Miracle-dz/UniFAR-FTUs huggingface dataset:
Matrix Structure Driven Interior Point Method for Quadrotor Real-Time Trajectory Planning

Sequential convex programming (SCP) has been recently employed in various trajectory planning problems, including entry flight, planetary landing, and aircraft formation. In SCP, convex programming subproblems are sequentially solved to obtain the optimum of original nonconvex problems. For SCP-based quadrotor trajectory planning, this paper proposes a matrix-structure-driven interior point method (MSD-IPM) to improve the efficiency of solving search directions in convex programming. In MSD-IPM, primal-dual systems for solving search directions are derived from the Karush–Kuhn–Tucher (KKT) conditions of quadrotor trajectory planning subproblems. Then, the successive elimination technique is used to solve the inverse of large-scale coefficient matrices of primal-dual systems by more efficient operations on small-scale matrices. In successive elimination, the positive definiteness of several small-scale matrices is used to enhance the numerical stability of computing search directions, and the specific diagonal structures of small-scale matrices are exploited to efficiently compute the search directions. The complexity analysis shows that the efficiency of the proposed method is about one order of magnitude higher than that of the standard IPM. The comparative studies on simulation experiments demonstrate that the MSD-IPM generally outperforms several well-known optimizers (e.g., MOSEK, SDPT3, and SeDuMi) in terms of efficiency and robustness. Finally, the indoor trajectory tracking experiments indicate that the proposed method can generate smooth trajectories for real-world applications.
Combining Deep Learning Technologies with Multi-Level Gabor Features for Facial Recognition in Biometric Automated Systems

: Face recognition is one of the most important abilities that humans use in their daily lives. It represents a natural, robust and non-intrusive manner for identifying individuals. However, it is also a very challenging problem in the field of computer vision and pattern recognition. A good face recognition algorithm should be able to automatically detect and recognize a face in an image, regardless of lightning, expression, illumination and pose. In this paper, we present a novel approach for the face model representation and matching issues in face recognition. Our approach is based on multi-level Gabor features and Deep Learning techniques. In the experiments presented in this paper, ORL, Caltech, Yale and Yale B databases were used in order to obtain the face recognition rate. The results show that the new face recognition algorithm outperforms the conventional methods such as global Gabor face recognition based on PCA in terms of recognition rate.
Aktivní fázované anténní systémy pro přibližovací radary

The thesis deals with the precision approach radar (PAR) problematics focusing on active antenna arrays with application of adaptive methods of angle of arrival estimation. First of all, the thesis introduces some possible PAR antenna arrays solutions. These solutions are then optimized to reduce the number of antenna elements and to enhance its angular resolution. The methods of angular resolution improvement are described and analysed in details. The difficulties of the adaptive methods of the angle of arrival estimation, especially of the coherent signals reception and the number of incoming signals estimation are analysed and their possible solutions are shown and experimentally verified.
Interactive logical analysis of planning domains

Kalyanam, Rajesh PhD, Purdue University, August 2016. Interactive Logical Analysis of Planning Domains. Major Professor: Robert L. Givan. Humans exhibit a significant ability to answer a wide range of questions about previously unencountered planning domains, and leverage this ability to construct “general-purpose” solution plans for the domain. The long term vision of this research is to automate this ability, constructing a system that utilizes reasoning to automatically verify claims about a planning domain. The system would use this ability to automatically construct and verify a generalized plan to solve any planning problem in the domain. The goal of this thesis is to start with baseline results from the interactive verification of claims about planning domains and develop the necessary knowledge representation and reasoning methods to progressively reduce the amount of human interaction required. To achieve this goal, a representation of planning domains in a class-based logic syntax was developed. A novel proof assistant was then used to perform semiautomatic machine analysis of two benchmark planning domains: Blocksworld and Logistics. This analysis was organized around the interactive formal verification of state invariants and specifications of the state-change effects of handwritten recursive program-like generalized plans. The human interaction required for these verifications was metered and qualitatively characterized. This characterization motivated several algorithmic changes to the proof assistant resulting in significant savings in the interactions required. A strict limit was enforced on the time spent by the base reasoner in response to user queries; interactions taking longer were studied to direct improvements to the inference engine’s efficiency. A complete account of these changes is provided.
The Aesthetic Point Of View Selected Essays

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Application of Three-Dimensional Motion Tracking of Low-Activity Fiducial Positron-Emitting Markers in Radiation Therapy and Positron Emission Tomography

Patient body motion limits the delivery accuracy of radiotherapy and creates blurring artefacts on positron emission tomography (PET) images. Those adverse effects can be mitigated by tracking the patient body motion and using the information appropriately. A technique that can track the three-dimensional motion of low-activity positron-emitting fiducial markers was developed. The application of this tracking technique, called PeTrack, for respiratory-gated radiotherapy and for motion-compensated PET imaging was evaluated. The feasibility of respiratory-gated radiotherapy using PeTrack was assessed. A respiratory gating interface was developed in LabVIEW to communicate with an Elekta Precise research linear accelerator and toggle the delivery of the beam. Radiochromic films were placed in a rod insert of an anthropomorphic dynamic thorax phantom to evaluate the dose distribution of the gated and non-gated delivery of a small square beam. A single low-activity source was used to track the motion of the phantom. Real patient breathing data were used as the basis of the motion of the phantom. Visual and quantitative assessments of the films confirmed that respiratory-gated radiotherapy using real-time tracking based on positron-emitting fiducial markers is achievable. The blurring of the dose distribution due to motion was greatly reduced on the gated deliveries compared to the non-gated cases. A modified version of the tracking algorithm was developed to track fiducial markers when high physiological tracer activity from a patient undergoing PET imaging is present. Monte Carlo and phantom studies demonstrated that the tracking can achieve submillimetre accuracy and precision. The motion traces from the tracking were used to generated respiratory-gated cardiac PET images. The performance of PeTrack-based gating was compared to the Varian RPM system. PeTrack-based gating captured more motion of the patient and performed similarly to the RPM system. The feasibility of event-by-event motion correction of the raw PET data based on the tracking from PeTrack was also evaluated. Motion-corrected reconstructed images showed reduced motion blurring. The potential for respiratory-gated radiotherapy and motion-compensated PET imaging using PeTrack was demonstrated.
COLOR IMAGE RETRIEVAL BASED ON OBJECT REGIONS

When an image is observed, it is natural that the image is to be divided into the object regions and the background regions according to the human vision. Generally, the image retrieval performance is evaluated mainly with the object-based measurement. A color image retrieval algorithm based on object regions is presented in this paper. First, each component grayscale image in the HSV color space of the color image is obtained, and then the binary edge image of each grayscale image is calculated. In the color image retrieval based on object regions, it is most important to extract the object regions from the color image effectively. According to the connectivity of the edge images, the object regions are extracted from the color image. During the image retrieval, the features of the subimages, which the object regions of the color image correspond to, are extracted and used in the image similarity matching process in place of the global image features. And two classes of experiments are conducted: the object regions extraction and the color image retrieval based on object regions. Experiments show that the color image retrieval algorithm based on object regions is superior to that based on the global image.
A survey of clustering algorithms in community detection of complex networks

Complex network analysis begins in the 1930s, and the community structure in complex networks is a major characteristic that has been widely concerned. This paper introduces the basic and core ideas of several typical clustering algorithms in community detecting, and analyzes the advantages and disadvantages of each one. And it also reviews the background, the significance and the performance of these algorithms. Some algorithms may perform well in some specific areas but not in community detecting, while the newest algorithms still need to be tested in the future. Keywords-Complex Networks; Clustering; Community Detection
Stochastic differential equations for the deterioration processes of engineering systems

This work was supported in part by the MAE Center at the University of Illinois at Urbana-Champaign and the National Institute of Standards and Technology (NIST) through the Center for Risk-Based Community Resilience Planning under Award No 70NANB15H044. Opinions and findings presented are those of the authors and do not necessarily reflect the views of the sponsors.
Scalable Three-Dimensional Grasping Mechanism

Dr. Mohammad Mayyas, Advisor In this work, we develop a scalable end-effector mechanism for grasping threedimensional objects with sizes ranging from micrometer to millimeter scale. The design architecture of the gripper comprises an array of identical fingers patented in a circular fashion. Each finger is designed from a novel linkage mechanism whose end effector is manipulated by two independent actuators. In this research, we study three finger gripper device, where each is obtained from a 3 linkage mechanism. The device is controlled by three independent piezo actuators, and one electro-magnetic solenoid common to each mechanism. The gripping capability depends on how fingers are controlled collectively and on the mechanical flexibility, which together provide variety of gripping performances that are necessary to handle a wide variety of objects. The gripping performance is defined here by grasping force at contact, motion range, and bandwidth. Optimization is done to design the link lengths for the best Geometric Advantage (GA), and the functionality evaluated using finite element analysis software, ANSYS.
An evaluation of the interactive video CAPTCHA method against automated attack

: CAPTCHA is designed to detect automated programs (called bots) by requiring them to perform tasks that are easy for humans but di ﬃ cult for automations. CAPTCHAs are vulnerable to relay attacks in which the challenges are relayed to remote human-solvers. In our previous paper, we proposed an interactive video type CAPTCHA that is strongly resistant to relay attacks. However, a quantitative evaluation of resistance to automated attacks still has not been carried out. Herein, we implement an automated attack for applying to our CAPTCHA and evaluate its resistance to automated attacks. Our results show the robustness of our proposed method against mean shift algorithm.
Market-Based Scheduling in Distributed Computing Systems

In verteilten Rechensystemen (bspw. im Cluster und Grid Computing) kann eine Knappheit der zur Verfugung stehenden Ressourcen auftreten. Hier haben Marktmechanismen das Potenzial, Ressourcenbedarf und -angebot durch geeignete Anreizmechanismen zu koordinieren und somit die okonomische Effizienz des Gesamtsystems zu steigern. Diese Arbeit beschaftigt sich anhand vier spezifischer Anwendungsszenarien mit der Frage, wie Marktmechanismen fur verteilte Rechensysteme ausgestaltet sein sollten.
IoT Database Forensics: An Investigation on HarperDB Security

The data that are generated by several devices in the IoT realm require careful and real time processing. Recently, researchers have concentrated on the usage of cloud databases for storing such data to improve efficiency. HarperDB aims at producing a DBMS that is relational and non-relational simultaneously, to help journeymen developers creating products and servers in the IoT space. Much of what the HarperDB team has talked about has been achieved, but from a security perspective, a lot of improvements need to be made. The team has clearly focused on the problems that exist from a database and data point of view, creating a structure that is unique, fast, easy to use and has great potential to grow with a startup. The functionality and ease of use of this DBMS is not in question, however as the trade-off triangle to the right suggests, this does entail an impact to security. In this paper, using multiple forensic methodologies, we performed an in-depth forensic analysis on HarperDB and found several areas of extreme concern, such as lack of logging functionalities, basic level of authorisation, exposure of users' access rights to any party using the database, There had to be a focus on preventative advice instead of reactive workarounds due to the nature of the flaws found in HarperDB. As such, we provide a number of recommendations for the users and developers.
Evaluation of causal Bayesian network search algorithms using simulated mesotheliomas gene expression data

To understand the physiology of a complex disease, such as mesotheliomas, it is necessary to learn how the genes that are involved in developing the disease interact with the environment. To this end, statistical methods that can detect these gene-environment interactions will help scientists in detecting causal relationships among genes. These predicted causal relationships among genes can then be later verified through actual laboratory experiments. In this paper, we have developed a novel causal discovery system that incorporates recent advances in Bayesian network search methods. We introduce a novel algorithm called Equivalence Checking Local Implicit latent variable scoring Method with Markov Chain Monte Carlo (EquLIM-MCMC) search algorithm that extends existing causal Bayesian network discovery algorithms, EquLIM and the Local Implicit latent variable scoring Method (LIM). Markov Chain Monte Carlo (MCMC) search has been shown to be very useful especially in analyzing datasets where the number of input variables greatly exceeds the number of cases that are collected (Friedman and Koller 2000; Hageman, Leduc et al. 2011). More and more datasets that are collected for gene expression studies have thousands of genes' expression levels (input variables) that are measured from tens or hundreds of subjects (cases). Datasets collected in gene-environment interactions studies will show similar trends. We use LIM with MCMC (LIM-MCMC) and EquLIM-MCMC to analyze purely observational simulated gene expression datasets. To test these algorithms' abilities to detect causal relationships from realistic data, we generate datasets from a gene regulation pathway model of malignant mesothelioma formation proposed by an expert. Using the metrics of Area Under Receiver Operating Characteristic (AUROC) curve, Positive Predictive Value (PPV), and Shannon Entropy, we show that EquLIM-MCMC exhibit clear advantages over LIM-MCMC with causal relationship predictions. EquLIM-MCMC therefore improves over LIM-MCMC's ability in detecting causal relationships in gene networks and gene-environment interactions from presently available observational gene expression data.
A BeiDou Navigation System Aided Method for Localization in Manually Deployed Wireless Sensor Networks

BeiDou Navigation System can provide PNT services for mainland China. In this paper we present the design, implementation and evaluation of a simple, practical and cost effective Localization solution for wireless sensor network notes. This method is designed especially for manual deployments of WSN. We evaluate the solution exclusively in real deployments of BeiDou models. Experiments results show that 100% of the deployed notes have been successfully located and that the average errors are within 2 meters. Keywords-WSN; beidou navigation system; location
Foreign accents reduce false recognition rates in the DRM paradigm

ABSTRACT More cognitive resources are required to comprehend foreign-accented than native speech. Focusing these cognitive resources on resolving the acoustic mismatch between the foreign-accented input and listeners’ stored representations of spoken words can affect other cognitive processes. Across two studies, we explored whether processing foreign-accented speech reduces the activation of semantic information. This was achieved using the DRM paradigm, in which participants study word lists and typically falsely remember non-studied words (i.e. critical lures) semantically associated with the studied words. In two experiments, participants were presented with word lists spoken both by a native and a foreign-accented speaker. In both experiments we observed lower false recognition rates for the critical lures associated with word lists presented in a foreign accent, compared to native speech. In addition, participants freely recalled more studied words when they had been presented in a native, compared to a foreign, accent, although this difference only emerged in Experiment 2, where the foreign speaker had a very strong accent. These observations suggest that processing foreign-accented speech modulates the activation of semantic information. Highlights The DRM paradigm was used to explore whether semantic activation is reduced when processing foreign-accented speech. Across two experiments, false recognition of non-studied semantic associates was lower when word lists were presented in a foreign accent, compared to native speech. The above results suggest semantic activation may be reduced when processing foreign-accented speech. Additionally, it was found that when the foreign speaker had a mild accent, correct recall of studied words was uninfluenced. If the foreign speaker had a strong accent, however, correct recall of studied words was reduced.
Uncertainty-Aware Visualization of Fish Populations (Demo)

Applying computer vision for monitoring ecosystems opens new perspectives for marine ecology research. Information on ﬁsh populations can be automatically extracted from underwater cameras. As scientists, ecologists need to understand the uncertainty in computer vision results. We present an interface for studying ﬁsh populations that addresses two challenges: provide information about ﬁsh populations as well as computer vision uncertainty; and enable open-ended data exploration. We introduce an uncertainty visualization for non-experts and a generalizable interaction design for data exploration.
Statistical Process Monitoring of the Tennessee Eastman Process Using Parallel Autoassociative Neural Networks and a Large Dataset

In this article, the statistical process monitoring problem of the Tennessee Eastman process is considered using deep learning techniques. This work is motivated by three limitations of the existing works for such problem. First, although deep learning has been used for process monitoring extensively, in the majority of the existing works, the neural networks were trained in a supervised manner assuming that the normal/fault labels were available. However, this is not always the case in real applications. Thus, in this work, autoassociative neural networks are used, which are trained in an unsupervised fashion. Another limitation is that the typical dataset used for the monitoring of the Tennessee Eastman process is comprised of just a small number of data samples, which can be highly limiting for deep learning. The dataset used in this work is 500-times larger than the typically-used dataset and is large enough for deep learning. Lastly, an alternative neural network architecture, which is called parallel autoassociative neural networks, is proposed to decouple the training of different principal components. The proposed architecture is expected to address the co-adaptation issue of the fully-connected autoassociative neural networks. An extensive case study is designed and performed to evaluate the effects of the following neural network settings: neural network size, type of regularization, training objective function, and training epoch. The results are compared with those obtained using linear principal component analysis, and the advantages and limitations of the parallel autoassociative neural networks are illustrated.
Performance Enhancement in LDPC coded high-dimension MIMO-OFDM systems

Currently, low-density parity check (LDPC) coded multiple–input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems becoming very important research area in wireless communication. Especially in high throughput wireless communication, due to its capacity increasing ability in a wireless fading environment. And also it is one of the research candidates for 5G communication when it is applied in large scale MIMO systems. It has been shown that LDPC coded MIMO systems have much attention in industrialization by some development work from its theory to commercial products in the semiconductor industry. And in this paper, we applied Expectation Propagation (EP) algorithm with A message-passing algorithm (MPA) and belief propagation (BP) decoding for LDPC coded MIMO-OFDM systems. In fact, Expectation Propagation (EP) algorithm is a low-complexity algorithm. It solves the posterior marginalization problem easily which it makes efficient work with high-dimension MIMO. Therefore, our proposed work is for high throughput providing channel coding in the high-dimension wireless channel.