Recent Submissions

  • Human Supervised Multirotor UAV System Design for Inspection Applications

    Shaqura, Mohammad; Alzuhair, Khalid; Abdellatif, Fadl; Shamma, Jeff S. (IEEE, 2018-09-20)
    Multirotor UAVs are widely used for aerial inspection applications where missions are accomplished either via manual or autonomous control. Human controlled UAVs require trained pilots which can be a barrier from using the technology for general inspection personnel. Fully autonomous navigation, which employs onboard sensing, planning and coverage algorithms, is effective but comes with the cost of development and operational complexities. A human supervised UAV system design is presented where a deployed aerial vehicle operates in semi-autonomous mode. An operator, who is equipped with a smart handheld laser pointer, gives the UAV global guiding directions to reach the inspection target. The UAV is equipped with onboard vision sensing for local planning and target identification in addition to video streaming or recording. System operation is validated in indoor fliaht tests.
  • High Power GaN-based Blue Superluminescent Diode Exceeding 450 mW

    Alatawi, Abdullah; Holguin Lerma, Jorge Alberto; Shen, Chao; Shakfa, Mohammad Khaled; Alhamoud, Abdullah A.; Albadri, Abdulrahman M.; Alyamani, Ahmed Y.; Ng, Tien Khee; Ooi, Boon S. (IEEE, 2018-09-19)
    We demonstrate a high-power blue emitting superluminescent diode (SLD) with a tilted-facet configuration. An optical power of 457 mW with a broad spectral bandwidth of 6.5 nm was obtained under pulsed current injection of 1A, leading to a large power-bandwidth product of ~2970 mW·nm.
  • Numerical simulation of the magnetic cement induction response in the borehole environment

    Eltsov, Timofey; Patzek, Tadeusz W. (Society of Exploration Geophysicists, 2018-09-12)
    We present a technique for the detection of integrity of the magnetic cement behind resistive fiberglass casing. Numerical simulations show that an optimized induction logging tool allows one to detect small changes in the magnetic permeability of cement through a non-conductive casing in a vertical (or horizontal) well. Changes in magnetic permeability influence mostly the real part of the vertical component of magnetic field. The signal attenuation is sensitive to a change of magnetic properties of the cement. Our simulations show that optimum separation between the transmitter and receiver coils ranges from 0.25 to 0.6 meters, and the most suitable magnetic field frequencies vary from 0.1 to 10 kHz. Our goal is to build cheap, long-lasting, low-temperature (<150°C) geothermal wells with water recirculation.
  • An Intelligent Gripper Design for Autonomous Aerial Transport with Passive Magnetic Grasping and Dual-Impulsive Release

    Fiaz, Usman A.; Abdelkader, M.; Shamma, Jeff S. (IEEE, 2018-09-07)
    We present a novel gripper design for autonomous aerial transport of ferrous objects with unmanned aerial vehicles (UAVs). The proposed design uses permanent magnets for grasping, and a novel dual-impulsive release mechanism, for achieving drop. The gripper can simultaneously lift up to four objects of arbitrary shape, in fully autonomous mode, with a 100% rate of successful drops. We optimize the system subject to realistic constraints, such as the simplicity of design and its sturdiness to aerial maneuvers, payload limits for multi-rotor UAVs, reliability of autonomous grasping irrespective of the environment of operation, active power consumption of the gripper, and its comparison with the existing technologies. We describe the design concepts, and the hardware, and perform extensive experiments is both indoor and outdoor environments, with two multi-rotor configurations. Several results, showcasing superior performance of the proposed system are provided as well.
  • Manufacturable Heterogeneous Integration for Flexible CMOS Electronics

    Hussain, Muhammad M.; Shaikh, Sohail F.; Sevilla, Galo A. Torres; Nassar, Joanna M.; Hussain, Aftab M.; Bahabry, Rabab R.; Khan, Sherjeel M.; Kutbee, Arwa T.; Rojas, Jhonathan P.; Ghoneim, Mohamed T.; Cruz, Melvin (IEEE, 2018-09-07)
    Nearly sixty years back when Jack Kilby built the first integrated circuit (IC), it was also the beginning of today's advanced and matured complementary metal oxide semiconductor (CMOS) technology whose arts and science of miniaturization has enabled Moore's Law to double up the number of devices in a given area in every two years. It has also been possible because CMOS technology has consistently adopted new materials and processes. High performance (data processing speed in computational devices), energy efficiency (for portable devices) and ultra-large-scale-integration (ULSI) density - all these features have been added to every major technology generation in additive manner. As we go forward and embrace Internet of Everything (IoE) where people, process, device and data are going to be seamlessly connected, we may want to ask ourselves a few fundamental questions about the future of CMOS electronics, enabling role of CMOS technology, potential benefits and application opportunities. Physically flexible electronics are increasingly getting attention as a critical and impactful expansion area for the general area of electronics. Many exciting demonstrations have been made to point out to its powerful prospect. Due to the paradox that traditional crystalline materials based electronics are useful in data management but they are naturally rigid and bulky, most of the researchers have resorted to two strategies: (i) non-silicon based fully flexible system with limited functionality and (ii) hybrid flexible electronic system with off-the-shelf ICs for data management. We do not consider this paradox is fundamental and a block-by-block approach using traditional CMOS technology can allow us to build fully flexible CMOS electronic systems [Fig. 1].
  • Multibit Memory Cells Based on Spin-Orbit Torque Driven Magnetization Switching of Nanomagnets with Configurational Anisotropy

    Wasef, Shaik; Amara, Selma; Alawein, Meshal; Fariborzi, Hossein (IEEE, 2018-09-07)
    In this work, we report the fabrication and characterization of novel four and six terminal current-driven magnetic memory cells. In particular, we experimentally demonstrate the magnetization switching of triangular and square magnets through spin-orbit torque by in-plane currents in a Pt/NiFe (Py) heterostructure. The spin torques, generated by applying a constant current in one of multiple Pt wires, are used to switch a Py film between its multiple stable magnetic states, as quantified by anisotropic magnetoresistance (AMR) and tunnel magnetoresistance (TMR) measurements at room temperature. The observations have also been confirmed by micromagnetic simulations.
  • Fully printed microwave sensor for simultaneous and independent level measurements of 8 liquids

    Karimi, Muhammad Akram; Arsalan, Muhammad; Shamim, Atif (IEEE, 2018-09-03)
    Level sensors find numerous applications in many industries to automate the processes involving chemicals. Currently, some commercial ultrasound, capacitance and microwave radar based level sensors are being used for medical and industrial usage [1]. Some of the desirable features in any level sensor are its non-intrusiveness, low cost and consistent performance. It is a common stereotype to consider microwaves sensing mechanism as being expensive. Unlike usual expensive, intrusive and bulky microwave methods of level sensing using guided radars, this paper presents an extremely low cost, fully printed, non-intrusive microwave sensor to reliably sense the level/volume of 8 different types of liquid independently and simultaneously. This paper presents a new microwave level sensor whose design is inspired by a T-reso-nator. A unique modification of the conventional T-resonator enables it to sense liquid level/volume present inside any metallic container of cylindrical shape. The proposed sensor can be thoroughly fabricated using additive manufacturing like 3D and screen printing making it easier, faster and cheaper to realize.
  • Super-Resolution and Sparse View CT Reconstruction

    Zang, Guangming; Aly, Mohamed; Idoughi, Ramzi; Wonka, Peter; Heidrich, Wolfgang (2018-09-01)
    We present a flexible framework for robust computed tomography (CT) reconstruction with a specific emphasis on recovering thin 1D and 2D manifolds embedded in 3D volumes. To reconstruct such structures at resolutions below the Nyquist limit of the CT image sensor, we devise a new 3D structure tensor prior, which can be incorporated as a regularizer into more traditional proximal optimization methods for CT reconstruction. As a second, smaller contribution, we also show that when using such a proximal reconstruction framework, it is beneficial to employ the Simultaneous Algebraic Reconstruction Technique (SART) instead of the commonly used Conjugate Gradient (CG) method in the solution of the data term proximal operator. We show empirically that CG often does not converge to the global optimum for tomography problem even though the underlying problem is convex. We demonstrate that using SART provides better reconstruction results in sparse-view settings using fewer projection images. We provide extensive experimental results for both contributions on both simulated and real data. Moreover, our code will also be made publicly available.
  • Flexible Magnetoresistive Sensors for Guiding Cardiac Catheters

    Hawsawi, M.; Amara, S.; Mashraei, Y.; Almansouri, A.; Mohammad, H.; Sevilla, G. Torres; Jakob, G.; Jaiswal, S.; Klaui, M.; Haneef, A.; Saoudi, A.; Hussain, M.; Kosel, J. (IEEE, 2018-08-20)
    Cardiac catheterization is a procedure, in which a long thin tube that is called a “catheter” is inserted into the heart for diagnosis or treatment. Due to the excessive use of x-ray doses and contrast agents for orientation detection during the surgery, there is a need to find a better alternative. This paper presents magnetic tunnel junction sensors on flexible Si attached to the catheter tip for orientation detection during minimally invasive surgeries. Due to the small size of catheters, extreme minimization in terms of size, weight, thickness and power consumption is needed for any device implemented on it. The fabricated flexible magnetic tunnel junctions fulfill those requirements with size, thickness, weight and power consumption of 150 μm 2 , 12 μm, 8 μg and 0.15 μW, respectively, while still providing a high sensitivity of 4.93 %/Oe. The sensors can be bent with up to 500 μm in diameter, which is more than needed for even the smallest catheters of size 1 mm (3 Fr) in diameter. This result is a stepping-stone towards the development of a versatile and low-cost smart catheter system that can help surgeons navigate inside the heart while minimizing the side effects.
  • Highly-Sensitive Magnetic Tunnel Junction Based Flow Cytometer

    Amara, Selma; Bu, Ride; Alawein, Meshal; Alsharif, Nouf; Khan, Mohammed Asadullah; Wen, Yan; Zhang, Xixiang; Kosel, Jurgen; Fariborzi, Hossein (IEEE, 2018-08-20)
    Flow cytometers are important instruments for biological and biomedical analyses. These instruments are large and expensive, and researchers are continuously striving to come up with smaller, cheaper, and more energy-efficient flow cytometers. In this work, we present a highly-sensitive magnetic tunnel junction (MTJ) based flow cytometer. An externally magnetized magnetic beads labeling cells were placed above an MTJ sensor that can measure the stray field surrounding the beads. It was found that each time labeled cells pass through the sensitive area of the sensor, a peak of signal was observed. The results demonstrate a novel MTJ based flow cytometer design approach for accurate detection of magnetically labeled cells.
  • Herdable Systems Over Signed, Directed Graphs

    Ruf, Sebastian F.; Egerstedt, Magnus; Shamma, Jeff S. (IEEE, 2018-08-17)
    This paper considers the notion of herdability, a set-based reachability condition, which asks whether the state of a system can be controlled to be element-wise larger than a non-negative threshold. The basic theory of herdable systems is presented, including a necessary and sufficient condition for herdability. This paper then considers the impact of the underlying graph structure of a linear system on the herdability of the system, for the case where the graph is represented as signed and directed. By classifying nodes based on the length and sign of walks from an input, we find a class of completely herdable systems as well as provide a complete characterization of nodes that can be herded in systems with an underlying graph that is a directed out-branching rooted at a single input.
  • Picking a Partner

    Alowayed, Yousef; Canini, Marco; Marcos, Pedro; Chiesa, Marco; Barcellos, Marinho (ACM Press, 2018-08-08)
    We tackle the problem of enabling Autonomous Systems to evaluate network providers on the basis of their adherence to Service Level Agreements (SLAs) regarding interconnection agreements. In current Internet practices, choices of interconnection partners are driven by factors such as word of mouth, personal relationships, brand recognition and market intelligence, and not by proofs of previous performance. Given that Internet eXchange Points provide increasingly more peering choices, rudimentary schemes for picking interconnection partners are not adequate anymore. Although the current interconnection ecosystem is shrouded in confidentiality, our key observation is that recently-emerged blockchain technology and advances in cryptography enable a privacy-preserving decentralized solution based on actual performance measurements. We propose the concept of SLA score to evaluate network providers and introduce a privacy-preserving protocol that allows networks to compute and verify SLA scores.
  • Real-Time Massively Distributed Multi-object Adaptive Optics Simulations for the European Extremely Large Telescope

    Ltaief, Hatem; Charara, Ali; Gratadour, Damien; Doucet, Nicolas; Hadri, Bilel; Gendron, Eric; Feki, Saber; Keyes, David (IEEE, 2018-08-06)
    The European Extremely Large Telescope (E-ELT) is one of today's most challenging projects in ground-based astronomy. Addressing one of the key science cases for the E-ELT, the study of the early Universe, requires the implementation of multi-object adaptive optics (MOAO), a dedicated concept relying on turbulence tomography. We use a novel pseudo-Analytical approach to simulate the performance of tomographic reconstruction of the atmospheric turbulence in an MOAO system on real datasets. We simulate simultaneously 4K galaxies in a common field of view on massively parallel supercomputers during a single night of observations. We are able to generate a first-ever high-resolution galaxy map at almost a real-Time throughput. This simulation scale opens new research horizons in numerical methods for experimental astronomy, some core components of the pipeline standing as pathfinders toward actual operations and future astronomic discoveries on the E-ELT.
  • Dynamic Embeddings for User Profiling in Twitter

    Liang, Shangsong; Zhang, Xiangliang; Ren, Zhaochun; Kanoulas, Evangelos (ACM Press, 2018-07-19)
    In this paper, we study the problem of dynamic user profiling in Twitter. We address the problem by proposing a dynamic user and word embedding model (DUWE), a scalable black-box variational inference algorithm, and a streaming keyword diversification model (SKDM). DUWE dynamically tracks the semantic representations of users and words over time and models their embeddings in the same space so that their similarities can be effectively measured. Our inference algorithm works with a convex objective function that ensures the robustness of the learnt embeddings. SKDM aims at retrieving top-K relevant and diversified keywords to profile users' dynamic interests. Experiments on a Twitter dataset demonstrate that our proposed embedding algorithms outperform state-of-the-art non-dynamic and dynamic embedding and topic models.
  • Multi-label Learning with Highly Incomplete Data via Collaborative Embedding

    Han, Yufei; Sun, Guolei; Shen, Yun; Zhang, Xiangliang (ACM Press, 2018-07-19)
    Tremendous efforts have been dedicated to improving the effectiveness of multi-label learning with incomplete label assignments. Most of the current techniques assume that the input features of data instances are complete. Nevertheless, the co-occurrence of highly incomplete features and weak label assignments is a challenging and widely perceived issue in real-world multi-label learning applications due to a number of practical reasons including incomplete data collection, moderate labels from annotators, etc. Existing multi-label learning algorithms are not directly applicable when the observed features are highly incomplete. In this work, we attack this problem by proposing a weakly supervised multi-label learning approach, based on the idea of collaborative embedding. This approach provides a flexible framework to conduct efficient multi-label classification at both transductive and inductive mode by coupling the process of reconstructing missing features and weak label assignments in a joint optimisation framework. It is designed to collaboratively recover feature and label information, and extract the predictive association between the feature profile and the multi-label tag of the same data instance. Substantial experiments on public benchmark datasets and real security event data validate that our proposed method can provide distinctively more accurate transductive and inductive classification than other state-of-the-art algorithms.
  • REST: A Reference-based Framework for Spatio-temporal Trajectory Compression

    Zhao, Yan; Shang, Shuo; Wang, Yu; Zheng, Bolong; Nguyen, Quoc Viet Hung; Zheng, Kai (ACM Press, 2018-07-19)
    The pervasiveness of GPS-enabled devices and wireless communication technologies results in massive trajectory data, incurring expensive cost for storage, transmission, and query processing. To relieve this problem, in this paper we propose a novel framework for compressing trajectory data, REST (Reference-based Spatio-temporal trajectory compression), by which a raw trajectory is represented by concatenation of a series of historical (sub-)trajectories (called reference trajectories) that form the compressed trajectory within a given spatio-temporal deviation threshold. In order to construct a reference trajectory set that can most benefit the subsequent compression, we propose three kinds of techniques to select reference trajectories wisely from a large dataset such that the resulting reference set is more compact yet covering most footprints of trajectories in the area of interest. To address the computational issue caused by the large number of combinations of reference trajectories that may exist for resembling a given trajectory, we propose efficient greedy algorithms that run in the blink of an eye and dynamic programming algorithms that can achieve the optimal compression ratio. Compared to existing work on trajectory compression, our framework has few assumptions about data such as moving within a road network or moving with constant direction and speed, and better compression performance with fairly small spatio-temporal loss. Extensive experiments on a real taxi trajectory dataset demonstrate the superiority of our framework over existing representative approaches in terms of both compression ratio and efficiency.
  • Task-Guided and Semantic-Aware Ranking for Academic Author-Paper Correlation Inference

    Zhang, Chuxu; Yu, Lu; Zhang, Xiangliang; Chawla, Nitesh V. (International Joint Conferences on Artificial Intelligence Organization, 2018-07-05)
    We study the problem of author-paper correlation inference in big scholarly data, which is to effectively infer potential correlated works for researchers using historical records. Unlike supervised learning algorithms that predict relevance score of author-paper pair via time and memory consuming feature engineering, network embedding methods automatically learn nodes' representations that can be further used to infer author-paper correlation. However, most current models suffer from two limitations: (1) they produce general purpose embeddings that are independent of the specific task; (2) they are usually based on network structure but out of content semantic awareness. To address these drawbacks, we propose a task-guided and semantic-aware ranking model. First, the historical interactions among all correlated author-paper pairs are formulated as a pairwise ranking loss. Next, the paper's semantic embedding encoded by gated recurrent neural network, together with the author's latent feature is used to score each author-paper pair in ranking loss. Finally, a heterogeneous relations integrative learning module is designed to further augment the model. The evaluation results of extensive experiments on the well known AMiner dataset demonstrate that the proposed model reaches significant better performance, comparing to a number of baselines.
  • Convergence Analysis of Gradient Descent for Eigenvector Computation

    Xu, Zhiqiang; Cao, Xin; Gao, Xin (International Joint Conferences on Artificial Intelligence Organization, 2018-07-05)
    We present a novel, simple and systematic convergence analysis of gradient descent for eigenvector computation. As a popular, practical, and provable approach to numerous machine learning problems, gradient descent has found successful applications to eigenvector computation as well. However, surprisingly, it lacks a thorough theoretical analysis for the underlying geodesically non-convex problem. In this work, the convergence of the gradient descent solver for the leading eigenvector computation is shown to be at a global rate O(min{ (lambda_1/Delta_p)^2 log(1/epsilon), 1/epsilon }), where Delta_p=lambda_p-lambda_p+1>0 represents the generalized positive eigengap and always exists without loss of generality with lambda_i being the i-th largest eigenvalue of the given real symmetric matrix and p being the multiplicity of lambda_1. The rate is linear at (lambda_1/Delta_p)^2 log(1/epsilon) if (lambda_1/Delta_p)^2=O(1), otherwise sub-linear at O(1/epsilon). We also show that the convergence only logarithmically instead of quadratically depends on the initial iterate. Particularly, this is the first time the linear convergence for the case that the conventionally considered eigengap Delta_1= lambda_1 - lambda_2=0 but the generalized eigengap Delta_p satisfies (lambda_1/Delta_p)^2=O(1), as well as the logarithmic dependence on the initial iterate are established for the gradient descent solver. We are also the first to leverage for analysis the log principal angle between the iterate and the space of globally optimal solutions. Theoretical properties are verified in experiments.
  • Mining Streaming and Temporal Data: from Representation to Knowledge

    Zhang, Xiangliang (International Joint Conferences on Artificial Intelligence Organization, 2018-07-05)
    In this big-data era, vast amount of continuously arriving data can be found in various fields, such as sensor networks, network management, web and financial applications. To process such data, algorithms are usually challenged by its complex structure and high volume. Representation learning facilitates the data operation by providing a condensed description of patterns underlying the data. Knowledge discovery based on the new representations will then be computationally efficient, and to certain extent be more effective due to the removal of noise and irrelevant information in the step of representation learning. In this paper, we will briefly review state-of-the-art techniques for extracting representation and discovering knowledge from streaming and temporal data, and demonstrate their performance at addressing several real application problems.
  • Bandit Online Learning on Graphs via Adaptive Optimization

    Yang, Peng; Zhao, Peilin; Gao, Xin (International Joint Conferences on Artificial Intelligence Organization, 2018-07-05)
    Traditional online learning on graphs adapts graph Laplacian into ridge regression, which may not guarantee reasonable accuracy when the data are adversarially generated. To solve this issue, we exploit an adaptive optimization framework for online classification on graphs. The derived model can achieve a min-max regret under an adversarial mechanism of data generation. To take advantage of the informative labels, we propose an adaptive large-margin update rule, which enjoys a lower regret than the algorithms using error-driven update rules. However, this algorithm assumes that the full information label is provided for each node, which is violated in many practical applications where labeling is expensive and the oracle may only tell whether the prediction is correct or not. To address this issue, we propose a bandit online algorithm on graphs. It derives per-instance confidence region of the prediction, from which the model can be learned adaptively to minimize the online regret. Experiments on benchmark graph datasets show that the proposed bandit algorithm outperforms state-of-the-art competitors, even sometimes beats the algorithms using full information label feedback.

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