An Objective Deghosting Quality Metric for HDR Images

Reconstructing high dynamic range (HDR) images of a complex scene involving moving objects and dynamic backgrounds is prone to artifacts. A large number of methods have been proposed that attempt to alleviate these artifacts, known as HDR deghosting algorithms. Currently, the quality of these algorithms are judged by subjective evaluations, which are tedious to conduct and get quickly outdated as new algorithms are proposed on a rapid basis. In this paper, we propose an objective metric which aims to simplify this process. Our metric takes a stack of input exposures and the deghosting result and produces a set of artifact maps for different types of artifacts. These artifact maps can be combined to yield a single quality score. We performed a subjective experiment involving 52 subjects and 16 different scenes to validate the agreement of our quality scores with subjective judgements and observed a concordance of almost 80%. Our metric also enables a novel application that we call as hybrid deghosting, in which the output of different deghosting algorithms are combined to obtain a superior deghosting result.     

Modeling discrete event scalable network systems

Scalability in simulation tools is one of the most important traits to measure performance of software. The reason is that today’s Internet is the main instance of a large-scale and highly complex system. Simulation of Internet-scale network systems has to be supported by any simulation tool. Despite this fact, many network simulators lacks support for building large models. In this work, in order to propose a new approach for scalability issue in network simulation tools, a network simulator is developed based on behavior of honeybees and high performance DEVS, modular and hierarchical system theoretic approach. A biologically-inspired discrete event modeling approach is described for studying networks’ scalability and performance traits. Since natural systems can offer important concepts for modeling network systems, key adaptive and emergent attributes of honeybees and their societal properties are incorporated into a set of simulation models that are developed using the discrete event system specification approach. Large-scale network models are simulated and evaluated to show the benefits of nature-inspired network models.     

An adaptive least squares channel estimation based on conjugate gradient approach for multipath fading CDMA systems

Multiple access interference (MAI) is the main factor affecting the performance of channel estimation techniques for code division multiple access (CDMA) systems. Although, several multi-user channel estimation algorithms have been proposed to mitigate MAI, these algorithms require high computational complexities. In this paper, we address the problem of iterative least squares (LS) mobile channel estimation at high channel efficiency that requires a short training sequence along with the spreading sequences. We employ an efficient iterative method based on conjugate gradient (CG) algorithm to reduce the computational complexity of the estimation method. Computer simulations illustrate that the proposed method performs almost identical to the exact LS estimate for reasonable training lengths.     

Classification of abnormal plant operation using multiple process variable trends

This paper illustrates two strategies for the detection and classification of abnormal process operating conditions in which multiple process variable trends are available. The first strategy uses a hidden Markov model (HMM) for overall process classification while the second method uses a back-propagation neural network (BPNN) to determine the overall process classification. The methods are compared in terms of their ability to detect and correctly diagnose a variety of abnormal operating conditions for a non-isothermal CSTR simulation. For the case study problem, the BPNN method resulted in better classification accuracy with a moderate increase in training time compared with the HMM approach.     

Groundwater legislation in Turkey: problems of conception and application

The uneven distribution of water resources, a growing population, urbanization and global climate change require new approaches for groundwater management in Turkey. “Safe yield” should yield to broader concepts such as “sustainability”. Groundwater management needs to consider future needs of the people and all ecosystems in accordance with basin development models. Groundwater law needs to be expanded beyond quantity to address quality concerns. A new institutional framework should be established and groundwater regulation should reflect new approaches and ideas, in particular to address problems of application.     

Thread vulnerability in parallel applications

Continuously reducing transistor sizes and aggressive low power operating modes employed by modern architectures tend to increase transient error rates. Concurrently, multicore machines are dominating the architectural spectrum today in various application domains. These two trends require a fresh look at resiliency of multithreaded applications against transient errors from a software perspective. In this paper, we propose and evaluate a new metric called the Thread Vulnerability Factor (TVF$TVF$). A distinguishing characteristic of TVF$TVF$ is that its calculation for a given thread (which is typically one of the threads of a multithreaded application) does not depend on its code alone, but also on the codes of the threads that share resources and data with that thread. As a result, we decompose TVF$TVF$ of a thread into two complementary parts: local and remote. While the former captures the TVF$TVF$ induced by the code of the target thread, the latter represents the vulnerability impact of the threads that interact with the target thread. We quantify the local and remote TVF$TVF$ values for three architectural components (register file, ALUs, and caches) using a set of ten multithreaded applications from the Parsec and Splash-2 benchmark suites. Our experimental evaluation shows that TVF$TVF$ values tend to increase as the number of cores increases, which means the system becomes more vulnerable as the core count rises. We further discuss how TVF$TVF$ metric can be employed to explore performance–reliability tradeoffs in multicores. Reliability-based analysis of compiler optimizations and redundancy-based fault tolerance are also mentioned as potential usages of our TVF$TVF$ metric.     

Identification of EMG signals using discriminant analysis and SVM classifier

The electromyography (EMG) signal is a bioelectrical signal variation, generated in muscles during voluntary or involuntary muscle activities. The muscle activities such as contraction or relaxation are always controlled by the nervous system. The EMG signal is a complicated biomedical signal due to anatomical/physiological properties of the muscles and its noisy environment. In this paper, a classification technique is proposed to classify signals required for a prosperous arm prosthesis control by using surface EMG signals. This work uses recorded EMG signals generated by biceps and triceps muscles for four different movements. Each signal has one single pattern and it is essential to separate and classify these patterns properly. Discriminant analysis and support vector machine (SVM) classifier have been used to classify four different arm movement signals. Prior to classification, proper feature vectors are derived from the signal. The feature vectors are generated by using mean absolute value (MAV). These feature vectors are provided as inputs to the identification/classification system. Discriminant analysis using five different approaches, classification accuracy rates achieved from very good (98%) to poor (96%) by using 10-fold cross validation. SVM classifier gives a very good average accuracy rate (99%) for four movements with the classification error rate 1%. Correct classification rates of the applied techniques are very high which can be used to classify EMG signals for prosperous arm prosthesis control studies.     

Natural frequencies of a flat viaduct road part simply supported on two ends

Viaduct roads have wide application in big cities with high traffic loads, in order to decrease traffic density and to connect subways to highways. Viaduct roads are constructed using steel structures instead of concrete ones in areas of earthquake risks. The low weight of steel structures however causes problems such as vibration and noise. There is increasing demand especially in populated areas to suppress vibration and noise on highway roads for reducing noise-related environmental pollution. In this study, bending vibrations of rectangular plate viaduct roads, which are supported by six fixed elements of rectangular cross-sectional elements are considered. Natural frequencies are obtained using the Rayleigh-Ritz technique, finite elements analysis, experimentally and neural networks (NN).     

Real-time monitoring of flank wear behavior of ceramic cutting tool in turning hardened steels

Flank wear of an alumina-based ceramic cutting tool was determined in hard turning two workpieces (AISI 4340 and 52100 hardened steels) at three cutting speeds (142, 181, and 264?m/min) to devise a real-time monitoring system. Results of the six turning tests were assessed using Kruskal?CWallis test, regression models, and linear trend analysis. Multiple non-linear regression models that explained variation in flank wear as a function of time (second) had a range of $ R_{\rm{adj}}^2 $ values of 27.7% for the test 4340-142 to 95% for the test 52100-181. Linear trend models revealed that the highest flank wear rate of the ceramic cutting tool belonged to the test 52100-181. Interaction effect of the three cutting speeds and the two workpiece types was determined to account for 82.2% of variation in flank wear (P?<?0.001). The real-time monitoring system designed in this study appeared to be promising in terms of determining and quantifying flank wear behavior of the ceramic cutting tool and optimal hard turning conditions.