Optimizing subclass discriminant error correcting output codes using particle swarm optimization

Error-Correcting Output Codes (ECOC) reveal a common way to model multi-class classification problems. According to this state of the art technique, a multi-class problem is decomposed into several binary ones. Additionally, on the ECOC framework we can apply the subclass technique (sub-ECOC), where by splitting the initial classes of the problem we create larger but easier to solve ECOC configurations. The multi-class problem's decomposition is achieved via a discriminant tree creation procedure. This discriminant tree's creation is controlled by a triplet of thresholds that define a set of user defined splitting standards. The selection of the thresholds plays a major role in the classification performance. In our work we show that by optimizing these thresholds via particle swarm optimization we improve significantly the classification performance. Moreover, using Support Vector Machines (SVMs) as classifiers we can optimize in the same time both the thresholds of sub-ECOC and the parameters C and φ of the SVMs, resulting in even better classification performance. Extensive experiments in both real and artificial data illustrate the superiority of the proposed approach in terms of performance. © 2010 IEEE

The severity of stages estimation during hemorrhage using error correcting output codes method

As a beneficial component with critical impact, computer-aided decision making systems have infiltrated many fields, such as economics, medicine, architecture and agriculture. The latent capabilities for facilitating human work propel high-speed development of such systems. Effective decisions provided by such systems greatly reduce the expense of labor, energy, budget, etc. The computer-aided decision making system for traumatic injuries is one type of such systems that supplies suggestive opinions when dealing with the injuries resulted from accidents, battle, or illness. The functions may involve judging the type of illness, allocating the wounded according to battle injuries, deciding the severity of symptoms for illness or injuries, managing the resources in the context of traumatic events, etc. The proposed computer-aided decision making system aims at estimating the severity of blood volume loss. Specifically speaking, accompanying many traumatic injuries, severe hemorrhage, a potentially life-threatening condition that requires immediate treatment, is a significant loss of blood volume in process resulting in decreased blood and oxygen perfusion of vital organs. Hemorrhage and blood loss can occur in different levels such as mild, moderate, or severe. Our proposed system will assist physicians by estimating information such as the severity of blood volume loss and hemorrhage , so that timely measures can be taken to not only save lives but also reduce the long-term complications as well as the cost caused by unmatched operations and treatments. The general framework of the proposed research contains three tasks and many novel and transformative concepts are integrated into the system. First is the preprocessing of the raw signals. In this stage, adaptive filtering is adopted and customized to filter noise, and two detection algorithms (QRS complex detection and Systolic/Diastolic wave detection) are designed. The second process is to extract features. The proposed system combines features from time domain, frequency domain, nonlinear analysis, and multi-model analysis to better represent the patterns when hemorrhage happens. Third, a machine learning algorithm is designed for classification of patterns. A novel machine learning algorithm, as a new version of error correcting output code (ECOC), is designed and investigated for high accuracy and real-time decision making. The features and characteristics of this machine learning method are essential for the proposed computer-aided trauma decision making system. The proposed system is tested agasint Lower Body Negative Pressure (LBNP) dataset, and the results indicate the accuracy and reliability of the proposed system

Quantum channels and their entropic characteristics

One of the major achievements of the recently emerged quantum information theory is the introduction and thorough investigation of the notion of quantum channel which is a basic building block of any data-transmitting or data-processing system. This development resulted in an elaborated structural theory and was accompanied by the discovery of a whole spectrum of entropic quantities, notably the channel capacities, characterizing information-processing performance of the channels. This paper gives a survey of the main properties of quantum channels and of their entropic characterization, with a variety of examples for finite dimensional quantum systems. We also touch upon the "continuous-variables" case, which provides an arena for quantum Gaussian systems. Most of the practical realizations of quantum information processing were implemented in such systems, in particular based on principles of quantum optics. Several important entropic quantities are introduced and used to describe the basic channel capacity formulas. The remarkable role of the specific quantum correlations - entanglement - as a novel communication resource, is stressed.Comment: review article, 60 pages, 5 figures, 194 references; Rep. Prog. Phys. (in press

Optimizing linear discriminant error correcting output codes using particle swarm optimization

Error Correcting Output Codes reveal an efficient strategy in dealing with multi-class classification problems. According to this technique, a multi-class problem is decomposed into several binary ones. On these created sub-problems we apply binary classifiers and then, by combining the acquired solutions, we are able to solve the initial multi-class problem. In this paper we consider the optimization of the Linear Discriminant Error Correcting Output Codes framework using Particle Swarm Optimization. In particular, we apply the Particle Swarm Optimization algorithm in order to optimally select the free parameters that control the split of the initial problem's classes into sub-classes. Moreover, by using the Support Vector Machine as classifier we can additionally apply the Particle Swarm Optimization algorithm to tune its free parameters. Our experimental results show that by applying Particle Swarm Optimization on the Sub-class Linear Discriminant Error Correcting Output Codes framework we get a significant improvement in the classification performance. © 2011 Springer-Verlag

SIGNAL PROCESSING TECHNIQUES AND APPLICATIONS

As the technologies scaling down, more transistors can be fabricated into the same area, which enables the integration of many components into the same substrate, referred to as system-on-chip (SoC). The components on SoC are connected by on-chip global interconnects. It has been shown in the recent International Technology Roadmap of Semiconductors (ITRS) that when scaling down, gate delay decreases, but global interconnect delay increases due to crosstalk. The interconnect delay has become a bottleneck of the overall system performance. Many techniques have been proposed to address crosstalk, such as shielding, buffer insertion, and crosstalk avoidance codes (CACs). The CAC is a promising technique due to its good crosstalk reduction, less power consumption and lower area. In this dissertation, I will present analytical delay models for on-chip interconnects with improved accuracy. This enables us to have a more accurate control of delays for transition patterns and lead to a more efficient CAC, whose worst-case delay is 30-40% smaller than the best of previously proposed CACs. As the clock frequency approaches multi-gigahertz, the parasitic inductance of on-chip interconnects has become significant and its detrimental effects, including increased delay, voltage overshoots and undershoots, and increased crosstalk noise, cannot be ignored. We introduce new CACs to address both capacitive and inductive couplings simultaneously.Quantum computers are more powerful in solving some NP problems than the classical computers. However, quantum computers suffer greatly from unwanted interactions with environment. Quantum error correction codes (QECCs) are needed to protect quantum information against noise and decoherence. Given their good error-correcting performance, it is desirable to adapt existing iterative decoding algorithms of LDPC codes to obtain LDPC-based QECCs. Several QECCs based on nonbinary LDPC codes have been proposed with a much better error-correcting performance than existing quantum codes over a qubit channel. In this dissertation, I will present stabilizer codes based on nonbinary QC-LDPC codes for qubit channels. The results will confirm the observation that QECCs based on nonbinary LDPC codes appear to achieve better performance than QECCs based on binary LDPC codes.As the technologies scaling down further to nanoscale, CMOS devices suffer greatly from the quantum mechanical effects. Some emerging nano devices, such as resonant tunneling diodes (RTDs), quantum cellular automata (QCA), and single electron transistors (SETs), have no such issues and are promising candidates to replace the traditional CMOS devices. Threshold gate, which can implement complex Boolean functions within a single gate, can be easily realized with these devices. Several applications dealing with real-valued signals have already been realized using nanotechnology based threshold gates. Unfortunately, the applications using finite fields, such as error correcting coding and cryptography, have not been realized using nanotechnology. The main obstacle is that they require a great number of exclusive-ORs (XORs), which cannot be realized in a single threshold gate. Besides, the fan-in of a threshold gate in RTD nanotechnology needs to be bounded for both reliability and performance purpose. In this dissertation, I will present a majority-class threshold architecture of XORs with bounded fan-in, and compare it with a Boolean-class architecture. I will show an application of the proposed XORs for the finite field multiplications. The analysis results will show that the majority class outperforms the Boolean class architectures in terms of hardware complexity and latency. I will also introduce a sort-and-search algorithm, which can be used for implementations of any symmetric functions. Since XOR is a special symmetric function, it can be implemented via the sort-and-search algorithm. To leverage the power of multi-input threshold functions, I generalize the previously proposed sort-and-search algorithm from a fan-in of two to arbitrary fan-ins, and propose an architecture of multi-input XORs with bounded fan-ins