Fairness of Transitions in Diagnosability of Discrete Event Systems

Failure diagnosability has been widely studied for discrete event system (DES) models because of modeling simplicity and computational efficiency due to abstraction. In the literature it is often held that for diagnosability, such models can be used not only for systems that fall naturally in the class of DES but also for the ones traditionally treated as continuous variable dynamic systems. A class of algorithms for failure diagnosability of DES models has been successfully developed for systems where fairness is not a part of the model. These algorithms are based on detecting cycles in the normal and the failure model that look identical. However, there exist systems with all transitions fair where the diagnosability condition that hinges upon this feature renders many failures non-diagnosable although they may actually be diagnosable by transitions out of a cycle. Hence, the diagnosability conditions based on cycle detection need to be modified to hold for many real-world systems where all transitions are fair. In this work, however, it is shown by means of an example that a system may have some transitions fair and some unfair. A new failure diagnosability mechanism is proposed for DES models with both fair and unfair transitions. Time complexity for deciding diagnosability of DES models with fair and unfair transitions is analyzed and compared with the time complexities of other DES diagnosability analysis methods reported in the literature.     

Spectral entropy and spectral shape based pre-quantization for real time speaker identification system

Pre-processing is one of the vital steps for developing robust and efficient recognition system. Better pre-processing not only aid in better data selection but also in significant reduction of computational complexity. Further an efficient frame selection technique can improve the overall performance of the system. Pre-quantization (PQ) is the technique of selecting less number of frames in the pre-processing stage to reduce the computational burden in the post processing stages of speaker identification (SI). In this paper, we develop PQ techniques based on spectral entropy and spectral shape to pick suitable frames containing speaker specific information that varies from frame to frame depending on spoken text and environmental conditions. The attempt is to exploit the statistical properties of distributions of speech frames at the pre-processing stage of speaker recognition. Our aim is not only to reduce the frame rate but also to maintain identification accuracy reasonably high. Further we have also analyzed the robustness of our proposed techniques on noisy utterances. To establish the efficacy of our proposed methods, we used two different databases, POLYCOST (telephone speech) and YOHO (microphone speech).     

An imperfect production process for time varying demand with inflation and time value of money – An EMQ model

The paper deals with an economic manufacturing quantity (EMQ) model for time-dependent (quadratic) demand pattern. Every manufacturing sector wants to produce perfect quality items. But in long run process, there may arise different types of difficulties like labor problem, machinery capabilities problems, etc., due to that the machinery systems shift from in-control state to out-of-control state as a result the manufacturing systems produce imperfect quality items. The imperfect items are reworked at a cost to become the perfect one. The rework cost may be reduced by improvements in product reliability i.e., the production process depend on time and also the reliability parameter. We want to determine the optimal product reliability and production rate that achieves the biggest total integrated profit for an imperfect manufacturing process using Euler–Lagrange theory to build up the necessary and sufficient conditions for optimality of the dynamic variables. Finally, a numerical example is discussed to test the model which is illustrated graphically also.     

Design of 5.5 GHz linear low noise amplifier using post distortion technique with body biasing

A highly linear 5.5 GHz low noise amplifier (LNA) has been designed exploiting source inductive degeneration topology by using post distortion linearization techniques in 0.18 m CMOS technology. This technique improves the input third order intercept point \((IIP_{3})\) of a low noise amplifier. For enhancing the linearity, this technique used a diode connected MOSFET as IMD sinker and forward body biased which is done in cadence tool. The proposed low noise amplifier achieves high \(IIP_{3}\) by using two transistors, main and auxiliary transistors. Also source inductive degeneration topology is employed in the proposed LNA to optimize the noise figure (NF) and \(S_{11}\) at high frequency. In order to reduce power consumption and threshold voltage, Forward Body Biased technique was implemented. In this paper, the first section discusses the most widely used eight linearization techniques and in the second section, the proposed circuit is represented along with its employed topology, techniques and the simulated results. The proposed LNA achieves a simulated third order input intercept \((IIP_{3})\) of 9.20 dBm while consuming 10.8 mW from a power supply of 1.8 V. it also exhibits a measured gain of 11.34 dB and NF, NF of 2.33 dB.     

Multi‐band pattern reconfigurable Yagi‐Uda antenna

In this study, novel designs of single‐band and tri‐band pattern reconfigurable antennas are proposed. The design of single‐band pattern reconfigurable antenna is accomplished by the use of varactor diodes with the parasitic elements placed on both sides of the driven conventional printed dipole antenna. By tuning the capacitance of varactor, the antenna operates in four different configurations of radiation pattern which include bi‐directional end‐fire, broadside, and uni‐directional end‐fire radiation patterns. The tri‐band pattern reconfigurable antenna design is achieved by the use of parasitic elements on both sides of a tri‐band driven dipole antenna. Dual‐band LC resonators are used as loading elements along the arms of printed dipole to get two lower order modes in addition to the reference dipole mode, resulting in a triband operation of the driven element. The electrical lengths of the parasitic elements with respect to the tri‐band driven element are controlled by suitably embedding varactor and PIN diodes with them. The proposed tri‐band antenna operates in ten different configurations of radiation patterns in the three operating bands. Fully functional prototypes of single‐band and tri‐band pattern reconfigurable antennas along with the DC bias networks have been fabricated to validate the results obtained in simulation.     

On negative results when using sentiment analysis tools for software engineering research

Recent years have seen an increasing attention to social aspects of software engineering, including studies of emotions and sentiments experienced and expressed by the software developers. Most of these studies reuse existing sentiment analysis tools such as SentiStrength and NLTK. However, these tools have been trained on product reviews and movie reviews and, therefore, their results might not be applicable in the software engineering domain. In this paper we study whether the sentiment analysis tools agree with the sentiment recognized by human evaluators (as reported in an earlier study) as well as with each other. Furthermore, we evaluate the impact of the choice of a sentiment analysis tool on software engineering studies by conducting a simple study of differences in issue resolution times for positive, negative and neutral texts. We repeat the study for seven datasets (issue trackers and Stack Overflow questions) and different sentiment analysis tools and observe that the disagreement between the tools can lead to diverging conclusions. Finally, we perform two replications of previously published studies and observe that the results of those studies cannot be confirmed when a different sentiment analysis tool is used.     

Improved gait recognition by gait dynamics normalization

Potential sources for gait biometrics can be seen to derive from two aspects: gait shape and gait dynamics. We show that improved gait recognition can be achieved after normalization of dynamics and focusing on the shape information. We normalize for gait dynamics using a generic walking model, as captured by a population Hidden Markov Model (pHMM) defined for a set of individuals. The states of this pHMM represent gait stances over one gait cycle and the observations are the silhouettes of the corresponding gait stances. For each sequence, we first use Viterbi decoding of the gait dynamics to arrive at one dynamics-normalized, averaged, gait cycle of fixed length. The distance between two sequences is the distance between the two corresponding dynamics-normalized gait cycles, which we quantify by the sum of the distances between the corresponding gait stances. Distances between two silhouettes from the same generic gait stance are computed in the linear discriminant analysis space so as to maximize the discrimination between persons, while minimizing the variations of the same subject under different conditions. The distance computation is constructed so that it is invariant to dilations and erosions of the silhouettes. This helps us handle variations in silhouette shape that can occur with changing imaging conditions. We present results on three different, publicly available, data sets. First, we consider the HumanlD Gait Challenge data set, which is the largest gait benchmarking data set that is available (122 subjects), exercising five different factors, i.e., viewpoint, shoe, surface, carrying condition, and time. We significantly improve the performance across the hard experiments involving surface change and briefcase carrying conditions. Second, we also show improved performance on the UMD gait data set that exercises time variations for 55 subjects. Third, on the CMU Mobo data set, we show results for matching across different walking speeds. It is worth noting that there was no separate training for the UMD and CMU data sets.     

A simple strategy for calibrating the geometry of light sources

We present a methodology for calibrating multiple light source locations in 3D from images. The procedure involves the use of a novel calibration object that consists of three spheres at known relative positions. The process uses intensity images to find the positions of the light sources. We conducted experiments to locate light sources in 51 different positions in a laboratory setting. Our data shows that the vector from a point in the scene to a light source can be measured to within 2.7±4° at α=.05 (6 percent relative) of its true direction and within 0.13±.02 m at α=.05 (9 percent relative) of its true magnitude compared to empirically measured ground truth. Finally, we demonstrate how light source information is used for color correction     

RAPID ONLINE MONITORING OF A HETEROGENEOUS CATALYST BY A PULSED REACTOR COUPLED TO GC-MS

A rapid screening system for heterogeneous catalyst discovery has been developed by coupling an in-house designed and fabricated high temperature vapor phase pulse reactor on-line to a GC-MS. The incorporation of gas chromatography for separation of the products with the mass spectrometry system allowed simultaneous identification and determination of reaction products and substrate conversion. This system was employed to study the vapor phase catalytic hydride transfer reduction (CHTR) of nitrobenzene with methanol as hydrogen donor on an MgO catalyst as a model reaction. Structural information of all the by-products that were formed was useful to understand the reaction mechanism. The products obtained with the new screening technique were in good agreement with conventional bench scale experiments. The rapid online screening provided an efficient methodology for optimization of reaction conditions such as catalyst loading, reaction temperature, and mole ratios. Response Surface Methodology (RSM) was used to optimize the conversion of reactants and selectivity of products.