In recent years, indoor positioning systems (IPSs) have received attention from many research fields, such as robotics, navigation, human–computer interaction, etc. However, IPS based on passive radio frequency (PRF) technology is still rare. This article proposes a 3-D IPS based on received signal strength (RSS) distribution and Gaussian process regression (GPR). Traditional RSS-based positioning systems have a transmitter with known frequencies, while in the proposed PRf signal of Opportunity—3D IPS (PRO-3DIPS), the system neither deploys new transmitters nor uses any a priori knowledge of transmitters. Furthermore, PRO-3DIPS integrates multiple Signal of Opportunity (SoOP) sources, shadowing, fading, and also captures scenario signatures. Data collection and analysis of PRF-based RSS distribution in 3-D space enables the capability of 3-D positioning. Three methods are applied and compared to find the frequency band most impacted by the scenario to achieve the best positioning performance as well as used in the estimation of RSS distribution. The RSS distribution is estimated by measuring the PRF spectrum on a fixed grid in the scenario. Using the RSS distribution, the GPR can accurately locate the receiver position. RSS at 90-gridded positions were collected in the experiment scenario, with one hundred samples at each position. The experimental result shows that a root-mean-square error (RMSE) of the proposed PRO-3DIPS is 0.292 m when the sampling distance is 1 m. The result demonstrates that the PRF spectrum is a new modality for the positioning task, which demonstrates better performance than most existing RF-based technologies.

Transform sensing is proposed to sense the space through certain beam patterns designed for a transformation basis. The received signals will be the result of transformation instead of the original raw data. In doing so, the process of generating an arbitrary beam pattern on the phased array and multiple-input multiple-output is described. We further propose a transform-sensing mechanism using wavelets, and compare it with traditional sensing methods. Simulations and experiments demonstrate how to generate the transmission patterns and sense through the transform-sensing mechanism. The results show that transform sensing obtains high-resolution data on the target area, while spending less time on nontarget ones that need low resolutions. In this way, the new sensing mechanism generates a multiresolution result, which balances resolution and sensing efficiency.

In this paper, a new radar waveform based on wavelet packets is proposed. This waveform shows significant advantages over conventional linear frequency modulated waveforms for range and velocity detections of moving targets. The proposed waveform can decouple its envelope and carrier for range and velocity estimations, respectively, because of which the Doppler spread term can be totally removed when estimating velocity and the Doppler effect ignored when estimating range. This way, the proposed waveform produces higher resolution in range or velocity detection. The proposed waveform can be applied to the synthetic aperture radar for improved performance.

We present a novel visual tracking method for measuring the speed of a moving vehicle within a structured environment using stationary stereo cameras. In the proposed method, visual stereo tracking and motion estimation in 3-D are integrated within the framework of particle filtering. The visual tracking processes in the two views are coupled with each other since they are dependent upon the same 3-D motion and correlated in the observations. Considering that the vehicle's motion is physically constrained by the environment, we further utilize the path constraint reconstructed from stereo views to reduce the uncertainty about the vehicle's motion and improve the accuracy for both tracking and speed measuring. The proposed method overcomes the challenges arising from the limitation of depth accuracy in a long-range stereo, and the experiments on the synthetic and real-world sequences have demonstrated its effectiveness and accuracy in both the tracking performance and the speed measurement.

Stereo vision provides us the ability to perceive objects in 3D. By continuously tracking an object in stereo videos, the information about object motion can be acquired. In this paper, we propose a novel method which unifies the tracking and speed measuring within the Bayesian filtering framework and solves this problem using particle filtering approach. Vehicle speed measuring is studied as a specific example. Environment constraints are integrated into the tracking for reducing the uncertainty to achieve more robust and accurate results. Experiment results on both synthetic and real traffic sequences demonstrate the accuracy and effectiveness of our proposed method.

Image registration is an essential step in many image processing applications that need visual information from multiple images for comparison, integration or analysis. Recently researchers have introduced image registration techniques using the log-polar transform (LPT) for its rotation and scale invariant properties. However, the accuracy of the approach limits by the number of samples used in the mapping process, which affects directly the computational cost. Motivated by the success of LPT based approach and its limitation, we propose a novel projective polar transform (PPT) based image registration method that is robust to translation, scale, and rotation and yields high accuracy while requires low computational cost. Unlike LPT that 2D interpolation is needed in the mapping process, our method uses one-to-one mapping mechanism that directly arranges image from Cartesian to polar coordinate according to pre-computed PPT map. An innovative projection mechanism is proposed to reduce the image from two to one dimensional vectors. With the proposed approach, the mapping procedure is accelerated and the matching process can be performed in 1D. This results in great reduction of the computational cost.

Image registration is an essential step in many image processing applications that need visual information from multiple images for comparison, integration, or analysis. Recently, researchers have introduced image registration techniques using the log-polar transform (LPT) for its rotation and scale invariant properties. However, it suffers from nonuniform sampling which makes it not suitable for applications in which the registered images are altered or occluded. Inspired by LPT, this paper presents a new registration algorithm that addresses the problems of the conventional LPT while maintaining the robustness to scale and rotation. We introduce a novel adaptive polar transform (APT) technique that evenly and effectively samples the image in the Cartesian coordinates. Combining APT with an innovative projection transform along with a matching mechanism, the proposed method yields less computational load and more accurate registration than that of the conventional LPT. Translation between the registered images is recovered with the new search scheme using Gabor feature extraction to accelerate the localization procedure. Moreover an image comparison scheme is proposed for locating the area where the image pairs differ. Experiments on real images demonstrate the effectiveness and robustness of the proposed approach for registering images that are subjected to occlusion and alteration in addition to scale, rotation, and translation.

Image registration is an essential task in many image processing applications. Log-polar transform (LPT) is a well known tool for image processing for its rotation and scale invariant properties. However it suffers from nonuniform sampling which makes it not suitable for the environment when registered images are altered or occluded. Inspired by LPT, this paper presents a new algorithm that addresses the problems of the conventional LPT while maintaining the invariant properties. We introduce adaptive polar transform (APT) technique and an innovative matching mechanism that serve as image processing tool for recovering scale and rotation change of the registered image. Unlike the conventional LPT that focuses only at the fovea area of the registered image, our proposed APT gives even consideration to the entire image. Translation of the registered image is recovered with the new search scheme by using Gabor feature extraction to accelerate the localization procedure. The experimental results indicate that our proposed method outperforms conventional LPT in registering images that are altered or occluded.

The newly developing facets of technology and sensory control algorithm fuselets that will enable the envisioned information flow between the unmanned aerial system agents, will rely on cognitive advances in embedded smart perception sensory systems. Smart perception system-on-a-chip (SP-SoC) systems that sense, receive, transmit, and process signals are the analogous eyes and ears for waveform diversity. The nature of context processing implies information processing that is cognitive in classifying waveform diversity from sensory wavelet data.

This paper proposes a rotation and scale invariant object recognition method which combines image feature extraction in Cartesian coordinate with the log-polar mapping and similarity measure techniques for classification. The method yields robustness for fast computation without any rescale needed in the target image. The method also does not use motion estimation. Thus, the method is also robust in recognizing objects without prior information of the objectpsilas motion or previous location. Experiments with real video sequences are provided to verify the effectiveness of the proposed approach in practice.

We address the problem of scheduling parallel applications onto Heterogeneous Chip Multi-Processors (H- CMPs) containing reconfigurable processing cores. To model reconfiguration, we introduce the novel Mutually Exclusive Processor Groups reconfiguration model, which captures many different modes of reconfiguration. The paper continues by proposing the Heterogeneous Earliest Finish Time with Mutually Exclusive Processor Groups (HEFT- MEG) scheduling heuristic that uses our new reconfiguration model; at compile-time, HEFT-MEG schedules reconfigurations to occur at runtime, with the goal of choosing the most efficient configuration for different application phases. Scheduling reconfiguration to occur at runtime with HEFT-MEG improves the performance of GPS Acquisition, a software radio application, by about 23%, compared to the best single-configuration schedule on the same hardware.

A simulation model for polymer film chemical sensors is developed based on a 1 dimensional diffusion equation. Using this model, electronic nose smell prints produced by the 32 sensor array of a Cyranose 320 are simulated to test pattern classification. A Haar wavelet Alter reduces noise and captures information about the diffusion rate of the analyte in each sensor. Inputs are encoded into a binary Hamming pattern and fed into a binary spiking neural network for pattern classification. The preprocessing circuit for the spiking neural network, including the wavelet Alter, is designed using standard cells for an 180 nm process. Real and simulated results from the spiking neural network classification algorithm are favorably compared to Bayes, canonical, and PCA-PNN classifiers.

A combined spatial- and temporal-domain wavelet shrinkage algorithm for video denoising is presented in this paper. The spatial-domain denoising technique is a selective wavelet shrinkage method which uses a two-threshold criteria to exploit the geometry of the wavelet subbands of each video frame, and each frame of the image sequence is spatially denoised independently of one another. The temporal-domain denoising technique is a selective wavelet shrinkage method which estimates the level of noise corruption as well as the amount of motion in the image sequence. The amount of noise is estimated to determine how much filtering is needed in the temporal-domain, and the amount of motion is taken into consideration to determine the degree of similarity between consecutive frames. The similarity affects how much noise removal is possible using temporal-domain processing. Using motion and noise level estimates, a video denoising technique is established which is robust to various levels of noise corruption and various levels of motion.

A selective wavelet shrinkage algorithm for digital image denoising is presented. The performance of this method is an improvement upon other methods proposed in the literature and is algorithmically simple for large computational savings. The improved performance and computational speed of the proposed wavelet shrinkage algorithm is presented and experimentally compared with established methods. The denoising method incorporated in the proposed algorithm involves a two-threshold validation process for real-time selection of wavelet coefficients. The two-threshold criteria selects wavelet coefficients based on their absolute value, spatial regularity, and regularity across multiresolution scales. The proposed algorithm takes image features into consideration in the selection process. Statistically, most images have regular features resulting in connected subband coefficients. Therefore, the resulting subbands of wavelet transformed images in large part do not contain isolated coefficients. In the proposed algorithm, coefficients are selected due to their magnitude, and only a subset of those selected coefficients which exhibit a spatially regular behavior remain for image reconstruction. Therefore, two thresholds are used in the coefficient selection process. The first threshold is used to distinguish coefficients of large magnitude and the second is used to distinguish coefficients of spatial regularity. The performance of the proposed wavelet denoising technique is an improvement upon several other established wavelet denoising techniques, as well as being computationally efficient to facilitate real-time image-processing applications.

The timely detection and classification of biological agents in a hostile environment is critical for increasing protection in critical areas. This paper proposes a strategy for identifying biological agents via distributed sensors with an artificial immune system (AIS). The proposed system is composed of networked sensors and nodes, communicating via wireless or wired connections. Measurements are continually taken via dispersed redundant sensors strategically placed in threat areas. These sensors continually measure and categorize air or liquid samples, alerting surrounding nodes when possible biological agents are detected. Detection is based upon the biological immune system (BIS) model of antigens and antibodies, and alerts are generated when a measured sample is determined to be a valid biological agent (antigen). Biological agent signatures (antibodies) are continually distributed throughout the system to adapt to changes in the environment or to new antigens.

An olfaction detection spiking neural network that detects binary odor patterns is analyzed and implemented. This paper presents a new method for inhibiting spiking neural networks by modulating a detection threshold. Interference noise from active odors is measured by a single inhibitory neuron. The inhibition neuron changes the detection threshold to create tolerance for a system with multiple odors present. A digital implementation of the inhibition is simulated. Comparative results prove that threshold modulation reduces false-positive detection error in high noise scenarios where fifteen odors are active simultaneously.

The methodology for the integration of design domains towards the purpose of controlling dynamic mechatronics systems is the current challenge of the modern engineer. Scaling issues for both the mechanical and electrical parameters are critical to the successful design and implementation of a mechatronic system. In approaching the scaling design methodology for future submicron fabrication, new disciplines of symbolic matrix techniques and dimensional analysis must be developed and applied in the design of these mechatronics systems. This paper presents both an overview of the techniques and insight using computer aided design packages for the blending of symbolic matrix techniques using the admittance matrix created by SPICE and dimensional analysis using Buckingham's /spl Pi/ parameters.