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Faculty Publications

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Full-Text Articles in Computer Engineering

Subsurface Mimo: A Beamforming Design In Internet Of Underground Things For Digital Agriculture Applications, Abdul Salam Aug 2019

Subsurface Mimo: A Beamforming Design In Internet Of Underground Things For Digital Agriculture Applications, Abdul Salam

Faculty Publications

In underground (UG) multiple-input and multiple-output (MIMO), the transmit beamforming is used to focus energy in the desired direction. There are three different paths in the underground soil medium through which the waves propagates to reach at the receiver. When the UG receiver receives a desired data stream only from the desired path, then the UG MIMO channel becomes three path (lateral, direct, and reflected) interference channel. Accordingly, the capacity region of the UG MIMO three path interference channel and degrees of freedom (multiplexing gain of this MIMO channel requires careful modeling). Therefore, expressions are required for the degree of ...


A Path Loss Model For Through The Soil Wireless Communications In Digital Agriculture, Abdul Salam Jul 2019

A Path Loss Model For Through The Soil Wireless Communications In Digital Agriculture, Abdul Salam

Faculty Publications

In this paper, a path loss model is developed to predict the impact of soil type, soil moisture, operation frequency, distance, and burial depth of sensors for through-the-soil wireless communications channel. The soil specific model is developed based on empirical measurements in a testbed and field settings. The model can be used in different soils for a frequency range of 100MHz to 1GHz. The standard deviation between measured and predicted path loss is from 4-6dB in the silt loam, sandy, and silty clay loam soil types. The model leads to development of sensor-guided irrigation system in the field of digital ...


Underground Soil Sensing Using Subsurface Radio Wave Propagation, Abdul Salam, Akhlaque Ahmad Jul 2019

Underground Soil Sensing Using Subsurface Radio Wave Propagation, Abdul Salam, Akhlaque Ahmad

Faculty Publications

Continuous sensing of soil moisture is essential for smart agriculture variable rate irrigation (VRI), real-time agricultural decision making, and water conservation. Therefore, development of simple techniques to measure the in-situ properties of soil is of vital importance. Moreover, permittivity estimation has applications in electromagnetic (EM) wave propagation analysis in the soil medium, depth analysis, subsurface imaging, and UG localization. Different methods for soil permittivity and moisture estimation are time-domain reflectometry (TDR), ground-penetrating radar (GPR) measurements, and remote sensing. One major bottleneck in the current laboratory-based permittivity estimation techniques is off-line measurement of the collected soil samples. At that, the remote ...


A Comparison Of Path Loss Variations In Soil Using Planar And Dipole Antennas, Abdul Salam Jul 2019

A Comparison Of Path Loss Variations In Soil Using Planar And Dipole Antennas, Abdul Salam

Faculty Publications

In this paper, an empirical investigation of propagation path loss variations with frequency in sandy and silty clay loam soils has been done using planar and dipole antennas. The path loss experiments are conducted using vector network analyzer (VNA) in sandy soil testbed, and greenhouse outdoor silty clay loam testbed for different operation frequencies and communication distances. The results show that the planar antenna can be used for subsurface communications in a wide range of operation frequencies. The comparison paves the way for development of sensor-guided irrigation system in the field of digital agriculture.


Urban Underground Infrastructure Monitoring Iot: The Path Loss Analysis, Abdul Salam, Syed Shah Apr 2019

Urban Underground Infrastructure Monitoring Iot: The Path Loss Analysis, Abdul Salam, Syed Shah

Faculty Publications

The extra quantities of wastewater entering the pipes can cause backups that result in sanitary sewer overflows. Urban underground infrastructure monitoring is important for controlling the flow of extraneous water into the pipelines. By combining the wireless underground communications and sensor solutions, the urban underground IoT applications such as real time wastewater and storm water overflow monitoring can be developed. In this paper, the path loss analysis of wireless underground communications in urban underground IoT for wastewater monitoring has been presented. It has been shown that the communication range of up to 4 kilometers can be achieved from an underground ...


An Underground Radio Wave Propagation Prediction Model For Digital Agriculture, Abdul Salam Apr 2019

An Underground Radio Wave Propagation Prediction Model For Digital Agriculture, Abdul Salam

Faculty Publications

Underground sensing and propagation of Signals in the Soil (SitS) medium is an electromagnetic issue. The path loss prediction with higher accuracy is an open research subject in digital agriculture monitoring applications for sensing and communications. The statistical data are predominantly derived from site-specific empirical measurements, which is considered an impediment to universal application. Nevertheless, in the existing literature, statistical approaches have been applied to the SitS channel modeling, where impulse response analysis and the Friis open space transmission formula are employed as the channel modeling tool in different soil types under varying soil moisture conditions at diverse communication distances ...


Underground Environment Aware Mimo Design Using Transmit And Receive Beamforming In Internet Of Underground Things, Abdul Salam Apr 2019

Underground Environment Aware Mimo Design Using Transmit And Receive Beamforming In Internet Of Underground Things, Abdul Salam

Faculty Publications

In underground (UG) multiple-input and multiple-output (MIMO), the transmit beamforming is used to focus energy in the desired direction. There are three different paths in the underground soil medium through which the waves propagates to reach at the receiver. When the UG receiver receives a desired data stream only from the desired path, then the UG MIMO channel becomes three path (lateral, direct, and reflected) interference channel. Accordingly, the capacity region of the UG MIMO three path interference channel and degrees of freedom (multiplexing gain of this MIMO channel requires careful modeling). Therefore, expressions are required derived the degrees of ...


Internet Of Things In Smart Agriculture: Enabling Technologies, Abdul Salam, Syed Shah Jan 2019

Internet Of Things In Smart Agriculture: Enabling Technologies, Abdul Salam, Syed Shah

Faculty Publications

In this paper, an IoT technology research and innovation roadmap for the field of precision agriculture (PA) is presented. Many recent practical trends and the challenges have been highlighted. Some important objectives for integrated technology research and education in precision agriculture are described. Effective IoT based communications and sensing approaches to mitigate challenges in the area of precision agriculture are presented.


A Neuromorphic Quadratic, Integrate, And Fire Silicon Neuron With Adaptive Gain, David Parent, Eric Basham Jul 2018

A Neuromorphic Quadratic, Integrate, And Fire Silicon Neuron With Adaptive Gain, David Parent, Eric Basham

Faculty Publications

An integrated circuit implementation of a silicon neuron was designed, manufactured, and tested. The circuit was designed using the Quadratic, Integrate, and Fire (QIF) neuron model in 0.5 µm silicon technology. The neuron implementation was optimized for low current consumption, drawing only 1.56 mA per QIF circuit and utilized hysteretic reset, non-inverting integrator, and voltage-squarer circuits. The final area of each circuit in silicon was 268 µm height × 400 µm width. This design is the first IC of its kind for this neuron model and is successfully able to output true spiking that follows the behaviors of bistability ...


Enabling Autonomous Navigation For Affordable Scooters, Kaikai Liu, Rajathswaroop Mulky Jun 2018

Enabling Autonomous Navigation For Affordable Scooters, Kaikai Liu, Rajathswaroop Mulky

Faculty Publications

Despite the technical success of existing assistive technologies, for example, electric wheelchairs and scooters, they are still far from effective enough in helping those in need navigate to their destinations in a hassle-free manner. In this paper, we propose to improve the safety and autonomy of navigation by designing a cutting-edge autonomous scooter, thus allowing people with mobility challenges to ambulate independently and safely in possibly unfamiliar surroundings. We focus on indoor navigation scenarios for the autonomous scooter where the current location, maps, and nearby obstacles are unknown. To achieve semi-LiDAR functionality, we leverage the gyros-based pose data to compensate ...


The 2018 Nvidia Ai City Challenge, Milind Naphade, Ming-Ching Chang, Anuj Sharma, David Anastasiu, Vamsi Jagarlamudi, Pranamesh Chakraborty, Tingting Huang, Shuo Wang, Ming-Yu Liu, Rama Chellappa, Jenq-Neng Hwang, Siwei Lyu Jun 2018

The 2018 Nvidia Ai City Challenge, Milind Naphade, Ming-Ching Chang, Anuj Sharma, David Anastasiu, Vamsi Jagarlamudi, Pranamesh Chakraborty, Tingting Huang, Shuo Wang, Ming-Yu Liu, Rama Chellappa, Jenq-Neng Hwang, Siwei Lyu

Faculty Publications

The NVIDIA AI City Challenge has been created to accelerate intelligent video analysis that helps make cities smarter and safer. With millions of traffic video cameras acting as sensors around the world, there is a significant opportunity for real-time and batch analysis of these videos to provide actionable insights. These insights will benefit a wide variety of agencies, from traffic control to public safety. The second edition of the NVIDIA AI City Challenge, being organized as a CVPR workshop, provided a forum to more than 70 academic and industrial research teams to compete and solve real-world problems using traffic camera ...


Teaching With Jupyter In-Class Activities: Lessons Learned And Next Steps, David Anastasiu Jan 2018

Teaching With Jupyter In-Class Activities: Lessons Learned And Next Steps, David Anastasiu

Faculty Publications

No abstract provided.


Patient Friendly Kidney Function Screening, Ragwa El Sayed, Rathna Ramesh, Alessandro Bellofiore, David Anastasiu, Melinda Simon Jan 2018

Patient Friendly Kidney Function Screening, Ragwa El Sayed, Rathna Ramesh, Alessandro Bellofiore, David Anastasiu, Melinda Simon

Faculty Publications

No abstract provided.


A Data-Driven Approach For Detecting Autism Spectrum Disorders, Manika Kapoor, David Anastasiu Jan 2018

A Data-Driven Approach For Detecting Autism Spectrum Disorders, Manika Kapoor, David Anastasiu

Faculty Publications

No abstract provided.


Parallel Cosine Nearest Neighbor Graph Construction, David Anastasiu, George Karypis Dec 2017

Parallel Cosine Nearest Neighbor Graph Construction, David Anastasiu, George Karypis

Faculty Publications

The nearest neighbor graph is an important structure in many data mining methods for clustering, advertising, recommender systems, and outlier detection. Constructing the graph requires computing up to n2 similarities for a set of n objects. This high complexity has led researchers to seek approximate methods, which find many but not all of the nearest neighbors. In contrast, we leverage shared memory parallelism and recent advances in similarity joins to solve the problem exactly. Our method considers all pairs of potential neighbors but quickly filters pairs that could not be a part of the nearest neighbor graph, based on similarity ...


Document Clustering, David Anastasiu, Andrea Tagarelli Nov 2017

Document Clustering, David Anastasiu, Andrea Tagarelli

Faculty Publications

In a world flooded with information, document clustering is an important tool that can help categorize and extract insight from text collections. It works by grouping similar documents, while simultaneously discriminating between groups. In this article, we provide a brief overview of the principal techniques used to cluster documents, and introduce a series of novel deep-learning based methods recently designed for the document clustering task. In our overview, we point the reader to salient works that can provide a deeper understanding of the topics discussed.


Efficient Identification Of Tanimoto Nearest Neighbors; All Pairs Similarity Search Using The Extended Jaccard Coefficient, David Anastasiu, George Karypis Nov 2017

Efficient Identification Of Tanimoto Nearest Neighbors; All Pairs Similarity Search Using The Extended Jaccard Coefficient, David Anastasiu, George Karypis

Faculty Publications

Tanimoto, or extended Jaccard, is an important similarity measure which has seen prominent use in fields such as data mining and chemoinformatics. Many of the existing state-of-the-art methods for market basket analysis, plagiarism and anomaly detection, compound database search, and ligand-based virtual screening rely heavily on identifying Tanimoto nearest neighbors. Given the rapidly increasing size of data that must be analyzed, new algorithms are needed that can speed up nearest neighbor search, while at the same time providing reliable results. While many search algorithms address the complexity of the task by retrieving only some of the nearest neighbors, we propose ...


Formal Performance Guarantees For An Approach To Human In The Loop Robot Missions, Damian Lyons, Ron Arkin, Shu Jiang, Matt O'Brien, Feng Tang, Peng Tang Oct 2017

Formal Performance Guarantees For An Approach To Human In The Loop Robot Missions, Damian Lyons, Ron Arkin, Shu Jiang, Matt O'Brien, Feng Tang, Peng Tang

Faculty Publications

Abstract— A key challenge in the automatic verification of robot mission software, especially critical mission software, is to be able to effectively model the performance of a human operator and factor that into the formal performance guarantees for the mission. We present a novel approach to modelling the skill level of the operator and integrating it into automatic verification using a linear Gaussians model parameterized by experimental calibration. Our approach allows us to model different skill levels directly in terms of the behavior of the lumped, robot plus operator, system.

Using MissionLab and VIPARS (a behavior-based robot mission verification module ...


The Nvidia Ai City Challenge, Milind Naphade, David Anastasiu, Anuj Sharma, Vamsi Jagrlamudi, Hyeran Jeon, Kaikai Liu, Ming-Ching Chang, Siwei Lyu, Zeyu Gao Aug 2017

The Nvidia Ai City Challenge, Milind Naphade, David Anastasiu, Anuj Sharma, Vamsi Jagrlamudi, Hyeran Jeon, Kaikai Liu, Ming-Ching Chang, Siwei Lyu, Zeyu Gao

Faculty Publications

Web image analysis has witnessed an AI renaissance. The ILSVRC benchmark has been instrumental in providing a corpus and standardized evaluation. The NVIDIA AI City Challenge is envisioned to provide similar impetus to the analysis of image and video data that helps make cities smarter and safer. In its first year, this Challenge has focused on traffic video data. While millions of traffic video cameras around the world capture data, albeit low-quality, very little automated analysis and value creation results. Lack of labeled data, and trained models that can be deployed at the edge of the city fabric, ensure that ...


Robust Classification Of City Roadway Objects For Traffic Related Applications, Niveditha Bhandary, Charles Mackay, Alex Richards, Ji Tong, David Anastasiu Aug 2017

Robust Classification Of City Roadway Objects For Traffic Related Applications, Niveditha Bhandary, Charles Mackay, Alex Richards, Ji Tong, David Anastasiu

Faculty Publications

The increasing prevalence of video data, particularly from traffic and surveillance cameras, is accompanied by a growing need for improved object detection, tracking, and classification techniques. In order to encourage development in this area, the AI City Challenge, sponsored by IEEE Smart World and NVIDIA, cultivated a competitive environment in which teams from all over the world sought to demonstrate the effectiveness of their models after training and testing on a common dataset of 114,766 unique traffic camera keyframes. Models were constructed for two distinct purposes; track 1 designs addressed object detection, localization and classification, while track 2 designs ...


Optimal Constrained Wireless Emergency Network Antenna Placement, Swapnil Gaikwad, David Anastasiu Aug 2017

Optimal Constrained Wireless Emergency Network Antenna Placement, Swapnil Gaikwad, David Anastasiu

Faculty Publications

Communication is paramount, especially during a natural disaster or other emergency. Even when traditional lines of communication become unavailable, emergency response teams must be able to communicate with each other and the outside world. To facilitate this need, major cities across the United States are deploying wireless emergency networks (WENs) that serve as a secure communication channel between emergency response points (police stations, shelters, food banks, hospitals, etc.) and the outside world. An important question when designing such networks is identifying the locations within the city where access points (APs) should be placed to construct a reliable WEN. We propose ...


An Approach To Robust Homing With Stereovision, Fuqiang Fu, Damian Lyons Apr 2017

An Approach To Robust Homing With Stereovision, Fuqiang Fu, Damian Lyons

Faculty Publications

Visual Homing is a bioinspired approach to robot navigation which can be fast and uses few assumptions. However, visual homing in a cluttered and unstructured outdoor environment offers several challenges to homing methods that have been developed for primarily indoor environments. One issue is that any current image during homing may be tilted with respect to the home image. The second is that moving through a cluttered scene during homing may cause obstacles to interfere between the home scene and location and the current scene and location. In this paper, we introduce a robust method to improve a previous developed ...


Efficient Neighborhood Graph Construction For Sparse High Dimensional Data, David Anastasiu Feb 2017

Efficient Neighborhood Graph Construction For Sparse High Dimensional Data, David Anastasiu

Faculty Publications

No abstract provided.


Impact Of Reviewer Social Interaction On Online Consumer Review Fraud Detection, Kunal Goswami, Younghee Park, Chungsik Song Jan 2017

Impact Of Reviewer Social Interaction On Online Consumer Review Fraud Detection, Kunal Goswami, Younghee Park, Chungsik Song

Faculty Publications

Background Online consumer reviews have become a baseline for new consumers to try out a business or a new product. The reviews provide a quick look into the application and experience of the business/product and market it to new customers. However, some businesses or reviewers use these reviews to spread fake information about the business/product. The fake information can be used to promote a relatively average product/business or can be used to malign their competition. This activity is known as reviewer fraud or opinion spam. The paper proposes a feature set, capturing the user social interaction behavior ...


Establishing A-Priori Performance Guarantees For Robot Missions That Include Localization Software, Damian Lyons, Ron Arkin, Shu Jiang, Matt O'Brien, Feng Tang, Peng Tang Jan 2017

Establishing A-Priori Performance Guarantees For Robot Missions That Include Localization Software, Damian Lyons, Ron Arkin, Shu Jiang, Matt O'Brien, Feng Tang, Peng Tang

Faculty Publications

One approach to determining whether an automated system is performing correctly is to monitor its performance, signaling when the performance is not acceptable; another approach is to automatically analyze the possible behaviors of the system a-priori and determine performance guarantees. Thea authors have applied this second approach to automatically derive performance guarantees for behaviorbased, multi-robot critical mission software using an innovative approach to formal verification for robotic software. Localization and mapping algorithms can allow a robot to navigate well in an unknown environment. However, whether such algorithms enhance any specific robot mission is currently a matter for empirical validation. Several ...


Performance Verification For Robot Missions In Uncertain Environments, Damian Lyons, Ron Arkin, Shu Jiang, Matt O'Brien, Feng Tang, Peng Tang Jan 2017

Performance Verification For Robot Missions In Uncertain Environments, Damian Lyons, Ron Arkin, Shu Jiang, Matt O'Brien, Feng Tang, Peng Tang

Faculty Publications

Abstract—Certain robot missions need to perform predictably in a physical environment that may have significant uncertainty. One approach is to leverage automatic software verification techniques to establish a performance guarantee. The addition of an environment model and uncertainty in both program and environment, however, means the state-space of a model-checking solution to the problem can be prohibitively large. An approach based on behavior-based controllers in a process-algebra framework that avoids state-space combinatorics is presented here. In this approach, verification of the robot program in the uncertain environment is reduced to a filtering problem for a Bayesian Network. Validation results ...


Robust And Agile System Against Fault And Anomaly Traffic In Software Defined Networks, Mihui Kim, Younghee Park, Rohit Kotalwar Jan 2017

Robust And Agile System Against Fault And Anomaly Traffic In Software Defined Networks, Mihui Kim, Younghee Park, Rohit Kotalwar

Faculty Publications

The main advantage of software defined networking (SDN) is that it allows intelligent control and management of networking though programmability in real time. It enables efficient utilization of network resources through traffic engineering, and offers potential attack defense methods when abnormalities arise. However, previous studies have only identified individual solutions for respective problems, instead of finding a more global solution in real time that is capable of addressing multiple situations in network status. To cover diverse network conditions, this paper presents a comprehensive reactive system for simultaneously monitoring failures, anomalies, and attacks for high availability and reliability. We design three ...


A Multi-Value Sequence Generated By Power Residue Symbol And Trace Function Over Odd Characteristic Field, Yasuyuki Nogami, Satoshi Uehara, Kazuyoshi Tsuchiya, Nasima Begum, Hiroto Ino, Robert Morelos-Zaragoza Dec 2016

A Multi-Value Sequence Generated By Power Residue Symbol And Trace Function Over Odd Characteristic Field, Yasuyuki Nogami, Satoshi Uehara, Kazuyoshi Tsuchiya, Nasima Begum, Hiroto Ino, Robert Morelos-Zaragoza

Faculty Publications

This paper proposes a new multi-value sequence generated by utilizing primitive element, trace, and power residue symbol over odd characteristic finite field. In detail, let p and k be an odd prime number as the characteristic and a prime factor of p-1, respectively. Our proposal generates k-value sequence T={ti | ti=fk(Tr(ωi)+A)}, where ω is a primitive element in the extension field $\F{p}{m}$, Tr(⋅) is the trace function that maps $\F{p}{m} \rightarrow \f{p}$, A is a non-zero scalar in the prime field $\f{p}$, and fk(⋅) is a certain mapping function based ...


Formal Performance Guarantees For Behavior-Based Localization Missions, Damian Lyons, Ron Arkin, Shu Jiang, Matt O'Brien, Feng Tang, Peng Tang Nov 2016

Formal Performance Guarantees For Behavior-Based Localization Missions, Damian Lyons, Ron Arkin, Shu Jiang, Matt O'Brien, Feng Tang, Peng Tang

Faculty Publications

Abstract— Localization and mapping algorithms can allow a robot to navigate well in an unknown environment. However, whether such algorithms enhance any specific robot mission is currently a matter for empirical validation. In this paper we apply our MissionLab/VIPARS mission design and verification approach to an autonomous robot mission that uses probabilistic localization software.

Two approaches to modeling probabilistic localization for verification are presented: a high-level approach, and a sample-based approach which allows run-time code to be embedded in verification. Verification and experimental validation results are presented for two different missions, each using each method, demonstrating the accuracy of ...


Landmark Detection With Surprise Saliency Using Convolutional Neural Networks, Feng Tang, Damian Lyons, Daniel Leeds Sep 2016

Landmark Detection With Surprise Saliency Using Convolutional Neural Networks, Feng Tang, Damian Lyons, Daniel Leeds

Faculty Publications

Abstract—Landmarks can be used as reference to enable people or robots to localize themselves or to navigate in their environment. Automatic definition and extraction of appropriate landmarks from the environment has proven to be a challenging task when pre-defined landmarks are not present. We propose a novel computational model of automatic landmark detection from a single image without any pre-defined landmark database. The hypothesis is that if an object looks abnormal due to its atypical scene context (what we call surprise saliency), it then may be considered as a good landmark because it is unique and easy to spot ...