Underwater Vehicle Localization with Complementary Filter: Performance Analysis in the Shallow Water Environment

Rapid development of underwater technology during the last two decades yielded more affordable sensors and underwater vehicles, and, as a result, expanded their use from exclusively offshore industry towards smaller interdisciplinary research groups. Regardless of application, knowing the location of the vehicle operating underwater is crucial. Relatively inexpensive solution is sensor fusion based on a dynamic model of the vehicle aided by a Doppler Velocity Log and a Ultra-Short Base Line position system. Raw data from the sensors are highly asynchronous and susceptible to outliers, especially in shallow water environment. This paper presents detailed sensor analysis based on experimental data gathered in shallow waters, identifies outliers, presents an intuitive and simple sensor fusion algorithm and finally, discusses outlier rejection. The approach has been experimentally verified on medium size remotely operated vehicle.     

干涉型集成光学加速度计信号处理及SOPC设计

介绍了集成光学芯片、光纤质量块简谐振子和可编程片上系统(SOPC)混合集成光学加速度计的设计方法。采用迈克尔逊干涉方法实现加速度信号光相位调制,综合交流相位跟踪零差补偿技术(PTAC)和合成外差信号解调技术(SHSD)补偿误差并解调出加速度信号,应用SOPC技术设计和实现数字信号处理(DSP)系统,同时实现DSP和智能化数据传输接口并行工作,使系统实时、线性地跟踪到加速度信号。系统设计灵敏度达4.8902V/g。实际测量灵敏度达4.62V/g,工作频带10~1066Hz。     

Estimation and setting starting values in ARMA algorithms

For the given observations set of the ARMA (autoregressive moving average) process, the likelihood function depends, not only on model parameters, but on the starting values of the input and output. Therefore, it is called theconditional likelihood function. Theunconditional likelihood function can be obtained in two ways. The first is to set the starting values to zero, as is often done, and the second is to set them to the properly estimated values. The difference between these two types of likelihood functions is significant when the given data sequence is short, and any of the zeros of the moving average part is close to the boundary of the unit circle.In this paper the direct method of starting value estimation and its application to two off-line ARMA estimation algorithms, the maximum likelihood (ML) algorithm and the iterative inverse filtering (ITIF) algorithm, is proposed. Experimental results prove both increased efficiency and stability of these algorithms.The importance of setting the starting values properly is also significant when the recursive algorithm, with previously estimated parameters, has to be restarted. The advantage of the proposed reinitialization method is shown on the recursive lattice algorithm working in the block mode.     

Frequency and Timing Offset Estimation Using Single Reference Symbol

In this paper we will investigate the performance of an estimator employing single OFDM reference symbol for timing and frequency offset estimation. We will show how both coarse and fine frequency offset estimation can be performed with this single OFDM reference symbol. The performance of this estimator will be compared with the ML estimator employing OFDM reference symbols of different length for coarse and fine frequency offset estimation. It will be shown that, unlike ML estimator, the estimation range of the analyzed estimator is not constrained.     

Spike detection using the continuous wavelet transform

This paper combines wavelet transforms with basic detection theory to develop a new unsupervised method for robustly detecting and localizing spikes in noisy neural recordings. The method does not require the construction of templates, or the supervised setting of thresholds. We present extensive Monte Carlo simulations, based on actual extracellular recordings, to show that this technique surpasses other commonly used methods in a wide variety of recording conditions. We further demonstrate that falsely detected spikes corresponding to our method resemble actual spikes more than the false positives of other techniques such as amplitude thresholding. Moreover, the simplicity of the method allows for nearly real-time execution.     

Optimal Scheduling of Utility Electric Vehicle Fleet Offering Ancillary Services

Vehicle‐to‐grid presents a mechanism to meet the key requirements of an electric power system, using electric vehicles (EVs) when they are parked. The most economic ancillary service is that of frequency regulation, which imposes some constraints regarding the period and duration of time the vehicles have to be connected to the grid. The majority of research explores the profitability of the aggregator, while the perspective of the EV fleet owner, in terms of their need for usage of their fleet, remains neglected. In this paper, the optimal allocation of available vehicles on a day‐ahead basis using queuing theory and fuzzy multi‐criteria methodology has been determined. The proposed methodology is illustrated on the daily scheduling of EVs in an electricity distribution company.     

On the Capacity of IEEE 802.11 DCF with Capture in Multipath-Faded Channels

In this paper, we estimated the influence of capture effect over the capacity of IEEE 802.11b DCF within a single picocell. The channel utilization is examined analytically by introducing two capture models based on Rayleigh-distributed envelopes of the captured and the interfering frames divided into two local-mean power classes. Simulations in a pure Rician-faded channel depict the conditions under which both Rayleigh-faded capture models can be used to accurately predict the peak network capacity. Unlike the RTS/CTS handshake access mode, Basic access mode is significantly sensitive to the capture ratio, i.e., the receiver design. The packet size threshold over which it is convenient to switch from Basic to RTS/CTS handshake access scheme is also sensitive to the capture effect.     

Implementation Issues in Turbo Decoding for 3GPP FDD Receiver

In the Frequency Division Duplex (FDD) mode of the Third Generation Partnership Project (3GPP) standard, implementation of the turbo decoder, especially for the mobile equipments, faces design decisions related to computational complexity, power efficiency, and memory requirements. In this paper we compare different approaches of low complexity implementation of the turbo decoder, with emphasis on the issues of signal scaling and quantization, the sliding window operation for memory size reduction and the iteration stopping algorithms. The demodulated signal at the output of the RAKE receiver may have a wide dynamic range and it may require many bits of precision. In order to overcome the numerical precision problem and to prevent Log Likelihood ratio (LLR) metric overflow, a scaling algorithm must be used. Our simulation results indicate that the Average Absolute (AA) algorithm using dynamic scaling outperforms other scaling schemes and it is less sensitive to the channel conditions. One of the major challenges in the implementation of a practical turbo decoder is optimization of memory requirements. In this paper we evaluate the performance of the sliding window algorithm using different main and guard window sizes. We show that the bit and block error rate performance of the sliding window scheme mainly depend on the guard window size rather than the main window size. The simulation results indicate that small guard window sizes can significantly decrease the iteration gain for large frames in fast fading channels. Iteration stopping algorithms reduce the power consumption and the latency of the decoder and help to dedicate more resources to other functions of the receiver. The block error distribution in the fading channels makes it even more essential to use an iteration stopping rule. Our simulations conclude that a rule called the minimum absolute value appears to be a very effective, low complexity and robust algorithm. Mohamadreza Marandian Hagh was born in Tabriz, Iran on January 1974. He received the B.S. and the M.S. degrees in electrical engineering from Tehran University with honors in 1996 and 1999, respectively. He is pursuing the Ph.D. degree in electrical engineering at Northeastern University, Boston. His research interests includes information theory, channel coding and iterative techniques for wireless communication systems. His current research is focused on low complexity designs for iterative receivers using Space-Time coding in time-dispersive channels. He is also interested in Exit-Chart analysis of iterative receivers. From 1996 to 1999, he was with Sana Pro Inc. as a system engineer, developing simulation tools for OFDM, WCDMA, CDMA2000. He is currently with Airvana Inc. in Chelmsford, MA and working on 1xEVDO wireless systems. Masoud Salehi received BS degree (Summa Cum Laude) from Tehran University and MS and Ph.D. degrees from Stanford University all in Electrical Engineering. Before joining Northeastern, he was with the Departments of Electrical and Computer Engineering, Isfahan University of Technology and Tehran University. From February 1988 to May 1989 Dr. Salehi was a visiting professor at the Information Theory Research Group, Department of Electrical Engineering, Eindhoven University of Technology, The Netherlands, where he did research in network information theory and coding for storage media.In 1989 Dr. Salehi joined Department of Electrical and Computer Engineering, Northeastern University. Professor Salehi is a member of the CDSP (Communication and Digital Signal Processing) Center. His main areas of research interest are network information theory, source-channel matching problems in single and multiple user systems, data compression, turbo coding, coding for fading channels, and digital watermarking. Professor Salehi’s research has been supported by research grants from the National Science Foundation (NSF), GTE, NUWC, CenSSIS, and Analog Devices. Professor Salehi has also done consulting to the industry including Teleco Oilfield Services and AT&T. Professor Salehi is currently a member of the Editorial Board of The International Journal of Electronics and Communications.Professor Salehi is the coauthor of the textbooks “Communication Systems Engineering”, Prentice-Hall 1994, 2002, “Contemporary Communication Systems Using MATLAB and Simulink” Thomson 1998, 2000, 2004, and “Fundamentals of Communication Systems”, Prentice-Hall 2005. Abhay Sharma received B.E. (Hons) Electrical and Electronics Engineering degree from Birla Institute of Technology and Science, Pilani, India in 1996 and M.S. Electrical Engineering degree from Ohio State University, Columbus in 2000. From 2000 to 2005 he was working with Analog Devices, RF and Wireless Systems Group, Wilmington, USA, where he was working on design and implementation of algorithms for the emerging cellular communication standards. Currently he is working with Allgo Embedded Systems, Bangalore, India, in the area of wireless networks and systems based on the emerging W-PAN wireless technologies. Zoran Zvonar received the Dipl. Ing. degree in 1986 and the M.S. degree in 1989, both from the Department of Electrical Engineering, University of Belgrade, Yugoslavia, and the Ph.D. degree in Electrical Engineering from the Northeastern University, Boston, in 1993.From 1986 to 1989 he was with the Department of Electrical Engineering, University of Belgrade, Belgrade, Yugoslavia, where he conducted research in the area of telecommunications. 1993 to 1994 he was a Post-Doctoral investigator at the Woods Hole Oceanographic Institution, Woods Hole, MA, anconducted research on multiple-access communications for underwater acoustic networks. Since 1994 he has been with the Analog Devices, Communications Division, Wilmington, USA. He is the Manager of the Systems Engineering Group focusing on the design of algorithms and architectures for wireless communications, with emphasis on integrated solutions and real-time software.He was a Guest Editor of the IEEE Transactions on Vehicular Technology, the International Journal of Wireless Information Networks and the ACM/Baltzer Wireless Networks, Associate Editor of the IEEE Communications Letters and a co-editor of the books GSM: Evolution Towards Third Generation Systems, Kluwer Academic Publishers, 1998, Wireless Multimedia Networks Technologies, Kluwer Academic Publishers, 1999 and Software Radio Technologies: Selected Reading, IEEE Press, 2001. Dr. Zvonar is currently Co-Editor of the Radio Communication Series in the IEEE Communications Magazine.     

The WINLAB Network Centric Cognitive Radio Hardware Platform—WiNC2R

This paper presents the design goals and architecture of WiNC2R—the WINLAB Network Centric Cognitive radio hardware platform. The platform has been designed for flexible processing at both the radio physical layer and MAC/network layers with sustained bit-rates of ~10 Mbps and higher. The hardware prototype supports multi band operation with fast spectrum scanning, the ability to dynamically switch between a number of OFDM and DSSS modems and multiple MAC protocols. The radio modems, MAC, and network-layer protocols are implemented in a flexible manner using general-purpose processing engines and a set of dynamically configurable hardware accelerators. An FPGA based platform implementation currently in progress is described in terms of key hardware components including the software-defined modem, the flexible MAC engine and network-level processor. Preliminary prototyping results are reported, and a roadmap for further evolution of the WiNC2R board is provided.