Question: Moving toward integrated scene text recognition and interpretation.

This paper delves into the Pascal Visual Object Classes (VOC) Challenge, a benchmark for visual object category recognition and detection. Since its inception in 2005, the challenge has served as a pivotal standard for the vision and machine learning communities, offering a standardized dataset of images and annotations, alongside evaluation procedures. Over the years, it has become the go-to benchmark for object detection. Here, we outline the dataset and the evaluation process, reviewing the latest advancements in classification and detection methods. We conduct a thorough analysis to determine if these methods exhibit statistically significant differences, what aspects of the images they are learning (such as the object itself or its context), and their performance in distinguishing between objects. Drawing from the insights gained over three years of the challenge's existence, we conclude with lessons learned and suggest avenues for future enhancement and expansion.

**Question**: Understanding the Impact of the COVID-19 Pandemic on Mental Health

**Documents**: The Psychological Impact of the COVID-19 Pandemic on the General Population
The COVID-19 pandemic has had a profound impact on mental health worldwide. This study aimed to assess the psychological impact of the pandemic on the general population. The study used a cross-sectional online survey to collect data from participants across various countries. The survey included questions on demographic characteristics, perceived risk of infection, and psychological distress. The results showed that the pandemic has led to increased levels of anxiety, depression, and stress in the general population. The study also found that individuals who perceived a higher risk of infection had higher levels of psychological distress. The findings highlight the need for mental health support during and after the pandemic.

**Similar Documents**: This research explores the profound psychological effects of the COVID-19 pandemic on the global populace. With the aim of gauging the mental health impact, a cross-sectional online survey was employed to gather insights from participants in multiple countries. The survey encompassed inquiries into demographic details, perceived susceptibility to infection, and indicators of psychological distress. Findings revealed a notable surge in anxiety, depression, and stress levels among the general public. Notably, individuals with heightened perceptions of infection risk exhibited greater psychological distress. These results underscore the critical importance of providing mental health support during and following the pandemic, emphasizing the need for comprehensive care in the face of such global health crises.

Recognizing lines of unconstrained handwritten text is a challenging task. The difficulty of segmenting cursive or overlapping characters, combined with the need to exploit surrounding context, has led to low recognition rates for even the best current recognizers. Most recent progress in the field has been made either through improved preprocessing or through advances in language modeling. Relatively little work has been done on the basic recognition algorithms. Indeed, most systems rely on the same hidden Markov models that have been used for decades in speech and handwriting recognition, despite their well-known shortcomings. This paper proposes an alternative approach based on a novel type of recurrent neural network, specifically designed for sequence labeling tasks where the data is hard to segment and contains long-range bidirectional interdependencies. In experiments on two large unconstrained handwriting databases, our approach achieves word recognition accuracies of 79.7 percent on online data and 74.1 percent on offline data, significantly outperforming a state-of-the-art HMM-based system. In addition, we demonstrate the network's robustness to lexicon size, measure the individual influence of its hidden layers, and analyze its use of context. Last, we provide an in-depth discussion of the differences between the network and HMMs, suggesting reasons for the network's superior performance.

As the intermediate level task connecting image captioning and object detection, visual relationship detection started to catch researchers’ attention because of its descriptive power and clear structure. It localizes the objects and captures their interactions with a subject-predicateobject triplet, e.g. 〈person-ride-horse〉. In this paper, the visual relationship is considered as a phrase with three components. So we formulate the visual relationship detection as three inter-connected recognition problems and propose a Visual Phrase reasoning Convolutional Neural Network (ViP-CNN) to address them simultaneously. In ViP-CNN, we present a Visual Phrase Reasoning Structure (VPRS) to set up the connection among the relationship components and help the model consider the three problems jointly. Corresponding non-maximum suppression method and model training strategy are also proposed. Experimental results show that our ViP-CNN outperforms the stateof-art method both in speed and accuracy. We further pretrain our model on our cleansed Visual Genome Relationship dataset, which is found to perform better than the pretraining on the ImageNet for this task.

The problem we are addressing in Alvey Project MMI149 is that of using computer vision to understand the unconstrained 3D world, in which the viewed scenes will in general contain too wide a diversity of objects for topdown recognition techniques to work. For example, we desire to obtain an understanding of natural scenes, containing roads, buildings, trees, bushes, etc., as typified by the two frames from a sequence illustrated in Figure 1. The solution to this problem that we are pursuing is to use a computer vision system based upon motion analysis of a monocular image sequence from a mobile camera. By extraction and tracking of image features, representations of the 3D analogues of these features can be constructed.

The advent of social media has brought Internet memes, a unique social phenomenon, to the front stage of the Web. Embodied in the form of images with text descriptions, little do we know about the “language of memes”. In this paper, we statistically study the correlations among popular memes and their wordings, and generate meme descriptions from raw images. To do this, we take a multimodal approach—we propose a robust nonparanormal model to learn the stochastic dependencies among the image, the candidate descriptions, and the popular votes. In experiments, we show that combining text and vision helps identifying popular meme descriptions; that our nonparanormal model is able to learn dense and continuous vision features jointly with sparse and discrete text features in a principled manner, outperforming various competitive baselines; that our system can generate meme descriptions using a simple pipeline.

Takeo Kanade, Robert T. Collins, and Alan J. Lipton Carnegie Mellon University, Pittsburgh, PA e-mail: fkanade,rcollins,ajlg@cs.cmu.edu homepage: http://www.cs.cmu.edu/ vsam P. Anandan, Peter Burt, and Lambert Wixson David Sarno Research Center, Princeton, NJ e-mail: fpanandan,pburt,lwixsong@sarno .com Abstract Carnegie Mellon University (CMU) and the David Sarno Research Center (Sarno ) have begun a joint, integrated feasibility demonstration in the area of Video Surveillance and Monitoring (VSAM). The objective is to develop a cooperative, multi-sensor video surveillance system that provides continuous coverage over large battle eld areas. Image Understanding (IU) technologies will be developed to: 1) coordinate multiple sensors to seamlessly track moving targets over an extended area, 2) actively control sensor and platform parameters to track multiple moving targets, 3) integrate multisensor output with collateral data to maintain an evolving, scene-level representation of all targets and platforms, and 4) monitor the scene for unusual \trigger" events and activities. These technologies will be integrated into an experimental testbed to support evaluation, data collection, and demonstration of other VSAM technologies developed within the DARPA IU community.

The caption retrieval task can be defined as follows: given a set of images I and a set of describing sentences S, for each image i in I we ought to find the sentence in S that best describes i. The most commonly applied method to solve this problem is to build a multimodal space and to map each image and each sentence to that space, so that they can be compared easily. A non-conventional model called Word2VisualVec has been proposed recently: instead of mapping images and sentences to a multimodal space, they mapped sentences directly to a space of visual features. Advances in the computation of visual features let us infer that such an approach is promising. In this paper, we propose a new Recurrent Neural Network model following that unconventional approach based on Gated Recurrent Capsules (GRCs), designed as an extension of Gated Recurrent Units (GRUs). We show that GRCs outperform GRUs on the caption retrieval task. We also state that GRCs present a great potential for other applications.

The idea described in this paper is to use the built-in cameras of consumer mobile phones as sensors for 2-dimensional visual codes. Such codes can be attached to physical objects in order to retrieve object-related information and functionality. They are also suitable for display on electronic screens. The proposed visual code system allows the simultaneous detection of multiple codes, introduces a position-independent coordinate system, and provides the phone’s orientation as a parameter. The ability to detect objects in the user’s vicinity offers a natural way of interaction and strengthens the role of mobile phones in a large number of application scenarios. We describe the hardware requirements, the design of a suitable visual code, a lightweight recognition algorithm, and present some example applications.

We introduce an effective and efficient method that grounds (i.e., localizes) natural sentences in long, untrimmed video sequences. Specifically, a novel Temporal GroundNet (TGN)1 is proposed to temporally capture the evolving fine-grained frame-by-word interactions between video and sentence. TGN sequentially scores a set of temporal candidates ended at each frame based on the exploited frameby-word interactions, and finally grounds the segment corresponding to the sentence. Unlike traditional methods treating the overlapping segments separately in a sliding window fashion, TGN aggregates the historical information and generates the final grounding result in one single pass. We extensively evaluate our proposed TGN on three public datasets with significant improvements over the stateof-the-arts. We further show the consistent effectiveness and efficiency of TGN through an ablation study and a runtime test.

This paper presents integration and testing scheme for managing a large Multilingual OCR Project. The project is an attempt to implement an integrated platform for OCR of different Indian languages. Software engineering, workflow management and testing processes have been discussed in this paper. The OCR has now been experimentally deployed for some specific applications and currently is being enhanced for handling the space and time constraints, achieving higher recognition accuracies and adding new functionalities.

This paper discusses the implementation of imrestaurant(), a MATLAB function for feature-based restaurant logo recognition. The algorithm works by first applying SIFT to a logo database and then creating a hierarchical vocabulary tree using K-means clustering. The input to the algorithm is a photograph of an image logo, which is first filtered to reduce noise. Then SIFT is applied to the input image, and the output descriptors are pushed to the vocabulary tree. Each image in the logo database is scored using TF-IDF weights and the ten highest scoring database logos are pairwise matched using the ratio test and RANSAC. The restaurant database image with the largest consensus set is searched for on yelp.com. This algorithm was shown to be robust against input image size, background noise, and shearing. Finally, future work in increasing the accuracy of feature detection as well as optical character recognition is discussed.


Question: A multiple-observer method for designing a vehicle velocity observer using a 6-D IMU.

This document details an innovative approach to vehicle position estimation for intelligent vehicles, emphasizing the need for high accuracy, reliability, and continuous data provision. Recognizing the limitations of low-cost Global Positioning System (GPS) receivers in maintaining accuracy, reliability, and continuity of position data, especially during GPS errors, the paper explores the use of Bayesian filters for sensor fusion. These filters, however, depend significantly on the chosen process model, which necessitates consideration of vehicle dynamics under varying driving conditions. To address this, the paper introduces a positioning algorithm that employs an Interacting Multiple Model (IMM) filter, integrating low-cost GPS with in-vehicle sensors. This integration allows for the adaptive adjustment of the vehicle model according to diverse driving conditions. The IMM filter's model set includes both kinematic and dynamic vehicle models. The proposed algorithm is subjected to rigorous simulation and real-world testing using a real-time embedded system. The results affirm the system's ability to deliver accurate and reliable positioning performance across a broad spectrum of driving scenarios. This approach represents a significant advancement in mitigating GPS errors and enhancing the overall performance of vehicle positioning systems.

Exact knowledge of the position of a vehicle is a fundamental problem in mobile robot applications. In search for a solution, researchers and engineers have developed a variety of systems, sensors, and techniques for mobile robot positioning. This paper provides a review of relevant mobile robot positioning technologies. The paper defines seven categories for positioning systems: 1. Odometry; 2. Inertial Navigation; 3. Magnetic Compasses; 4. Active Beacons; 5. Global Positioning Systems; 6. Landmark Navigation; and 7. Model Matching. The characteristics of each category are discussed and examples of existing technologies are given for each category. The field of mobile robot navigation is active and vibrant, with more great systems and ideas being developed continuously. For this reason the examples presented in this paper serve only to represent their respective categories, but they do not represent a judgment by the authors. Many ingenious approaches can be found in the literature, although, for reasons of brevity, not all could be cited in this paper. 1) (Corresponding Author) The University of Michigan, Advanced Technologies Lab, 1101 Beal Avenue, Ann Arbor, MI 48109-2110, Ph.: 313-763-1560, Fax: 313-944-1113. Email: johannb@umich.edu 2) Naval Command, Control, and Ocean Surveillance Center, RDT&E Division 5303, 271 Catalina Boulevard, San Diego, CA 92152-5001, Email: Everett@NOSC.MIL 3) The University of Michigan, Advanced Technologies Lab, 1101 Beal Avenue, Ann Arbor, MI 481092110, Email: Feng@engin.umich.edu 4) The University of Michigan, Dept. of Nuclear Engineering and Radiological Sciences, 239 Cooley Bldg., Ann Arbor, MI 48109, Email: dkw@umich.edu

Robot manipulator designs are increasingly focused on low cost approaches, especially those envisioned for use in unstructured environments such as households, office spaces and hazardous environments. The cost of angular sensors varies based on the precision offered. For tasks in these environments, millimeter order manipulation errors are unlikely to cause drastic reduction in performance. In this paper, estimates the joint angles of a manipulator using low cost triaxial accelerometers by taking the difference between consecutive acceleration vectors. The accelerometer-based angle is compensated with a uniaxial gyroscope using a complementary filter to give robust measurements. Three compensation strategies are compared: complementary filter, time varying complementary filter, and extended Kalman filter. This sensor setup can also accurately track the joint angle even when the joint axis is parallel to gravity and the accelerometer data does not provide useful information. In order to analyze this strategy, accelerometers and gyroscopes were mounted on one arm of a PR2 robot. The arm was manually moved smoothly through different trajectories in its workspace while the joint angle readings from the on-board optical encoders were compared against the joint angle estimates from the accelerometers and gyroscopes. The low cost angle estimation strategy has a mean error 1.3° over the three joints estimated, resulting in mean end effector position errors of 6.1 mm or less. This system provides an effective angular measurement as an alternative to high precision encoders in low cost manipulators and as redundant measurements for safety in other manipulators.

Multiple cameras are increasingly prevalent on robotic and human-driven vehicles. These cameras come in a variety of wide-angle, fish-eye, and catadioptric models. Furthermore, wheel odometry is generally available on the vehicles on which the cameras are mounted. For robustness, vision applications tend to use wheel odometry as a strong prior for camera pose estimation, and in these cases, an accurate extrinsic calibration is required in addition to an accurate intrinsic calibration. To date, there is no known work on automatic intrinsic calibration of generic cameras, and more importantly, automatic extrinsic calibration of a rig with multiple generic cameras and odometry. We propose an easy-to-use automated pipeline that handles both intrinsic and extrinsic calibration; we do not assume that there are overlapping fields of view. At the begining, we run an intrinsic calibration for each generic camera. The intrinsic calibration is automatic and requires a chessboard. Subsequently, we run an extrinsic calibration which finds all camera-odometry transforms. The extrinsic calibration is unsupervised, uses natural features, and only requires the vehicle to be driven around for a short time. The intrinsic parameters are optimized in a final bundle adjustment step in the extrinsic calibration. In addition, the pipeline produces a globally-consistent sparse map of landmarks which can be used for visual localization. The pipeline is publicly available as a standalone C++ package.

In this paper, we address the estimation, control, navigation and mapping problems to achieve autonomous inspection of penstocks and tunnels using aerial vehicles with on-board sensing and computation. Penstocks and tunnels have the shape of a generalized cylinder. They are generally dark and featureless. State estimation is challenging because range sensors do not yield adequate information and cameras do not work in the dark. We show that the six degrees of freedom (DOF) pose and velocity can be estimated by fusing information from an inertial measurement unit (IMU), a lidar and a set of cameras. This letter discusses in detail the range-based estimation part while leaving the details of vision component to our earlier work. The proposed algorithm relies only on a model of the generalized cylinder and is robust to changes in shape of the tunnel. The approach is validated through real experiments showing autonomous and shared control, state estimation and environment mapping in the penstock at Center Hill Dam, TN. To our knowledge, this is the first time autonomous navigation and mapping has been achieved in a penstock without any external infrastructure such GPS or external cameras.

The Hemispherical Resonator Gyro (HRG) has proven itself to be an ultra-reliable technology for space application with over 20 million operation hours and 100% mission success. Northrop Grumman Navigation Systems Division is developing a terrestrial inertial navigation system, INS, based on the proven space technology that can be used for precision pointing applications. The Precision Pointing System (PPS) design yields a small size and lightweight system and will require only a few watts of power to operate. To achieve this small sized INS, the PPS utilizes a new golf-ball sized milli-HRG (mHRG) that is based on the current HRG 130P production gyro design used in extremely accurate space pointing systems. The power reduction is derived from a new electronics design based around low power elements. The new mHRG gyro design has demonstrated bias stability performance better than the navigation grade gyros and will quickly attain this accuracy due to the extremely low noise characteristics of the HRG. Instrumental in the success of the mHRG performance has been the implementation of a calibration mechanization that eliminates the requirement for thermal control or modeling. This implementation will allow the INS to align quickly and will be advantageous for applications that have a quick response time requirement. Additionally, due to the stability of the mHRG the system can operate without GPS aiding for greater than an hour, while maintaining the attitude accuracy required for precision pointing, before an internal realignment is needed. The simplified design of mHRG, has reduced the parts count by roughly 90% when compared to the current space qualified HRG production unit. With the major parts reduction it is projected that the mHRG can be produced efficiently and at a cost making it a viable choice for terrestrial applications.

Mobile robots such as unmanned vehicles integrate heterogeneous capabilities in sensing, computation, and control. They are representative cyber-physical systems where the cyberspace and the physical world are strongly coupled. However, the safety of mobile robots is significantly threatened by cyber/physical attacks and software/hardware failures. These threats can thwart normal robot operations and cause robot misbehaviors. In this paper, we propose a novel anomaly detection system, which leverages physical dynamics of mobile robots to detect misbehaviors in sensors and actuators. We explore issues raised in real-world implementations, e.g., distinctive robot dynamic models, sensor quantity and quality, decision parameters, etc., for practicality purposes. We implement the detection system on two types of mobile robots and evaluate the detection performance against various misbehavior scenarios, including signal interference, sensor spoofing, logic bomb and physical jamming. The experiments show detection effectiveness and small detection delays.

There have been increasing demands for developing robotic system combining camera and inertial measurement unit in navigation task, due to their low-cost, lightweight and complementary properties. In this paper, we present a Visual Inertial Odometry (VIO) system which can utilize sparse depth to estimate 6D pose in GPS-denied and unstructured environments. The system is based on Multi-State Constraint Kalman Filter (MSCKF), which benefits from low computation load when compared to optimization-based method, especially on resource-constrained platform. Features are enhanced with depth information forming 3D landmark position measurements in space, which reduces uncertainty of position estimate. And we derivate measurement model to access compatibility with both 2D and 3D measurements. In experiments, we evaluate the performance of the system in different in-flight scenarios, both cluttered room and industry environment. The results suggest that the estimator is consistent, substantially improves the accuracy compared with original monocular-based MSKCF and achieves competitive accuracy with other research.

LiDAR (Light detection and ranging) sensors are widely used in research and development. As such, they build the base for the evaluation of newly developed ADAS (Advanced Driver Assistance Systems) functions in the automotive field where they are used for ground truth establishment. However, the factory calibration provided for the sensors is not able to satisfy the high accuracy requirements by such applications. In this paper we propose a concept to easily improve the existing calibration of a Velodyne LiDAR sensor without the need for special calibration setups which can even be used to enhance already recorded data.

The mission of tracking a pedestrian is valuable for many applications including walking distance estimation for the purpose of pervasive healthcare, museum and shopping mall guides, and locating emergency responders. In this paper, we show how accurate and ubiquitous tracking of a pedestrian can be performed using only the inertial sensors embedded in his/her mobile phone. Our work depends on performing dead reckoning to track the user's movement. The main challenge that needs to be addressed is handling the noise of the low cost low quality inertial sensors in cell phones. Our proposed system combines two novel contributions: a novel step count estimation technique and a gait-based accurate variable step size detection algorithm. The step count estimation technique is based on a lightweight finite state machine approach that leverages orientation-independent features. In order to capture the varying stride length of the user, based on his changing gait, we employ a multi-class hierarchical Support Vector Machine classifier. Combining the estimated number of steps with the an accurate estimate of the individual stride length, we achieve ubiquitous and accurate tracking of a person in indoor environments. We implement our system on different Android-based phones and compare it to the state-of-the-art techniques in indoor and outdoor testbeds with arbitrary phone orientation. Our results in two different testbeds show that we can provide an accurate step count estimation with an error of 5.72%. In addition, our gait type classifier has an accuracy of 97.74%. This leads to a combined tracking error of 6.9% while depending only on the inertial sensors and turning off the GPS sensor completely. This highlights the ability of the system to provide ubiquitous, accurate, and energy efficient tracking.

We present a framework for the estimation of driver behavior at intersections, with applications to autonomous driving and vehicle safety. The framework is based on modeling the driver behavior and vehicle dynamics as a hybrid-state system (HSS), with driver decisions being modeled as a discrete-state system and the vehicle dynamics modeled as a continuous-state system. The proposed estimation method uses observable parameters to track the instantaneous continuous state and estimates the most likely behavior of a driver given these observations. This paper describes a framework that encompasses the hybrid structure of vehicle-driver coupling and uses hidden Markov models (HMMs) to estimate driver behavior from filtered continuous observations. Such a method is suitable for scenarios that involve unknown decisions of other vehicles, such as lane changes or intersection access. Such a framework requires extensive data collection, and the authors describe the procedure used in collecting and analyzing vehicle driving data. For illustration, the proposed hybrid architecture and driver behavior estimation techniques are trained and tested near intersections with exemplary results provided. Comparison is made between the proposed framework, simple classifiers, and naturalistic driver estimation. Obtained results show promise for using the HSS-HMM framework.


Question: Design of a vehicle velocity observer utilizing a 6-D IMU and a multiple-observer technique.

This paper introduces a robust, high-integrity navigation solution for road vehicles, specifically designed to meet the escalating demands for performance and reliability in Global Navigation Satellite System (GNSS)-dependent applications. With the advent of safety systems, electronic fee-collection mechanisms, and traveler information services, there's a critical need for onboard equipment (OBE) that can provide a highly accurate and available position, even in challenging environments with limited satellite visibility such as urban canyons or tunnels, all while maintaining affordability. Moreover, there's a growing expectation for OBEs to not only deliver continuous, accurate positioning but also to provide integrity information about the reliability of the position. This is particularly crucial in life-critical applications where high-integrity monitored positioning is indispensable. The proposed solution integrates GNSS and inertial sensors (Global Positioning System/satellite-based augmentation system/inertial navigation system) and employs an extended Kalman filter combined with an interactive multimodel method (IMM-EKF). This integrated system ensures continuous, marketable positioning with a quantifiable level of trust in the solution's accuracy. The efficacy of the IMM-EKF implementation is substantiated through a series of controlled and real-world scenario tests, which demonstrate its superiority over low-cost GNSS-based solutions, dead reckoning systems, single-model EKF, and other filtering methods currently documented in the literature.

Robot manipulator designs are increasingly focused on low cost approaches, especially those envisioned for use in unstructured environments such as households, office spaces and hazardous environments. The cost of angular sensors varies based on the precision offered. For tasks in these environments, millimeter order manipulation errors are unlikely to cause drastic reduction in performance. In this paper, estimates the joint angles of a manipulator using low cost triaxial accelerometers by taking the difference between consecutive acceleration vectors. The accelerometer-based angle is compensated with a uniaxial gyroscope using a complementary filter to give robust measurements. Three compensation strategies are compared: complementary filter, time varying complementary filter, and extended Kalman filter. This sensor setup can also accurately track the joint angle even when the joint axis is parallel to gravity and the accelerometer data does not provide useful information. In order to analyze this strategy, accelerometers and gyroscopes were mounted on one arm of a PR2 robot. The arm was manually moved smoothly through different trajectories in its workspace while the joint angle readings from the on-board optical encoders were compared against the joint angle estimates from the accelerometers and gyroscopes. The low cost angle estimation strategy has a mean error 1.3° over the three joints estimated, resulting in mean end effector position errors of 6.1 mm or less. This system provides an effective angular measurement as an alternative to high precision encoders in low cost manipulators and as redundant measurements for safety in other manipulators.

This paper presents an intelligent control scheme which utilizes a single neuron PID controller to an experimental propeller setup called the twin rotor multi-input multi-output system (TRMS). The control objective is to make the TRMS move quickly and accurately to the desired attitudes. The pitch angle and the azimuth angle in the conditions of decoupled and cross-coupled between vertical and horizontal axes are considered. It is difficult to design a suitable controller because of the influence between two axes and nonlinear movement. For easy demonstrate the movement on the vertical plane and horizontal plane separately, the TRMS is decoupled first by the main rotor and tail rotor. After successful simulations in decoupled condition, the more difficult simulations of cross-coupled condition are performed. Simulation results show that the new approach can improve the tracking performance.

The data association algorithm plays the vital role of forming an appropriate and valid set of tracks from the available tracks at the fusion center, which are delivered by different sensor's local tracking systems. The architecture of the data association module has to be designed taking into account the fusion strategy of the sensor fusion system, the granularity and the quality of the data provided by the sensors. The current generation environment perception sensors used for automotive sensor fusion are capable of providing estimated kinematic and as well as non-kinematic information on the observed targets. This paper focuses on integrating the kinematic and non-kinematic information in a track-to-track association (T2TA) procedure. A scalable framework called Combi-Tor is introduced here that is designed to calculate the association decision using likelihood ratio tests based on the available kinematic and non-kinematic information on the targets, which are tracked and classified by different sensors. The calculation of the association decision includes the uncertainty in the sensor's local tracking and classification modules. The required sufficient statistical derivations are discussed. The performance of this T2TA framework and the traditional T2TA scheme considering only the kinematic information are evaluated using Monte-Carlo simulation. The initial results obtained using the real world sensor data is presented.

This work is to establish an auto-calibration around-view monitoring system to avoid the inconsistent performance from deviations of camera installation. First, a vehicle is parked at a specific area which has a special pattern on the ground. Second, the captured images from four embedded cameras are compared with the reference pattern images to obtain the deviations in angle, X-axis and Y-axis. Finally, these deviations are used to compensate the coefficients of transformation matrices associated with view-point conversion and image stitching. Particularly, the adaptive edge recovery scheme is developed to help on precise alignments. The experimental results show that the proposed system implemented at the TI DaVinci DSP platform can accomplish auto-calibration of a vehicle within 60 seconds and have deviation rates smaller than 38% at stitched regions and 25% at the other regions. Additionally, ±2° deviation errors at angles, X-axis and Y-axis are allowed. Particularly, the proposed system can generate precise 2-meter around-view pictures at a vehicle speed of 30km/h. Therefore, the auto-calibration system proposed herein can be widely applied to vehicle manufacture, verification and repair.

This paper studies the estimation of the frictional torque of the synchromesh during the gear shifting operation in an electric vehicle equipped with a clutchless automated manual transmission (AMT). The clutchless drivetrain of the electric vehicle is discussed and the dynamical model of the powertrain from the electric traction motor to the synchromesh system of a two-speed AMT is developed. In order to estimate the frictional torque of the synchromesh, which is indeed an unknown input to the dynamical system, it is assumed to be generated by a fictitious autonomous system. Thereafter, the augmented state- space representation of the actual and fictitious state variables, which forms the basis for the observer design, is provided and the observability of this augmented system is discussed. A deterministic Luenberger observer and a stochastic Kalman-Bucy filter are designed in order to estimate the frictional torque of the synchromesh. The estimation is based on the measuring angular velocities of the electric motor and synchro ring together with the known electromagnetic torque of the traction motor. The performance of the observers is assessed experimentally by means of a test rig. The results demonstrate the satisfactory performance of the stochastic observer when the system encounters process and measurement noise which are likely to happen in practice.

A method is described which quantifies the speed and direction of several moving objects in a sequence of digital images. A relationship between the time variation of intensity, the spatial gradient, and velocity has been developed which allows the determination of motion using clustering techniques. This paper describes these relationships, the clustering technique, and provides examples of the technique on real images containing several moving objects.

The objective of this paper is to review the theories and approaches of multisensor fusion and integration (MFI) with its application in mechatronics. MFI helps the system perceiving changes of the environment and monitoring the system itself. Since each individual sensor has its own inherent defects and limitations, MFI merges the redundant information acquired by multiple sensors synergistically to provide a more accurate perception and make an optimal decision in further. The wide application spectrum of MFI in mechatronics includes the industrial automation, the development of intelligent robots, military applications, biomedical applications, etc. In this paper, the architecture and algorithms of MFI are reviewed, and some implementation examples in industrial automation and robotic applications are presented. Furthermore, sensor fusion methods at different levels, namely, estimation methods, classification methods and inference methods, the most frequently used algorithms in previous researches with their advantages and limitations are summarized. Applications of MFI in robotics and mechatronics are discussed. Future perspectives of MFI deployment are included in the concluding remarks.

This second edition of Dr. Grove's book (the original was published in 2008) could arguably be considered a new work. At just under 1,000 pages (including the 11 appendices on the DVD), the second edition is 80% longer than the original. Frankly, the word "book" hardly seems adequate, considering the wide range of topics covered. "Mini-encyclopedia" seems more appropriate. The hardcover portion of the book comprises 18 chapters, and the DVD includes the aforementioned appendices plus 20 fully worked examples, 125 problems or exercises (with answers), and MATLAB routines for the simulation of many of the algorithms discussed in the main text. Here is a brief overview of the contents: ▸ Chapters 1–3: an overview of the diversity of positioning techniques and navigation systems; fundamentals of coordinate frames, kinematics and earth models; introduction to Kaiman filtering ▸ Chapters 4–6: inertial sensors, inertial navigation, and lower-cost dead reckoning systems ▸ Chapters 7–12: principles of radio positioning, short-, medium-, and long-range radio navigation, as well as extensive coverage of global navigation satellite systems (GNSS) ▸ Chapter 13: environmental feature matching. ▸ Chapters 14–16: various integration topics, including inertial navigation system (INS)/GNSS integration, alignment, zero-velocity updates, and multisensor integration ▸ Chapter 17: fault detection. ▸ Chapter 18: applications and trends. In summary, this book is an excellent reference (with numerous nuggets of wisdom) that should be readily handy on the shelf of every practicing navigation engineer. In the hands of an experienced instructor, the book will also serve students as a great textbook. However, the lack of examples integrated in the main text makes it difficult for the book to serve as a self-study guide for those that are new to the field.

Future Mars exploration missions rely heavily on high-mobility autonomous rovers equipped with sophisticated scientific instruments and possessing advanced navigational capabilities. Increasing their navigation velocity and localization accuracy is essential for enabling these rovers to explore large areas on Mars. Contemporary Mars rovers move slowly, partially due to the long execution time of complex computer vision algorithms running on their slow space-grade CPUs. This paper exploits the advent of high-performance space-grade field-programmable gate arrays (FPGAs) to accelerate the navigation of future rovers. Specifically, it focuses on visual odometry (VO) and performs HW/SW codesign to achieve one order of magnitude faster execution and improved accuracy. Conforming to the specifications of the European Space Agency, we build a proof-of-concept system on an HW/SW platform with processing power resembling that to be available onboard future rovers. We develop a codesign methodology adapted to the rover's specifications, design parallel architectures, and customize several feature extraction, matching, and motion estimation algorithms. We implement and evaluate five distinct HW/SW pipelines on a Virtex6 FPGA and a 150 MIPS CPU. We provide a detailed analysis of their cost-time-accuracy tradeoffs and quantify the benefits of employing FPGAs for implementing VO. Our solution achieves a speedup factor of 16× over a CPU-only implementation, handling a stereo image pair in less than 1 s, with a 1.25% mean positional error after a 100 m traverse and an FPGA cost of 54 K LUTs and 1.46-MB RAM.

In this paper, we present an infrastructure-free mapping and localization framework for rail vehicles using only a lidar sensor. Our method is designed to handle the pathological environment found in modern underground tunnels: narrow, parallel, and relatively smooth concrete walls with very little infrastructure to break up the empty spaces in the tunnel. By using an RQE-based, point-cloud alignment approach, we are able to implement a sliding-window algorithm, used for both mapping and localization. We demonstrate the proposed method with datasets gathered on a subway train travelling at high speeds (up to 70 km/h) in an underground tunnel for a total of 20 km across 6 runs. Our method is capable of mapping the tunnel with less than 0.6% error over the total length of the generated map. It is capable of continuously localizing, relative to the generated map, to within 10 cm in stations and at crossovers, and 1.8 m in pathological sections of tunnel. This method improves rail-based localization in a tunnel, which can be used to increase capacity on existing railways and for automated trains.


Question: Designing a vehicle velocity observer using a 6-D IMU and a multiple-observer technique.

This paper introduces an innovative method for achieving lane-level integrity in navigation and map matching, addressing significant challenges in the field. Integrity is particularly crucial in safety-critical applications or those with stringent performance demands, such as satellite-based electronic toll collection systems. A navigation solution capable of operating at the lane level while ensuring the integrity of the data is therefore highly advantageous. The paper outlines a groundbreaking approach to simultaneous positioning and map matching with lane-level integrity. This system integrates data from a global navigation satellite system (GNSS) receiver, an odometer, and a gyroscope, alongside detailed road information from enhanced digital maps. A multiple-hypothesis particle-filter-based algorithm is employed to process these inputs effectively. Through a series of real-world tests conducted in France and Germany, the method has demonstrated exceptional performance in positioning accuracy, map matching reliability, and integrity assurance, thus validating the practicality of the proposed solution.

Lane-level positioning and map matching are some of the biggest challenges for navigation systems. Additionally, in safety applications or in those with critical performance requirements (such as satellite-based electronic fee collection), integrity becomes a key word for the navigation community. In this scenario, it is clear that a navigation system that can operate at the lane level while providing integrity parameters that are capable of monitoring the quality of the solution can bring important benefits to these applications. This paper presents a pioneering novel solution to the problem of combined positioning and map matching with integrity provision at the lane level. The system under consideration hybridizes measurements from a global navigation satellite system (GNSS) receiver, an odometer, and a gyroscope, along with the road information stored in enhanced digital maps, by means of a multiple-hypothesis particle-filter-based algorithm. A set of experiments in real environments in France and Germany shows the very good results obtained in terms of positioning, map matching, and integrity consistency, proving the feasibility of our proposal.

This paper presents a new shared control method for lane keeping assist (LKA) systems of intelligent vehicles. The proposed method allows the LKA system to effectively share the control authority with a human driver by avoiding or minimizing the conflict situations between these two driving actors. To realize the shared control scheme, the unpredictable driver-automation interaction is explicitly taken into account in the control design via a fictive driver activity variable. This latter is judiciously introduced into the driver–road–vehicle system to represent the driver's need for assistance in accordance with his/her real-time driving activity. Using Lyapunov stability arguments, Takagi–Sugeno fuzzy model-based design conditions are derived to handle not only the time-varying driver activity variable, but also a large variation range of vehicle speed. Both simulation and hardware experiments are presented to demonstrate that the proposed control strategy together with a linear matrix inequality design formulation provide an effective tool to deal with the challenging shared steering control issue. In particular, a fuzzy output feedback control scheme is exploited to achieve the shared control goal without at least two important vehicle sensors. These physical sensors are widely employed in previous works to measure the lateral speed and the steering rate for the control design and real-time implementation. The successful results of this idea of sensor-reduction control has an obvious interest from practical viewpoint.

This paper presents a summary of the current state-of-the-art in INS-based navigation systems in AUVs manufactured by Bluefin Robotics Corporation. A detailed description of the successful integrations of the Kearfott T-24 Ring Laser Gyro and the IXSEA PHINS III Fiber Optic Gyro into recent Bluefin Robotics AUVs is presented. Both systems provide excellent navigation accuracy for high quality data acquisition. This paper provides a comprehensive assessment of the primary advantages and disadvantages of each INS, paying particular attention to navigation accuracy, power draw, physical size, and acoustic radiated noise. Additionally, a brief presentation of a recently integrated Synthetic Aperture Sonar system will be used to highlight how critical a high-performance INS is to hydrographic, mine countermeasures, and other SAS applications.

We present a mobile vision system for multi-person tracking in busy environments. Specifically, the system integrates continuous visual odometry computation with tracking-by-detection in order to track pedestrians in spite of frequent occlusions and egomotion of the camera rig. To achieve reliable performance under real-world conditions, it has long been advocated to extract and combine as much visual information as possible. We propose a way to closely integrate the vision modules for visual odometry, pedestrian detection, depth estimation, and tracking. The integration naturally leads to several cognitive feedback loops between the modules. Among others, we propose a novel feedback connection from the object detector to visual odometry which utilizes the semantic knowledge of detection to stabilize localization. Feedback loops always carry the danger that erroneous feedback from one module is amplified and causes the entire system to become instable. We therefore incorporate automatic failure detection and recovery, allowing the system to continue when a module becomes unreliable. The approach is experimentally evaluated on several long and difficult video sequences from busy inner-city locations. Our results show that the proposed integration makes it possible to deliver stable tracking performance in scenes of previously infeasible complexity.

We introduce a new image-based visual navigation algorithm that allows the Cartesian velocity of a robot to be defined with respect to a set of visually observed features corresponding to previously unseen and unmapped world points. The technique is well suited to mobile robot tasks such as moving along a road or flying over the ground. We describe the algorithm in general form and present detailed simulation results for an aerial robot scenario using a spherical camera and a wide angle perspective camera, and present experimental results for a mobile ground robot.

A path-tracking criterion for the so-called LHD (load-haul-dump) truck used in underground mining is proposed in this paper. It exploits the particular configuration of this vehicle, composed of two units connected by an actuated articulation. The task is to follow the path represented by the middle of the tunnel, maintaining the whole vehicle at a reduced distance from the path itself, to decrease the risk of crashes against the walls of the tunnel. This is accomplished via feedback through the synthesis of an appropriate path-tracking criterion. The criterion is based on monitoring the distances of the midpoints of both axles of the vehicle from their orthogonal projections on the path, using two different moving frames simultaneously. Local asymptotic stability to paths of constant curvature is achieved by means of linear-state feedback. KEY WORDS—mobile robots, articulated vehicles, mining trucks, path tracking, off-tracking, feedback stabilization

This paper studies the estimation of the frictional torque of the synchromesh during the gear shifting operation in an electric vehicle equipped with a clutchless automated manual transmission (AMT). The clutchless drivetrain of the electric vehicle is discussed and the dynamical model of the powertrain from the electric traction motor to the synchromesh system of a two-speed AMT is developed. In order to estimate the frictional torque of the synchromesh, which is indeed an unknown input to the dynamical system, it is assumed to be generated by a fictitious autonomous system. Thereafter, the augmented state- space representation of the actual and fictitious state variables, which forms the basis for the observer design, is provided and the observability of this augmented system is discussed. A deterministic Luenberger observer and a stochastic Kalman-Bucy filter are designed in order to estimate the frictional torque of the synchromesh. The estimation is based on the measuring angular velocities of the electric motor and synchro ring together with the known electromagnetic torque of the traction motor. The performance of the observers is assessed experimentally by means of a test rig. The results demonstrate the satisfactory performance of the stochastic observer when the system encounters process and measurement noise which are likely to happen in practice.

This paper presents a stereo vision-based autonomous navigation system using a GPS and a modified version of the VFH algorithm. In order to obtain a high-accuracy disparity map and meet the time constraints of the real time navigation system, this work proposes the use of a semi-global stereo method. By not suffering the same issues of the regularly used local stereo methods, the employed stereo technique enables the generation of a highly dense, efficient, and accurate disparity map. Obstacles are detected using a method that checks for relative slopes and heights differences. Experimental tests using an electric vehicle in an urban environment were performed to validate the proposed approach.

Exact knowledge of the position of a vehicle is a fundamental problem in mobile robot applications. In search for a solution, researchers and engineers have developed a variety of systems, sensors, and techniques for mobile robot positioning. This paper provides a review of relevant mobile robot positioning technologies. The paper defines seven categories for positioning systems: 1. Odometry; 2. Inertial Navigation; 3. Magnetic Compasses; 4. Active Beacons; 5. Global Positioning Systems; 6. Landmark Navigation; and 7. Model Matching. The characteristics of each category are discussed and examples of existing technologies are given for each category. The field of mobile robot navigation is active and vibrant, with more great systems and ideas being developed continuously. For this reason the examples presented in this paper serve only to represent their respective categories, but they do not represent a judgment by the authors. Many ingenious approaches can be found in the literature, although, for reasons of brevity, not all could be cited in this paper. 1) (Corresponding Author) The University of Michigan, Advanced Technologies Lab, 1101 Beal Avenue, Ann Arbor, MI 48109-2110, Ph.: 313-763-1560, Fax: 313-944-1113. Email: johannb@umich.edu 2) Naval Command, Control, and Ocean Surveillance Center, RDT&E Division 5303, 271 Catalina Boulevard, San Diego, CA 92152-5001, Email: Everett@NOSC.MIL 3) The University of Michigan, Advanced Technologies Lab, 1101 Beal Avenue, Ann Arbor, MI 481092110, Email: Feng@engin.umich.edu 4) The University of Michigan, Dept. of Nuclear Engineering and Radiological Sciences, 239 Cooley Bldg., Ann Arbor, MI 48109, Email: dkw@umich.edu

In this paper, we aim to explore path following. We implement a path following component by referring to the existing Pure Pursuit algorithm. Using the simulation and field operational test, we identified the problem in the path following component. The main problems identified were with respect to vehicles meandering off the path, turning a corner, and the instability of steering control. Therefore, we apply some modifications to the Pure Pursuit[1] algorithm. We have also conducted the simulation and field operational tests again to evaluate these modifications.


Question: Design of a vehicle velocity observer utilizing a 6-D IMU and a multiple-observer technique.

This document explores an advanced method for object tracking in highly dynamic driving scenarios, utilizing the Interacting Multiple Model (IMM) filter. Unlike conventional approaches that rely on a single, optimized Kalman-filter model for a specific driving maneuver, the IMM filter innovatively supports multiple models running concurrently. These models are weighted and combined to generate a comprehensive state estimate. By selecting models tailored to various driving modes, such as maintaining a constant speed, accelerating, or undergoing significant acceleration changes, the IMM filter can significantly enhance the accuracy of object state estimation during highly dynamic maneuvers, particularly in Stop&Go situations. The paper delves into the statistical analysis of these scenarios and systematically parametrizes the IMM method based on the gathered data. The efficacy of the IMM approach is substantiated through real-world testing, leveraging sensor data from laser scanners, radar systems, and video image processing units to evaluate its performance.

This work deals with navigation of an omni-directional mobile robot with active caster wheels. Initially, the posture of the omni-directional mobile robot is calculated by using the odometry information. Next, the position accuracy of the mobile robot is measured through comparison of the odometry information and the external sensor measurement. Finally, for successful navigation of the mobile robot, a motion planning algorithm that employs kinematic redundancy resolution method is proposed. Through experiments for multiple obstacles and multiple moving obstacles, the feasibility of the proposed navigation algorithm was verified.

Recent advances in automotive radar technology have led to increasing sensor resolution and hence a more detailed image of the environment with multiple measurements per object. This poses several challenges for tracking systems: new algorithms are necessary to fully exploit the additional information and algorithms need to resolve measurement-to-object association ambiguities in cluttered multi-object scenarios. Also, the information has to be fused if multi-sensor setups are used to obtain redundancy and increased fields of view. In this paper, a Labeled Multi-Bernoulli filter for tracking multiple vehicles using multiple high-resolution radars is presented. This finite-set-statistics-based filter tackles all three challenges in a fully probabilistic fashion and is the first Monte Carlo implementation of its kind. The filter performance is evaluated using radar data from an experimental vehicle.

Both simultaneous localization and mapping (SLAM) and detection and tracking of moving objects (DTMO) play key roles in robotics and automation. For certain constrained environments, SLAM and DTMO are becoming solved problems. But for robots working outdoors, and at high speeds, SLAM and DTMO are still incomplete. In earlier works, SLAM and DTMO are treated as two separate problems. In fact, they can be complementary to one another. In this paper, we present a new method to integrate SLAM and DTMO to solve both problems simultaneously for both indoor and outdoor applications. The results of experiments carried out with CMU Navlab8 and Navlab11 vehicles with the maximum speed of 45 mph in crowded urban and suburban areas verify the described work.

This paper presents a novel stabilized platform with three UPS-type and one RPU-type active limbs based on the shipboard helicopter's landing process and its kinematics are studied systematically. First, a prototype of this stabilized platform is constructed and its dof is analyzed, Second, analytic formulas for solving the inverse displacement kinematics are derived Third, analytic formulas for solving the forward displacement kinematics are derived and verified. Theoretical formulas and results provide a basis for the further optimization, control, and manufacturing.

This work is to establish an auto-calibration around-view monitoring system to avoid the inconsistent performance from deviations of camera installation. First, a vehicle is parked at a specific area which has a special pattern on the ground. Second, the captured images from four embedded cameras are compared with the reference pattern images to obtain the deviations in angle, X-axis and Y-axis. Finally, these deviations are used to compensate the coefficients of transformation matrices associated with view-point conversion and image stitching. Particularly, the adaptive edge recovery scheme is developed to help on precise alignments. The experimental results show that the proposed system implemented at the TI DaVinci DSP platform can accomplish auto-calibration of a vehicle within 60 seconds and have deviation rates smaller than 38% at stitched regions and 25% at the other regions. Additionally, ±2° deviation errors at angles, X-axis and Y-axis are allowed. Particularly, the proposed system can generate precise 2-meter around-view pictures at a vehicle speed of 30km/h. Therefore, the auto-calibration system proposed herein can be widely applied to vehicle manufacture, verification and repair.

This paper proposes a method for detecting moving obstacles on roads, by using a vehicle mounted monocular camera. To detect various moving obstacles, such as vehicles and pedestrians, the ego-motion of the vehicle is initially estimated from images captured by the camera. There are two problems in ego-motion estimation. Firstly, a typical road scene contains moving obstacles. This causes false estimation of the ego-motion. Secondly, roads possess fewer features, when compared to the number associated with background structures. This reduces the accuracy of ego-motion estimation. In our approach, the ego-motion is estimated from the correspondences of dispersed feature points extracted from various regions other than those that contain moving obstacles. After estimating the ego-motion, any moving obstacles are detected by tracking the feature points over consecutive frames. In our experiments, it has been shown that the proposed method is able to detect moving obstacles

While indoor portable navigation using portable devices is becoming increasingly required, it faces many challenges in obtaining accurate positioning performance. One of the methods to improve navigation results is to recognize the mode of motion of the user carrying the portable device containing low-cost microelectromechanical systems (MEMS) motion sensors. Pattern recognition methodology has been employed to detect a group of motion modes that are common indoors, namely, walking, stationary, going up/down stairs, standing on an escalator, walking on an escalator, standing on a moving walkway, and walking on a moving walkway. The performance of the motion mode recognition module was examined on different types of mobile computing devices, including various brands of smartphones, tablets, smartwatches, and smartglasses, and the results obtained showed the capability of enhancing positioning performance. The module does not require satellite or wireless positioning signals, and depends only on MEMS sensors.

Underwater high resolution 3D mapping implies a very accurate sensor pose estimation to fuse the sensor readings over time into one consistent model. In the case of optical mapping systems, the needed accuracy can easily lie outside of the specification of Doppler Velocity Logs normally used for pose estimation by remotely operated and autonomous underwater vehicles. This is especially the case for difficult terrains or for low altitude situations. Therefore, to improve performance in such situations, a self-referenced laser system is proposed performing micro navigation to significantly improve the resolution of optical underwater 3D seabed mapping.

Robot manipulator designs are increasingly focused on low cost approaches, especially those envisioned for use in unstructured environments such as households, office spaces and hazardous environments. The cost of angular sensors varies based on the precision offered. For tasks in these environments, millimeter order manipulation errors are unlikely to cause drastic reduction in performance. In this paper, estimates the joint angles of a manipulator using low cost triaxial accelerometers by taking the difference between consecutive acceleration vectors. The accelerometer-based angle is compensated with a uniaxial gyroscope using a complementary filter to give robust measurements. Three compensation strategies are compared: complementary filter, time varying complementary filter, and extended Kalman filter. This sensor setup can also accurately track the joint angle even when the joint axis is parallel to gravity and the accelerometer data does not provide useful information. In order to analyze this strategy, accelerometers and gyroscopes were mounted on one arm of a PR2 robot. The arm was manually moved smoothly through different trajectories in its workspace while the joint angle readings from the on-board optical encoders were compared against the joint angle estimates from the accelerometers and gyroscopes. The low cost angle estimation strategy has a mean error 1.3° over the three joints estimated, resulting in mean end effector position errors of 6.1 mm or less. This system provides an effective angular measurement as an alternative to high precision encoders in low cost manipulators and as redundant measurements for safety in other manipulators.

A method is described which quantifies the speed and direction of several moving objects in a sequence of digital images. A relationship between the time variation of intensity, the spatial gradient, and velocity has been developed which allows the determination of motion using clustering techniques. This paper describes these relationships, the clustering technique, and provides examples of the technique on real images containing several moving objects.


Question: Applying artificial intelligence for forecasting stock market trends.

Forecasting Stock Market Trends with Advanced Machine Learning Techniques
The prediction of stock market trends has long captivated the interest of researchers across diverse disciplines. Notably, a plethora of studies have employed sophisticated machine learning methodologies, including support vector machines (SVM) and reinforcement learning, to forecast stock market movements. This study introduces a novel predictive algorithm that harnesses the temporal interdependencies among global stock markets and a variety of financial instruments to anticipate next-day stock market directions. Utilizing SVM, the algorithm achieves a prediction accuracy of 74.4% for NASDAQ, 76% for S&P500, and 77.6% for DJIA. Additionally, the same algorithm is applied in conjunction with various regression techniques to track the actual market growth. To evaluate its practical utility, a straightforward trading model is developed to compare the performance of the proposed predictive algorithm against established benchmarks.

As macroeconomic fundamentals based modelling of FX timeseries have been shown not to fit the empirical evidence at horizons of less than one year, interest has moved towards microstructure-based approaches. Order flow data has recently been receiving an increasing amount of attention in equity market analyses and thus increasingly in foreign exchange as well. In this paper, order flow data is coupled with order book derived indicators and we explore whether pattern recognition techniques derived from computational learning can be applied to successfully infer trading strategies on the underlying timeseries. Due to the limited amount of data available the results are preliminary. However, the approach demonstrates promise and it is shown that using order flow and order book data is usually superior to trading on technical signals alone.

Foreign exchange markets are notoriously difficult to predict. For many years academics and practitioners alike have tried to build trading models, but history has not been kind to their efforts. Consistently predicting FX markets has seemed like an impossible goal but recent advances in financial research now suggest otherwise. With newly developed computational techniques and newly available data, the development of successful trading models is looking possible. The Centre for Financial Research (CFR) at Cambridge University’s Judge Institute of Management has been researching trading techniques in foreign exchange markets for a number of years. Over the last 18 months a joint project with HSBC Global Markets has looked at how the bank’s proprietary information on customer order flow and on the customer limit order book can be used to enhance the profitability of technical trading systems in FX markets. Here we give an overview of that research and report our results.

The banking industry has undergone various changes in the way they conduct the business and focus on modern technologies to compete the market. The banking industry has started realizing the importance of creating the knowledge base and its utilization for the benefits of the bank in the area of strategic planning to survive in the competitive market. In the modern era, the technologies are advanced and it facilitates to generate, capture and store data are increased enormously. Data is the most valuable asset, especially in financial industries. The value of this asset can be evaluated only if the organization can extract the valuable knowledge hidden in raw data. The increase in the huge volume of data as a part of day to day operations and through other internal and external sources, forces information technology industries to use technologies like data mining to transform knowledge from data. Data mining technology provides the facility to access the right information at the right time from huge volumes of raw data. Banking industries adopt the data mining technologies in various areas especially in customer segmentation and profitability, Predictions on Prices/Values of different investment products, money market business, fraudulent transaction detections, risk predictions, default prediction on pricing. It is a valuable tool which identifies potentially useful information from large amount of data, from which organization can gain a clear advantage over its competitors. This study shows the significance of data mining technologies and its advantages in the banking and financial sectors.

The importance of accurate estimation of student’s future performance is essential in order to provide the student with adequate assistance in the learning process. To this end, this research aimed at investigating the use of Bayesian networks for predicting performance of a student, based on values of some identified attributes. We presented empirical experiments on the prediction of performance with a data set of high school students containing 8 attributes. The paper demonstrates an application of the Bayesian approach in the field of education and shows that the Bayesian network classifier has a potential to be used as a tool for prediction of student performance.

ACKNOWLEDGEMENT The timely and successful completion of the book could hardly be possible without the helps and supports from a lot of individuals. I will take this opportunity to thank all of them who helped me either directly or indirectly during this important work. First of all I wish to express my sincere gratitude and due respect to my supervisor Dr. R.K. Agrawal, Associate Professor SC & SS, JNU. I am immensely grateful to him for his valuable guidance, continuous encouragements and positive supports which helped me a lot during the period of my work. I would like to appreciate him for always showing keen interest in my queries and providing important suggestions. I also express whole hearted thanks to my friends and classmates for their care and moral supports. The moments, I enjoyed with them during my M.Tech course will always remain as a happy memory throughout my life. I owe a lot to my mother for her constant love and support. She always encouraged me to have positive and independent thinking, which really matter in my life. I would like to thank her very much and share this moment of happiness with her. Last but not the least I am also thankful to entire faculty and staff of SC & SS for their unselfish help, I got whenever needed during the course of my work.ABSTRACT Time series modeling and forecasting has fundamental importance to various practical domains. Thus a lot of active research works is going on in this subject during several years. Many important models have been proposed in literature for improving the accuracy and effeciency of time series modeling and forecasting. The aim of this book is to present a concise description of some popular time series forecasting models used in practice, with their salient features. In this book, we have described three important classes of time series models, viz. the stochastic, neural networks and SVM based models, together with their inherent forecasting strengths and weaknesses. We have also discussed about the basic issues related to time series modeling, such as stationarity, parsimony, overfitting, etc. Our discussion about different time series models is supported by giving the experimental forecast results, performed on six real time series datasets. While fitting a model to a dataset, special care is taken to select the most parsimonious one. To evaluate forecast accuracy as well as to compare among different models …

This paper shows an evolutionary algorithm application to generate profitable strategies to trade futures contracts on foreign exchange market (Forex). Strategy model in approach is based on two decision trees, responsible for taking the decisions of opening long or short positions on Euro/US Dollar currency pair. Trees take into consideration only technical analysis indicators, which are connected by logic operators to identify border values of these indicators for taking profitable decision(s). We have tested the efficiency of presented approach on learning and test time-frames of various characteristics.

Attempting to successfully and accurately predict the financial market has long attracted the interests and attention of economists, bankers, mathematicians and scientists alike. The financial markets form the bedrock of any economy. There are a large number of factors and parameters that influence the direction, volume, price and flow of traded stocks. This coupled with the markets’ vulnerability to external and non-finance related factors and the resulting intrinsic volatility makes the development of a robust and accurate financial market prediction model an interesting research and engineering problem. In an attempt to solve this engineering problem, the authors of this paper present a rough set theory based predictive model for the financial markets. Rough set theory has, as its base, imperfect data analysis and approximation. The theory is used to extract a set of reducts and a set of trading rules based on trading data of the Johannesburg Stock Exchange (JSE) for the period 1 April 2006 to 1 April 2011. To increase the efficiency of the model four of dicretization algorithms were used on the data set, namely (Equal Frequency Binning (EFB), Boolean Reasoning, Entropy and the Naïve Algorithm. The EFB algorithm gives the least number of rules and highest accuracy. Next, the reducts are extracted using the Genetic Algorithm and finally the set of dependency rules are generated from the set of reducts. A rough set confusion matrix is used to assess the accuracy of the model. The model gave a prediction accuracy of 80.4% using the Standard Voting classifier. Keywords-rough set theory; financial market modelling, neural networks, discretization, classification. INTRODUCTION Around the world, trading in the stock market has gained enormous popularity as a means through which one can reap huge profits. Attempting to successfully and accurately predict the financial market has long attracted the interests and attention of economists, bankers, mathematicians and scientists alike. Thus, stock price movement prediction has long been a cherished desire of investors, speculators and industries [1]. For a long time statistical techniques such as Bayesian models, regression and some econometric techniques have dominated research activities in prediction [2]. The primary approach to financial forecasting has been the identification of a stock price trend and continuation of the investment strategy until evidence suggests that the trend has reversed [3]. One of the biggest problems with the use of regression methods is that they fail to give satisfactory forecasting results for some series’ because of their linear structure and other inherent limitations [8, 9]. However the emergence of computational intelligence techniques as a viable alternative to the “traditional” statistical models that have dominated this area since the 1930’s [2,3] has given impetus to the increasing usage of these techniques in fields such as economics and finance [3]. Apart from these, there have been many other successful applications of intelligent systems to decision support and complex automation tasks [4 6]. Since the year of its inception in 1982, rough set theory has been extensively used as an effective data mining and knowledge discovery technique in numerous applications in the finance, investment and banking fields [4, 7, 11, 12, 20]. Data mining is a discipline in computational intelligence that deals with knowledge discovery, data analysis, and full and semi-autonomous decision making [7]. It entails the analysis of data sets such that unsuspected relationships among data objects are found. Predictive modeling is the practice of deriving future inferences based on these relationships. The financial markets form the bedrock of any economy. There are a large number of factors and parameters that influence the direction, volume, price and flow of traded stocks. This coupled with the markets’ vulnerability to external and nonfinance related factors and the resulting intrinsic volatility makes the development of a robust and accurate financial market prediction model an interesting research and engineering problem. This paper presents a generic stock price prediction model based on rough set theory. The model is derived on data from the daily movements of the Johannesburg Stock Exchange’s All Share Index. The data used was collected over a five year period from 1 April 2006 to 1 April 2011. The methodology used in this paper is as follows: data preprocessing, data splitting, data discreetization, redundant attribute elimination, reduct generation, rule generation and prediction. 71 The rest of this paper is organized as follows: Section II covers the theoretical foundations of rough set theory, Section III gives the design of the proposed prediction model while an analysis of the results and conclusions are presented in Sections IV and V respectively. THEORETICAL FOUNDATIONS OF ROUGH SETS Rough set theory (RST) was introduced by Pawlak in 1982. The theory can be regarded as a mathematical tool used for imperfect data analysis [10]. Thus RST has proved useful in applications spanning the engineering, financial and decision support domains to mention but a few. It is based on the assumption that “with every object in the universe of discourse, some information (data or knowledge) is associated” [10]. In practical applications, the “universe of discourse” described in [10] is usually a table called the decision table in which the rows are objects or data elements and the columns are attributes and the entries are called the attribute values [3]. The objects or data elements described by the same attributes are said to be indiscernible (indistinguishable) by the attribute set. Any set of indiscernible data elements forms a granule or atom of knowledge about the entire “universe of discourse” (information system framework) [10]. A union of these elementary sets (granules) is said to be a precise or crisp set, other-wise the set is said to be rough [1,2,7,10]. Every rough set will have boundary cases i.e data objects which cannot certainly be classified as belonging to the set or its complement when using the available information [10]. Associated with every rough set is a pair of sets called the lower and upper approximation of the rough set. The lower approximation consists of those objects which one can definitively say belong to the target set. The upper approximation consists of those objects which possibly belong to the target set. The difference between the two sets is the boundary region. The decision rule derived specifies an outcome based on certain conditions. Where the derived rule uniquely identifies outcomes based on some conditions the rule is said to be certain else it is uncertain. Every decision rule has a pair of probabilities associated with it, the certainty and coverage coefficients [3]. These conditional probabilities also satisfy Bayes’ theorem [7,10]. The certainty coefficient is the conditional probability that an object that belongs to the decision class outlined by the rule given that it satisfies the conditions of the rule. The coverage coefficient on other hand expresses the conditional probability of reasons given some decision [10]. Clearly RST can be seen to overlap with many other theories in the realm of imperfect knowledge analysis such as evidence theory, Bayesian inference, fuzzy sets etc [1, 3, 4, 10, 11, 12]. To define rough sets mathematically, we begin by defining an information system S = (U,A), where U and A are finite and non-empty sets that represent the data objects and attributes respectively. Every attribute has a set of possible values Va. Va is called the domain of a. A subset of A say B will determine a binary relation I(B) on U, which is called the indiscerniblity relation. The relation is defined as follows: ( ) ( ) if and only if a(x) = a(y) for every a in B, where a(x) denotes the value of attribute a for data object x [10]. I(B) is an equivalence relation. All equivalence classes of I(B) as U/I(B). An equivalence class of I(B) containing x is denoted as B(x). If (x,y) belong to I(B) they are said to be indiscernible with respect to B. All equivalence classes of the indescernibility relation, I(B), are referred to as B-granules or B-elementary sets [10]. In the information system defined above, we define as in [10]: (1) And, (2) We now define the two operators assigned to every (1) two sets called the upper and lower approximation of X. The two sets are defined as follows [10]: ( ) ⋃ { ( ) ( ) } (3) And, ( ) ⋃ { ( ) ( ) } (4) Thus, the lower approximation is the union of all Belementary sets that are included in the target set, whilst the upper approximation is the union of all B-elementary sets that have a non-empty intersection with the target set. The difference between the two sets is called the boundary of region of X.

This study proposed a new insight in comparing common methods used in predicting based on data series i.e statistical method and machine learning. The corresponding techniques are use in predicting Forex (Foreign Exchange) rates. The Statistical method used in this paper is Adaptive Spline Threshold Autoregression (ASTAR), while for machine learning, Support Vector Machine (SVM) and hybrid form of Genetic Algorithm-Neural Network (GA-NN) are chosen. The comparison among the three methods accurate rate is measured in root mean squared error (RMSE). It is found that ASTAR and GA-NN method has advantages depend on the period time intervals.

This This study aims to investigate the robustness of prediction model by comparing artificial neural networks (ANNs), and support vector machine (SVMs) model. The study employs ten years monthly data of six types of macroeconomic variables as independent variables and the average rate of return of one-month time deposit of Indonesian Islamic banks (RR) as dependent variable. Finally, the performance is evaluated through graph analysis, statistical parameters and accuracy rate measurement. This research found that ANNs outperforms SVMs empirically resulted from the training process and overall data prediction. This is indicating that ANNs model is better in the context of capturing all data pattern and explaining the volatility of RR.

India is an agricultural country which largely depends on monsoon for irrigation purpose. A large amount of water is consumed for industrial production, crop yield and domestic use. Rainfall forecasting is thus very important and necessary for growth of the country. Weather factors including mean temperature, dew point temperature, humidity, pressure of sea and speed of wind and have been used to forecasts the rainfall. The dataset of 2245 samples of New Delhi from June to September (rainfall period) from 1996 to 2014 has been collected from a website named Weather Underground. The training dataset is used to train the classifier using Classification and Regression Tree algorithm, Naive Bayes approach, K nearest Neighbour and 5-10-1 Pattern Recognition Neural Network and its accuracy is tested on a test dataset. Pattern Recognition networks has given 82.1% accurate results, KNN with 80.7% correct forecasts ranks second, Classification and Regression Tree(CART) gives 80.3% while Naive Bayes provides 78.9% correctly classified samples.


Question: Applying artificial intelligence for forecasting trends in the stock market.

This study employs a Long Short-Term Memory (LSTM) network to forecast stock returns in the Chinese market, highlighting its predictive capabilities in a complex and volatile environment. The historical data from the Chinese stock market was structured into sequences, each 30 days in length, encompassing 10 learning features, with labels indicating the 3-day earning rate. The LSTM model was trained on a dataset of 900,000 sequences and validated using an additional 311,361 sequences. Compared to random prediction methods, the LSTM model significantly enhanced the accuracy of stock return predictions, increasing it from a mere 14.3% to a commendable 27.2%. This improvement underscores the robustness of LSTM in deciphering patterns and predicting trends within the intricate dynamics of the Chinese stock market, showcasing its superiority over traditional methods in handling the inherent unpredictability of this domain.

This research concentrates upon predictive analysis of enrolled pupil using forecasting based on data mining technique. The Microsoft SQL Server Data Mining Add-ins Excel 2007 was employed as a software mining tool for predicting enrollment of pupils. The time series algorithm was used for experiment analysis. U-DISE (Unified District Information System for Education) Dataset of Aurangabad district in Maharashtra (India) was obtained from SSA (Sarva Shiksha Abhiyan) and was used for analysis. Data mining for primary pupil enrollment research provides a feasible way to analyze the trend and to addresses the interest of pupil. The dataset was studied and analyzed to forecast registered pupils in government for upcoming years. We conclude that for upcoming year, pupil strength in government school will be dropped down and number of teachers will be supersizing in the said district. Keywords— Data mining, Microsoft SQL Server Data Mining Add-ins Excel 2007, Time series algorithm, U-DISE

A system for predicting the results of football matches that beats the bookmakers’ odds is presented. The predictions for the matches are based on previous results of the teams involved.

According to UNESCO, education is a fundamental human right and every nation's citizens should be granted universal access with equal quality to it. Because this goal is yet to be achieved in most countries, in particular in the developing and underdeveloped countries, it is extremely important to find more effective ways to improve education. This paper presents a model based on the application of computational intelligence (data mining and data science) that leads to the development of the student's knowledge profile and that can help educators in their decision making for best orienting their students. This model also tries to establish key performance indicators to monitor objectives' achievement within individual strategic planning assembled for each student. The model uses random forest for classification and prediction, graph description for data structure visualization and recommendation systems to present relevant information to stakeholders. The results presented were built based on the real dataset obtained from a Brazilian private k-9 (elementary school). The obtained results include correlations among key data, a model to predict student performance and recommendations that were generated for the stakeholders.

Artificial neural network is a valuable tool for time series forecasting. In the case of performing multiperiodic forecasting with artificial neural networks, two methods, namely iterative and direct, can be used. In iterative method, first subsequent period information is predicted through past observations. Afterwards, the estimated value is used as an input; thereby the next period is predicted. The process is carried on until the end of the forecast horizon. In the direct forecast method, successive periods can be predicted all at once. Hence, this method is thought to yield better results as only observed data is utilized in order to predict future periods. In this study, forecasting was performed using direct and iterative methods, and results of the methods are compared using grey relational analysis to find the method which gives a better result. 2008 Elsevier Ltd. All rights reserved.

Each and every sector in this digital world is undergoing a dramatic change due to the influence of IT field. The agricultural sector needs more support for its development in developing countries like India. Price prediction helps the farmers and also Government to make effective decision. Based on the complexity of vegetable price prediction, making use of the characteristics of neural networks such as self-adapt, self-study and high fault tolerance, to build up the model of Back-propagation neural network to predict vegetable price. A prediction model was set up by applying the neural network. Taking tomato as an example, the parameters of the model are analyzed through experiment. At the end of the result of Back-propagation neural network shows absolute error percentage of monthly and weekly vegetable price prediction and analyze the accuracy percentage of the price prediction.

This paper presents improvements in financial time series prediction using a Deep Neural Network (DNN) in conjunction with a Discrete Wavelet Transform (DWT). When comparing our model to other three alternatives, including ARIMA and other deep learning topologies, ours has a better performance. All of the experiments were conducted on High-Frequency Data (HFD). Given the fact that DWT decomposes signals in terms of frequency and time, we expect this transformation will make a better representation of the sequential behavior of high-frequency data. The input data for every experiment consists of 27 variables: The last 3 one-minute pseudo-log-returns and last 3 oneminute compressed tick-by-tick wavelet vectors, each vector is a product of compressing the tick-by-tick transactions inside a particular minute using a DWT with length 8. Furthermore, the DNN predicts the next one-minute pseudo-log-return that can be transformed into the next predicted one-minute average price. For testing purposes, we use tick-by-tick data of 19 companies in the Dow Jones Industrial Average Index (DJIA), from January 2015 to July 2017. The proposed DNN’s Directional Accuracy (DA) presents a remarkable forecasting performance ranging from 64% to 72%.

Artificial Neural Networks (ANNs) have been applied to predict many complex problems. In this paper ANNs are applied to horse racing prediction. We employed Back-Propagation, Back-Propagation with Momentum, QuasiNewton, Levenberg-Marquardt and Conjugate Gradient Descent learning algorithms for real horse racing data and the performances of five supervised NN algorithms were analyzed. Data collected from AQUEDUCT Race Track in NY, include 100 actual races from 1 January to 29 January 2010. The experimental results demonstrate that NNs are appropriate methods in horse racing prediction context. Results show that BP algorithm performs slightly better than other algorithms but it needs a longer training time and more parameter selection. Furthermore, LM algorithm is the fastest. Key-Words: Artificial Neural Networks, Time Series Analysis, Horse Racing Prediction, Learning Algorithms, BackPropagation

*This work is supported by a grant from National Science Foundation (IIS-1111092) and a strategic research grant from City University of Hong Kong (7002770) *The work was done when the first author was visiting University of Illinois at Chicago. Abstract • The Web has seen a tremendous rise in social media. • People’s experiences and opinions expressed in Social Media (e.g., Twitter, Facebook) contain rich social signals (e.g., about consumer sentiment, stock market, economic conditions, etc.). • The goal of this paper is to exploit public sentiment for stock market prediction. • Topic based sentiment is an important factor in stock prediction because it reflects public opinions about current issues/topics. • We employ a Bayesian non-parametric topic-based sentiment time series approach learned from streaming tweets to predict the S&P100 Stock Index.

Algorithms which construct classifiers from sample data -such as neural networks, radial basis functions, and decision trees -have attracted growing attention for their wide applicability. Researchers in the fields of Statistics, Artificial Intelligence, Machine Learning, Data Mining, and Pattern Recognition are continually introducing (or rediscovering) induction methods, and often publishing implementing code. It is natural for practitioners and potential users to wonder, "Which classification technique is best?", or more realistically, "What subset of methods tend to work well for a given type of dataset?". This book provides perhaps the best current answer to that question.

The support vector regression (SVR) has been employed to deal with stock price forecasting problems. However, the selection of appropriate kernel parameters is crucial to obtaining satisfactory forecasting performance. This paper proposes a novel approach for forecasting stock prices by combining the SVR with the firefly algorithm (FA). The proposed forecasting model has two stages. In the first stage, to enhance the global convergence speed, a modified version of the FA, which is termed the MFA, is developed in which the dynamic adjustment strategy and the opposition-based chaotic strategy are introduced. In the second stage, a hybrid SVR model is proposed and combined with the MFA for stock price forecasting, in which the MFA is used to optimize the SVR parameters. Finally, comparative experiments are conducted to show the applicability and superiority of the proposed methods. Experimental results show the following: (1) Compared with other algorithms, the proposed MFA algorithm possesses superior performance, and (2) The proposed MFA-SVR prediction procedure can be considered as a feasible and effective tool for forecasting stock prices.