• This study introduces an unsupervised approach for assessing pothole severity using vibration data. • The methodology incorporates IoT-enabled accelerometers facilitating scalable pavement monitoring. • A detailed performance comparison with a traditional wavelet-based changepoint algorithm is conducted. • The proposed system is validated using a full-scale study detecting pavement anomalies under realistic traffic conditions. • The adaptability of the proposed method is verified experimentally across varying test scenarios. The rapid deterioration of transportation infrastructure, accelerated by extreme weather events and increasing traffic loads, poses significant challenges for roadway maintenance. Potholes, a common form of pavement distress, not only compromise road safety but also increase vehicle maintenance costs and disrupt economic productivity. To address this issue, this study presents a mobile Internet-of-Things (IoT)-based pavement monitoring system for the automated detection and evaluation of potholes. The proposed system can detect and estimate pothole size based on real-time vibration data collected from unmanned ground vehicles (UGVs) by combining IoT-enabled accelerometers and a novel unsupervised threshold-based methodology. This paper introduces a scalable and cost-efficient framework that integrates IoT data acquisition technology with advanced pavement monitoring algorithms, providing municipalities and infrastructure managers with an automated solution for identifying and prioritizing road repairs. The proposed system was validated through multiple field trials and a full-scale study, where it accurately identified potholes of varying sizes across different road conditions. The threshold-based proposed approach is compared with a wavelet-based changepoint detection algorithm to demonstrate its versatility in delivering reliable and robust results even in adverse environmental conditions. The proposed method is validated using nine potholes of varying sizes which are successfully identified and estimated without any user intervention.

Rapid advances in smart devices tremendously facilitate our day-to-day lives. However, these can be exploited remotely via existing cyber vulnerabilities to cause disruption at the physical infrastructure level. In this paper, we discover a novel distributed and stealthy attack that uses malicious actuation of a large number of small-scale loads residing within a distribution network (DN). This attack is capable of cumulatively violating the underlying operational system limits, leading to widespread and prolonged disruptions. A key element of this attack is the efficient use of attack resources, planned via Stackelberg games. To mitigate this type of an attack, we propose a countermeasure strategy which adaptively suppresses adverse effects of the attack when detected in a timely manner. The effectiveness of the proposed mitigation strategy is demonstrated via theoretical convergence studies, practical evaluations, and comparisons with the state-of-the-art strategies using realistic load flow and DN infrastructure models.

Advanced intrusion detection systems are beginning to utilize the power and flexibility offered by Complex Event Processing (CEP) engines. Adapting to new attacks and optimizing CEP rules are two challenges in this domain. Optimizing CEP rules requires a complete framework which can be ported to stream processors because a CEP rule cannot run without a stream processor. External dependencies of stream processors make CEP rule a black box which is hard to optimize. In this paper, we present a novel adaptive and functionally autoscaling stream processor: “Wisdom” with a built-in hybrid optimizer developed using Particle Swarm Optimization, and Bisection algorithms to optimize CEP rule parameters. We show that an adaptive “Wisdom” rule tuned by the proposed optimization algorithm is able to detect selected attacks in CICIDS 2017 dataset with an average precision of 99.98% and an average recall of 93.42% while processing over 2.5 million events per second. The proposed distributed functionally autoscaling deployment mode consumes significantly fewer system resources than the monolithic deployment of CEP rules.

In magnetic resonance (MR)-targeted, 3-D transrectal ultrasound (TRUS)-guided biopsy, prostate motion during the procedure increases the needle targeting error and limits the ability to accurately sample MR-suspicious tumor volumes. The robustness of the 2-D-3-D registration methods for prostate motion compensation is impacted by local optima in the search space. In this paper, we analyzed the prostate motion characteristics and investigated methods to incorporate such knowledge into the registration optimization framework to improve robustness against local optima. Rigid motion of the prostate was analyzed adopting a mixture-of-Gaussian (MoG) model using 3-D TRUS images acquired at bilateral sextant probe positions with a mechanically assisted biopsy system. The learned motion characteristics were incorporated into Powell's direction set method by devising multiple initial search positions and initial search directions. Experiments were performed on data sets acquired during clinical biopsy procedures, and registration error was evaluated using target registration error (TRE) and converged image similarity metric values after optimization. After incorporating the learned initialization positions and directions in Powell's method, 2-D-3-D registration to compensate for motion during prostate biopsy was performed with rms ± std TRE of 2.33 ± 1.09 mm with ~3 s mean execution time per registration. This was an improvement over 3.12 ± 1.70 mm observed in Powell's standard approach. For the data acquired under clinical protocols, the converged image similarity metric value improved in ≥8% of the registrations whereas it degraded only ≤1% of the registrations. The reported improvements in optimization indicate useful advancements in robustness to ensure smooth clinical integration of a registration solution for motion compensation that facilitates accurate sampling of the smallest clinically significant tumors.

This paper presents our recent work on human activity detection based on smart phone embedded sensors and learning algorithms. The proposed human activity detection system recognizes human activities including walking, running, and sitting. While walking and running can be recorded as daily fitness activities, falling will also be detected as anomalous situations and alerting messages can be sent as needed. Embedded sensors including a tri-axial accelerometer, tri-axial linear accelerometer, gyroscope sensor, and orientation sensors are used for motion data collection. A two-stage data analysis approach is used for prediction model generation: short period statistical analysis (max, min, mean, and standard deviation) and long period data analysis using machine learning. The system is implemented in an Android smart phone platform.

Single Carrier Frequency Division Multiple Access (SC-FDMA) is used for uplink data transmission in Long Term Evolution (LTE) systems. SC-FDMA requires contiguous resource blocks (RBs) allocation for each user, which challenges the uplink resource allocation in LTE. The contiguity constraint turns the allocation problem into a non-convex optimization problem. The optimal solution is achieved by solving a binary integer programming (BIP) problem which is computationally-expensive. In this work, we propose a genetic algorithm that is able to solve the resource allocation problem in the LTE uplink. The proposed algorithm maintains all the system constraints and provides a solution with lower complexity compared with the optimal solution. The proposed GA is evaluated and compared with the optimal approach in terms of efficiency and time complexity.

The well-established low-cost broadband OFDM based wireless systems in license-exempt bands, including worldwide interoperability for microwave access (WiMAX) systems, have drawn considerable attention for radar related applications over the past decade. This paper explores an idea of utilizing Commercial Off-The-Shelf (COTS) WiMAX base stations (BSs) in unlicensed band for monostatic airborne synthetic aperture radar (SAR) application. The OFDM PHY of the WiMAX base station has been redesigned to meet the requirements of airborne stripmap SAR applications. The pulse repetition frequency (PRF) offered by standard WiMAX BSs can neither satisfy Doppler bandwidth nor expand SAR slant range. To increase PRF, we propose an RF front modification by employing WiMAX BSs together with a microwave double-pole-double-throw (DPDT) switch and two absorbing loads. Further, a standard WiMAX BS receiver can be directly used to acquire raw data of targets. Simulation results show the proposed scheme can significantly increase the range with limited transmission power.

The study of human metaphase chromosomes is an important aspect in clinical diagnosis of genetic disorders. Although many image processing techniques have been developed for chromosomal karyotyping to assist in laboratory diagnosis, they fail to provide reliable results in segmenting and extracting the centerline of chromosomes due to their shape variability when placed on microscope slides. In this paper we propose a hybrid algorithm that uses Gradient Vector Flow active contours, Discrete Curve Evolution based skeleton pruning and morphological thinning to provide a reliable centerline that is robust to shape variations of the chromosomes. Effective identification of the chromosome outline with its centerline provides a basis for further operations such as automated chromosome classification and centromere identification.

Mutual information (MI), as a similarity measure has been very successful in multi-modal image registration. However, the iterative nature of the method for finding optimal transformation makes it time consuming and mis-registration can happen due to local extrema. In this paper, a heuristic approach based on straight line detection and matching is proposed for rigid registration to speed up mutual information-based method and avoid local extrema by converting the 3-D optimization problem into a 1-D search along the corresponding lines. The proposed method is suitable for images rich with line segments, such as those used in remote sensing applications.

In the last decade, graph-cut optimization has been popular for a variety of labeling problems. Typically, graph-cut methods are used to incorporate smoothness constraints on a labeling, encouraging most nearby pixels to have equal or similar labels. In addition to smoothness, ordering constraints on labels are also useful. For example, in object segmentation, a pixel with a "car wheel" label may be prohibited above a pixel with a "car roof" label. We observe that the commonly used graph-cut \alpha-expansion move algorithm is more likely to get stuck in a local minimum when ordering constraints are used. For a certain model with ordering constraints, we develop new graph-cut moves which we call order-preserving. The advantage of order-preserving moves is that they act on all labels simultaneously, unlike \alpha-expansion. More importantly, for most labels \alpha, the set of \alpha-expansion moves is strictly smaller than the set of order-preserving moves. This helps to explain why in practice optimization with order-preserving moves performs significantly better than \alpha-expansion in the presence of ordering constraints. We evaluate order-preserving moves for the geometric class scene labeling (introduced by Hoiem et al.) where the goal is to assign each pixel a label such as "sky," "ground," etc., so ordering constraints arise naturally. In addition, we use order-preserving moves for certain simple shape priors in graph-cut segmentation, which is a novel contribution in itself.

Robust feature tracking is a requirement for many computer vision tasks such as indoor robot navigation. However, indoor scenes are characterized by poorly localizable features. As a result, indoor feature tracking without artificial markers is challenging and remains an attractive problem. We propose to solve this problem by constraining the locations of a large number of nondistinctive features by several planar homographies which are strategically computed using distinctive features. We experimentally show the need for multiple homographies and propose an illumination-invariant local-optimization scheme for motion refinement. The use of a large number of nondistinctive features within the constraints imposed by planar homographies allows us to gain robustness. Also, the lesser computation cost in estimating these nondistinctive features helps to maintain the efficiency of the proposed method. Our local-optimization scheme produces subpixel accurate feature motion. As a result, we are able to achieve robust and accurate feature tracking.

In the last decade, graph-cut optimization has been popular for a variety of pixel labeling problems. Typically graph-cut methods are used to incorporate a smoothness prior on a labeling. Recently several methods incorporated ordering constraints on labels for the application of object segmentation. An example of an ordering constraint is prohibiting a pixel with a ldquocar wheelrdquo label to be above a pixel with a ldquocar roofrdquo label. We observe that the commonly used graph-cut based alpha-expansion is more likely to get stuck in a local minimum when ordering constraints are used. For certain models with ordering constraints, we develop new graph-cut moves which we call order-preserving moves. Order-preserving moves act on all labels, unlike alpha-expansion. Although the global minimum is still not guaranteed, optimization with order-preserving moves performs significantly better than alpha-expansion. We evaluate order-preserving moves for the geometric class scene labeling (introduced by Hoiem et al.) where the goal is to assign each pixel a label such as ldquoskyrdquo, ldquogrounrdquo, etc., so ordering constraints arise naturally. In addition, we use order-preserving moves for certain simple shape priors in graphcut segmentation, which is a novel contribution in itself.

Atherosclerosis at the carotid bifurcation resulting in cerebral emboli is a major cause of ischemic stroke. Most strokes associated with carotid atherosclerosis can be prevented by lifestyle/dietary changes and pharmacological treatments if identified early by monitoring carotid plaque changes. Registration of 3-D ultrasound (US) images of carotid plaque obtained at different time points is essential for sensitive monitoring of plaque changes in volume and surface morphology. This registration technique should be nonrigid, since different head positions during image acquisition sessions cause relative bending and torsion in the neck, producing nonlinear deformations between the images. We modeled the movement of the neck using a "twisting and bending" model with only six parameters for nonrigid registration. We evaluated the algorithm using 3-D US carotid images acquired at two different head positions to simulate images acquired at different times. We calculated the mean registration error (MRE) between the segmented vessel surfaces in the target image and the registered image using a distance-based error metric after applying our "twisting and bending" model-based nonrigid registration algorithm. We achieved an average registration error of 0.80 +/-0.26 mm using our nonrigid registration technique, which was a significant improvement in registration accuracy over rigid registration, even with reduced degrees-of-freedom compared to the other nonrigid registration algorithms.

In this work, we propose a framework that can provide performance metrics with regards to the usability of an assistive device designed to provide visual servoing of a reaching task performed by a visually impaired individual. The framework provides a model and methodology from which performance metrics can be synthesized based on robotic manipular kinematics and an adaptation of Fitts' law for reaching task motions in a 3D environment. The purpose of which is to facilitate an understanding of how well the system's design accommodates the user's natural movement style when performing the reaching task. Use of advances in visual tracking techniques employing scale, view-point, and illumination invariant features are incorporated to allow for a further decoupling of the feature space from the joint-level control space.

Text that appears in images contains important and useful information. Detection and extraction of text in images have been used in many applications. In this paper, we propose a multiscale edge-based text extraction algorithm, which can automatically detect and extract text in complex images. The proposed method is a general-purpose text detection and extraction algorithm, which can deal not only with printed document images but also with scene text. It is robust with respect to the font size, style, color, orientation, and alignment of text and can be used in a large variety of application fields, such as mobile robot navigation, vehicle license detection and recognition, object identification, document retrieving, page segmentation, etc

In this work, we have taken the first step towards the creation of a computerized seeing-eye guide dog. The system we presented extends the development of assistive technology for the visually impaired into a new area: object tracking and visual servoing. The system uses computer vision to provide a kind of surrogate sight for the human user; sensing information from the environment and communicating it through haptic signalling. Our proof-of concept prototype is a low-cost wearable system which uses a colour camera to analyze a scene and recognize a desired object, then generate tactile cues to the wearer to steer his or her hand towards the object. We have proved the system in trials with random users in an unstructured environment.

This paper proposes a method to fill in the missing range information of a planar region in the depth map of an image obtained from a commercial stereo vision system. The Digiclops stereo vision system, a fast three-camera module range measurement device, is used to provide the initial depth image. The edge information of the reference image from the system is extracted by the embedded confidence edge detection technique. Hough transform (HT) is then applied to the binary edge image to extract the straight-line boundary edges and the planar region is determined by the intersections of the edges. A 3-D planar equation is then used to determine the missing depth information of the region. The range data in the world coordinate system is projected back into the image coordinate system using a pixel-to-pixel projection algorithm. Results demonstrate the accuracy of the method in filling the missing depth information in a corrupted region in a depth map.

In this paper, we present an automatic model based image segmentation system, which combines a multi-resolution discrete dynamic contour (DDC) model refinement procedure and the domain knowledge of the image class. The segmentation begins on a low-resolution image by defining an open DDC model, followed by a contour growing process generates the closed DDC model, which deforms progressively towards higher resolution images. A combination of knowledge based fuzzy inference system (FIS) and a set of adaptive region based operators is used to enhance the edges of interest and to govern the DDC model deformation. With the above process we were able to greatly reduce the sensitivity to the initial model, thus paving the way for automatic segmentation on noisy images. Domain knowledge of a particular class of images is encapsulated within the FIS such that it can be easily changed for different image classes. We applied this algorithm successfully to detect the organ boundary in ultra-sound images of prostates and examples are shown in order to illustrate the advantages of the proposed method.

Modeling textures using Gaussian Markov random fields (GMRF) has been successfully used in classifying textures. However, these models do not perform well for self-similar textures such as those generated from fractional Brownian motion. The authors show that by using the difference images at different scales instead of the original image, one can significantly increase the performance of classifying self-similar texture patterns using GMRF models.>

The authors have applied mathematical morphology for fractal analysis on bone X-ray images. The digitized gray level image is treated as a three-dimensional surface whose fractal dimension is calculated by performing a series of dilations on this surface and plotting the area of the resulting set of surfaces against the size of the structuring element. This approach has the added advantage of encoding structural information via the use of a structuring element. The algorithm has been applied to several bone radiographs, and the results demonstrate that the fractal dimension using mathematical morphology gives a robust texture measure of trabecular bone structures.

Mathematical morphology was applied for fractal analysis of bone X-ray images. Fractal dimension is calculated by performing a series of dilations on the given image and plotting the surface area of the resulting object against the size of the structuring element. This approach gives the added advantage of encoding structural information via the use of a structuring element. The algorithm was applied to several bone radiographs and the results demonstrate that fractal dimension using mathematical morphology gives us a robust texture measure of trabecular bone structures.>