Buyurman Baykal

Wireless passive sensor networks

The primary challenge in wireless sensor network deployment is the limited network lifetime due to finite-capacity batteries. Hence, the vast majority of research efforts thus far have focused on the development of energy-efficient communication and computing mechanisms for WSNs. In this article a fundamentally different approach and hence completely new WSN paradigm, the wireless passive sensor network, is introduced. The objective of the WPSN is to eliminate the limitation on system lifetime of the WSN. In a WPSN power is externally supplied to the sensor network node via an external RF source. Modulated backscattering is discussed as an alternative communication scheme for WPSNs. The feasibility is investigated along with the open research challenges for reliable communication and networking in WPSNs.

Real-Time coordination and routing in wireless sensor and actor networks

In Wireless Sensor Actor Networks (WSAN), sensor nodes perform the sensing task and actor nodes take action based on the sensed phenomena in the field. To ensure efficient and accurate operations of WSAN, new communication protocols are imperative to provide sensor-actor coordination in order to achieve energy-efficient and reliable communication. Moreover, the protocols must honor the application-specific real-time delay bounds for the effectiveness of the actors in WSAN. In this paper, we propose a new real-time coordination and routing (RCR) framework for WSAN. It addresses the issues of coordination among sensors and actors and honors the delay bound for routing in distributed manner. RCR configures sensors to form hierarchical clusters and provides delay-constrained energy aware routing (DEAR) mechanism. It uses only cluster-heads to coordinate with sink/actors in order to save the precious energy resources. The DEAR algorithm integrates the forwardtracking and backtracking routing approaches to establish paths from source nodes to sink/actors. In the presence of the sink in WSAN, it implements the centralized version of DEAR (C-DEAR) to coordinate with the actors through the sink. In the absence of sink or ignoring its presence, there is a distributed DEAR (D-DEAR) to provide coordination among sensors and actors. Cluster-heads then select the path among multiple alternative paths to deliver the packets to the actors within the given delay bound in an efficient way. Simulation experiments prove that RCR achieves the goal to honor the realistic application-specific delay bound.

Complexity reduction in radial basis function (RBF) networks by using radial B-spline functions

Abstract In this paper, new basis consisting of radial cubic and quadratic B-spline functions are introduced together with the CORDIC algorithm, within the context of RBF networks as a means of reducing computational complexity in real-time signal-processing applications. The new basis are compared with two other existing and popularly used basis families, namely the Gaussian functions and the inverse multiquadratic functions (IVMQ) in terms of approximation performance and computational requirements. The new basis are shown to achieve approximation performance very similar to the Gaussian basis functions and are better than the IVMQ functions with less computational load and without any need for approximation methods such as table-lookup.

Underdetermined-order recursive least-squares adaptive filtering: the concept and algorithms

Underdetermined recursive least-squares (URLS) adaptive filtering is introduced. In particular, the URLS algorithm is derived and shown to be a direct consequence of the principle of minimal disturbance. By exploiting the Hankel structure of the filter input matrix, the fast transversal filter (FTF) version of the URLS algorithm (URLS-FTF) is derived including sliding window and growing window types. The computational complexity is reduced to O(N)+O(m), where N is the adaptive filter length, and m is the order of the URLS algorithm. In addition, the efficient URLS (EURLS) algorithm, which does not compute the filter coefficients explicitly, thereby significantly reducing the computational load, is presented. Some earlier adaptive algorithms such as the averaged LMS, filtered-X LMS, and fast conjugate gradient are shown to be suboptimal approximations of the URLS algorithm. Instrumental variable approximations are also discussed. The URLS algorithm has a whitening effect on the input, signal, which provides immunity to the eigenvalue spread of the input signal correlation matrix. Although the algorithm is sensitive to observation noise, it has good tracking characteristics, and tradeoffs can be found by tuning the step size. The utility of the URLS algorithms, in its basic form and FTF realization, depends heavily on the practical applicability of the mth-order sliding window estimate of the covariance matrix and mth-order PTF relations. The feasibility of the URLS family in practical applications is demonstrated in channel equalization and acoustic echo cancellation.

Residual echo signal in critically sampled subband acoustic echo cancellers based on IIR and FIR filter banks

The residual echo signal characteristics of critically sampled subband acoustic echo cancellers are analyzed. For finite impulse response (FIR) filter banks, the residual echo signal usually has a relatively broad spectral nature around the subband edges. The residual echo signal of power symmetric infinite impulse response (PS-IIR) filter banks, on the other hand, has very narrowband spectral components around the subband edges. These components can be efficiently removed with PS-IIR notch filters that integrate neatly into the filter banks without introducing perceptually noticeable degradation to the near-end speech. This solution has very low computational complexity and does not impinge on the system performance. Simulation studies with recordings from the cockpit of a car, based on a fast QR least-squares adaptive algorithm, demonstrate the potential of this approach for a practical AEC system.

Using continuous integration and automated test techniques for a robust C4ISR system

We have used CI (Continuous Integration) and various software testing techniques to achieve a robust C4ISR (Command, Control, Communications, Computers, Intelligence, Surveillance, and Reconnaissance) multi-platform system. Because of rapid changes in the C4ISR domain and in the software technology, frequent critical design adjustments and in turn vast code modifications or additions become inevitable. Defect fixes might also incur code changes. These unavoidable code modifications may put a big risk in the reliability of a mission critical system. Also, in order to stay competitive in the C4ISR market, a company must make recurring releases without sacrificing quality. We have designed and implemented an XML driven automated test framework that enabled us developing numerous high quality tests rapidly. While using CI with automated software test techniques, we have aimed at speeding up the delivery of high quality and robust software by decreasing integration procedure, which is one of the main bottleneck points in the industry. This work describes how we have used CI and software test techniques in a large-scaled, multi-platform, multi-language, distributed C4ISR project and what the benefits of such a system are.

Matrix quantization and mixed excitation based linear predictive speech coding at very low bit rates

Abstract A matrix quantization scheme and a very low bit rate vocoder is developed to obtain good quality speech for low capacity communication links. The new matrix quantization method operates at bit rates between 400 and 800 bps and using a 25 ms linear predictive coding (LPC) analysis frame, spectral distortion about 1 dB is achieved at 800 bps. Techniques for improving the performance at very low bit rate vocoding include quantization of residual line spectral frequency (LSF) vectors, multistage matrix quantization, joint quantization of pitch and voiced/unvoiced/mixed decisions and a technique to obtain voiced/unvoiced/mixed decisions. In the new matrix quantization based mixed excitation (MQME) vocoder, the residual LSF vectors for two consecutive frames are obtained using autoregressive moving average (ARMA) prediction, then grouped into a superframe and jointly quantized. For other speech parameters, quantization is made in each frame. The residual LSF vector quantization yields bit rate reduction in the vocoder. For the MQME vocoder, listening tests have proven that an efficient and high quality coding has been achieved at a bit rate of 1200 bps. Test results are compared with the mixed excitation based 2400 bps MELP vocoder which is chosen as the new federal standard, and it is observed that the degradation in speech quality is tolerable and the performance is near the 2400 bps MELP vocoder particularly in quiet environments.

A new family of blind adaptive equalization algorithms

New blind adaptive channel equalization techniques based on a deterministic optimization criterion are presented. A family of nonlinear functions is proposed which constitutes a generic class of blind algorithms. They have been shown to have better performance than the conventional constant modulus algorithm (CMA)-like approaches. The advantages include the relaxed stability range on the step size and that an automatic gain control unit which estimates the gain of the channel, is no longer of crucial importance.

Tracker-aware adaptive detection: An efficient closed-form solution for the Neyman--Pearson case

A promising line of research for radar systems attempts to optimize the detector thresholds so as to maximize the overall performance of a radar detector-tracker pair. In the present work, we attempt to move in a direction to fulfill this promise by considering a particular dynamic optimization scheme which relies on a non-simulation performance prediction (NSPP) methodology for the probabilistic data association filter (PDAF), namely, the modified Riccati equation (MRE). By using a suitable functional approximation, we propose a closed-form solution for the special case of a Neyman-Pearson (NP) detector. The proposed solution replaces previously proposed iterative solution formulations and results in dramatic improvement in computational complexity without sacrificed system performance. Moreover, it provides a theoretical lower bound on the detection signal-to-noise ratio (SNR) concerning when the whole tracking system should be switched to the track before detect (TBD) mode.

Target localization methods for frequency-only MIMO radar

In Multi Input-Multi Output (MIMO) radar, the target localization is possible by using only the frequencies of received signals instead of using whole data on the received signals for moving targets. As target is moving, received frequency will be shifted as doppler frequency. Received frequencies which are scattered from non-manouvering and constant speed target, can be written in two dimensional (2D) space with respect to the target coordinates. A cost function can be defined by the help of these frequencies, and than by using grid search, local minimum of this cost function can be found in (x, y) coordinates. In this work, a new target localization method is poroposed for frequency-only MIMO radar.