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Articles 3541 - 3570 of 9024

Full-Text Articles in Computer Sciences

Privacy Preserving Search Services Against Online Attack, Yi Zhao, Jianting Nian, Kaitai Liang, Yanqi Zhao, Liqun Chen, Bo Yang Aug 2020

Privacy Preserving Search Services Against Online Attack, Yi Zhao, Jianting Nian, Kaitai Liang, Yanqi Zhao, Liqun Chen, Bo Yang

Research Collection School Of Computing and Information Systems

Searchable functionality is provided in many online services such as mail services or outsourced data storage. To protect users privacy, data in these services is usually stored after being encrypted using searchable encryption. This enables the data user to securely search encrypted data from a remote server without leaking data and query information. Public key encryption with keyword search is one of the research branches of searchable encryption; this provides privacy-preserving searchable functionality for applications such as encrypted email systems. However, it has an inherent vulnerability in that the information of a query may be leaked using a keyword guessing …

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Accelerating Exact Constrained Shortest Paths On Gpus, Shengliang Lu, Bingsheng He, Yuchen Li, Hao Fu Aug 2020

Accelerating Exact Constrained Shortest Paths On Gpus, Shengliang Lu, Bingsheng He, Yuchen Li, Hao Fu

Research Collection School Of Computing and Information Systems

The recently emerging applications such as software-defined networks and autonomous vehicles require efficient and exact solutions for constrained shortest paths (CSP), which finds the shortest path in a graph while satisfying some user-defined constraints. Compared with the common shortest path problems without constraints, CSP queries have a significantly larger number of subproblems. The most widely used labeling algorithm becomes prohibitively slow and impractical. Other existing approaches tend to find approximate solutions and build costly indices on graphs for fast query processing, which are not suitable for emerging applications with the requirement of exact solutions. A natural question is whether and …

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An Analysis Of Sketched Irls For Accelerated Sparse Residual Regression, Daichi Iwata, Michael Waechter, Wen-Yan Lin, Yasuyuki Matsushita Aug 2020

An Analysis Of Sketched Irls For Accelerated Sparse Residual Regression, Daichi Iwata, Michael Waechter, Wen-Yan Lin, Yasuyuki Matsushita

Research Collection School Of Computing and Information Systems

This paper studies the problem of sparse residual regression, i.e., learning a linear model using a norm that favors solutions in which the residuals are sparsely distributed. This is a common problem in a wide range of computer vision applications where a linear system has a lot more equations than unknowns and we wish to find the maximum feasible set of equations by discarding unreliable ones. We show that one of the most popular solution methods, iteratively reweighted least squares (IRLS), can be significantly accelerated by the use of matrix sketching. We analyze the convergence behavior of the proposed method …

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Off-Policy Reinforcement Learning For Efficient And Effective Gan Architecture Search, Tian Yuan, Wang Qin, Zhiwu Huang, Wen Li, Dengxin Dai, Minghao Yang, Jun Wang, Olga Fink Aug 2020

Off-Policy Reinforcement Learning For Efficient And Effective Gan Architecture Search, Tian Yuan, Wang Qin, Zhiwu Huang, Wen Li, Dengxin Dai, Minghao Yang, Jun Wang, Olga Fink

Research Collection School Of Computing and Information Systems

In this paper, we introduce a new reinforcement learning (RL) based neural architecture search (NAS) methodology for effective and efficient generative adversarial network (GAN) architecture search. The key idea is to formulate the GAN architecture search problem as a Markov decision process (MDP) for smoother architecture sampling, which enables a more effective RL-based search algorithm by targeting the potential global optimal architecture. To improve efficiency, we exploit an off-policy GAN architecture search algorithm that makes efficient use of the samples generated by previous policies. Evaluation on two standard benchmark datasets (i.e., CIFAR-10 and STL-10) demonstrates that the proposed method is …

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Refactoring From 9 To 5? What And When Employees And Volunteers Contribute To Oss, Luiz Felipe Dias, Caio Barbosa, Gustavo Pinto, Igor Steinmacher, Baldoino Fonseca, Márcio Ribeiro, Christoph Treude, Daniel Alencar Da Costa Aug 2020

Refactoring From 9 To 5? What And When Employees And Volunteers Contribute To Oss, Luiz Felipe Dias, Caio Barbosa, Gustavo Pinto, Igor Steinmacher, Baldoino Fonseca, Márcio Ribeiro, Christoph Treude, Daniel Alencar Da Costa

Research Collection School Of Computing and Information Systems

In this paper we characterize the contributions made by employees (developers that work for GitHub, the company) and volunteers (developers that use GitHub, the platform) to OSS projects maintained by GitHub (the company) on GitHub (the platform). By mining activities performed in five well-known company-owned OSS projects, we investigate what they do and when they do it. We found that the majority of the volunteers' contributions are related to reengineering (e.g., refactoring), while employees focus more on management (e.g., documentation). When it comes to the working hours, we found that contributions are made mostly from 9am-5pm, even for the volunteers.

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How Practitioners Perceive Automated Bug Report Management Techniques, Weiqin Zou, David Lo, Zhenyu Chen, Xin Xia, Yang Feng, Baowen Xu Aug 2020

How Practitioners Perceive Automated Bug Report Management Techniques, Weiqin Zou, David Lo, Zhenyu Chen, Xin Xia, Yang Feng, Baowen Xu

Research Collection School Of Computing and Information Systems

Bug reports play an important role in the process of debugging and fixing bugs. To reduce the burden of bug report managers and facilitate the process of bug fixing, a great amount of software engineering research has been invested into automated bug report management techniques. However, the verdict is still open whether such techniques are actually required and applicable outside of the theoretical research domain. To fill this gap, in this paper, we conducted a survey among 327 practitioners to gain their insights into various categories of automated bug report management techniques. Specifically, in the survey, we asked them to …

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Dual-Dropout Graph Convolutional Network For Predicting Synthetic Lethality In Human Cancers, Ruichu Cai, Xuexin Chen, Yuan Fang, Min Wu, Yuexing Hao Aug 2020

Dual-Dropout Graph Convolutional Network For Predicting Synthetic Lethality In Human Cancers, Ruichu Cai, Xuexin Chen, Yuan Fang, Min Wu, Yuexing Hao

Research Collection School Of Computing and Information Systems

Motivation: Synthetic lethality (SL) is a promising form of gene interaction for cancer therapy, as it is able to identify specific genes to target at cancer cells without disrupting normal cells. As high-throughput wet-lab settings are often costly and face various challenges, computational approaches have become a practical complement. In particular, predicting SLs can be formulated as a link prediction task on a graph of interacting genes. Although matrix factorization techniques have been widely adopted in link prediction, they focus on mapping genes to latent representations in isolation, without aggregating information from neighboring genes. Graph convolutional networks (GCN) can capture …

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Meta-Learning On Heterogeneous Information Networks For Cold-Start Recommendation, Yuanfu Lu, Yuan Fang, Chuan Shi Aug 2020

Meta-Learning On Heterogeneous Information Networks For Cold-Start Recommendation, Yuanfu Lu, Yuan Fang, Chuan Shi

Research Collection School Of Computing and Information Systems

Cold-start recommendation has been a challenging problem due to sparse user-item interactions for new users or items. Existing efforts have alleviated the cold-start issue to some extent, most of which approach the problem at the data level. Earlier methods often incorporate auxiliary data as user or item features, while more recent methods leverage heterogeneous information networks (HIN) to capture richer semantics via higher-order graph structures. On the other hand, recent meta-learning paradigm sheds light on addressing cold-start recommendation at the model level, given its ability to rapidly adapt to new tasks with scarce labeled data, or in the context of …

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A Systematic Density-Based Clustering Method Using Anchor Points, Yizhang Wang, Di Wang, Wei Pang, Ah-Hwee Tan, You Zhou Aug 2020

A Systematic Density-Based Clustering Method Using Anchor Points, Yizhang Wang, Di Wang, Wei Pang, Ah-Hwee Tan, You Zhou

Research Collection School Of Computing and Information Systems

Clustering is an important unsupervised learning method in machine learning and data mining. Many existing clustering methods may still face the challenge in self-identifying clusters with varying shapes, sizes and densities. To devise a more generic clustering method that considers all the aforementioned properties of the natural clusters, we propose a novel clustering algorithm named Anchor Points based Clustering (APC). The anchor points in APC are characterized by having a relatively large distance from data points with higher densities. We take anchor points as centers to obtain intermediate clusters, which can divide the whole dataset more appropriately so as to …

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An Exact Algorithm For Agile Earth Observation Satellite Scheduling With Time-Dependent Profits, Guansheng Peng, Guopeng Song, Lining Xing, Aldy Gunawan, Pieter Vansteenwegen Aug 2020

An Exact Algorithm For Agile Earth Observation Satellite Scheduling With Time-Dependent Profits, Guansheng Peng, Guopeng Song, Lining Xing, Aldy Gunawan, Pieter Vansteenwegen

Research Collection School Of Computing and Information Systems

The scheduling of an Agile Earth Observation Satellite (AEOS) consists of selecting and scheduling a subset of possible targets for observation in order to maximize the collected profit related to the images while satisfying its operational constraints. In this problem, a set of candidate targets for observation is given, each with a time-dependent profit and a visible time window. The exact profit of a target depends on the start time of its observation, reaching its maximum at the midpoint of its visible time window. This time dependency stems from the fact that the image quality is determined by the look …

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A Longitudinal Study Of A Capstone Course, Benjamin Gan, Eng Lieh Ouh, Yin Yin Fiona Lee Aug 2020

A Longitudinal Study Of A Capstone Course, Benjamin Gan, Eng Lieh Ouh, Yin Yin Fiona Lee

Research Collection School Of Computing and Information Systems

This is a 7 years study on a capstone course completed by 1700+ students for 200+ organizations involving 300+ projects. Student teams deliver a system to solve real-world problems proposed by industry partners. We want to understand what independent variables influence student performance. We analyzed the deployment status of systems delivered, the type of organization/industry, the number of meetings and the technology used. Our results show some organization value proof of concept over fully deployed systems, student strengths are in Infocomm and Finance projects, the number of meetings is a weak correlation to performance and best performing projects are fully …

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A Multicut Outer-Approximation Approach For Competitive Facility Location Under Random Utilities, Tien Mai, Andrea Lodi Aug 2020

A Multicut Outer-Approximation Approach For Competitive Facility Location Under Random Utilities, Tien Mai, Andrea Lodi

Research Collection School Of Computing and Information Systems

This work concerns the maximum capture facility location problem with random utilities, i.e., the problem of seeking to locate new facilities in a competitive market such that the captured demand of users is maximized, assuming that each individual chooses among all available facilities according to a random utility maximization model. The main challenge lies in the nonlinearity of the objective function. Motivated by the convexity and separable structure of such an objective function, we propose an enhanced implementation of the outer approximation scheme. Our algorithm works in a cutting plane fashion and allows to separate the objective function into a …

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Rethinking Pruning For Accelerating Deep Inference At The Edge, Dawei Gao, Xiaoxi He, Zimu Zhou, Yongxin Tong, Ke Xu, Lothar Thiele Aug 2020

Rethinking Pruning For Accelerating Deep Inference At The Edge, Dawei Gao, Xiaoxi He, Zimu Zhou, Yongxin Tong, Ke Xu, Lothar Thiele

Research Collection School Of Computing and Information Systems

There is a growing trend to deploy deep neural networks at the edge for high-accuracy, real-time data mining and user interaction. Applications such as speech recognition and language understanding often apply a deep neural network to encode an input sequence and then use a decoder to generate the output sequence. A promising technique to accelerate these applications on resource-constrained devices is network pruning, which compresses the size of the deep neural network without severe drop in inference accuracy. However, we observe that although existing network pruning algorithms prove effective to speed up the prior deep neural network, they lead to …

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Adaptive Task Sampling For Meta-Learning, Chenghao Liu, Zhihao Wang, Doyen Sahoo, Yuan Fang, Kun Zhang, Steven C. H. Hoi Aug 2020

Adaptive Task Sampling For Meta-Learning, Chenghao Liu, Zhihao Wang, Doyen Sahoo, Yuan Fang, Kun Zhang, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Meta-learning methods have been extensively studied and applied in computer vision, especially for few-shot classification tasks. The key idea of meta-learning for few-shot classification is to mimic the few-shot situations faced at test time by randomly sampling classes in meta-training data to construct fewshot tasks for episodic training. While a rich line of work focuses solely on how to extract meta-knowledge across tasks, we exploit the complementary problem on how to generate informative tasks. We argue that the randomly sampled tasks could be sub-optimal and uninformative (e.g., the task of classifying “dog” from “laptop” is often trivial) to the meta-learner. …

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Social Participation Performance Of Wheelchair Users Using Clustering And Geolocational Sensor's Data, Yukun Yin, Kar Way Tan Aug 2020

Social Participation Performance Of Wheelchair Users Using Clustering And Geolocational Sensor's Data, Yukun Yin, Kar Way Tan

Research Collection School Of Computing and Information Systems

For wheelchair users, social participation and physical mobility play a significant part in determining their mental health and quality of life outcomes. However, little is known about how wheelchair users move about and engage in social interactions within their life-spaces. In this project, we investigate the social participation performance of the wheelchair users based on a combination of geolocational and lifestyle survey data collected over a period of three months. This paper adopts a multi-variate approach combining geolocational travel patterns and various factors such as independence, willingness and self-perception to provide multi-faceted analysis to their lifestyles. We provide profiles of …

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An Ensemble Of Epoch-Wise Empirical Bayes For Few-Shot Learning, Yaoyao Liu, Bernt Schiele, Qianru Sun Aug 2020

An Ensemble Of Epoch-Wise Empirical Bayes For Few-Shot Learning, Yaoyao Liu, Bernt Schiele, Qianru Sun

Research Collection School Of Computing and Information Systems

Few-shot learning aims to train efficient predictive models with a few examples. The lack of training data leads to poor models that perform high-variance or low-confidence predictions. In this paper, we propose to meta-learn the ensemble of epoch-wise empirical Bayes models (E3BM) to achieve robust predictions. “Epoch-wise'' means that each training epoch has a Bayes model whose parameters are specifically learned and deployed. ”Empirical'' means that the hyperparameters, e.g., used for learning and ensembling the epoch-wise models, are generated by hyperprior learners conditional on task-specific data. We introduce four kinds of hyperprior learners by considering inductive vs. transductive, and epoch-dependent …

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Feature Pyramid Transformer, Dong Zhang, Hanwang Zhang, Jinhui Tang, Meng Wang, Xian-Sheng Hua, Qianru Sun Aug 2020

Feature Pyramid Transformer, Dong Zhang, Hanwang Zhang, Jinhui Tang, Meng Wang, Xian-Sheng Hua, Qianru Sun

Research Collection School Of Computing and Information Systems

Feature interactions across space and scales underpin modern visual recognition systems because they introduce beneficial visual contexts. Conventionally, spatial contexts are passively hidden in the CNN’s increasing receptive fields or actively encoded by non-local convolution. Yet, the non-local spatial interactions are not across scales, and thus they fail to capture the non-local contexts of objects (or parts) residing in different scales. To this end, we propose a fully active feature interaction across both space and scales, called Feature Pyramid Transformer (FPT). It transforms any feature pyramid into another feature pyramid of the same size but with richer contexts, by using …

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A Visual Analytics Tool For Personalized Competency Feedback, Joelle Elmaleh, Shankararaman, Venky Aug 2020

A Visual Analytics Tool For Personalized Competency Feedback, Joelle Elmaleh, Shankararaman, Venky

Research Collection School Of Computing and Information Systems

In this paper we report our study on the design and implementation of a visual analytics tool, Competency Analytics System (CAS), which provides feedback to instructors on both the cohort and individual student’s competency acquisition rate, as well as provide personalized dashboard to each student on his or her competency acquisition for a specific course. We present the key functionalities of CAS and describe a case study on the implementation of CAS in a first-year programming course. Data from a student survey indicates that the personalized dashboard provided by CAS contributed to enhancing their ability to clearly identify the extent …

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An Attention-Based Rumor Detection Model With Tree-Structured Recursive Neural Networks, Jing Ma, Wei Gao, Shafiq Joty, Kam-Fai Wong Aug 2020

An Attention-Based Rumor Detection Model With Tree-Structured Recursive Neural Networks, Jing Ma, Wei Gao, Shafiq Joty, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

Rumor spread in social media severely jeopardizes the credibility of online content. Thus, automatic debunking of rumors is of great importance to keep social media a healthy environment. While facing a dubious claim, people often dispute its truthfulness sporadically in their posts containing various cues, which can form useful evidence with long-distance dependencies. In this work, we propose to learn discriminative features from microblog posts by following their non-sequential propagation structure and generate more powerful representations for identifying rumors. For modeling non-sequential structure, we first represent the diffusion of microblog posts with propagation trees, which provide valuable clues on how …

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Prevalence, Contents And Automatic Detection Of Kl-Satd, Leevi Rantala, Mika Mantyla, David Lo Aug 2020

Prevalence, Contents And Automatic Detection Of Kl-Satd, Leevi Rantala, Mika Mantyla, David Lo

Research Collection School Of Computing and Information Systems

When developers use different keywords such as TODO and FIXME in source code comments to describe self-admitted technical debt (SATD), we refer it as Keyword-Labeled SATD (KL-SATD). We study KL-SATD from 33 software repositories with 13,588 KL-SATD comments. We find that the median percentage of KL-SATD comments among all comments is only 1,52%. We find that KL-SATD comment contents include words expressing code changes and uncertainty, such as remove, fix, maybe and probably. This makes them different compared to other comments. KL-SATD comment contents are similar to manually labeled SATD comments of prior work. Our machine learning classifier using logistic …

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A Generalised Bound For The Wiener Attack On Rsa, Willy Susilo, Joseph Tonien, Guomin Yang Aug 2020

A Generalised Bound For The Wiener Attack On Rsa, Willy Susilo, Joseph Tonien, Guomin Yang

Research Collection School Of Computing and Information Systems

Since Wiener pointed out that the RSA can be broken if the private exponent d is relatively small compared to the modulus N, it has been a general belief that the Wiener attack works for d

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Tenet: Triple Excitation Network For Video Salient Object Detection, Sucheng Ren, Chu Han, Xin Yang, Guoqiang Han, Shengfeng He Aug 2020

Tenet: Triple Excitation Network For Video Salient Object Detection, Sucheng Ren, Chu Han, Xin Yang, Guoqiang Han, Shengfeng He

Research Collection School Of Computing and Information Systems

In this paper, we propose a simple yet effective approach, named Triple Excitation Network, to reinforce the training of video salient object detection (VSOD) from three aspects, spatial, temporal, and online excitations. These excitation mechanisms are designed following the spirit of curriculum learning and aim to reduce learning ambiguities at the beginning of training by selectively exciting feature activations using ground truth. Then we gradually reduce the weight of ground truth excitations by a curriculum rate and replace it by a curriculum complementary map for better and faster convergence. In particular, the spatial excitation strengthens feature activations for clear object …

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A Fast Anderson-Chebyshev Acceleration For Nonlinear Optimization, Zhize Li, Jian Li Aug 2020

A Fast Anderson-Chebyshev Acceleration For Nonlinear Optimization, Zhize Li, Jian Li

Research Collection School Of Computing and Information Systems

Anderson acceleration (or Anderson mixing) is an efficient acceleration method for fixed point iterations $x_{t+1}=G(x_t)$, e.g., gradient descent can be viewed as iteratively applying the operation $G(x) \triangleq x-\alpha\nabla f(x)$. It is known that Anderson acceleration is quite efficient in practice and can be viewed as an extension of Krylov subspace methods for nonlinear problems. In this paper, we show that Anderson acceleration with Chebyshev polynomial can achieve the optimal convergence rate $O(\sqrt{\kappa}\ln\frac{1}{\epsilon})$, which improves the previous result $O(\kappa\ln\frac{1}{\epsilon})$ provided by (Toth and Kelley, 2015) for quadratic functions. Moreover, we provide a convergence analysis for minimizing general nonlinear problems. Besides, …

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Measuring Privacy Concerns With Government Surveillance And Right-To-Be-Forgotten In Nomological Net Of Trust And Willingness-To-Share, Gaurav Bansal, Fiona Fui-Hoon Nah Aug 2020

Measuring Privacy Concerns With Government Surveillance And Right-To-Be-Forgotten In Nomological Net Of Trust And Willingness-To-Share, Gaurav Bansal, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

In the post Snowden revelations era, concerns related to government surveillance and oversight have come to the forefront. The ability of the Internet to remember “everything” (or forget anything) also raises a privacy concern associated with the “right to be forgotten”. Hence, in this paper, we propose and examine privacy concerns by extending the Hong and Thong’s (2013) model with the addition of two dimensions: right to be forgotten as well as government surveillance and oversight. We tested two different measurement models using privacy concerns as a second-order and a third-order construct within a nomological net that includes trusting beliefs …

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Interface Design Of Web-Based Educational Platforms For Young Students, Lurong He, Fiona Fui-Hoon Nah Aug 2020

Interface Design Of Web-Based Educational Platforms For Young Students, Lurong He, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

With the outbreak of the Covid-19 pandemic, almost all the school programs in the United States are closed. Most educational programs have moved online. Both teachers and students are facing challenges adapting to this new teaching and learning mode. This paper will focus on the interface design of webbased educational platforms for students and teachers from elementary and middle schools. The age of students from elementary and middle schools typically ranges from 5 to 13 years old. Their computer literacy level is still preliminary. So traditional online teaching platforms may not be appropriate for them. The design of learning systems …

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Predictive Insights For Improving The Resilience Of Global Food Security Using Artificial Intelligence, Meng Leong How, Yong Jiet Chan, Sin Mei Cheah Aug 2020

Predictive Insights For Improving The Resilience Of Global Food Security Using Artificial Intelligence, Meng Leong How, Yong Jiet Chan, Sin Mei Cheah

Research Collection Lee Kong Chian School Of Business

Unabated pressures on food systems affect food security on a global scale. A human-centric artificial intelligence-based probabilistic approach is used in this paper to perform a unified analysis of data from the Global Food Security Index (GFSI). The significance of this intuitive probabilistic reasoning approach for predictive forecasting lies in its simplicity and user-friendliness to people who may not be trained in classical computer science or in software programming. In this approach, predictive modeling using a counterfactual probabilistic reasoning analysis of the GFSI dataset can be utilized to reveal the interplay and tensions between the variables that underlie food affordability, …

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Love A Restaurant? Swipe Right On Foodrecce, Hady W. Lauw, Smu Office Of Research Jul 2020

Love A Restaurant? Swipe Right On Foodrecce, Hady W. Lauw, Smu Office Of Research

Research@SMU Infographics

A bunch of your friends wants to meet for dinner, but nobody can agree on where and what to eat? FoodRecce can help! FoodRecce is an app, developed under the Preferred.AI initiative, that provides recommendations on restaurants based on users' locations and past preferences.

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Deep Learning For Real-World Object Detection, Xiongwei Wu Jul 2020

Deep Learning For Real-World Object Detection, Xiongwei Wu

Dissertations and Theses Collection (Open Access)

Despite achieving significant progresses, most existing detectors are designed to detect objects in academic contexts but consider little in real-world scenarios. In real-world applications, the scale variance of objects can be significantly higher than objects in academic contexts; In addition, existing methods are designed for achieving localization with relatively low precision, however more precise localization is demanded in real-world scenarios; Existing methods are optimized with huge amount of annotated data, but in certain real-world scenarios, only a few samples are available. In this dissertation, we aim to explore novel techniques to address these research challenges to make object detection algorithms …

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Deep Learning Of Facial Embeddings And Facial Landmark Points For The Detection Of Academic Emotions, Hua Leong Fwa Jul 2020

Deep Learning Of Facial Embeddings And Facial Landmark Points For The Detection Of Academic Emotions, Hua Leong Fwa

Research Collection School Of Computing and Information Systems

Automatic emotion recognition is an actively researched area as emotion plays a pivotal role in effective human communications. Equipping a computer to understand and respond to human emotions has potential applications in many fields including education, medicine, transport and hospitality. In a classroom or online learning context, the basic emotions do not occur frequently and do not influence the learning process itself. The academic emotions such as engagement, frustration, confusion and boredom are the ones which are pivotal to sustaining the motivation of learners. In this study, we evaluated the use of deep learning on FaceNet embeddings and facial landmark …

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Skin-Mimo: Vibration-Based Mimo Communication Over Human Skin, Dong Ma, Yuezhong Wu, Ming Ding, Mahbub Hassan, Wen Hu Jul 2020

Skin-Mimo: Vibration-Based Mimo Communication Over Human Skin, Dong Ma, Yuezhong Wu, Ming Ding, Mahbub Hassan, Wen Hu

Research Collection School Of Computing and Information Systems

We explore the feasibility of Multiple-Input-Multiple-Output (MIMO) communication through vibrations over human skin. Using off-the-shelf motors and piezo transducers as vibration transmitters and receivers, respectively, we build a 2x2 MIMO testbed to collect and analyze vibration signals from real subjects. Our analysis reveals that there exist multiple independent vibration channels between a pair of transmitter and receiver, confirming the feasibility of MIMO. Unfortunately, the slow ramping of mechanical motors and rapidly changing skin channels make it impractical for conventional channel sounding based channel state information (CSI) acquisition, which is critical for achieving MIMO capacity gains. To solve this problem, we …

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