In this paper, we propose a scheme to strengthen network-based moving target defense with disposable identifiers. The main idea is to change disposable identifiers for each packet to maximize unpredictability with large hopping space and substantially high hopping frequency. It allows network-based moving target defense to defeat active scanning, passive scanning, and passive host profiling attacks. Experimental results show that the proposed scheme changes disposable identifiers for each packet while requiring low overhead.

At Eurocrypt 2011, Lindell presented practical static and adaptively UC-secure commitment schemes based on the DDH assumption. Later, Blazy et al. (at ACNS 2013) improved the efficiency of the Lindell's commitment schemes. In this paper, we present static and adaptively UC-secure commitment schemes based on the same assumption and further improve the communication and computational complexity, as well as the size of the common reference string.

Surveillance through aerial systems is in place for years. Such systems are expensive, and a large fleet is in operation around the world without upgrades. These systems have low resolution and multiple analog cameras on-board, with Digital Video Recorders (DVRs) at the control station. Generated digital videos have multi-scenes from multi-feeds embedded in a single video stream and lack video stabilization. Replacing on-board analog cameras with the latest digital counterparts requires huge investment. These videos require stabilization and other automated video analysis prepossessing steps before passing it to the mosaicing algorithm. Available mosaicing software are not tailored to segregate feeds from different cameras and scenes, automate image enhancements, and stabilize before mosaicing (image stitching). We present "AirMatch", a new automated system that first separates camera feeds and scenes, then stabilize and enhance the video feed of each camera; generates a mosaic of each scene of every feed and produce a super quality mosaic by stitching mosaics of all feeds. In our proposed solution, state-of-the-art video analytics techniques are tailored to work on videos from vintage cameras in aerial applications. Our new framework is independent of specialized hardware requirements and generates effective mosaics. Affine motion transform with smoothing Gaussian filter is selected for the stabilization of videos. A histogram-based method is performed for scene change detection and image contrast enhancement. Oriented FAST and rotated BRIEF (ORB) is selected for feature detection and descriptors in video stitching. Several experiments on a number of video streams are performed and the analysis shows that our system can efficiently generate mosaics of videos with high distortion and artifacts, compared with other commercially available mosaicing software.

Kurotto and Juosan are Nikoli's pencil puzzles. We study the computational complexity of Kurotto and Juosan puzzles. It is shown that deciding whether a given instance of each puzzle has a solution is NP-complete.

As a research hotspot and difficulty in the field of computer vision, pedestrian detection has been widely used in intelligent driving and traffic monitoring. The popular detection method at present uses region proposal network (RPN) to generate candidate regions, and then classifies the regions. But the RPN produces many erroneous candidate areas, causing region proposals for false positives to increase. This letter uses improved residual attention network to capture the visual attention map of images, then normalized to get the attention score map. The attention score map is used to guide the RPN network to generate more precise candidate regions containing potential target objects. The region proposals, confidence scores, and features generated by the RPN are used to train a cascaded boosted forest classifier to obtain the final results. The experimental results show that our proposed approach achieves highly competitive results on the Caltech and ETH datasets.

Ethernet has become an indispensable technology for communications, and has come into use for many applications. At the IEEE, high-speed standardization has been discussed and has seen the adoption of new technologies such as multi-level modulation formats, high baud rate modulation and dense wave length division multiplexing. The MSA transceiver form factor has also been discussed following IEEE standardization. Optical devices such as TOSA and ROSA have been required to become more compact and higher-speed, because each transceiver form factor has to be miniaturized for high-density construction. We introduce the technologies for realizing 100GbE and those applicable to 400GbE. We also discuss future packages for optical devices. There are many similarities between optical device packages and electrical device packages, and we predict that optical device packages will follow the trends seen in electrical devices. But there are also differences between optical and electrical devices. It is necessary to utilize new technology for specific optical issues to employ advanced electrical packaging and catch up the trends.

In this paper, a proof-of-concept sensor platform for an all-in-one wireless bio sensor chip was developed and evaluated. An on-chip battery, an on-chip electrochromic display (ECD), a micro processor, a voltage converter and analog switches were implemented on a printed circuit board. Instead of bio-sensor, a temperature sensor was used to evaluate the functionality of the platform. The platform successfully worked in an electrolyte and the encoded measurement result was displayed on the ECD. The displayed data was captured by a CMOS digital camera and the measured data could be successfully decoded by a computer program.

The research on inertial sensor based human action detection and recognition (HADR) is a new area in machine learning. We propose a novel time sequence based interval convolutional neutral networks framework for HADR by combining interesting interval proposals generator and interval-based classifier. Experiments demonstrate the good performance of our method.

A lot of vision systems have been embedded in devices around us, like mobile phones, vehicles and UAVs. Many of them still need interactive operations of human users. However, specifying accurate object information could be a challenging task due to video jitters caused by camera shakes and target motions. In this paper, we first collect practical hand drawn bounding boxes on real-life videos which are captured by hand-held cameras and UAV-based cameras. We give a deep look into human-computer interactive operations on unstable images. The collected data shows that human input suffers heavy deviations which are harmful to interaction accuracy. To achieve robust interactions on unstable platforms, we propose a target-focused video stabilization method which utilizes a proposal-based object detector and a tracking-based motion estimation component. This method starts with a single manual click and outputs stabilized video stream in which the specified target stays almost stationary. Our method removes not only camera jitters but also target motions simultaneously, therefore offering an comfortable environment for users to do further interactive operations. The experiments demonstrate that the proposed method effectively eliminates image vibrations and significantly increases human input accuracy.

Two wideband circularly polarized (CP) equilateral triangular slot (ETS) antennas are proposed for communication system and the Global Navigation Satellite System (GNSS) Radio Occultation (RO) sensor of the GAIA-I microsatellite. These wide slot antennas use the ring slot antenna CP generation method due to their shape. The compact antennas employ truncated corners, grounded equilateral triangular perturbation patch and branched feed line to create CP radiation. A 3-dB axial ratio bandwidth (ARBW) enhancement is achieved by inserting a pair of slits into the ETS. A parametric study on the influence of those shape modifications in reflection coefficient and axial ratio is presented. An ETS antenna for communication system of the GAIA-I is fabricated and measured, which is shown to agree well with its simulated performance by providing CP fractional bandwidth of 52%. An ETS antenna designed for the GNSS RO sensor of GAIA-I delivers 3-dB ARBW of 41.6%. The ETS antenna offers uni-directional radiation by mounting a 3D printed truncated cone reflector underneath which also enhances antenna gain.

SAR tomography is one of the methods that can perform 3-dimensional (3-D) imaging with multiple SAR datasets by using the Direction-of-arrival (DOA) estimation technique to estimate the height distribution of scatterers. Several reports on SAR tomography have been issued. However, experimental results of the SAR tomography by the Pi-SAR2-X, Japanese airborne SAR operated by the NICT, have not been reported yet. This paper is the first to report the results of experiments on the Japanese SAR platform. High-resolution 2-dimensional image can be obtained by the X-band SAR. However the image is generated by projecting 3-D objects in to a 2-D image plane, hence the target responses having the same slant-range distance locate at the same image pixel. This is well known as the layover problem. When we employ the X-band SAR tomography, we can obtain 3-D high-resolution images without the layover and also foreshortening problem. It will be useful for disaster damage monitoring, especially in urban areas. The main difficulty of the SAR tomography comes from the phase error caused by inaccurate flight-path data. In many cases, the dataset are preprocessed and compensated so as to parallelize their flight-path to carry out the phase calibration and the DOA estimation easily. However, it is often difficult for common users to obtain such preprocessed datasets. In this paper, we propose a simple calibration method by using a flat-surface area with known altitude. Experiments show that the proposed method is effective for the Pi-SAR2-X standard products without parallelized preprocessing or precise flight-path information.

Bayesian networks are a powerful approach for representation and reasoning under conditions of uncertainty. Of the many good algorithms for learning Bayesian networks from data, the bio-inspired search algorithm is one of the most effective. In this paper, we propose a hybrid mutual information-modified binary particle swarm optimization (MI-MBPSO) algorithm. This technique first constructs a network based on MI to improve the quality of the initial population, and then uses the decomposability of the scoring function to modify the BPSO algorithm. Experimental results show that, the proposed hybrid algorithm outperforms various other state-of-the-art structure learning algorithms.

In this paper, we consider the problem of generating uniformly random mosaic floorplans. We propose a polynomial-time algorithm that generates such floorplans with f faces. Two modified algorithms are created to meet additional criteria.

Recently, object-proposal methods have attracted more and more attention of scholars and researchers for its utility in avoiding exhaustive sliding window search in an image. Object-proposal method is inspired by a concept that objects share a common feature. There exist many object-proposal methods which are either in segmentation fashion or engineering categories depending on low-level feature. Among those object-proposal methods, Edge Boxes, which is based on the number of contours that a bounding box wholly contains, has the state of art performance. Since Edge Boxes sometimes misses proposing some obvious objects in some images, we propose an appropriate version of it based on our two observations. We call the appropriate version as Improved Edge Boxes. The first of our observations is that objects have a property which can help us distinguish them from the background. It is called object saliency. An appropriate way we employ to calculate object saliency can help to retrieve some objects. The second of our observations is that objects ‘prefer’ to appear at the center part of images. For this reason, a bounding box that appears at the center part of the image is likely to contain an object. These two observations are going to help us retrieve more objects while promoting the recall performance. Finally, our results show that given just 5000 proposals we achieve over 89% object recall but 87% in Edge Boxes at the challenging overlap threshold of 0.7. Further, we compare our approach to some state-of-the-art approaches to show that our results are more accurate and faster than those approaches. In the end, some comparative pictures are shown to indicate intuitively that our approach can find more objects and more accurate objects than Edge Boxes.

This paper proposes an efficient video object segmentation approach that is tolerant to complex scene dynamics. Unlike existing approaches that rely on estimating object-like proposals on an intra-frame basis, the proposed approach employs temporally consistent foreground hypothesis using nonlinear regression of saliency guided proposals across a video sequence. For this purpose, we first generate salient foreground proposals at superpixel level by leveraging a saliency signature in the discrete cosine transform domain. We propose to use a random forest based nonlinear regression scheme to learn both appearance and shape features from salient foreground regions in all frames of a sequence. Availability of such features can help rank every foreground proposals of a sequence, and we show that the regions with high ranking scores are well correlated with semantic foreground objects in dynamic scenes. Subsequently, we utilize a Markov Random Field to integrate both appearance and motion coherence of the top-ranked object proposals. A temporal nonlinear regressor for generating salient object support regions significantly improves the segmentation performance compared to using only per-frame objectness cues. Extensive experiments on challenging real-world video sequences are performed to validate the feasibility and superiority of the proposed approach for addressing dynamic scene segmentation.

A Bayer-like White-RGB (W-RGB) color filter array (CFA) was invented for overcoming the weaknesses of commonly used RGB based Bayer CFA. In order to reproduce full-color images from the Bayer-like W-RGB CFA, a demosaicing or a CFA interpolation process which estimates missing color channels of raw mosaiced images from CFA is an essential process for single sensor digital cameras having CFA. In the case of Bayer CFA, numerous demosaicing methods which have remarkable performance were already proposed. In order to take advantage of both remarkable performance of demosaicing method for Bayer CFA and the characteristic of high-sensitive Bayer-like W-RGB CFA, a new method of transforming Bayer-like W-RGB to Bayer pattern is required. Therefore, in this letter, we present a new method of transforming Bayer-like W-RGB pattern to Bayer pattern. The proposed method mainly uses the color difference assumption between different channels which can be applied to practical consumer digital cameras.

Recently a compact code of mosaic floorplans with ƒ inner face was proposed by He. The length of the code is 3ƒ-3 bits and asymptotically optimal. In this paper, we propose a new code of mosaic floorplans with ƒ inner faces including k boundary faces. The length of our code is at most $3f - rac{k}{2} - 1$ bits. Hence our code is shorter than or equal to the code by He, except for few small floorplans with k=ƒ≤3. Coding and decoding can be done in O(ƒ) time.

In this letter, we prove that the Kurosawa-Desmedt (KD) scheme [10], which belongs to the hybrid framework, is KDM-CCA secure w.r.t. an ensemble proposed by Qin et al. in [12] under the decisional Diffie-Hellman assumption. Since our proof does not rely on the random oracle model, we partially answer the question presented by Davies and Stam in [7], where they hope to achieve the KDM-CCA security for hybrid encryption scheme in the standard model (i.e. not random oracle model). Moreover, our result may also make sense in practice since KD-scheme is (almost) the most efficient CCA secure scheme.

Most images obtained with imaging sensors contain Bayer patterns and suffer from blurring caused by the lens. In order to convert a blurred Bayer-patterned image into a viewable image, demosaicing and deblurring are needed. These concepts have been major research areas in digital image processing for several decades. Despite their importance, their performance and efficiency are not satisfactory when considered independently. In this paper, we propose a joint deblurring and demosaicing method in which edge direction and edge strength are estimated in the Bayer domain and then edge adaptive deblurring and edge-oriented interpolation are performed simultaneously from the estimated edge information. Experimental results show that the proposed method produces better image quality than conventional algorithms in both objective and subjective terms.

In this paper, we study conjunctive decentralized diagnosis of discrete event systems (DESs). In most existing works on decentralized diagnosis of DESs, it is implicitly assumed that diagnosis decisions of all local diagnosers are available to detect a failure. However, it may be possible that some local diagnosis decisions are not available, due to some reasons. Letting n be the number of local diagnosers, the notion of (n,k)-conjunctive codiagnosability guarantees that the occurrence of any failure is detected in the conjunctive architecture as long as at least k of the n local diagnosis decisions are available. We propose an algorithm for verifying (n,k)-conjunctive codiagnosability. To construct a reliable conjunctive decentralized diagnoser, we need to compute the delay bound within which the occurrence of any failure can be detected as long as at least k of the n local diagnosis decisions are available. We show how to compute the delay bound.