• Combined Probabilistic Shaping and Nyquist Pulse Shaping for PAM8 Signal Transmission in WDM Systems

      Han, Xiao; Yang, Mingwei; Djordjevic, Ivan B.; Yue, Yang; Wang, Qiang; Qu, Zhen; Anderson, Jon; Univ Arizona, ECE Dept (IEEE, 2019)
      We use the LDPC-coded probabilistically shaped PAM8 signaling combined with Nyquist pulse shaping to improve the transmission performance in a WDM system. We find that the combination of these shaping schemes offers great performance improvement. (C) 2019 The Author(s)
    • Computer Aided System for Users with Visual Impairments

      Kirboyun, Sevgi; Univ Arizona, Coll Educ, Dept Disabil & Psychoeduc Studies (IEEE, 2018)
      Screen readers enable people with visual impairment (VI) to have equal access to written information both on computers and smartphones. This paper presents an overview of screen readers that allow users with VI read the text materials and describe images on the computer screen by using a special software program to synthesize as speech. We study three aspects of the screen readers in terms of their accessibility. First, we invest on developing websites, apps, and software programs by following web content accessibility guidelines to ensure accessibility. We than conduct research on compatibility of the websites, apps, and software programs with screen readers. Last, we inform about descriptions of the images by creating alternative texts.
    • A CONCEPTUAL FRAMEWORK TO CLASSIFY THE EXTENSIONS OF DEVS FORMALISM AS VARIANTS AND SUBCLASSES

      Blas, Maria J.; Gonnet, Silvio M.; Leone, Horacio P.; Zeigler, Bernard P.; Univ Arizona, RTSync Corp (IEEE, 2018)
      The Discrete Event System Specification (DEVS) is a general modeling formalism with sound semantics founded on a system theoretic basis. It can be used as a base for the development of specialized modeling formalisms. Usually, the extensions of DEVS expand the classes of systems models that can be represented in DEVS. However, with a growing number in new variants of DEVS and an increasing number of problems to be solved using discrete simulation techniques, it is necessary to define the relations among different approaches. This paper presents a conceptual modeling perspective applied to DEVS extensions that structure a framework over the traditional modeling and simulation approach. The framework provides a multilevel structure to analyze the features required for each extension type. Two main types of extensions are identified: variants and subclasses. In order to illustrate the proposed guidelines, the Routed DEVS formalism is presented as example of the subclass type.
    • Control of Spherical Robots on Uneven Terrains

      Sabet, Sahand; Poursina, Mohammad; Nikravesh, Parviz E.; University of Arizona, Department of Aerospace and Mechanical Engineering (IEEE, 2021-09-27)
      Hybrid robots incorporate the advantages of both aerial-only and terrestrial-only vehicles to achieve enhanced mobility and better energy efficiency. Among hybrid vehicles, spherical robots offer the best maneuverability. While operating on uneven surfaces is one of the main benefits of spherical robots, the current literature only covers control of these robots on flat surfaces. This work presents two control algorithms to track a desired trajectory and angular velocity of spherical robots on uneven terrains. The proposed control algorithms can be used when the terrain is known analytically or empirically (i.e., point cloud). By allowing the controller to use empirical information about the terrain profile, this work broadens the implementation of spherical robots in real applications.
    • Decentralized Control of Distributed Actuation in a Segmented Soft Robot Arm

      Doroudchi, Azadeh; Shivakumar, Sachin; Fisher, Rebecca E.; Marvi, Hamid; Aukes, Daniel; He, Ximin; Berman, Spring; Peet, Matthew M.; Univ Arizona, Coll Med Phoenix, Dept Basic Med Sci (IEEE, 2018)
      Continuum robot manipulators present challenges for controller design due to the complexity of their infinite-dimensional dynamics. This paper develops a practical dynamics-based approach to synthesizing state feedback controllers for a soft continuum robot arm composed of segments with local sensing, actuation, and control capabilities. Each segment communicates its states to its two adjacent neighboring segments, requiring a tridiagonal feedback matrix for decentralized controller implementation. A semi-discrete numerical approximation of the Euler-Bernoulli beam equation is used to represent the robot arm dynamics. Formulated in state space representation, this numerical approximation is used to define an H-infinity optimal control problem in terms of a Bilinear Matrix Inequality. We develop three iterative algorithms that solve this problem by computing the tridiagonal feedback matrix which minimizes the H-infinity norm of the map from disturbances to regulated outputs. We confirm through simulations that all three controllers successfully dampen the free vibrations of a cantilever beam that are induced by an initial sinusoidal displacement, and we compare the controllers' performance.
    • Decision of Learning Status Based on Modeling of the Information Measurement of Social Behavioral Tasks in Rhesus Monkeys

      Lee, SeungHyun; Rozenblit, Jerzy W.; Gothard, Katalin M.; University of Arizona, Department of Electrical and Computer Engineering; College of Medicine, University of Arizona, Department of Physiology (IEEE, 2021-07-19)
      We are interested in identifying the learning status of the social behavioral tasks in the rhesus monkey. In addition, we define the characteristic of stimulus with a numerical quantification. We allow monkeys to interact with individuals of different social status, while we monitor the viewer monkey's behavior by tracking its scan paths. With these observations, we can understand the learning status of this animal via looking behavior analysis on the stimulus. First, the viewer monkey shows different looking patterns among six different classes. Therefore, we can generate different data descriptors of these classes and observe the classification performance of the machine learning algorithm. Second, we design the ground truth model based on the characteristic of each stimulus. We define the distribution of information from the ratio of the face, body, and background area in the stimulus. Lastly, we link them to figure out whether the viewer monkey learned enough about the information in the stimulus.
    • Deep Learning Classification of Chest X-Ray Images

      Majdi, Mohammad S.; Salman, Khalil N.; Morris, Michael F.; Merchant, Nirav C.; Rodriguez, Jeffrey J.; Univ Arizona, Dept Elect & Comp Engn; Univ Arizona, Dept Med Imaging; Univ Arizona, Data Sci Inst; Univ Arizona, Dept Radiol (IEEE, 2020-05-18)
      We propose a deep learning based method for classification of commonly occurring pathologies in chest X-ray images. The vast number of publicly available chest X-ray images provides the data necessary for successfully employing deep learning methodologies to reduce the misdiagnosis of thoracic diseases. We applied our method to the classification of two example pathologies, pulmonary nodules and cardiomegaly, and we compared the performance of our method to three existing methods. The results show an improvement in AUC for detection of nodules and cardiomegaly compared to the existing methods.
    • Design of volumetric sub-THz negative refractive index metamaterial with gain

      Kantemur, A.; Tang, Q.; Xin, H.; Department of Electrical and Computer Engineering, University of Arizona; This work is supported in part by AFOSR grant under FA9550-13-1-0209 (IEEE, 2016-06)
      Conventional passive metamaterials always suffer from the limitation of loss and dispersion due to fundamental causality issue. Especially it becomes severe due to material loss at terahertz frequency. Our work resolves the loss problem by introducing gain device into the metamaterial structure. A passive volumetric metamaterial is firstly designed on the quartz substrate. A negative resistance is inserted into the wire of the structure to provide the gain. We have identified resonant tunneling diodes that work up into THz frequency and shown in simulation that simultaneous negative index and gain can be obtained.
    • DESIGNING CARE PATHWAYS USING SIMULATION MODELING AND MACHINE LEARNING

      Elbattah, Mahmoud; Molloy, Owen; Zeigler, Bernard P.; Univ Arizona, Dept Elect & Comp Engn (IEEE, 2018)
      The development of care pathways is increasingly becoming an instrumental artefact towards improving the quality of care and cutting costs. This paper presents a framework that incorporates Simulation Modeling along with Machine Learning (ML) for the purpose of designing pathways and evaluating the return on investment of implementation. The study goes through a use case in relation to elderly healthcare in Ireland, with a particular focus on the hip-fracture care scheme. Initially, unsupervised ML is utilized to extract knowledge from the Irish Hip Fracture Database. Data clustering is specifically applied to learn potential insights pertaining to patient characteristics, care-related factors, and outcomes. Subsequently, the data-driven knowledge is utilized within the process of simulation model development. Generally, the framework is conceived to provide a systematic approach for developing healthcare policies that help optimize the quality and cost of care.
    • Designing Finite Alphabet Iterative Decoders of LDPC Codes Via Recurrent Quantized Neural Networks

      Xiao, Xin; Vasic, Bane; Tandon, Ravi; Lin, Shu; Univ Arizona, Dept Elect & Comp Engn (IEEE, 2020-04-06)
      In this paper, we propose a new approach to design finite alphabet iterative decoders (FAIDs) for Low-Density Parity Check (LDPC) codes over binary symmetric channel (BSC) via recurrent quantized neural networks (RQNN). We focus on the linear FAID class and use RQNNs to optimize the message update look-up tables by jointly training their message levels and RQNN parameters. Existing neural networks for channel coding work well over Additive White Gaussian Noise Channel (AWGNC) but are inefficient over BSC due to the finite channel values of BSC fed into neural networks. We propose the bit error rate (BER) as the loss function to train the RQNNs over BSC. The low precision activations in the RQNN and quantization in the BER cause a critical issue that their gradients vanish almost everywhere, making it difficult to use classical backward propagation. We leverage straight-through estimators as surrogate gradients to tackle this issue and provide a joint training scheme. We show that the framework is flexible for various code lengths and column weights. Specifically, in high column weight case, it automatically designs low precision linear FAIDs with superior performance, lower complexity, and faster convergence than the floating-point belief propagation algorithms in waterfall region.
    • Detecting Cyber Threats in Non-English Dark Net Markets: A Cross-Lingual Transfer Learning Approach

      Ebrahimi, Mohammadreza; Surdeanu, Mihai; Surdeanu, Mihai; Chen, Hsinchun; Univ Arizona, Dept Management Informat Syst; Univ Arizona, Dept Comp Sci (IEEE, 2018)
      Recent advances in proactive cyber threat intelligence rely on early detection of cyber threats in hacker communities. Dark Net Markets (DNMs) are growing platforms in hacker community that provide hackers with highly specialized tools and products which may not be found in other platforms. While text classification techniques have been used for cyber threat detection in English DNMs, the task is hindered in non-English platforms due to the language barrier and lack of ground-truth data. Current approaches use monolingual models on machine translated data to overcome these challenges. However, the translation errors can deteriorate the classification results. The abundance of data in English DNMs can be leveraged in learning non-English threats without using machine translation. In this study, we show that a deep cross-lingual model that can jointly learn the common language representation from two languages, significantly outperforms a monolingual model learned on machine translated data for identifying cyber threats in non-English DNMs. Unlike most studies, our approach does not require any external data source such as bilingual word embeddings or bilingual lexicons. Our experiments on Russian DNMs show that this approach can achieve better performance than state-of-the-art methods for non-English cyber threat detection in malicious hacker community.
    • Digital Behavioral Biometrics and Privacy: Methods for Improving Business Processes without Compromising Customer Privacy

      Valacich, J. S.; Jenkins, J. L.; Cisic, D.; University of Arizona, Management Information Systems Department (IEEE, 2022-05-23)
      To enable people to interact with online websites and systems, browsers capture a variety of events that occur on the page - such as how a person is moving the computer mouse, what a person clicks on, what a person types, and whether a person is scrolling. These events represent a user's behavior on a page, referred to as the DOM or Document Object Model, and are recorded at a millisecond precision rate (e.g., the exact millisecond timestamp when a key goes down and when it comes back up). Research and practice alike have found that these behavioral events can provide powerful insight into the users' experience, such as whether users are frustrated, and even help distinguish between legitimate and fraudulent users. In this paper, we present six best practices for responsibly collecting these digital behavioral biometric data to help protect user privacy as well as encourage proper interpretation. For each principle, we discuss its rationale and practical application.
    • Discretized Gaussian Modulation-Based Continuous Variable (CV)-QKD

      Djordjevic, Ivan B.; Univ Arizona, Dept Elect & Comp Engn (IEEE, 2019-09)
      To overcome the low-reconciliation-efficiency problem of Gaussian modulation (GM)-based-CV-QKD, we propose to use discretized-GM-based-CV-QKD. This scheme has complexity and reconciliation-efficiency similar to discrete modulation (DM)-based-CV-QKD and at the same time solves for the problem of nonexistence of strict security proofs for DM-CV-QKD under collective attacks.
    • A DNN-LSTM based Target Tracking Approach using mmWave Radar and Camera Sensor Fusion

      Sengupta, Arindam; Jin, Feng; Cao, Siyang; Univ Arizona, Dept Elect & Comp Engn (IEEE, 2020-04-09)
      A new sensor fusion study for monocular camera and mmWave radar using deep neural network and LSTMs is presented. The proposed study includes a decision framework to produce reliable output when either sensor fails. Experiment results to demonstrate single sensor uncertainty and the proposed method's advantages are also presented.
    • Dual-comb laser system for time-resolved studies of fireballs in the MIR

      Rhoades, Ryan T.; Lecaplain, Caroline; Schunemann, Peter G.; Jones, R. Jason; Univ Arizona, Coll Opt Sci (IEEE, 2019)
      We present a high power all-fiber dual-comb system suitable for time-resolved spectroscopy measurements in the mid-infrared to study explosion dynamics and detection of chemical species. (C) 2019 The Author(s)
    • Dual-wavelength fiber laser above 2 mu m based on cascaded single mode-multimode-single mode structures

      Fu, Shijie; Shi, Guannan; Sheng, Quan; Shi, Wei; Yao, Jianquan; Zhu, Xiushan; Peyghambarian, N.; College of Optical Sciences, University of Arizona (IEEE, 2016-06)
      A stable dual-wavelength Tm:Ho co-doped fiber laser operating above 2 mu m based on cascaded single mode-multimode-single mode (SMS) all-fiber structures has been proposed and experimentally demonstrated for the first time.
    • Efficient Data Access in Hybrid Cloud Storage

      Samy, Islam; Koyluoglu, O. Ozan; Rawat, Ankit Singh; Univ Arizona, ECE, Tucson, AZ 85721 USA; Univ Calif Berkeley, EECS, Berkeley, CA 94720 USA; MIT, RLE, Cambridge, MA 02141 USA (IEEE, 2018-01-18)
      Hybrid cloud is a widely adopted framework where on-premise storage and/or compute resources are combined with public cloud system. This paper explores the storage aspect of this framework, which requires designing coding schemes that are aware of both local and global components of the available storage space. The coding schemes should provide efficient repair mechanisms for the data stored on the public cloud (global storage space) and utilize the local storage space to facilitate seamless access to the overall information stored on the hybrid cloud storage. This paper presents a mathematical model for hybrid cloud storage which takes all these requirements into account. The paper then extends the information flow graph approach to characterize the fundamental limits on access bandwidth of the system, i.e., the amount of data downloaded from the public cloud during the data reconstruction process. This paper also presents several explicit coding schemes that utilize the available local storage space to attain the fundamental limit on the access bandwidth. The setup where multiple clients with varying local storage spaces are supported by a single global storage space is also addressed.
    • An Efficient Instanton Search Algorithm for LP Decoding of LDPC Codes Over the BSC

      Chilappagari, Shashi Kiran; Chertkov, Michael; Vasic, Bane; Univ Arizona, Dept Elect & Comp Engn (IEEE, 2011-06-20)
      We consider linear programming (LP) decoding of a fixed low-density parity-check (LDPC) code over the binary symmetric channel (BSC). The LP decoder fails when it outputs a pseudo-codeword which is not equal to the transmitted codeword. We design an efficient algorithm termed the Instanton Search Algorithm (ISA) which generates an error vector called the BSC-instanton. We prove that: (a) the LP decoder fails for any error pattern with support that is a superset of the support of an instanton; (b) for any input, the ISA outputs an instanton in the number of steps upper-bounded by twice the number of errors in the input error vector. We then find the number of unique instantons of different sizes for a given LDPC code by running the ISA sufficient number of times.
    • Eliminating trapping sets in low-density parity-check codes by using Tanner graph covers

      Ivkovic, Milos; Chilappagari, Shashi Kiran; Vasic, Bane; Univ Arizona, Dept Elect & Comp Engn (IEEE, 2008-08-01)
      We discuss error floor asympotics and present a method for improving the performance of low-density parity-check (LDPC) codes in the high SNR (error floor) region. The method is based on Tanner graph covers that do not have trapping sets from the original code. The advantages of the method are that it is universal, as it can be applied to any LDPC code/channel/decoding algorithm and it improves performance at the expense of increasing the code length, without losing the code regularity, without changing the decoding algorithm, and, under certain conditions, without lowering the code rate. The proposed method can be modified to construct convolutional LDPC codes also. The method is illustrated by modifying Tanner, MacKay and Margulis codes to improve performance on the binary symmetric channel (BSC) under the Gallager B decoding algorithm. Decoding results on AWGN channel are also presented to illustrate that optimizing codes for one channel/decoding algorithm can lead to performance improvement on other channels.
    • Enhanced Photovoltaic Power Model Fidelity Using On-Site Irradiance and Degradation-Informed Performance Input

      Dzurick, Matthew; Potter, B.G.; Holmgren, William F.; Simmons-Potter, Kelly; Univ Arizona (IEEE, 2019-06)
      The impact of irradiance data collection location and degradation-informed module performance on irradiance-to-power model accuracy is evaluated. A decrease in RMSE of over 20% is found for output power predictions using NREL PVWatts when site-specific irradiance data are coupled with maximum power point output characteristics obtained using accelerated lifecycle testing.