Ivica Kostanic

Determination of glucose concentrations in an aqueous matrix from NIR spectra using optimal time-domain filtering and partial least-squares regression

The authors have investigated the use of a time-domain optimal filtering method to simultaneously minimize both the baseline variation and high-frequency noise in near-infrared (NIR) spectrophotometric absorption data of glucose dissolved in a simple aqueous (deionized water) matrix. By coupling a third-order (6-pole) digital Butterworth bandpass filter with partial least-squares (PLS) regression modeling, glucose concentrations were determined for a set of test data with a standard error of prediction (SEP) of 10.53 mg/dl (mean percent error: 4.24%) using 7 PLS factors. Compared to the unfiltered test data for 6 PLS factors and a SEP=17.00 (mean percent error: 7.38%) this results shows more than a 38% decrease in the error. The glucose concentrations ranged from 51 mg/dl to 493 mg/dl, and the NIR spectral region between 2088 nm and 2354 nm (4789 cm/sup -1/ and 4248 cm/sup -1/) was used to develop the optimal PLS model. The optimal PLS model was determined from a sequence of 3-dimensional performance response maps for different numbers of PLS factors (2-10). A total of 99 NIR spectra were generated for glucose dissolved in deionized water using a NIRsystems 5000 dispersive spectrophotometer. Nine of these spectra were generated for only water, which were averaged and subtracted from the remaining 90 spectra to generate the training and test data sets, thereby, removing the intrinsic high background absorption due to the water. The training set consisted of 57 spectra and associated glucose concentration target values, and the test set was comprised of the remaining 33 spectra and target values. Performance results were compared for 3 different digital Butterworth bandpass filters (4-poles, 6-poles, and 8-poles), and a digital Gaussian filter design approach (i.e., Fourier filtering).

Single antenna interference cancellation (SAIC) for GSM networks

This paper presents field trial results for downlink interference cancellation in a live GSM network. Uplink interference cancellation techniques have been developed in the past, which exploit the use of multiple receive antennas. However, downlink techniques are typically limited to just the use of a single receive antenna due to space limitations, cost considerations and aesthetics associated with current mobile station designs. A prototype mobile station, using a low complexity single antenna interference cancellation (SAIC) algorithm, was constructed, and used to assess the network gain in both asynchronous and synchronous GSM networks. The performance results measured show that SAIC techniques can provide significant gains in C/I. In addition, network simulations indicate that this same algorithm can support voice capacity gains of 39-57%. Thus, SAIC is seen to be a very viable technology for GSM capacity improvement, which can be realized in the next generation of mobile stations.

Multivariate determination of glucose concentrations from optimally filtered frequency-warped NIR spectra of human blood serum.

Glucose concentrations over the 39-160 mg dl-1 range have been determined from 357 NIR (near-infrared) spectra of human blood serum in the spectral region from 6766 cm-1 to 4003 cm-1. A frequency-warping procedure was applied to the NIR data to compress 511 spectral components into 102 in the 6766-4003 cm-1 spectral region. Before the data compression process was carried out, the NIR spectrum of deionized water was subtracted from each of the blood serum spectra to remove the intrinsic high background absorption due to the water. PLS (partial least-squares) regression was coupled with time-domain digital Butterworth bandpass filtering in an optimization procedure. The optimization procedure was carried out over a range of centre frequencies and bandwidths for first- (two-pole), second- (four-pole) and third- (six-pole) order bandpass filters, and over a range of PLS factors. The optimal PLS model and filter parameters were determined from a sequence of three-dimensional performance response maps for different numbers of PLS factors and filter orders. As a basis for comparison, the same optimization process was carried out for a Gaussian filter design approach (i.e., Fourier filtering). Using the optimally filtered frequency-warped NIR spectral data, an SEP (standard error of prediction) of 13.2 mg dl-1 was achieved fro the test (monitoring) data using 14 PLS factors and a simple first-order (two-pole) digital Butterworth bandpass filter.

Multiresponse Optimization of Stochastic WSN Deployment Using Response Surface Methodology and Desirability Functions

Due to reliance on stochastic deployment, delivery of large-scale WSN presents a major problem in the application of wireless sensor networks (WSN) technology. When deployed in a stochastic manner, the WSN has the utmost challenge of guaranteeing acceptable operational efficiency upon deployment. The research presented in this paper evaluates application of the response surface methodology (RSM) and desirability functions for analysis and optimization of stochastic WSN deployments based on multiple efficiency metrics. Through case studies, the approach is proven successful in modeling individual efficiency metrics, and in providing a way for analyzing deployments, based on numerous efficiency metrics. Additionally, the approach may be used to quantify the effects of optimizing partial efficiency metrics on the overall deployment efficiency.

Measurements of the vehicle penetration loss characteristics at 800 MHz

This paper presents a series of vehicle penetration loss measurements performed in 800 MHz-frequency band. Measurements were conducted for three different vehicle types (mini van, full size car and sports car), two different propagation environments (urban and suburban), and two characteristic positions of the mobile receiver antenna. The statistical properties of the vehicle penetration loss are examined in order to determine benchmark parameters to be used in the design of wireless communication systems.

Measurement based QoS comparison of cellular communication networks

A Quality of Service (QoS) assessment methodology for cellular communication networks is described. The methodology is based on the data collected through drive testing; it is focused on the end user perception of service quality and it is independent of access technologies implemented by the cellular networks. QoS assessment for both the circuit switched and packet switched side of the network is discussed. The end goal of the proposed methodology is QoS comparison between cellular networks implementing different cellular technologies. Illustrative examples based on live network measurements are used in support of the presented methodology.

A Wireless Sensor Networks' Analytics System for Predicting Performance in On-Demand Deployments

The need for advanced tools that provide efficient design of on-demand deployment of wireless sensor networks (WSN) is critical for meeting our nations demand for increased intelligence, reconnaissance, and surveillance. For practical applications, WSN deployments can be time consuming and error prone since they have the utmost challenge of guaranteeing connectivity and proper area coverage upon deployment. This creates an unmet demand for decision-support systems that help manage this complex process. This paper presents research to develop a system for predicting optimal deployments of WSN. Specifically, it presents results of image processing algorithms for terrain classification, results of modeling WSN signal propagation under different terrain conditions, results of optimization and visualization techniques for high-dimensional deployments, and system architecture for efficient integration and future deployment. Results show a feasible approach that can be used to automatically determine areas of high signal obstruction-which is essential to estimate obstruction parameters in simulations-and mapping of accurate WSN path-loss models to enhance the overall decision-making process during predeployment of large-scale WSN.

A chain-type wireless sensor network for monitoring long range infrastructures

We present in this paper an investigation on a special class of wireless sensor networks for monitoring critical infrastructures that may extend for hundreds of miles in distances. Such networks are fundamentally different from traditional sensor networks in that the sensor nodes in this class of networks are deployed along narrowly elongated geographical areas and form a chain-type topology. Based on careful analysis of existing sensor network architectures, we first demonstrate the needs to develop new architecture and networking protocols to match the unique topology of chain-type sensor networks. We then propose hierarchical network architecture that consists of clusters of sensor nodes to enable the chain-type sensor networks to be scalable to cover typically long range of infrastructure with tolerable delay in network-wide data collection. To maintain energy efficient operations and maximize the lifetime for such a chain-type sensor network, we devise a smart strategy for the deployment of cluster heads. Protocols for network initialization and seamless operations of the chain-type sensor networks are also developed to match the proposed hierarchical architecture and cluster head deployment strategy. Simulations have been carried out to verify the performance of the hierarchical architecture, the smart node deployment strategy, and the corresponding network initialization and operation protocols.

A multi-hop, multi-segment architecture for perimeter security over extended geographical regions using wireless sensor networks

This paper presents a novel wireless sensor network architecture designed for perimeter security monitoring over extended geographical regions. The architecture relies on customized protocols and deployment techniques to disseminate perimeter event detection and tracking data to the sink, which maybe located miles away from the perimeter of interest. Through consideration of application specific characteristics, the proposed architecture reduces the number of deployed nodes, which results in reduced network complexity and cost without sacrificing the mission success.

A support vector machine for terrain classification in on-demand deployments of wireless sensor networks

Terrain characteristics can significantly alter the quality of the results provided by the deployment methodology of large-scale wireless sensor networks. For example, transmissions between nodes that are heavily obstructed will require additional transmission power to establish connection between nodes. In some cases, heavily obstructed areas may prevent nodes from establishing a connection at all. Therefore, terrain analysis and classification of specific deployment areas should be incorporated in the methodology process for evaluation and optimization of the performance of wireless sensor networks upon deployment. Although there exists radio frequency (RF) models capable of modeling obstructions, such as vegetation, foliage, etc., automatic assignment of parameter values for these models may be troublesome, specifically in highly irregular deployments terrains, where proximity of poor and optimal conditions for signal propagation may be adjacent to each other. In these situations, parameter estimation for modeling terrain obstruction may result in overly optimistic or pessimistic results, causing characterizations or predictions that deviate from the true performance of the WSN once deployed. This paper presents the results of employing a support vector machine for automatic terrain classification. The approach can be used to automatically determine areas of high obstruction, which is essential to estimate obstruction parameters in simulations and enhancing the overall decision-making process during pre-deployment of large-scale and irregular deployment terrains.