In many screen to camera communication (S2C) systems, the barcode preprocessing method is a significant prerequisite because barcodes may be deformed due to various environmental factors. However, previous studies have focused on barcode detection under static conditions; to date, few studies have been carried out on dynamic conditions (for example, the barcode video stream or the transmitter and receiver are moving). Therefore, we present a detection and tracking method for dynamic barcodes based on a Siamese network. The backbone of the CNN in the Siamese network is improved by SE-ResNet. The detection accuracy achieved 89.5%, which stands out from other classical detection networks. The EAO reaches 0.384, which is better than previous tracking methods. It is also superior to other methods in terms of accuracy and robustness. The SE-ResNet in this paper improved the EAO by 1.3% compared with ResNet in SiamMask. Also, our method is not only applicable to static barcodes but also allows real-time tracking and segmentation of barcodes captured in dynamic situations.

For flexibility in supporting mobility and multihoming in edge networks and scalability of the backbone routing system, future Internet is expected to be based on the concept of ID/locator split. Heterogeneity Inclusion and Mobility Adaptation through Locator ID Separation (HIMALIS) has been designed as a generic future network architecture based on ID/locator split concept. It can natively support mobility, multihoming, scalable backbone routing and heterogeneous protocols in the network layer of the new generation network or future Internet. However, HIMALIS still lacks security functions to protect itself from various attacks during the procedures of storing, updating, and retrieving of ID/locator mappings, such as impersonation attacks. Therefore, in this paper, we address the issues of security functions design and implementation for the HIMALIS architecture. We present an integrated security scheme consisting of mapping registration and retrieval security, network access security, communication session security, and mobility security. Through the proposed scheme, the hostname to ID and locator mapping records can be securely stored and updated in two types of name registries, domain name registry and host name registry. Meanwhile, the mapping records retrieved securely from these registries are utilized for securing the network access process, communication sessions, and mobility management functions. The proposed scheme provides comprehensive protection of both control and data packets as well as the network infrastructure through an effective combination of asymmetric and symmetric cryptographic functions.

To secure a wireless sensor and actuator network (WSAN) in cyber-physical systems, trust management framework copes with misbehavior problem of nodes and stimulate nodes to cooperate with each other. The existing trust management frameworks can be classified into reputation-based framework and trust establishment framework. There, however, are still many problems with these existing trust management frameworks, which remain unsolved, such as frangibility under possible attacks. To design a robust trust management framework, we identify the attacks to the existing frameworks, present the countermeasures to them, and propose a hybrid trust management framework (HTMF) to construct trust environment for WSANs in the paper. HTMF includes second-hand information and confidence value into trustworthiness evaluation and integrates the countermeasures into the trust formation. We preform extensive performance evaluations, which show that the proposed HTMF is more robust and reliable than the existing frameworks.

Crack detection is a vital task to maintain a bridge's health and safety condition. Traditional computer-vision based methods easily suffer from disturbance of noise and clutters for a real bridge inspection. To address this limitation, we propose a two-stage crack detection approach based on Convolutional Neural Networks (CNN) in this letter. A predictor of small receptive field is exploited in the first detection stage, while another predictor of large receptive field is used to refine the detection results in the second stage. Benefiting from data fusion of confidence maps produced by both predictors, our method can predict the probability belongs to cracked areas of each pixel accurately. Experimental results show that the proposed method is superior to an up-to-date method on real concrete surface images.

Rapid building damage identification plays a vital role in rescue operations when disasters strike, especially when rescue resources are limited. In the past years, supervised machine learning has made considerable progress in building damage identification. However, the usage of supervised machine learning remains challenging due to the following facts: 1) the massive samples from the current damage imagery are difficult to be labeled and thus cannot satisfy the training requirement of deep learning, and 2) the similarity between partially damaged and undamaged buildings is high, hindering accurate classification. Leveraging the abundant samples of auxiliary domains, domain adaptation aims to transfer a classifier trained by historical damage imagery to the current task. However, traditional domain adaptation approaches do not fully consider the category-specific information during feature adaptation, which might cause negative transfer. To address this issue, we propose a novel domain adaptation framework that individually aligns each category of the target domain to that of the source domain. Our method combines the variational autoencoder (VAE) and the Gaussian mixture model (GMM). First, the GMM is established to characterize the distribution of the source domain. Then, the VAE is constructed to extract the feature of the target domain. Finally, the Kullback-Leibler (KL) divergence is minimized to force the feature of the target domain to observe the GMM of the source domain. Two damage detection tasks using post-earthquake and post-hurricane imageries are utilized to verify the effectiveness of our method. Experiments show that the proposed method obtains improvements of 4.4% and 9.5%, respectively, compared with the conventional method.

Nowadays, malware is a serious threat to the Internet. Traditional signature-based malware detection method can be easily evaded by code obfuscation. Therefore, many researchers use the high-level structure of malware like function call graph, which is impacted less from the obfuscation, to find the malware variants. However, existing graph match methods rely on approximate calculation, which are inefficient and the accuracy cannot be effectively guaranteed. Inspired by the successful application of graph convolutional network in node classification and graph classification, we propose a novel malware similarity metric method based on graph convolutional network. We use graph convolutional network to compute the graph embedding vectors, and then we calculate the similarity metric of two graph based on the distance between two graph embedding vectors. Experimental results on the Kaggle dataset show that our method can applied to the graph based malware similarity metric method, and the accuracy of clustering application with our method reaches to 97% with high time efficiency.

In this letter, we propose a novel discriminative representation for patterned fabric defect inspection when only limited negative samples are available. Fisher criterion is introduced into the loss function of deep learning, which can guide the learning direction of deep networks and make the extracted features more discriminating. A deep neural network constructed from the encoder part of trained autoencoders is utilized to classify each pixel in the images into defective or defectless categories, using as context a patch centered on the pixel. Sequentially the confidence map is processed by median filtering and binary thresholding, and then the defect areas are located. Experimental results demonstrate that our method achieves state-of-the-art performance on the benchmark fabric images.

Aiming at the problem of non-line of sight (NLOS) signal recognition for Ultra Wide Band (UWB) positioning, we utilize the concepts of Neural Network Clustering and Neural Network Pattern Recognition. We propose a classification algorithm based on self-organizing feature mapping (SOM) neural network batch processing, and a recognition algorithm based on convolutional neural network (CNN). By assigning different weights to learning, training and testing parts in the data set of UWB location signals with given known patterns, a strong NLOS signal recognizer is trained to minimize the recognition error rate. Finally, the proposed NLOS signal recognition algorithm is verified using data sets from real scenarios. The test results show that the proposed algorithm can solve the problem of UWB NLOS signal recognition under strong signal interference. The simulation results illustrate that the proposed algorithm is significantly more effective compared with other algorithms.

In this paper, a real-valued reweighted l1 norm minimization method based on data reconstruction in monostatic multiple-input multiple-output (MIMO) radar is proposed. Exploiting the special structure of the received data, and through the received data reconstruction approach and unitary transformation technique, a one-dimensional real-valued received data matrix can be obtained for recovering the sparse signal. Then a weight matrix based on real-valued MUSIC spectrum is designed for reweighting l1 norm minimization to enhance the sparsity of solution. Finally, the DOA can be estimated by finding the non-zero rows in the recovered matrix. Compared with traditional l1 norm-based minimization methods, the proposed method provides better angle estimation performance. Simulation results are presented to verify the effectiveness and advantage of the proposed method.

Aperiodic quadriphase Z-complementary sequences, which include the conventional complementary sequences as special cases, are introduced. It is shown that, the aperiodic quadriphase Z-complementary pairs are normally better than binary ones of the same length, in terms of the number of Z-complementary pairs, and the maximum zero correlation zone. New notions of elementary transformations on quadriphase sequences and elementary operations on sets of quadriphase Z-complementary sequences are presented. In particular, new methods for analyzing the relations among the formulas relative to sets of quadriphase Z-complementary sequences and for describing the sets are proposed. The existence problem of Z-complementary pairs of quadriphase sequences with zero correlation zone equal to 2, 3, and 4 is investigated. Constructions of sets of quadriphase Z-complementary sequences and their mates are given.

We are researching a mobile and sensor access platform network for the future called NerveNet, which accommodates ubiquitous sensors and provides services in local areas. The realization of reliable and accountable information collection, processing and provision over NerveNet poses a challenging and fundamental issue in promotion of the sensor business field. As a first step toward a reliable NerveNet, we investigate privacy preservation and the collection of reliable sensor information for prospective personalized sensor applications. The privacy requirement impels the logic separation between sensor networks and the communication platform in the design of NerveNet architecture. To enable sensor network users (SNUs) to reliably interact with sensors managed by different sensor network owners (SNOs), we designed a secure sensor sharing framework (S3F) based on two business models – the Ad Hoc Sales Model (AHSM) and Shopping Center Sales Model (SCSM). With S3F-AHSM, an SNU acquires permission from an SNO each time she wants to obtain information from a sensor. On the other hand, with S3F-SCSM, an SNU can obtain the access privilege to a flexible set of sensors based on the queried preferences via a third party called a sensor network service provider (SNSP). In S3F-SCSM, SNSPs that share the sensors owned by various SNOs have the ability to search the preferred sensors and provide the authorization certificate to the SNUs.

Grey model (abbreviated as GM), which is based on Deng's grey theory, has been established as a prediction model. At present, it has been widely applied in many research fields to solve efficiently the predicted problems of uncertainty systems. However, this model has irrational problems concerning the calculation of derivative and background value z since the predicted accuracy of GM is unsatisfying when original data shows great randomness. In particular, the predicted accuracy falls in case of higher-order derivative or multivariate greatly. In this paper, the new calculation methods of derivative and background value z are first proposed to enhance the predicted power according to cubic spline function. The newly generated model is defined as 3spGM. To further improve predicted accuracy, Taylor approximation method is then applied to 3spGM model. We call the improved version as T-3spGM. Finally, the effectiveness of the proposed model is validated with three real cases.

Information-Centric or Content-Centric Networking (ICN/CCN) is a promising novel network architecture that naturally integrates in-network caching, multicast, and multipath capabilities, without relying on centralized application-specific servers. Software platforms are vital for researching ICN/CCN; however, existing platforms lack a focus on extensibility and lightweight implementation. In this paper, we introduce a newly developed software platform enabling CCN, named Cefore. In brief, Cefore is lightweight, with the ability to run even on top of a resource-constrained device, but is also easily extensible with arbitrary plugin libraries or external software implementations. For large-scale experiments, a network emulator (Cefore-Emu) and network simulator (Cefore-Sim) have also been developed for this platform. Both Cefore-Emu and Cefore-Sim support hybrid experimental environments that incorporate physical networks into the emulated/simulated networks. In this paper, we describe the design, specification, and usage of Cefore as well as Cefore-Emu and Cefore-Sim. We show performance evaluations of in-network caching and streaming on Cefore-Emu and content fetching on Cefore-Sim, verifying the salient features of the Cefore software platform.

Signed networks are ubiquitous in the real world. It is of great significance to study the problem of community detection in signed networks. In general, the behaviors of nodes in a signed network are rational, which coincide with the players in the theory of game that can be used to model the process of the community formation. Unlike unsigned networks, signed networks include both positive and negative edges, representing the relationship of friends and foes respectively. In the process of community formation, nodes usually choose to be in the same community with friends and between different communities with enemies. Based on this idea, we proposed a game theory model to address the problem of community detection in signed networks. Taking nodes as players, we build a gain function based on the numbers of positive edges and negative edges inside and outside a community, and prove the existence of Nash equilibrium point. In this way, when the game reaches the Nash equilibrium state, the optimal strategy space for all nodes is the result of the final community division. To systematically investigate the performance of our method, elaborated experiments on both synthetic networks and real-world networks are conducted. Experimental results demonstrate that our method is not only more accurate than other existing algorithms, but also more robust to noise.

Wireless access networks of the future could provide a variety of context-aware services with the use of sensor information in order to solve regional social problems and improve the quality of residents' lives as a part of the regional infrastructure. NerveNet is a conceptual regional wireless access platform in which multiple service providers provide their own services with shared use of the network and sensors, enabling a range of context-aware services. The platform acts like a human nervous system. Densely located, interconnected access points with databases and data processing units will provide mobility to terminals without a location server and enable secure sensor data transport on a highly reliable, managed mesh network. This paper introduces the motivations, concept, architecture, system configuration, and preliminary performance results of NerveNet.

In resent years, providing location services for mobile targets in a closed environment has been a growing interest. In order to provide good localization and tracking performance for drones in GPS-denied scenarios, this paper proposes a multi-tag radio frequency identification (RFID) system that is easy to equip and does not take up the limited resources of the drone which is not susceptible to processor performance and cost constraints compared with computer vision based approaches. The passive RFID tags, no battery equipped, have an ultra-high resolution of millimeter level. We attach multiple tags to the drone and form multiple sets of virtual antenna arrays during motion, avoiding arranging redundant antennas in applications, and calibrating the speed chain to improve tracking performance. After combining the strap-down inertial navigation system (SINS) carried by the drone, we have established a coupled integration model that can suppress the drift error of SINS with time. The experiment was designed in bi-dimensional and three-dimensional scenarios, and the integrated positioning system based on SINS/RFID was evaluated. Finally, we discussed the impact of some parameters, this innovative approach is verified in real scenarios.

Heat map is an important tool for eye tracking data analysis and visualization. It is very intuitive to express the area watched by observer, but ignores saccade information that expresses gaze shift. Based on conventional heat map generation method, this paper presents a novel heat map generation method for eye tracking data. The proposed method introduces a mixed data structure of fixation points and saccades, and considers heat map deformation for saccade type data. The proposed method has advantages on indicating gaze transition direction while visualizing gaze region.

Android operating system occupies a high share in the mobile terminal market. It promotes the rapid development of Android applications (apps). However, the emergence of Android malware greatly endangers the security of Android smartphone users. Existing research works have proposed a lot of methods for Android malware detection, but they did not make the utilization of apps' functional category information so that the strong similarity between benign apps in the same functional category is ignored. In this paper, we propose an Android malware detection scheme based on the functional classification. The benign apps in the same functional category are more similar to each other, so we can use less features to detect malware and improve the detection accuracy in the same functional category. The aim of our scheme is to provide an automatic application functional classification method with high accuracy. We design an Android application functional classification method inspired by the hyperlink induced topic search (HITS) algorithm. Using the results of automatic classification, we further design a malware detection method based on app similarity in the same functional category. We use benign apps from the Google Play Store and use malware apps from the Drebin malware set to evaluate our scheme. The experimental results show that our method can effectively improve the accuracy of malware detection.

The Next Generation Internet Initiative was launched in the U.S. to advance key networking technologies that will enable a new wave of applications on the Internet. Now, in its third year, the program has launched and fostered over one hundred new research projects in partnership with academic, industrial and government laboratories. One key research area that has been emphasized within the program is the next-generation optical networking. Given the ever increasing demand for network bandwidth, and the recent phenomenal advances in WDM technologies, the Next Generation Internet is expected to be an IP-based optical WDM network. As IP over WDM networking technologies mature, a number of important architectural, management and control issues have surfaced. These issues need to be addressed before a true Next Generation Optical Internet can emerge. This paper provides a brief introduction to the overall goals and activities of DARPA's NGI program and describes the key architectural, management, and control issues for the Optical Internet. We review the different IP/WDM networking architectural models and their tradeoffs. We outline and discuss several management and control issues and possible solutions related to the configuration, fault, and performance management of IP over dynamic WDM networks. We present an analysis and supporting simulation results demonstrating the potential benefits of dynamic IP over WDM networks. We then discuss the issues related to IP/WDM traffic engineering in more detail, and present the approach taken in the NGI SuperNet Network Control and Management Project funded by DARPA. In particular, we motivate and present an innovative integrated traffic-engineering framework for re-configurable IP/WDM networks. It builds on the strength of Multi-Protocol Label Switching (MPLS) for fine-grain IP load balancing, and on the strength of Re-configurable WDM networking for reducing the IP network's weighted-hop-distance, and for expanding the bottleneck bandwidth.

Middle-level parts have attracted great attention in the computer vision community, acting as discriminative elements for objects. In this paper we propose an unsupervised approach to mine discriminative parts for object detection. This work features three aspects. First, we introduce an unsupervised, exemplar-based training process for part detection. We generate initial parts by selective search and then train part detectors by exemplar SVM. Second, a part selection model based on consistency and distinctiveness is constructed to select effective parts from the candidate pool. Third, we combine discriminative part mining with the deformable part model (DPM) for object detection. The proposed method is evaluated on the PASCAL VOC2007 and VOC2010 datasets. The experimental results demons-trate the effectiveness of our method for object detection.