Miracle-dz/UniFAR-FTUs huggingface dataset:
Cooperative Multiuser Beamforming in mmWave Distributed Antenna Systems

In the fifth-generation (5G) system, millimeter-wave (mmWave) communication has been recognized as a candidate technique. To overcome the high transmission loss of mmWave, distributed antenna systems that consist of a base station (BS) connecting with multiple remote radio units (RRUs) were studied to improve the energy efficiency. In this letter, by utilizing the propagation characteristics of mmWave, we propose a cooperative multiuser beamforming method for energy-efficient transmissions in mmWave distributed antenna systems. The RRUs first decide the local serving group separately and the BS makes an optimization globally with limited data exchange. From simulation results, compared to the state-of-the-art method, it is shown that higher energy efficiency can be realized by the proposed method for the multiuser mmWave distributed system.
A Cooperation Scheme for User Fairness and Performance Enhancement in NOMA-HCN

Rapid increase in number of cellular users and high demand for data has lead to the formation of multi-tier networks. Non-orthogonal multiple access (NOMA) has proved to be an efficient method to cater to the paradigm shift from 4G to 5G. This paper employs NOMA in an heterogeneous cellular network consisting of a macro base station (MBS) tier underlaid with femto base station (FBS) tier and device-to-device (D2D) tier, where NOMA is employed in FBS and D2D tier only. The congestion at the MBS tier is relieved by offloading macro users (MU) to the FBS tier. The offloaded MU are further supported by the D2D tier when the FBS tier fails to find a corresponding pairing user for the incoming offloaded MU. Since, absence of pairing user means outage for offloaded MU, D2D cooperation is employed, which decreases the rate outage probability by $86.87\%$ for the MU offloaded as cell edge user (CEU) in comparison to no cooperation. Also, a three times increase in ergodic rate and four times increase in sum ergodic rate for MU offloaded as CEU is achieved using cooperation from D2D tier. Verification of the results is done using Monte Carlo simulations.
Iris super-resolution using CNNs: is photo-realism important to iris recognition?

The use of low-resolution images adopting more relaxed acquisition conditions such as mobile phones and surveillance videos is becoming increasingly common in iris recognition nowadays. Concurrently, a great variety of single image super-resolution techniques are emerging, especially with the use of convolutional neural networks (CNNs). The main objective of these methods is to try to recover finer texture details generating more photo-realistic images based on the optimisation of an objective function depending basically on the CNN architecture and training approach. In this work, the authors explore single image super-resolution using CNNs for iris recognition. For this, they test different CNN architectures and use different training databases, validating their approach on a database of 1.872 near infrared iris images and on a mobile phone image database. They also use quality assessment, visual results and recognition experiments to verify if the photo-realism provided by the CNNs which have already proven to be effective for natural images can reflect in a better recognition rate for iris recognition. The results show that using deeper architectures trained with texture databases that provide a balance between edge preservation and the smoothness of the method can lead to good results in the iris recognition process.
Fast Network Configuration in Software Defined Networking

Software defined networking (SDN) provides a framework to dynamically adjust and re-program the data plane with the use of flow rules. The realization of highly adaptive SDNs with the ability to respond to changing demands or recover after a network failure in a short period of time, hinges on efficient updates of flow rules. We model the time to deploy a set of flow rules by the update time at the bottleneck switch, and formulate the problem of selecting paths to minimize the deployment time under feasibility constraints as a mixed integer linear program (MILP). To reduce the computation time of determining flow rules, we propose efficient heuristics designed to approximate the minimum-deployment-time solution by relaxing the MILP or selecting the paths sequentially. Through extensive simulations we show that our algorithms outperform current, shortest path-based solutions by reducing the total network configuration time up to 55% while having similar packet loss, in the considered scenarios. We also demonstrate that in a networked environment with a certain fraction of failed links, our algorithms are able to reduce the average time to reestablish disrupted flows by 40%.
Cloud-Enabled Differentially Private Multiagent Optimization With Constraints

We present an optimization framework for solving multiagent convex programs subject to inequality constraints while keeping the agents’ state trajectories private. Each agent has an objective function depending only upon its own state and the agents are collectively subject to global constraints. The agents do not directly communicate with each other but instead route messages through a trusted cloud computer. The cloud adds noise to data being sent to the agents in accordance with the framework of differential privacy and, thus, keeps each agent's state trajectory private from all other agents and any eavesdroppers. This private problem can be viewed as a stochastic variational inequality, and it is solved using a projection-based method for solving variational inequalities that resemble a noisy primal-dual gradient algorithm. Convergence of the optimization algorithm in the presence of noise is proven, and a quantifiable tradeoff between privacy and convergence is extracted from this proof. Simulation results are provided that demonstrate numerical convergence for both <inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula>-differential privacy and <inline-formula><tex-math notation="LaTeX">$(\epsilon, \delta)$</tex-math></inline-formula>-differential privacy.
Connectivity Information-Aided Belief Propagation for Cooperative Localization

This letter proposes a cooperative localization algorithm based on connectivity information-aided belief propagation that can enhance belief propagation, which relies on the mutual exchange of location information with uncertainty. The connectivity information in the network is used to reduce the location uncertainty in the belief. As a result, it is possible to prevent the message flow of unnecessary location information which makes the location distribution too broad. The proposed algorithm is compared with sum-product algorithm over a wireless network, and shown to provide not only the performance enhancement in the localization accuracy, but also the computational gain when it is implemented by the sampling method.
Flow-Aware Routing and Forwarding for SDN Scalability in Wireless Data Centers

Software-defined networking (SDN) paradigm and high-speed wireless technologies have been widely adopted by industries to improve the performance of data center networks. In SDN, a switch commonly uses ternary content addressable memory (TCAM) to store rules for fast flow table lookup. However, due to the limited TCAM entries, rule replacement will occur frequently when traffic load in data centers is heavy. This issue will be more serious when flow characteristics in data centers are diverse. Although wireless transmissions can benefit for reducing TCAM usage, we observe that routing and forwarding designs without considering the flow characteristics will result in rule replacement storm in switches and significantly harm the SDN scalability for wireless data centers. As a result, switches will issue massive flow setup requests which cannot be processed by an SDN controller in real time. This paper studies the SDN scalability problem with the objective of minimizing the number of flow setup requests under the hybrid architecture with wired and wireless links. We propose a routing and forwarding algorithm with the consideration of flow characteristics to tackle the problem. A series of simulation results agrees our observations and shows that our algorithm outperforms two well-known solutions for wired data centers and three approaches for wireless data centers. The results also provide useful insights and guidelines for designing SDN-based wireless data centers.
Modeling and Control Design for Quadrotors: A Controlled Hamiltonian Systems Approach

In this paper, a new mathematical model of quadrotor is proposed by using the Hamiltonian approach, which has more advantages than Newtonian and Lagrangian approaches in some aspects such as compactness, easiness of controller design, and its symplectic structure. The Hamiltonian model of a quadrotor is a first-order differential equation whose states include generalized position and momentum that contain information about both the corresponding velocity and generalized mass. A nonlinear controller is proposed based on the proposed symplectic Hamiltonian model. The controller includes two sub-controllers—attitude sub-controller and position sub-controller. Furthermore, the stability of the designed controller is verified by Lyapunov stability theorem and the controller is provided with exponential stability. The effectiveness of the proposed model and the designed controller is demonstrated not only by software-in-the-loop simulations but also by hardware-in-the-loop simulations. Real-time experimental results show that the proposed controller is able to carry out the tasks of taking off, tracking fixed point, hovering, and landing.
Reducing the Monitoring Footprint on Controllers in Software-Defined Networks

A decisive advantage of software-defined networking (SDN) is its support for flexible network reconfigurations. Considering that, software-defined networks require accurate and timely data-plane state information. Network monitoring mechanisms usually require considerable resources on SDN controllers as well as on the data-plane elements. In this paper, we propose an optimization of the statistic transmission to reduce costs on both control- and data-plane regardless of the used monitoring application and statistic provisioning tool. To this end, we intercept the statistic message exchange and 1) aggregate multiple requests coming from different monitoring applications/controllers, 2) filter irrelevant statistic messages with respect to their information gain before delivering them to the control applications, and 3) deploy statistic caching. The proposed system, denoted STATISTIC REQUEST RELAY, forms a logically centralized statistic relay between controllers and the managed data-plane network. Our evaluation shows that the number of statistics processed on controllers as well as statistic requests on switches is reduced significantly while the performance penalty is negligible when using statistic aggregation and filtering as proposed here.
Load-Optimal Local Fast Rerouting for Dense Networks

Reliable and highly available computer networks must implement resilient fast rerouting mechanisms: upon a link or node failure, an alternative route is determined quickly, without involving the network control plane. Designing such fast failover mechanisms capable of dealing with multiple concurrent failures, however, is challenging, as failover rules need to be installed proactively, i.e., ahead of time, without knowledge of the actual failures happening at runtime. Indeed, only little is known today about the design of resilient routing algorithms. This paper introduces a general framework to reason about and design local failover algorithms that minimize the resulting load after failover on dense networks, beyond destination-based routing. We show that due to the inherent locality of the failover decisions at runtime, the problem is fundamentally related to the field of distributed algorithms without coordination. We derive an intriguing lower bound on the inherent network load overhead any local fast failover scheme that will introduce in the worst case, even though globally seen, much more balanced traffic allocations exist. We then present different randomized and deterministic failover algorithms and analyze their overhead load. In particular, we build upon the theory of combinatorial designs and develop a novel deterministic failover mechanism based on symmetric block design theory, which tolerates a maximal number of link failures while ensuring low loads.
Toward Symbiotic Human-AI Interaction Focusing on Programming by Example

Title of Dissertation: TOWARD SYMBIOTIC HUMAN-AI INTERACTION FOCUSING ON PROGRAMMING BY EXAMPLE Tak Yeon Lee, Doctor of Philosophy, 2017 Dissertation directed by: Professor Benjamin B. Bederson Computer Science Programming has become a new literacy, but is still inaccessible to ordinary people. Programming-by-example (PBE) is an alternative approach that allows people to teach computers repetitive tasks by demonstrating couple input and output examples of the tasks. While the advancements of PBE have been mainly driven by algorithmic improvements, a growing community of researchers started realizing the importance of issues on the human side of PBE. For instance, inexperienced users often find it hard to provide complete and consistent examples, which is crucial for computers to learn the correct programs. Unfortunately, most PBE systems have limited ways to communicate with users about what it can or cannot do, and how to handle unsuccessful situations. The lack of symbiotic interaction between human users and PBE engines remain as a major hurdle against a widespread adoption of PBE techniques. To address the issues on the human side of PBE, this dissertation has four research threads. First, we began with two formative studies to establish a better understanding of inexperienced users' needs and mental models. Second, based on the findings of the formative studies, we developed a Visual Environment for Symbiotic Programming, called VESPY. VESPY interleaves visual programming and PBE techniques, enabling users (1) to decompose complex tasks into small modules on its 2-d grid, and (2) to complete each module by providing input and output examples. Four sample programs demonstrate VESPY's remarkable versatility. However, we also noticed that VESPY still had a number of usability issues. Third, to better understand the usability issues and how to help users out from common mistakes, we conducted an online user study that observed how inexperience users perform program decomposition and disambiguation, which are the two core activities of PBE. We identified seven types of mistakes, and reaffirmed that informative feedback on those mistakes is crucial for designing usable systems. Finally, we explored the design space of feedback components, in order to understand their impact on user's experience. My dissertation contributes to the AI and HCI communities with: (i) identification of unmet needs of end-users of the Web; (ii) characterization of non-programmers’ mental model; (iii) design process of interleaving visual programming and PBE; (iv) identification of mistakes people make while using PBE; and (v) design and assessment of feedback components for PBE users. TOWARD SYMBIOTIC HUMAN-AI INTERACTION FOCUSING ON PROGRAMMING BY EXAMPLE
The Safety Analysis: Disagreement of Wireless Communication-Based Consensus

This letter considers the recent and practically relevant problem of safety in cyber-physical systems (CPS). Practical applications of CPS can be designed through the use of wireless multi-agent systems (MAS) since they provide flexibility and interoperability of the system functions. On the other hand, the performance of wireless MAS is prone to jamming attacks and the destructive effects of wireless channels. The aim of this letter is to determine safety bounds on wireless CPS under jamming attacks in the presence of additive white Gaussian noise (AWGN) and wireless fading channels. The impact of both wireless channel impairments and jamming attacks on the performance of wireless MAS has been investigated for the first time. The disagreement vector, defined as the deviation from the desired consensus value of MAS based on the quantization error, is analyzed and investigated for AWGN and fading channels. Theoretical results are supported by matching simulation results.
Hybrid segmentation method based on multi-scale Gaussian kernel fuzzy clustering with spatial bias correction and region-scalable fitting for breast US images

Automated segmentation of tumors in breast ultrasound (US) images is challenging due to poor image quality, presence of speckle noise, shadowing effects and acoustic enhancement. This paper improves the multi-scale Gaussian kernel induced fuzzy C-means clustering method with spatial bias correction (MsGKFCM_S). Furthermore, it presents a hybrid segmentation method, using both the features of the MsGKFCM_S clustering and active contour driven by a region-scalable fitting energy function. The result obtained from the MsGKFCM_S method is utilised to initialise the contour that spreads to identify the estimated regions. It also helps to estimate the several controlling parameters of the curve evolution process. The proposed approach is evaluated on a database of 127 breast US images consisting of 75 malignant and 52 solid benign cases. The performance of proposed approach is compared with other related techniques, using performance measures such as Jaccard Index, dice similarity, shape similarity, Hausdroff difference, area difference, accuracy and F-measure. Results indicate that the proposed approach can successfully detect lesions in breast US images, with high accuracy of 97.889 and 97.513%. Moreover, the proposed approach has the capability of handling shadowing effects, acoustic enhancement and multiple lesions.
Robust gait recognition: a comprehensive survey

Gait recognition has emerged as an attractive biometric technology for the identification of people by analysing the way they walk. However, one of the main challenges of the technology is to address the effects of inherent various intra-class variations caused by covariate factors such as clothing, carrying conditions, and view angle that adversely affect the recognition performance. The main aim of this survey is to provide a comprehensive overview of existing robust gait recognition methods. This is intended to provide researchers with state of the art approaches in order to help advance the research topic through an understanding of basic taxonomies, comparisons, and summaries of the state-of-the-art performances on several widely used gait recognition datasets.
Approximate Computing Techniques for Low Power and Energy Efficiency

Title of dissertation: APPROXIMATE COMPUTING TECHNIQUES FOR LOW POWER AND ENERGY EFFICIENCY Mingze Gao Doctor of Philosophy, 2018 Dissertation directed by: Professor Gang Qu Electrical & Computer Engineering Department and The Institute for Systems Research Approximate computing is an emerging computation paradigm in the era of the Internet of things, big data and AI. It takes advantages of the error-tolerable feature of many applications, such as machine learning and image/signal processing, to reduce the resources consumption and delivers a certain level of computation quality. In this dissertation, we propose several data format oriented approximate computing techniques that will dramatically increase the power/energy efficiency with the insignificant loss of computational quality. For the integer computations, we propose an approximate integer format (AIF) and its associated arithmetic mechanism with controllable computation accuracy. In AIF, operands are segmented at runtime such that the computation is performed only on part of operands by computing units (such as adders and multipliers) of smaller bit-width. The proposed AIF can be used for any arithmetic operation and can be extended to fixed point numbers. AIF requires additional customized hardware support. We also provide a method that can optimize the bit-width of the fixed point computations that run on the general purpose hardware. The traditional bit-width optimization methods mainly focus on minimizing the fraction part since the integer part is restricted by the data range. In our work, we utilize the dynamic fixed point concept and the input data range as the prior knowledge to get rid of this limitation. We expand the computations into data flow graph (DFG) and propose a novel approach to estimate the error during propagation. We derive the function of energy consumption and apply a more efficient optimization strategy to balance the tradeoff between the accuracy and energy. Next, to deal with the floating point computation, we propose a runtime estimation technique by converting data into the logarithmic domain to assess the intermediate result at every node in the data flow graph. Then we evaluate the impact of each node to the overall computation quality, and decide whether we should perform an accurate computation or simply use the estimated value. To approximate the whole graph, we propose three algorithms to make the decisions at certain nodes whether these nodes can be truncated. Besides the low power and energy efficiency concern, we propose a design concept that utilizes the approximate computing to address the security concerns. We can encode the secret keys into the least significant bits of the input data, and decode the final output. In the future work, the input-output pairs will be used for device authentication, verification, and fingerprint. APPROXIMATE COMPUTING TECHNIQUES FOR LOW POWER AND ENERGY EFFICIENCY
An Advanced Cyber Physical Framework for Micro Devices Assembly

The design and implementation of an Internet of Things (IoT) based cyber physical framework in the context of Industry 4.0 is discussed for the field of micro devices assembly. Such frameworks hold the potential to facilitate rapid and agile collaborations among distributed engineering partners. This paper outlines the key cyber and physical components which collaborate using cloud-based principles and emerging next generation global environment for network innovation Internet technologies. An information centric systems engineering approach is proposed to help design the cyber physical interactions, which provide a foundation for implementing this cyber physical framework. The cyber modules are capable of assembly planning, path planning, virtual reality-based assembly simulation, and physical command generation. The physical assembly activities are accomplished using micro assembly work cells. An IoT-based cyber physical test bed has been created to test and validate the design and implementation aspects of the proposed framework.
Introducing CloudLab: Scientific Infrastructure for Advancing Cloud Architectures and Applications

Do you have an idea for improving cloud computing? Do you need to instantiate a complete cloud stack so that you can improve part of it? Replace part of it? Tune it to better support a particular scientific workload? This article introduces CloudLab, a new multi-site facility that we are building to support cloud research.