Searcharxiv⌕ Search

arXiv subjects

Yu Chen

Publications and source records attributed to Yu Chen.

At least 595 records · Page 33Linked to original sources
arXiv2018

A Microservice-enabled Architecture for Smart Surveillance using Blockchain Technology

While the smart surveillance system enhanced by the Internet of Things (IoT) technology becomes an essential part of Smart Cities, it also brings new concerns in security of the data. Compared to the traditional surveillance systems that is built following a monolithic architecture to carry out lower level operations, such as monitoring and recording, the modern surveillance systems are expected to support more scalable and decentralized solutions for advanced video stream analysis at the large volumes of distributed edge devices. In addition, the centralized architecture of the conventional surveillance systems is vulnerable to single point of failure and privacy breach owning to the lack of protection to the surveillance feed. This position paper introduces a novel secure smart surveillance system based on microservices architecture and blockchain technology. Encapsulating the video analysis algorithms as various independent microservices not only isolates the video feed from different sectors, but also improve the system availability and robustness by decentralizing the operations. The blockchain technology securely synchronizes the video analysis databases among microservices across surveillance domains, and provides tamper proof of data in the trustless network environment. Smart contract enabled access authorization strategy prevents any unauthorized user from accessing the microservices and offers a scalable, decentralized and fine-grained access control solution for smart surveillance systems.

cs.DC↗
arXiv2018

Real-Time Index Authentication for Event-Oriented Surveillance Video Query using Blockchain

Information from surveillance video is essential for situational awareness (SAW). Nowadays, a prohibitively large amount of surveillance data is being generated continuously by ubiquitously distributed video sensors. It is very challenging to immediately identify the objects of interest or zoom in suspicious actions from thousands of video frames. Making the big data indexable is critical to tackle this problem. It is ideal to generate pattern indexes in a real-time, on-site manner on the video streaming instead of depending on the batch processing at the cloud centers. The modern edge-fog-cloud computing paradigm allows implementation of time sensitive tasks at the edge of the network. The on-site edge devices collect the information sensed in format of frames and extracts useful features. The near-site fog nodes conduct the contextualization and classification of the features. The remote cloud center is in charge of more data intensive and computing intensive tasks. However, exchanging the index information among devices in different layers raises security concerns where an adversary can capture or tamper with features to mislead the surveillance system. In this paper, a blockchain enabled scheme is proposed to protect the index data through an encrypted secure channel between the edge and fog nodes. It reduces the chance of attacks on the small edge and fog devices. The feasibility of the proposal is validated through intensive experimental analysis.

cs.DC↗
arXiv2018

Hall Conductance of a Non-Hermitian Chern Insulator

In this letter we study the Hall conductance for a non-Hermitian Chern insulator and quantitatively describe how the Hall conductance deviates from a quantized value. We show the effects of the non-Hermitian terms on the Hall conductance are two folds. On one hand, it broadens the density-of-state of each band, because of which there always exists a non-universal bulk contribution. On the other hand, it adds decay term to the edge state, because of which the topological contribution also deviates from the quantized Chern number. We provides a simple formula for the topological contribution for a general two-band non-Hermitian Chern insulator, as a non-Hermitian version of the Thouless-Kohmoto-Nightingale-de Nijs formula. It shows that the derivation from quantized value increases either when the strength of the non-Hermitian term increases, or when the momentum dependence of the non-Hermitian term increases. Our results can be directly verified in synthetic non-Hermitian topological systems where the strength of the non-Hermitian terms can be controlled.

cond-mat.mes-hall↗
arXiv2018

A Novel PMU Fog based Early Anomaly Detection for an Efficient Wide Area PMU Network

Based on phasor measurement units (PMUs), a synchronphasor system is widely recognized as a promising smart grid measurement system. It is able to provide high-frequency, high-accuracy phasor measurements sampling for Wide Area Monitoring and Control (WAMC) applications. However, the high sampling frequency of measurement data under strict latency constraints introduces new challenges for real time communication. It would be very helpful if the collected data can be prioritized according to its importance such that the existing quality of service (QoS) mechanisms in the communication networks can be leveraged. To achieve this goal, certain anomaly detection functions should be conducted by the PMUs. Inspired by the recent emerging edge-fog-cloud computing hierarchical architecture, which allows computing tasks to be conducted at the network edge, a novel PMU fog is proposed in this paper. Two anomaly detection approaches, Singular Spectrum Analysis (SSA) and K-Nearest Neighbors (KNN), are evaluated in the PMU fog using the IEEE 16-machine 68-bus system. The simulation experiments based on Riverbed Modeler demonstrate that the proposed PMU fog can effectively reduce the data flow end-to-end (ETE) delay without sacrificing data completeness.

cs.NI↗
arXiv2018

Explore Recurrent Neural Network for PUE Attack Detection in Practical CRN Models

The proliferation of the Internet of Things (IoTs) and pervasive use of many different types of mobile computing devices make wireless communication spectrum a precious resource. In order to accommodate the still fast increasing number of devices requesting wireless connection, more efficient, fine-grained spectrum allocation and sharing schemes are badly in need. Cognitive radio networks (CRNs) have been widely recognized as one promising solution, in which the secondary users (SUs) are allowed to share channels with licensed primary users (PUs) as long as bringing no interference to the normal operations of the PUs. However, malicious attackers or selfish SUs may mimic the behavior of PUs to occupy the channels illegally. It is nontrivial to accurately, timely detect such kind of primary user emulation (PUE) attacks. In this paper, an efficient PUE attacked detection method is introduced leveraging the recurrent neural network (RNN). After a fundamental algorithm using basic RNN, an advanced version taking advantage of long-short-term-memory (LSTM) is proposed, which is more efficient on processing time series with long term memory. The experimental study has provided deeper insights about the different performances the RNNs achieved and validated the effectiveness of the proposed detectors.

cs.NI↗
arXiv2018

A Federated Capability-based Access Control Mechanism for Internet of Things (IoTs)

The prevalence of Internet of Things (IoTs) allows heterogeneous embedded smart devices to collaboratively provide intelligent services with or without human intervention. While leveraging the large-scale IoT-based applications like Smart Gird and Smart Cities, IoT also incurs more concerns on privacy and security. Among the top security challenges that IoTs face is that access authorization is critical in resource and information protection over IoTs. Traditional access control approaches, like Access Control Lists (ACL), Role-based Access Control (RBAC) and Attribute-based Access Control (ABAC), are not able to provide a scalable, manageable and efficient mechanisms to meet requirement of IoT systems. The extraordinary large number of nodes, heterogeneity as well as dynamicity, necessitate more fine-grained, lightweight mechanisms for IoT devices. In this paper, a federated capability-based access control (FedCAC) framework is proposed to enable an effective access control processes to devices, services and information in large scale IoT systems. An identity-based capability token management strategy is presented, which involves registering, propagation and revocation of the access authorization. Through delegating centralized authorization decision-making policy to local domain delegator, the access authorization process is locally conducted on the service provider that integrates situational awareness (SAW) and customized contextual conditions. Implemented and tested on both resources-constrained devices, like smart sensors and Raspberry PI, and non-resource-constrained devices, like laptops and smart phones, our experimental results demonstrate the feasibility of the proposed FedCAC approach to offer a scalable, lightweight and fine-grained access control solution to IoT systems connected to a system network.

cs.NI↗
arXiv2018

Rigidity of Einstein metrics as critical points of some quadratic curvature functionals on complete manifolds

In this paper, we consider some rigidity results for the Einstein metrics as the critical points of some known quadratic curvature functionals on complete manifolds, characterized by some point-wise inequalities. Moreover, we also provide rigidity results by the integral inequalities involving the Weyl curvature, the trace-less Ricci curvature and the Sobolev constant, accordingly.

math.DG↗
arXiv2018

An Adaptive Primary User Emulation Attack Detection Mechanism for Cognitive Radio Networks

The proliferation of advanced information technologies (IT), especially the wide spread of Internet of Things (IoTs) makes wireless spectrum a precious resource. Cognitive radio network (CRN) has been recognized as the key to achieve efficient utility of communication bands. Because of the great difficulty, high complexity and regulations in dynamic spectrum access (DSA), it is very challenging to protect CRNs from malicious attackers or selfish abusers. Primary user emulation (PUE) attacks is one type of easy-to-launch but hard-to-detect attacks in CRNs that malicious entities mimic PU signals in order to either occupy spectrum resource selfishly or conduct Denial of Service (DoS) attacks. Inspired by the physical features widely used as the fingerprint of variant electronic devices, an adaptive and realistic PUE attack detection technique is proposed in this paper. It leverages the PU transmission features that attackers are not able to mimic. In this work, the transmission power is selected as one of the hard-to-mimic features due to the intrinsic discrepancy between PUs and attackers, while considering constraints in real implementations. Our experimental results verified the effectiveness and correctness of the proposed mechanism.

cs.NI↗
arXiv2018

BlendCAC: A BLockchain-ENabled Decentralized Capability-based Access Control for IoTs

The prevalence of Internet of Things (IoTs) allows heterogeneous embedded smart devices to collaboratively provide smart services with or without human intervention. While leveraging the large scale IoT based applications like Smart Gird or Smart Cities, IoTs also incur more concerns on privacy and security. Among the top security challenges that IoTs face, access authorization is critical in resource sharing and information protection. One of the weaknesses in today's access control (AC) is the centralized authorization server, which can be the performance bottleneck or the single point of failure. In this paper, BlendCAC, a blockchain enabled decentralized capability based AC is proposed for the security of IoTs. The BlendCAC aims at an effective access control processes to devices, services and information in large scale IoT systems. Based on the blockchain network, a capability delegation mechanism is suggested for access permission propagation. A robust identity based capability token management strategy is proposed, which takes advantage of smart contract for registering, propagation and revocation of the access authorization. In the proposed BlendCAC scheme, IoT devices are their own master to control their resources instead of being supervised by a centralized authority. Implemented and tested on a Raspberry Pi device and on a local private blockchain network, our experimental results demonstrate the feasibility of the proposed BlendCAC approach to offer a decentralized, scalable, lightweight and fine grained AC solution to IoT systems.

cs.NI↗
arXiv2018

Real-Time Human Detection as an Edge Service Enabled by a Lightweight CNN

Edge computing allows more computing tasks to take place on the decentralized nodes at the edge of networks. Today many delay sensitive, mission-critical applications can leverage these edge devices to reduce the time delay or even to enable real time, online decision making thanks to their onsite presence. Human objects detection, behavior recognition and prediction in smart surveillance fall into that category, where a transition of a huge volume of video streaming data can take valuable time and place heavy pressure on communication networks. It is widely recognized that video processing and object detection are computing intensive and too expensive to be handled by resource limited edge devices. Inspired by the depthwise separable convolution and Single Shot Multi-Box Detector (SSD), a lightweight Convolutional Neural Network (LCNN) is introduced in this paper. By narrowing down the classifier's searching space to focus on human objects in surveillance video frames, the proposed LCNN algorithm is able to detect pedestrians with an affordable computation workload to an edge device. A prototype has been implemented on an edge node (Raspberry PI 3) using openCV libraries, and satisfactory performance is achieved using real world surveillance video streams. The experimental study has validated the design of LCNN and shown it is a promising approach to computing intensive applications at the edge.

cs.CV↗
arXiv2018

Effective Capacity Analysis in Ultra-Dense Wireless Networks with Random Interference

Ultra-dense networks (UDNs) provide a promising paradigm to cope with exponentially increasing mobile traffic. However, little work has to date considered unsaturated traffic with quality-of-service (QoS) requirements. This paper presents a new cross-layer analytical model to capture the unsaturated traffic of a UDN in the presence of QoS requirements. The effective capacity (EC) of the UDN is derived, taking into account small-scale channel fading and possible interference. Key properties of the EC are revealed. The amount of traffic impacts effective capacity of the UDN due to the sophisticated interactions among small base stations operating in the same frequency. The maximization of total effective capacity is formulated as a non-cooperative game in the paper. The best-response function is derived, iteratively searching the Nash equilibrium point. System simulation results indicate that our proposed model is accurate. The simulations also show the maximum allowed arrival rate with the QoS guarantee, compared with the full interference model.

cs.IT↗
arXiv2018

Quantum ergodicity and mixing and their classical limits with quantum kicked rotor

We study the ergodicity and mixing of quantum kicked rotor (QKR) with two distinct approaches. In one approach, we use the definitions of quantum ergodicity and mixing recently proposed in [Phys. Rev. E 94, 022150 (2016)], which involve only eigen-energies (Floquet quasi-energies for QKR). In the other approach, we study ergodicity and mixing with quantum Poincarè section, which is plotted with a method that maps a wave function unitarily onto quantum phase space composed of Planck cells. Classical Poincarè section can be recovered with the effective Planck constant gradually diminishing. We demonstrate that the two approaches can capture the quantum and classical characteristics of ergodicity and mixing of QKR, and give consistent results with classical model at semiclassical limit. Therefore, we establish a correspondence between quantum ergodicity (mixing) and classical ergodicity (mixing).

quant-ph↗
arXiv2018

Who is Smarter? Intelligence Measure of Learning-based Cognitive Radios

Cognitive radio (CR) is considered as a key enabling technology for dynamic spectrum access to improve spectrum efficiency. Although the CR concept was invented with the core idea of realizing cognition, the research on measuring CR cognitive capabilities and intelligence is largely open. Deriving the intelligence measure of CR not only can lead to the development of new CR technologies, but also makes it possible to better configure the networks by integrating CRs with different cognitive capabilities. In this paper, for the first time, we propose a data-driven methodology to quantitatively measure the intelligence factors of the CR with learning capabilities. The basic idea of our methodology is to run various tests on the CR in different spectrum environments under different settings and obtain various performance data on different metrics. Then we apply factor analysis on the performance data to identify and quantize the intelligence factors and cognitive capabilities of the CR. More specifically, we present a case study consisting of 144 different types of CRs. The CRs are different in terms of learning-based dynamic spectrum access strategies, number of sensors, sensing accuracy, processing speed, and algorithmic complexity. Five intelligence factors are identified for the CRs through our data analysis.We show that these factors comply well with the nature of the tested CRs, which validates the proposed intelligence measure methodology.

cs.NI↗
arXiv2018

Low Loss Multi-Layer Wiring for Superconducting Microwave Devices

Complex integrated circuits require multiple wiring layers. In complementary metal-oxide-semiconductor (CMOS) processing, these layers are robustly separated by amorphous dielectrics. These dielectrics would dominate energy loss in superconducting integrated circuits. Here we demonstrate a procedure that capitalizes on the structural benefits of inter-layer dielectrics during fabrication and mitigates the added loss. We separate and support multiple wiring layers throughout fabrication using SiO$_2$ scaffolding, then remove it post-fabrication. This technique is compatible with foundry level processing and the can be generalized to make many different forms of low-loss multi-layer wiring. We use this technique to create freestanding aluminum vacuum gap crossovers (airbridges). We characterize the added capacitive loss of these airbridges by connecting ground planes over microwave frequency $λ/4$ coplanar waveguide resonators and measuring resonator loss. We measure a low power resonator loss of $\sim 3.9 \times 10^{-8}$ per bridge, which is 100 times lower than dielectric supported bridges. We further characterize these airbridges as crossovers, control line jumpers, and as part of a coupling network in gmon and fuxmon qubits. We measure qubit characteristic lifetimes ($T_1$'s) in excess of 30 $μ$s in gmon devices.

quant-ph↗
arXiv2018

Use of Two-Mode Circuitry and Optimal Energy-Efficient Power Control Under Target Delay-Outage Constraints

An accurate energy efficiency analytical model based on a two-mode circuitry was recently proposed; and the model showed that the use of this circuitry can significantly improve a system's energy efficiency. In this paper, we use this analytical model to develop a new power control scheme, a scheme that is capable of allocating a minimum transmission power precisely within the delay-outage probability constraint. Precision brings substantial benefits as numerical results show that the energy efficiency using our scheme is much higher than other schemes. Results further suggest that data rate values affect energy efficiency non-uniformly, i.e., there exists a specific data rate value that achieves maximum energy efficiency.

eess.SP↗
arXiv2018

Cross-domain Human Parsing via Adversarial Feature and Label Adaptation

Human parsing has been extensively studied recently due to its wide applications in many important scenarios. Mainstream fashion parsing models focus on parsing the high-resolution and clean images. However, directly applying the parsers trained on benchmarks to a particular application scenario in the wild, e.g., a canteen, airport or workplace, often gives non-satisfactory performance due to domain shift. In this paper, we explore a new and challenging cross-domain human parsing problem: taking the benchmark dataset with extensive pixel-wise labeling as the source domain, how to obtain a satisfactory parser on a new target domain without requiring any additional manual labeling? To this end, we propose a novel and efficient cross-domain human parsing model to bridge the cross-domain differences in terms of visual appearance and environment conditions and fully exploit commonalities across domains. Our proposed model explicitly learns a feature compensation network, which is specialized for mitigating the cross-domain differences. A discriminative feature adversarial network is introduced to supervise the feature compensation to effectively reduce the discrepancy between feature distributions of two domains. Besides, our model also introduces a structured label adversarial network to guide the parsing results of the target domain to follow the high-order relationships of the structured labels shared across domains. The proposed framework is end-to-end trainable, practical and scalable in real applications. Extensive experiments are conducted where LIP dataset is the source domain and 4 different datasets including surveillance videos, movies and runway shows are evaluated as target domains. The results consistently confirm data efficiency and performance advantages of the proposed method for the cross-domain human parsing problem.

cs.CV↗
arXiv2018

Spot the Difference by Object Detection

In this paper, we propose a simple yet effective solution to a change detection task that detects the difference between two images, which we call "spot the difference". Our approach uses CNN-based object detection by stacking two aligned images as input and considering the differences between the two images as objects to detect. An early-merging architecture is used as the backbone network. Our method is accurate, fast and robust while using very cheap annotation. We verify the proposed method on the task of change detection between the digital design and its photographic image of a book. Compared to verification based methods, our object detection based method outperforms other methods by a large margin and gives extra information of location. We compress the network and achieve 24 times acceleration while keeping the accuracy. Besides, as we synthesize the training data for detection using weakly labeled images, our method does not need expensive bounding box annotation.

cs.CV↗