surgical wound care cannot never be underestimated. Poorly managing surgical wounds may cause many serious complications. Thus, it raises the necessity to develop a patient-friendly self-care system which can help both patients and medical professionals to ensure the state of the surgical wounds without any special medical equipment. In this paper, a surgical wound assessment system for self-care is proposed. The proposed system is designed to enable patients capture surgical wound images of themselves by using a mobile device and upload these images for analysis. Combining image-processing and machine-learning techniques, the proposed method is composed of four phases. First, images are segmented into superpixels where each superpixel contains the pixels in the similar color distribution. Second, these superpixels corresponding to the skin are identified and the area of connected skin superpixels is derived. Third, surgical wounds will be extracted from this area based on the observation of the texture difference between skin and wounds. Lastly, state and symptoms of surgical wound will be assessed. Extensive experimental results are conducted. With the proposed method, more than 90% state assessment results are correct and more than 91% symptom assessment results consistent with the actual diagnosis. Moreover, case studies are provided to show the advantage and limitation of this system. These results show that this system could perform well in the practical self-care scenario.

Asthma is a complex multifactorial disease, increasing evidence shows that environmental and lifestyle factors may modify epigenetic mechanism of asthma development. Hence, the monitoring of asthma is essential for disease control and management. As mobile health technology has been widely applied in the prevention and management of health problem, smart phone applications (apps) providing social connection offer a potentially powerful approach to behavioral change through delivering convenient tailored intervention. However, there is limited evidence supporting apps can improve asthma self-management. Hence, the purposes of this study were to (1) develop an age-appropriate tailored KidsHealth app to improve healthy lifestyles for school-age children with asthma; (2) evaluate the feasibility and accessibility of interventions of the app; and (3) investigate the effectiveness of the app in improving healthy behaviors and outcomes for the users.

A description of patient conditions should consist of the changes in and combination of clinical measures. Traditional data-processing method and classification algorithms might cause clinical information to disappear and reduce prediction performance. To improve the accuracy of clinical-outcome prediction by using multiple measurements, a new multiple-time-series data-processing algorithm with period merging is proposed. Clinical data from 83 hepatocellular carcinoma (HCC) patients were used in this research. Their clinical reports from a defined period were merged using the proposed merging algorithm, and statistical measures were also calculated. After data processing, multiple measurements support vector machine (MMSVM) with radial basis function (RBF) kernels was used as a classification method to predict HCC recurrence. A multiple measurements random forest regression (MMRF) was also used as an additional evaluation/classification method. To evaluate the data-merging algorithm, the performance of prediction using processed multiple measurements was compared to prediction using single measurements. The results of recurrence prediction by MMSVM with RBF using multiple measurements and a period of 120 days (accuracy 0.771, balanced accuracy 0.603) were optimal, and their superiority to the results obtained using single measurements was statistically significant (accuracy 0.626, balanced accuracy 0.459, P < 0.01). In the cases of MMRF, the prediction results obtained after applying the proposed merging algorithm were also better than single-measurement results (P < 0.05). The results show that the performance of HCC-recurrence prediction was significantly improved when the proposed data-processing algorithm was used, and that multiple measurements could be of greater value than single.

Cloud computing changes the conventional way of virtualization resource allocation, and gives users high flexibility and convenience. However, when the cloud service providers offer Infrastructure as a Service (IaaS), there are many practical problems to be resolved. We propose a cloud system's prototype, through a composition of cloud services to automate workflow and exchange data between services based on Message-Oriented Middleware, and then achieve the goal of virtualization platform integration. We develop efficient schemes for users to use functions, which include a single web portal, virtual machine templates and message translation, to quickly deploy virtual machines in a large-scale.

Telehealthcare is a global trend affecting clinical practice in the world. With the progress and development of telecommunication technologies, telecom facilities have afforded telehealthcare a new approach for chronic disease management. The electrocardiogram (ECG) is commonly used to detect abnormal heart rhythms and to investigate the cause of heart abnormalities. To reduce the cardiologists' loading and to provide a continuously telehealthcare, we developed a clinical decision and support system (CDSS) with automatic recognition of the ECG in real-time analysis. In addition, we adopted the approach of noise reduction and feature extraction for support vector machine (SVM) implementation with automatic learning algorithms. The automatic interpretation of ECG could provide assistance to physicians in decision-making, especially with large volumes of data. According to the preliminary results of automatic classification models, we acquired 88.4% sensitivity, for noise detection model, 85.9% specificity for sinus classification model and 89.1% sensitivity for disease classification model, respectively. However, it is not reliable enough to obviate the need for physician's diagnosis and confirmation. We should put much effort on enhancing the performance of ECG interpretation in the future.

We present a system to detect seizure and spike in Epilepsy Electroencephalogram (EEG) analysis and characterize different epilepsy EEG types. After extracting features from three EEG types, Normal, Seizure and Spike, with Empirical Mode Decomposition (EMD), we do Analysis of variance (ANOVA) to classify conspicuous features and low-resolution features, and build Gaussian distributions of conspicuous features for probability density function (PDF) to do classification. Using EMD, the recognition rate improved from 70% to 90%. With ANOVA, the recognition rate can reach 99%. The linear model accelerates the system from 2 hours to 90 seconds compare to the previous approach.

A web-based telehealthcare system was integrated with the mobile application for diet people. In order to comprehensively deal with the over-weight problem, the user interface with four important aspects on weight, diet, exercise and sleep records is implemented. In addition, online courses and knowledge of diet are provided in the system. The electronic medical record of National Taiwan University Hospital is also embedded in the system for the case managers and doctors to evaluate the status of the over-weight patients. We utilized Pearson's Correlation Coefficient which represented the correlative degree of two variables to discover the relationship between the reduced weight of people and their lifestyle records. We found that three aspects of records (compliancy, violation, exercise) were important for people who wanted to lose weight.

The metabolic disorders may hinder an infant's normal physical or mental development during the neonatal period. The metabolic diseases can be treated by effective therapies if the diseases are discovered in the early stages. Therefore, newborn screening program is essential to prevent neonatal from these damages. In the paper, a support vector machine (SVM) based algorithm is introduced in place of cut-off value decision to evaluate the analyte elevation raw data associated with Phenylketonuria. The data were obtained from tandem mass spectrometry (MS/MS) for newborns. In addition, a combined feature selection mechanism is proposed to compare with the cut-off scheme. By adapting the mechanism, the number of suspected cases is reduced substantially, it also handles the medical resources effectively and efficiently.

In recent years, the rapid development of trade and traffic in our country has resulted in the increasing occurrence of various types of disasters and emergency injuries. According to the statistics from Fire Departments of Taiwan, medical emergency dispatch frequencies have exceeded 300,000, and the frequency of medical emergency dispatches has increased each year. These figures reflect the phenomenon that, despite the continual growth and progression of the community, various accidents and unforeseen situations are becoming increasingly common. Therefore, fire units have established a medical emergency care system with the objective of minimizing injuries, patient suffering, and the risk of mortality, reducing social medical costs, and alleviating the financial burden of medical emergencies on families. Many countries have special medical emergency dispatch systems, but these do not meet Taiwan's rules and emergency procedures. Thus, our research has focused on compiling a symptom-based medical emergency dispatch guide designed to match Taiwan's medical emergency dispatch system. We collate national medical emergency dispatch guides and present a medical emergency dispatch system to support medical staff and to educate these staff on the correct methodology in a short time. This system achieves the following objectives: (1) to enhance the quality of medical emergency services in Taiwan, (2) to reduce the response time of medical emergency dispatch, (3) to reduce the unnecessary waste of medical resources.

Multiclass classification is an important technique to many complex biomedicine problems. Genetic algorithms (GA) are proven to be effective to select features prior to multiclass classification by support vector machines (SVM). However, their use is computation intensive. Based on SOA (Service Oriented Architecture) design principles, this paper proposes a cloud computing framework that exploits the inherent parallelism of GA-SVM classification to speed up the work. The performance evaluations on an mRNA benchmark cancer dataset have shown the effectiveness and efficiency of the framework. With a user-friendly web interface, the framework provides researchers an easy way to investigate the unrevealed secrets in the fast-growing repository of biomedical data.

Financial derivative valuation is the key of the adoption of the International Financial Reporting Standards (IFRS), which are based on fair value accounting. When the derivatives do not have an active market, the inputs and methods for estimating their fair value will be more subjective and, the derivative valuation will be less reliable. The goal of this research is to design a derivative valuation service to aid the accounting professionals to meet the reliability requirement of the IFRS which, in the derivative setting, requires derivative valuation be objective and free from errors. First, we incorporate the various valuation models and their risk factors into the service as the basis for providing an objective valuation result. Second, we provide a user interface to simplified management of parameters and provisions of term sheets of derivatives to avoid users' errors in parsing the term sheets. Third, when the users hold a large number of derivatives, the derivative valuation meets real-time constraints in financial reporting. We thus developed the service in parallel computing environment to reduce computational time of the valuation process. Experiment cal results of derivative valuation are also presented.

Epilepsy is a common chronic neurological disorder characterized by recurrent unprovoked seizures. The electroencephalogram (EEG) signals play an important role in the diagnosis of epilepsy. In addition, multi-channel EEG signals have much more discrimination information than a single channel. However, traditional recognition algorithms of EEG signals are lack of multichannel EEG signals. In this paper, we propose a new method of epileptic seizure detection based on multichannel EEG signals. Both unipolar and bipolar EEG signals are considered in our approach. We make use of approximate entropy (ApEn) and statistic values to extract features. Furthermore, we tested the performance of four different Support Vector Machines (SVMs). The results reveal that the grid SVM achieves the highest totally classification accuracy (98.91%).

Network-on-Chip (NoC) is an on-chip communication method with good scalability and reliability. One of the most important issues of NoC design is low transmission latency design. In this paper, we propose a priority based output arbitration method to eliminate the congestion state of the NoC. By detecting and dispatching the packet requirements from different directions, the packets have different priorities. According to the priorities, the packets can pass the congested router in order. Simulation results demonstrate that the proposed method can reduce the transmission latency about 20% in uniformly distributed traffic and about 30% in hot-spot traffic when compare with the conventional router. In the router design, the new arbiter area overhead is less than 1%.

In modern Network-on-Chip (NoC) design, how to guarantee the data correctness from source IP to destination IP becomes an important issue. Error detection can be one of the solutions of this problem. In this paper, a distinguishable error detection method and the corresponding router are proposed to solve the data correctness problem. In the proposed method, the significant or critical data will be checked strictly while the other data will be checked slightly. Simulation results demonstrate that the proposed method not only provides an effective error detection scheme but also has better power-delay product than conventional technique.

802.11 Wireless Local Area Network (WLAN) technology is now common in power sensitive devices like smart phones and personal digital assistants (PDAs). In this article, we present an accurate power consumption model based on the Bianchi model [13] and the power measurement in PHY layer [9] to predict the power consumption of 802.11n and Multiple-Inputs-Multiple-Outputs (MIMO) mode of 802.11n. In this model, we calculate the total power consumption by summing six consumed powers: the idle listening power consumed in the DIFS period, the back-off stage, the power consumed in transmitting a frame, the idle listening power consumed in the SIFS turn-around time, the power consumed in receiving an ACK frame from AP and the power consumed in the collision for one frame transmission. Our analyses and simulations show that the power consumed in idle listing will dominate the total power consumption when the number of the active stations is large. The successful possibility and MAC efficiency of 802.11n are also analyzed and simulated in this article. The imperfect channel scenario will be evaluated, too. This power analysis model to our knowledge has not been presented in the previous works so far.

The clinical symptoms of metabolic disorders during neonatal period are often not apparent, if not treated early irreversible damages such as mental retardation may occur, even death. Therefore, practicing newborn screening is very important to prevent neonatal from these damages. In this paper, the newborn screening system used support vector machines (SVM) classification technique is proposed in place of cut-off value decision to evaluate the metabolic substances concentration raw data obtained from tandem mass spectrometry (MS/MS) and determine whether the newborn has some kinds of metabolic disorder diseases. On the basis of the proposed features, new analytic combinations are identified with superior discriminatory performance compared with the best published combinations. Classifiers built with the feature selection to find C3/C2, C3 and C16 of three key point features achieved diagnostic sensitivities, specificities and accuracy approaching 100%.

Bio-signal analysis is one of the most important approaches to biomedical engineering. The health information such as ECG, PCG, EMG and EEG are often recorded in digital format to be analyzed. In this paper, a bio-signal analyzing Web service system using Support Vector Machines (SVM) classifier technique is proposed. The bio-signals are recorded in digital format as the input of the system. In addition, based on the concept of Service-Oriented Architecture (SOA), the system under Web services .NET is designed. The system provides features including heterogeneous platform, protocols, as well as applications. The system has been designed with the purpose of seamless integration into the Health Information System.

Many efforts in past years have been made to lower the linguistic barriers for non-native English speakers to access the Internet. Internet standard RFC 3490, referred to as IDNA (Internationalizing Domain Names in Applications), focuses on access to IDNs (Internationalized Domain Names) in a range of scripts that is broader in scope than the original ASCII. However, the use of character variants that have similar appearances and/or interpretations could create confusion. A variant IDL (Internationalized Domain Label), derived from an IDL by replacing some characters with their variants, should match the original IDL; and thus a variant IDN does. In RFC 3743, referred to as JET (Joint Engineering Team) Guidelines, it is suggested that zone administrators model this concept of equivalence as an atomic IDL package. When an IDL is registered, an IDL package is created that contains its variant IDLs generated according to the zone-specific Language Variant Tables (LVTs). In addition to the registered IDL, the name holder can request the domain registry to activate some of the variant IDLs, free or by an extra fee. The activated variant IDLs are stored in the zone files, and thus become resolvable. However, an issue of scalability arises when there is a large number of variant IDLs to be activated. In this article, the authors present a resolution protocol that resolves the variant IDLs into the registered IDL, specifically for Han character variants. Two Han characters are said to be variants of each other if they have the same meaning and are pronounced the same. Furthermore, Han character variants usually have similar appearances. It is not uncommon that a Chinese IDL has a large number of variant IDLs. The proposed protocol introduces a new RR (resource record) type, denoted as VarIdx RR, to associate a variant expression of the variant IDLs with the registered IDL. The label of the VarIdx RR, denoted as the variant index, is assigned by an indexing function that is designed to give the same value to all of the variant IDLs enumerated by the variant expression. When one of the variant IDLs is accessed, Internet applications can compute the variant index, look up the VarIdx RRs, and resolve the variant IDL into the registered IDL. The authors examine two sets of Chinese IDLs registered in TWNIC and CNNIC, respectively. The results show that for a registered Chinese IDL, a very small number of VarIdx RRs, usually one or two, are sufficient to activate all of its variant IDLs. The authors also represent a Web redirection service that employs the proposed resolution protocol to redirect a URL addressed by a variant IDN to the URL addressed by the registered IDN. The experiment results show that the proposed protocol successfully resolves the variant IDNs into the registered IDNs.

The paper presents a multi-tier, integrated, distributed, inpatient healthcare information system based on service oriented architecture (SOA) .NET environment in National Taiwan University Hospital (NTUH). The architecture and outcomes of the newly developed inpatient information system (IIS) platform are discussed in details. We also present mechanisms of integration as well as interoperability among the components and multi-database in IIS via health level seven (HL7) Middleware layer. The preliminary performance of the current operating IIS is evaluated and analyzed to verify the efficiency and effectiveness of the architecture we designed.

In this paper, a higher-order-statistics (HOS)-based radial basis function (RBF) network for signal enhancement is introduced. In the proposed scheme, higher order cumulants of the reference signal were used as the input of HOS-based RBF. An HOS-based supervised learning algorithm, with mean square error obtained from higher order cumulants of the desired input and the system output as the learning criterion, was used to adapt weights. The motivation is that the HOS can effectively suppress Gaussian and symmetrically distributed non-Gaussian noise. The influence of a Gaussian noise on the input of HOS-based RBF and the HOS-based learning algorithm can be mitigated. Simulated results indicate that HOS-based RBF can provide better performance for signal enhancement under different noise levels, and its performance is insensitive to the selection of learning rates. Moreover, the efficiency of HOS-based RBF under the nonstationary Gaussian noise is stable.

This article describes the successful experiences of National Taiwan University Hospital (NTUH) in moving from IBM Mainframe to connected networking computer systems. We use multi-tier architecture and HL7 standard to implement our new outpatient hospital information system (HIS). The NTUH HIS is a complex environment with several operating systems, databases, and information systems. We adopt service-oriented architecture (SOA) to reduce the complex relations between systems and solve data consistency problems among databases. We also show that the distributed architecture can provide us stable and reasonable system performances. Our main contribution is proving that the distributed environment with HL7 standard and SOA can sustain in a highly demanding environment

The 802.11 has emerged as the prominent wireless LAN technology as the mobile computing devices such as notebooks and PDA have replaced the desktop computers to be the main trend products. However, if the number of active stations is large for the legacy backoff algorithm (DCF) of the 802.11, the capacity will be very low due to high collision cost. In this paper, we introduce the TDMA concept to partition all numerous active stations into several groups to avoid all stations transmitting the frames simultaneously. When Point Coordinator (PC, generally referring to AP) finds that the number of active stations (M) is large i.e. bigger than 16, it broadcasts grouping number and grouping head bits (such as 00000100 00000000) information in the TIM field of the beacon frame. Once all stations receive this instruction, the stations which last two LSB bits of the MAC address are 00 belonging to group 0 will transfer their frame first. On the contrary, all stations belonging to other groups will set their waiting time, that is, Network Allocation Vector (NAV) much more precisely. Analysis shows that the capacity of our GDCF will be near to the theoretical capacity of 802.11 WLAN even if the distributions of all active stations among all groups are not so uniform. This capacity could be independent of the number of active stations and CWmax(contention window maximum).

In this paper, we propose a method to solve the multiple supply voltage scheduling problem which is to assign the operational nodes of a control/data flow graph to a voltage level to minimize the average power consumption within a given computation time. Different from the previous researches focused on the operational nodes in the critical path and utilized the slack time to change the voltage of other nodes, our method can deal with all nodes without considering whether the node is in the critical path or not, and the benefit is that the voltage assignment of each node becomes more flexible. The proposed method consists of two phases, the scheduling phase and the adjusting phase, and considers both the power (delay) of the computational components and the power (delay) of the level shifters. Experimental result shows that using three voltages on a number of standard benchmarks, an average power saving of 34.23% can be obtained if the delay overhead is set as 0, and 48.07% can be obtained if the total delay is set as 1.6 times of the original delay.

The 802.11 has emerged as the prominent wireless LAN technology as the mobile computing devices such as notebooks and PDA have replaced the desktop computers to be the main trend products. However, if the number of active stations is large, that is high-loading condition for the legacy DCF of 802.11, the capacity will be very low due to high collision costs. In this paper, we introduce the TDMA concept to partition all numerous active stations into several groups to avoid all stations transmitting the frames simultaneously. When Point Coordinator (PC, generally referring to AP) finds that the number of active stations (M) is large i.e. bigger than 8, it broadcasts number of groups (Ng) and group head (Nh) bits (such as 00000100 00000000) information in the TIM field of the beacon frame. Once all stations receive this instruction, the stations which last two LSB bits of the MAC address (IEEE EUI-48 or EUI-64) are 00 belonging to group 0 will transfer their frame first. On the contrary, all stations belonging to other groups will set their waiting time, that is, Network Allocation Vector (NAV) much more precisely. Analysis shows that the capacity of our GB-DCF will be near to the theoretical capacity limit of 802.11 WLAN even if the distributions of all active stations among all groups are not so uniform. This capacity could be independent of the number of active stations and CWMax (Contention window maximum). In this article, we also introduce the grouping cycle concept to our scheme, called GB-DCF+, to reduce the heavy overhead of the legacy DCF and to increase the MAC layer throughput of the upcoming 802.11n protocol. The key idea of GB-DCF+ is that DIFS, SIFS, and ACK are added to the grouping cycle which consists of the transmissions of all groups' slot instead of a single frame. Simulations show that the capacity of our scheme could approach to 45.1% when the PHY data rate, frame size, number of stations, and number of groups are 216 Mbps, 2160 bytes, 128, and 32 respectively. On the contrary, the capacity of DCF will be low to 19.3% with the same scenario. This is due to the tremendous collision costs as well as fixed and heavy overhead of the legacy DCF.

Threshold signature is an important aspect of threshold cryptography, and represents almost the core of threshold cryptography research. Based on the threshold signature method, this work was made for resisting conspiracy attack by controlling the group signature issuance rights. The proposed method suffers a flaw for it requires establishing a secretary for signature issuance. By the way, the proposed strategy can be widely applied to various group signature crypto systems, in which the participant conspirators disable to forge the group signature

Traffic smoothing is an efficient means to reduce the bandwidth requirement for transmitting a variable-bit-rate video stream. Several traffic-smoothing algorithms have been presented to offline compute the transmission schedule for a prerecorded video. For live video applications, Sen present a sliding-window algorithm, referred to as SLWIN(k), to online compute the transmission schedule on the fly. SLWIN(k) looks ahead W video frames to compute the transmission schedule for the next k frametimes, where klesw. Note that W is upper bounded by the initial delay of the transmission. The time complexity of SLWIN(k) is O(W*N/k) for an N frame live video. In this paper, we present an O(N) online traffic-smoothing algorithm and two variants, denoted as FOS, FOS1 and FOS2, respectively. Note that O(N) is a trivial lower bound of the time complexity of the traffic-smoothing problem. Thus, the proposed algorithm is optimal. We compare the performance of our algorithms with SLWIN(k) based on several benchmark video clips. Experiment results show that FOS2, which adopts the aggressive workahead heuristic, further reduces the bandwidth requirement and better utilizes the client buffer for real-time interactive applications in which the initial delays are small

The composition of traditional domain names are restricted to ASCII letters, digits, and hyphens (abbreviated as LDH). This makes it difficult for many to use their native language to name and access their Internet hosts. The IETF IDN (Internationalized Domain Name) Working Group proposes a mechanism, IDNA (Internationalizing Domain Names in Applications), for internationalized access to multilingual domain names. The proposal uses a preparation process that converts a Unicode IDN into an ACE (ASCII Compatible Encoding) string that uses only LDH. Thus, applications can look up the ACE string by using the existing DNS infrastructure. However, some of the domain name strings embedded in multilingual content do not have any charset tag so they cannot appropriately be converted into ACE strings. We noticed that many Internet applications allow users to use non-ASCII domain names. We were motivated to design an architecture for IDN resolution as well as to minimize the cost of modifying legacy Internet applications. We specifically focus on designing an IDN server proxy, which is located on the domain name server side, to handle domain names in multiple encodings. In this article, we study several architecture design issues including detection of charset encoding, routing of non-ACE IDN lookup requests, and so on.With respect to these design issues, we present an IDN server proxy architecture which stores ACE IDNs in domain name servers. Note that traditional domain name servers can be used without modification. An IDN server proxy, called Octopus, is employed on the domain name server side. Octopus converts a non-ACE IDN string into ACE upon receiving an IDN lookup request from remote users or autonomous systems. The ACE string is then forwarded to backend domain name servers (where the traditional domain names and ACE IDNs are stored) for further processing. This allows Internet users to access IDNs without having to upgrade their software.Based on the design and implementation of Octopus, we deployed a CDN (Chinese domain name) trial in July 2002. In this article, we present the results of testing Octopus IDN lookup functions as well as our experiences in providing CDN Web services. Several types of errors can occur if applications are unable to handle IDNs adequately. For example, a Web browser may erroneously parse an IDN within a URL. Many legacy Web servers are unable to process the IDN of a virtual host. Web application servers may have trouble completing some actions such as redirecting Web pages to alternative Web pages. Our studies help service providers understand potential problems when non-ASCII domain names are used and the best common practice at this stage. As well, the experiences give some guidance for software developers to develop IDNA-compliant Internet applications.

The growth of the Internet inspires lots of new network applications, and many of them are based on group communication models. Key trees are ideal for a group of users to efficiently and securely share a common secret key, the group key, which can encrypt transmitted data, or other session keys that protect group communication. An efficient group key management protocol is proposed for centralized dynamic groups, and one-way key derivation is integrated with key trees to reduce the communication overhead of rekeying operations. The server does not have to send new keys to those members who can derive the keys by themselves, and the total number of encrypted keys transmitted per rekeying operation becomes fewer. It is shown that the technique can be applied to both synchronous and asynchronous rekeying operations. The proposed protocol outperforms the other group key management protocols from our analysis and simulation, and is suitable for practical systems.

In this article, we present a bipartition dual-encoding architecture for low-power pipelined circuits. We exploit the bipartition approach as well as encoding techniques to reduce power dissipation not only of combinational logic blocks but also of the pipeline registers. Based on Shannon expansion, we partition a given circuit into two subcircuits such that the number of different outputs of both subcircuits are reduced, and then encode the output of both subcircuits to minimize the Hamming distance for transitions with a high switching probability. We measure the benefits of four different combinational bipartitioning and encoding architectures for comparison. The transistor-level simulation results show that bipartition dual-encoding can effectively reduce power by 72.7% for the pipeline registers and 27.1% for the total power consumption on average. To the best of our knowledge, it is the first work that presents an in-depth study on bipartition and encoding techniques to optimize power for pipelined circuits.

The growth of the Internet inspires lots of new network applications, and many of them are based on group communication models, In this paper, we propose an approach to reduce the path length of a binary key tree managed by a centralized group key server by recomposing it, such that subsequent group operations can benefit from fewer auxiliary key updates, fewer encryptions, and less multicast bandwidth. The server can recompose the key tree in a join or a leave operation without the use of additional auxiliary keys or encryptions. An optimal composition algorithm is presented. It is shown in our analysis that the algorithm is effective to reduce the path length of a binary key tree in join operations and leave operations.

Most microprocessors employ the on-chip caches to bridge the performance gap between the processor and main memory. However, the cache accesses usually contribute significantly to the total power consumption of the chip. Based on the observation that an overwhelming majority of the cache access bits are '0', in this paper we propose a value-conscious (VC) cache to reduce the average cache power consumption during an access. Unlike the conventional cache with differential-bitline implementation, the VC cache is a single-bitline design. Depending on the access bit value, the VC cache can dynamically prevent the bitline from being discharged such that the power dissipated in accessing '0' is much less than the power dissipated in accessing '1'. The implementation of the VC cache is a circuit-level technique, which is software independent and orthogonal to other low power techniques at architecture-level. The experimental results based on the SPEC2000 and MediaBench traces show that without compromise of both performance and stability, by exploiting the prevalence of '0' bits in access data the VC cache can reduce the average cache read and write power by about 18%/spl sim/22% and 36%/spl sim/40%, respectively.

Most microprocessors employ the on-chip caches to bridge the performance gap between the processor and the main memory. However, the cache accesses usually contribute significantly to the total power consumption of the chip. Based on the observation that an overwhelming majority of the values written to the cache are "0", in this paper we propose a zero-aware SRAM cell with an asymmetric inverter pair, called ZA cell, to minimize the cache power consumption in writing "0". The ZA cell uses a circuit-level technique, which is software independent and orthogonal to other low-power techniques at architecture-level. Compared to the conventional SRAM cell, the experimental results based on the SPEC2000 and MediaBench traces show that without compromise of both performance and stability, the ZA cell can reduce the average cache write power consumption over 60% for both the baseline instruction and data caches. In particular, the ZA cell is attractive in the data caches, which reveal the high write-zero rate.

The waveform data, which is gathered during digital simulation of large and complex systems easily fill up huge amounts of disk space on the simulation computer. This is specially true for many deep-submicron designs. Hence, we developed a set of algorithms and techniques which can be used to efficiently compress digital waveforms without losing any significant information from the original data. Experimental results show that compression ratios of up to 300 compared to a original VCD formatted file can be achieved.

Low power caches have become a critical component of both hand-held devices and high-performance processors. Based on the observation that an overwhelming majority of the data written to the cache are '0', in this paper we propose a power-aware SRAM cell with one single-bitline write port and one differential-bitlines read port, called SWDR cell, to minimize the cache power consumption in writing '0'. The SWDR cell uses a circuit-level technique, which is software independent and orthogonal to other low power techniques at architecture-level. Compared to the conventional SRAM cell, the experimental results show that without compromise of both performance and stability, the SWDR cell can result in 73%∼92% reduction in average cache write power dissipated in bitlines.

Power consumption is an increasingly pressing problem in modern processor design. Since the on-chip caches usually consume a significant amount of power, it is one of the most attractive targets for power reduction. This paper presents a two-level filter scheme, which consists of the L1 and L2 filters, to reduce the power consumption of the on-chip cache. The main idea of the proposed scheme is motivated by the substantial unnecessary activities in conventional cache architecture. We use a single block buffer as the L1 filter to eliminate the unnecessary cache accesses. In the L2 filter, we then propose a new sentry-tag architecture to further filter out the unnecessary way activities in case of the L1 filter miss. We use SimpleScalar to simulate the SPEC2000 benchmarks and perform the HSPICE simulations to evaluate the proposed architecture. Experimental results show that the two-level filter scheme can effectively reduce the cache power consumption by eliminating most unnecessary cache activities, while the compromise of system performance is negligible. Compared to a conventional instruction cache (32 kB, two-way) implemented with only the L1 filter, the use of a two-level filter can result in roughly 30% reduction in total cache power consumption. Similarly, compared to a conventional data cache (32 kB, four-way) implemented with only the L1 filter, the total cache power reduction is approximately 46%.

For physical caches, the address translation delay can be partially masked, but it is hard to avoid completely. In this paper, we propose a cache partition architecture, called paged cache, which not only masks the address translation delay completely but also reduces the tag area dramatically. In the paged cache, we divide the entire cache into a set of partitions, and each partition is dedicated to only one page cached in the TLB. By restricting the range in which the cached block can be placed, we can eliminate the total or partial tag depending on the partition size. In addition, because the paged cache can be accessed without waiting for the generation of physical address, i.e., the paged cache and the TLB are accessed in parallel, the extended cache access time can be reduced significantly. We use SimpleScalar to simulate SPEC2000 benchmarks and perform HSPICE simulations (with a 0.18 /spl mu/m technology and 1.8 V voltage supply) to evaluate the proposed architecture. Experimental results show that the paged cache is very effective in reducing tag area of the on-chip Ll caches, while the average extended cache access time can be improved dramatically.

In this paper we propose a bipartition dual-encoding architecture for low power pipelined circuit. Pipelined circuits consist of combinational logic blocks separated by registers which usually consume a large amount of power Although the clock gated technique is a promising approach to reduce switching activities of the pipelined registers, this approach is restricted by the placement of the registers and the additional control signals that must be generated. Thus, we propose a technique for optimizing power dissipation of a pipelined circuit addressing registers and combinational logic blocks at the same time. Our approach modifies the registers using bipartition and encoding techniques. In our experiments power consumption were reduced by 72.9% for pipelined registers and 30.4% for the total pipelined stage on average.

With the popularity of group-oriented applications, secure and efficient communication among all group members has become a major issue. We propose a distributed key management protocol for group members to share a secret group key, which can be used to protect shared information. The protocol manages the group key of a dynamic group, where members can freely join or leave, and each time the key is updated using two broadcast messages. The protocol algorithms provide group key secrecy, forward group key secrecy, and backward group key secrecy. The complexities of the group key computation time, the storage space for every member, and the total communication bandwidth to update the group key are approximately of logarithmic order of the group size, which make the protocol attractive for environments with less computation power and smaller storage.

This work proposes a new algorithm to synthesize low power bipartition-codec architecture for pipelined circuits. The bipartition-codec architecture has been introduced as an effective power reduction technique for circuit design. The entropy-based partition-codec (ENPCO) algorithm extends this approach as it optimizes for both: power and area. It uses entropy as a criterion to balance between power and area. The ENPCO algorithm is composed of two phases: first, it clusters the output vectors with high occurrence into a group, moving all remaining output vectors into another group. The first group will be encoded in order to save power. Secondly, based on circuit entropy, output patterns are moved between both groups in order to balance power consumption and area overhead. A number of Microelectronic Center of North Carolina (MCNC) benchmarks were used to verify the effectiveness of our algorithm. Results demonstrate that ENPCO algorithm can achieve low power with less area overhead than the single-phase algorithm introduced previously by Shanq-Jang Ruan et al. (1999).

We propose a new framework for key management to construct a secure multicasting environment. When rekeying, only one message needs to be generated, and the message size is a constant. Especially when the group size is large, our framework largely reduces the traffic in the network. Two solutions are given to carry out this framework, which are closed curve solution (CCS) and perpendicular space solution (PSS), respectively. One is based on a geometric approach and the other on a linear algebraic approach. Our framework is also compatible to other group communication protocols.

This paper proposes a new approach to synthesize pipelined circuits with low-power consideration. We treat each output value of a combinational circuit as one state of a finite-state machine (FSM). If the output of a combinational circuit transits mainly among some few states, we could extract those states (output) and the corresponding input to build a subcircuit. After bipartitioning the circuit, we apply the encoding technique to the highly active subcircuit for further power reduction. In this paper, we formulate the bipartition problem and present a probabilistic-driven algorithm to bipartition a circuit so as to minimize the power dissipation. Our experimental results show that an average power reduction on several Microelectronic Center of North Carolina (MCNC) benchmarks of 31.6% is achievable.

Multicasting has been widely utilized for delivering messages from one sender to multiple recipients. Nowadays in some applications such as pay per view or video-conferencing systems, the messages delivered via multicasting should be available to authorized recipients only. Therefore, secure multicasting becomes an important design issue in a distributed environment. To achieve secure multicasting, all authorized recipients form a group and share a group key; messages should be encrypted by the key before they are multicasted. For the sake of security, we need a new group key if someone joins/leaves the group. Consequently, our goal is aimed at finding a way to distribute the new key securely and efficiently. We propose a new solution to the problem; we distribute new keys through a special function called the secure filter. Compared with related work, our solution requires less system resources to change group members.

Proposes a new bipartition-codec architecture that may reduce the power consumption of pipelined circuits. We treat each output value of a pipelined circuit as one state of a finite-state machine. If the output of a pipelined circuit mainly transitions among just a few states, we can partition the combinational portion of a pipelined circuit into two blocks: the one that contains the few states of high activity is small, and the other (that contains the remainder of low activity) is large. Consequently, the state transitions are confined to the small block most of the time. Then we replace the small block with a codec circuit, which consists of an encoder and a decoder, to reduce the internal switching activity of the block. The encoder minimizes the number of bit changes during state transitions; thus, the switching which propagates into the decoder is reduced considerably. We present experimental results on several MCNC benchmarks and obtain power savings of up to 63.7% by using our new architecture.

Fuzzy information often appears in the system requirements. Fuzzy Petri nets (FPN) are Petri nets in which certain fuzzy truth values are assigned to its transitions. We show how the FPN model can be used for formal specification and verification of digital systems. The consistent FPN model is actually a state machine, from which we can obtain a consistent marked Petri net (MPN) model. Based on the consistent MPN model, the hardware prototype at register transfer level can be easily induced by using the optimization rules. Finally, main results are presented in the form of three theorems and are supported by some experiments.

We propose an intelligent stock trading decision support system that can forecast the buying and selling signals according to the prediction of short-term and long-term trends using rule-based neural networks. A rule-based neural network allows us to use domain knowledge in the form of inference rules to set up the initial structure of the neural network, and to extract refined domain knowledge from the trained network. With this information, users can understand why and how a decision is made by the system without the need to trust the output of the network blindly. The performance of the proposed system was evaluated by trading the TSEWPI (Taiwan Stock Exchange Weighted Price Index) from 1992 to 1995, and the result was encouraging.

This paper introduces a novel superscalar micro-architecture, called IAS-S, and its related software techniques. We treat two basic problems in superscalar machines. First, we seek a feasible hardware platform which allows the compiler to perform more aggressive instruction scheduling. Second, we develop a good way of communication between the instruction scheduler and register allocator to avoid inadequate register allocation resulting in poor instruction schedules. For the first part, IAS-S employs the Conjugate Register File (CRF) scheme to support multilevel instruction boosting so that a greater amount of instruction-level parallelism in a program can be identified at compile time. For the second part, the instruction scheduling in the IAS-S compiler consists of two passes, prepass and postpass, and a scheduling-conflict graph is built for the register allocator during the prepass scheduling. In this manner, the register allocator can take the potential benefit for later postpass instruction scheduling into account and thus prevents inadequate register allocation.

This article is concerned with creating more realistic images of 3D scenes which are moving relative to the viewer at such high speeds that the propagation delay of light signals and other relativistic effects can not be neglected. Creating images of 3D scenes in relativistic motion might have important applications to science-fiction films, computer games, and virtual environments. We shall discuss the following problems: (1) how to determine the visual appearance of a rapidly moving object, (2) how to determine the apparent radiance of a scene point on a moving object, (3) how to determine the incident irradiance at a scene point coming from a moving light source, (4) how to determine the color of a rapidly moving object, and (5) how to generate shadows when there are relative motions between the viewer, the scenes, and the light sources. Detailed examples are also given to show the result of shading with the relativistic effects taken into account.

This paper describes a simple tree pattern matching algorithm for the code generator of compilers. The intermediate code (Register Transfer Language) is matched with the tree-rewriting rules of the instruction description which describes the target architecture to generate the assembly code. The hashing function is used in our system to transform a tree pattern matching problem into a simple number comparison. Compared with GNU C compiler (gcc), the tree pattern matching time can be reduced by 69% and the compiler time by 6%, and the space of the instruction descriptions can be reduced by 4.10 times on DLX and 2.14 on SPARC. The size of table, which is necessary for the code generator, is quite small in our method.

Proposes a common global variable reassignment and an integrated approach which takes advantage of the complementary relationship of (1) in-lining and (2) interprocedural register allocation to reduce the procedure call overhead without causing any additional negative effect. Our approach is based on the observation of analyzed program characteristics to identify the heavily called procedure regions, and on register usage information to optimize the placement of resister save/restore code. This method also takes full advantage of free-use registers at each procedure call site. The average performance improvement is 1.233 compared with previous schemes that performed either (1) or (2) independently.

A hierarchical and modular local asynchronous transfer mode (ATM) switch is proposed. In order to improve queuing performance, the output queuing technique is adopted and several outputs are allowed to partially share the same output buffer space. The performance of the proposed switch is evaluated under uniform and nonuniform traffic patterns. Comparisons with the knockout switch, the recursive switch, and the Christmas tree switch show that, in terms of complexity, crosstalk, scalability, and synchronization, the proposed switch is superior.

IAS-S Supports boosting with “semantic register” and boosting boundary register to remove the dependences caused by conditional branches. In IAS-S, there is no dedicated shadow register file, and multiple levels of boosting is supported without multiple copies of register files. Any general-purpose register in IAS-S can be regarded as a sequential register or a shadow register flexibly. Furthermore, multi-way jump mechanism is combined with boosting to reduce the penalty due to frequent control transfers.