Automatic detection of premature ventricular contraction using quantum neural networks
Premature ventricular contractions (PVCs) are ectopic heart beats originating from ventricular area. It is a common form of heart arrhythmia. Electrocardiogram (ECG) recordings have been widely used to assist cardiologists to diagnose the problem. In this paper, we study the automatic detection of PVC using a fuzzy artificial neural network named Quantum Neural Network (QNN). With the quantum neurons in the network, trained QNN can model the levels of uncertainty arising from complex classification problems. This fuzzy feature is expected to enhance the reliability of the algorithm, which is critical for the applications in the biomedical domain. Experiments were conducted on ECG records in the MIT-BIH Arrhythmia Database. Results showed consistently higher or same reliability of QNN on all the available records compared to the backpropagation network. QNN, however, has a relatively higher resource requirement for training.

title: Recognition of handwritten numerals by Quantum Neural Network with fuzzy features
abstract: This paper describes a new kind of neural network – Quantum Neural Network (QNN) – and its application to the recognition of handwritten numerals. QNN combines the advantages of neural modelling and fuzzy theoretic principles. Novel experiments have been designed for in-depth studies of applying the QNN to both real data and confusing images synthesized by morphing. Tests on synthesized data examine QNN's fuzzy decision boundary with the intention to illustrate its mechanism and characteristics, while studies on real data prove its great potential as a handwritten numeral classifier and the special role it plays in multi-expert systems. An effective decision-fusion system is proposed and a high reliability of 99.10% has been achieved.

title: MAYA: Integrating hybrid network modeling to the physical world
abstract: The flourish of large-scale network applications across the Internet and or MANET has raised a challenge to network modeling environments that support experimentation and analysis of close interactions between real applications and network dynamics. To facilitate such experimentations, this paper presents MAYA, a multiparadigm network modeling framework including discrete event models, analytical models and physical network interfaces, together with its illustrative implementation using QualNet, fluid flow TCP model and physical network interface. MAYA framework allows users to interface simulated networks directly with physical networks, while attaining real-time constraints even for large-scale networks by incorporating above multiparadigm network modeling techniques. It also gives user the flexibility to emulate applications on nodes in both real and simulated networks. Experiments are conducted to validate the interoperation of QualNet and fluid flow model, to examine the performance of MAYA as well as to evaluate the optimization techniques, namely interleaved execution of fluid flow model and causality-preserve realtime synchronization relaxation. Experimental results indicate that MAYA is a scalable and extensible solution to modeling of close interactions between real application and network dynamics.

title: Efficient Signature Validation Based on a New PKI
abstract: Digital signatures usually serve as irrefutable cryptographic evidence to support dispute resolution in electronic transactions. Trusted time-stamping and certificate revocation services, although very costly in practice, must be available to prevent big loss due to compromising of the signing key. In this paper, we employ a revocation-free public-key framework to improve the efficiency in maintaining the validity of digital signatures as non-repudiation evidence. The new PKI allows an end user to control the validity of his own public-key certificate and enables certificate verification without retrieving the revocation information from the CA. Based on this new PKI, we could validate generic digital signatures using a TSA only. Moreover, we could validate forward-secure digital signatures without the TTP’s involvement.

title: Electronic Payment Systems with Fair On-line Verification
abstract: Electronic payment systems have been intensively studied in recent years. Generally speaking, e-payment systems can be classified into two categories, i.e., the systems with on-line verification and the systems with off-line verification. The former establishes security under the principle of preventionbefore-the-fact while the latter’s security is based on the principle of detectionafter-the-fact. On-line systems are less risky, thus more likely to be adopted by the business community. A critical issue for on-line systems is how to make a transaction fair to each transacting party such that neither party can gain advantages over the other party in a transaction. This can be addressed with a fundamental technique of fair on-line verification. In this paper, we propose a solution to fair on-line verification for electronic payment systems, in which the customer gives verifiable encryption (by a TTP’s public key) of the digital money to the shop for on-line verification, instead of giving shop the money itself. The paper not only shows the principle of applying verifiable encryption to e-payment systems, but also focuses on building up a concrete payment system.

title: Buffering databse operations for enhanced instruction cache performance
abstract: As more and more query processing work can be done in main memory access is becoming a significant cost component of database operations. Recent database research has shown that most of the memory stalls are due to second-level cache data misses and first-level instruction cache misses. While a lot of research has focused on reducing the data cache misses, relatively little research has been done on improving the instruction cache performance of database systems.We first answer the question "Why does a database system incur so many instruction cache misses?" We demonstrate that current demand-pull pipelined query execution engines suffer from significant instruction cache thrashing between different operators. We propose techniques to buffer database operations during query execution to avoid instruction cache thrashing. We implement a new light-weight "buffer" operator and study various factors which may affect the cache performance. We also introduce a plan refinement algorithm that considers the query plan and decides whether it is beneficial to add additional "buffer" operators and where to put them. The benefit is mainly from better instruction locality and better hardware branch prediction. Our techniques can be easily integrated into current database systems without significant changes. Our experiments in a memory-resident PostgreSQL database system show that buffering techniques can reduce the number of instruction cache misses by up to 80% and improve query performance by up to 15%.

title: Performance Evaluation of Directional Adaptive Range Control in Mobile Ad Hoc Networks
abstract: This paper presents DARC (Directional Adaptive Range Control), a range control mechanism using directional antennas to be implemented across multiple layers. DARC uses directional reception for range control rather than directional transmission in order to achieve both range extension and high spatial reuse. It adaptively controls the communication range by estimating dynamically changing local network density based on the transmission activities around each network node. The experimental results using simulation with detailed physical layer, IEEE 802.11 DCF MAC, and AODV protocol models have shown the successful adaptation of communication range with DARC for varied network densities and traffic loads. DARC improves the packet delivery ratio by a factor of 9 at the maximum for sparse networks while it maintains the increased network capacity for dense networks. Further, as each node adaptively changes the communication range, the network delivers up to 20% more packets with DARC compared to any fixed range configurations.