Self-phase modulation and spectral broadening of optical pulses insemiconductor laser amplifiers

Amplification of ultrashort optical pulses in semiconductor laser amplifiers is shown to result in considerable spectral broadening and distortion as a result of the nonlinear phenomenon of self-phase modulation (SPM). The physical mechanism behind SPM is gain saturation, which leads to intensity-dependent changes in the refractive index in response to variations in the carrier density. The effect of the shape and the initial frequency chirp of input pulses on the shape and the spectrum of amplified pulses is discussed in detail. Particular attention is paid to the case in which the input pulsewidth is comparable to the carrier lifetime so that the saturated gain has time to recover partially before the trailing edge of the pulse arrives. The experimental results, performed by using picosecond input pulses from a 1.52-μm mode-locked semiconductor laser, are in agreement with the theory. When the amplified pulse is passed through a fiber, it is initially compressed because of the frequency chirp imposed on it by the amplifier. This feature can be used to compensate for fiber dispersion in optical communication systems     

A simple decision tree-based disturbance monitoring system for VSC-based HVDC transmission link integrating a DFIG wind farm

Fault detection and classification is a key challenge for the protection of High Voltage DC (HVDC) transmission lines. In this paper, the Teager–Kaiser Energy Operator (TKEO) algorithm associated with a decision tree-based fault classi f ier is proposed to detect and classify various DC faults. The Change Identification Filter is applied to the average and differential current components, to detect the first instant of fault occurrence (above threshold) and register a Change Identified Point (CIP). Further, if a CIP is registered for a positive or negative line, only three samples of currents (i.e., CIP and each side of CIP) are sent to the proposed TKEO algorithm, which produces their respective 8 indices through which the, fault can be detected along with its classification. The new approach enables quicker detection allowing utility grids to be restored as soon as possible. This novel approach also reduces computing complexity and the time required to identify faults with classification. The importance and accuracy of the proposed scheme are also thor oughly tested and compared with other methods for various faults on HVDC transmission lines.     

Global tracking of nonlinear systems with simultaneous unknown time‐varying state and input delays

This paper presents the design of a robust control law for a class of nonlinear dynamical systems subjected to parametric uncertainty and simultaneous unknown, variable state and input delays. A novel controller is developed, which consists of a filtered tracking error and the integral of previous values of control input where the limits of integration are dependent on the known bound of the input delay. Lyapunov‐Krasovskii functionals–based stability analysis guarantees a global uniformly ultimately bounded tracking result where sufficient conditions on controller gains and maximum allowable delay are derived. The performance and robustness of the controller are evaluated by simulation on a two‐link robot manipulator for different combinations of time‐varying state and input delays.     

Identification of critical erosion prone areas in the small agricultural watershed using USLE,GIS and remote sensing

In the present study, Karso watershed of Hazaribagh, Jharkhand State, India was divided into 200 × 200 grid cells and average annual sediment yields were estimated for each grid cell of the watershed to identify the critical erosion prone areas of watershed for prioritization purpose. Average annual sediment yield data on grid basis was estimated using Universal Soil Loss Equation (USLE). In general, a major limitation in the use of hydrological models has been their inability to handle the large amounts of input data that describe the heterogeneity of the natural system. Remote sensing (RS) technology provides the vital spatial and temporal information on some of these parameters. A recent and emerging technology represented by Geographic Information System (GIS) was used as the tool to generate, manipulate and spatially organize disparate data for sediment yield modeling. Thus, the Arc Info 7.2 GIS software and RS (ERDAS IMAGINE 8.4 image processing software) provided spatial input data to the erosion model, while the USLE was used to predict the spatial distribution of the sediment yield on grid basis. The deviation of estimated sediment yield from the observed values in the range of 1.37 to 13.85 percent indicates accurate estimation of sediment yield from the watershed.     

Real-time gaseous, PM and ultrafine particle emissions from a modern marine engine operating on biodiesel

Emissions from harbor-craft significantly affect air quality in populated regions near ports and inland waterways. This research measured regulated and unregulated emissions from an in-use EPA Tier 2 marine propulsion engine on a ferry operating in a bay following standard methods. A special effort was made to monitor continuously both the total Particulate Mass (PM) mass emissions and the real-time Particle Size Distribution (PSD). The engine was operated following the loads in ISO 8178-4 E3 cycle for comparison with the certification standards and across biodiesel blends. Real-time measurements were also made during a typical cruise in the bay. Results showed the in-use nitrogen oxide (NOx) and PM(2.5) emission factors were within the not to exceed standard for Tier 2 marine engines. Comparing across fuels we observed the following: a) no statistically significant change in NO(x) emissions with biodiesel blends (B20, B50); b) ～ 16% and ～ 25% reduction of PM(2.5) mass emissions with B20 and B50 respectively; c) a larger organic carbon (OC) to elemental carbon (EC) ratio and organic mass (OM) to OC ratio with B50 compared to B20 and B0; d) a significant number of ultrafine nuclei and a smaller mass mean diameter with increasing blend-levels of biodiesel. The real-time monitoring of gaseous and particulate emissions during a typical cruise in the San Francisco Bay (in-use cycle) revealed important effects of ocean/bay currents on emissions: NO(x) and CO(2) increased 3-fold; PM(2.5) mass increased 6-fold; and ultrafine particles disappeared due to the effect of bay currents. This finding has implications on the use of certification values instead of actual in-use emission values when developing inventories. Emission factors for some volatile organic compounds (VOCs), carbonyls, and poly aromatic hydrocarbons (PAHs) are reported as supplemental data.     

Analysis and Optimization of the Energy Efficiency in the 802.11 DCF

This paper introduces an analytical model to investigate the energy efficiency of the IEEE 802.11 distributed coordinated function (DCF). This model not only accounts for the number of contending nodes, the contention window, but also the packet size, and the channel condition. Based on this model, we identify the tradeoff in choosing optimum parameters to optimize the energy efficiency of DCF in the error-prone environment. The effects of contention window and packet size on the energy efficiency are examined and compared for both DCF basic scheme and DCF with four-way handshaking. The maximum energy efficiency can be obtained by combining both the optimal packet size and optimal contention window. To validate our analysis, we have done extensive simulations in ns-2, and simulation results seem to match well with the presented analytical results. The Ohio Board of Regents Doctoral Enhancements Funds and the National Science Foundation under Grant CCR 0113361 have supported this work. Xiaodong Wang received his B.S. degree in communication engineering from Beijing Information Technical Institute of China in 1995, and his M.S. degree in electric engineering from Beijing University of Aeronautics and Astronautics of China in 1998. He joined China Telecom in 1998 where he worked on communication protocols for telecommunication. From June 2000 to July 2002, he worked on GSM base station software development at Bell-labs China, Beijing, China. Currently he is a Ph.D. student in Computer Engineering at University of Cincinnati. His research activities include wireless MAC protocols, energy saving for wireless sensor networks. He is a student member of the IEEE. Jun Yin received the BS degree in automatic control from Dalian Railway Institute of China in 1997, and the MS degree in flight control from Beijing University of Aeronautics and Astronautics of China in 2001. Since 2001 she has been a Ph.D. student in the OBR Research Center for Distributed and Mobile Computing at the University of Cincinnati. Her research interests include performance evaluation of 802.11 MAC protocol, wireless ad hoc networks and sensor networks. She is a student member of the IEEE. Dharma P.Agrawal IEEE Fellow, 1987; ACM Fellow, 1998; AAAS Fellow, 2003 Dr. Agrawal is the Ohio Board of Regents Distinguished Professor of Computer Science and Computer Engineering in the department of Electrical and Computer Engineering and Computer Science, University of Cincinnati, OH. He has been a faculty member at Wayne State University, (1977–1982) and North Carolina State University (1982–1998). He has been a consultant to the General Dynamics Land Systems Division, Battelle, Inc., and the U. S. Army. He has held visiting appointment at AIRMICS, Atlanta, GA, and the AT&T Advanced Communications Laboratory, Whippany, NJ. He has published a number of papers in the areas of Parallel System Architecture, Multi computer Networks, Routing Techniques, Parallelism Detection and Scheduling Techniques, Reliability of Real-Time Distributed Systems, Modeling of C-MOS Circuits, and Computer Arithmetic. His recent research interest includes energy efficient routing, information retrieval, and secured communication in ad hoc and sensor networks, effective handoff handling and multicasting in integrated wireless networks, interference analysis in piconets and routing in scatternet, use of smart directional antennas (multibeam) for enhanced QoS, Scheduling of periodic real-time applications and automatic load balancing in heterogeneous workstation environment. He has four approved patents and three patent filings in the area of wireless cellular networks.     

Multiresolution approach for multiple human detection using moments and local binary patterns

Human detection is a central problem in development of any surveillance application. In this study, we present a simple and efficient, multi-resolution gray scale invariant approach for multiple human detection. The multiresolution is important for objects of different size and gray scale invariance is important due to uneven illumination and within-class variability. The proposed method is based on integration of central moments upon multi-resolution gray scale invariant local binary patterns operator. Since, the local binary patterns operator is invariant against different resolutions of space scale and monotonic change in gray scale, therefore the proposed method is robust in terms of variations in space scale as well as gray scale. Another advantage is high computational accuracy of the method due to use of moment operator which enhances the efficiency of the proposed method. Moreover, the proposed method is simple, as these operations can be performed within a few steps in a small neighborhood and a lookup table. The proposed method is tested on multiple human images and experimentally found appropriate for multiple human detection. The proposed method has been evaluated over two datasets, one is our own created dataset and the other is standard INRIA human detection dataset. Experimental results obtained from the proposed method demonstrate that better discrimination can be achieved for human and non-human objects in real scenes.     

Snowpack response in the Assiniboine-Red River basin associated with projected global warming of 1.0 °C to 3.0 °C

This study assesses snow response in the Assiniboine-Red River basin, located in the Lake Winnipeg watershed, due to anthropogenic climate change. We use a process-based distributed snow model driven by an ensemble of eight statistically downscaled global climate models (GCMs) to project future changes under policy-relevant global mean temperature (GMT) increases of 1.0 °C to 3.0 °C above the pre-industrial period. Results indicate that basin scale seasonal warmings generally exceed the GMT increases, with greater warming in winter months. The majority of GCMs project wetter winters and springs, and drier summers, while autumn could become either drier or wetter. An analysis of snow water equivalent (SWE) responses under GMT changes reveal higher correlations of snow cover duration (SCD), snowmelt rate, maximum SWE (SWE_max) and timing of SWE_max with winter and spring temperatures compared to precipitation, implying that these variables are predominantly temperature controlled. Consequently, under the GMT increases from 1.0 °C to 3.0 °C, the basin will experience successively shorter SCD, slower snowmelt, smaller monthly SWE and SWE_max, earlier SWE_max, and a transition from snow-dominated to rain-snow hybrid regime. Further, while the winter precipitation increases for some GCMs compensate the temperature-driven changes in SWE, the increases for most GCMs occur as rainfall, thus limiting the positive contribution to snow storage. Overall, this study provides a detailed diagnosis of the snow regime changes under the policy-relevant GMT changes, and a basis for further investigations on water quantity and quality changes.