In automotive frequency modulated continuous wave (FMCW) radar based on multiple ramps with different slope, an effective pairing algorithm is required to simultaneously detect the target range and velocity. That is, as finding beat-frequencies intersecting at a single point of the range-Doppler map, we extract the range and velocity of a target. Unlike the ideal case, however, in a real radar system, even though multiple beat frequencies are originated from the same target, these beat frequencies have many different intersection values, resulting in mismatch pairing during the pairing step. Moreover, this problem also reduces the detection accuracy and the radar detection performance. In this study, we found that mismatch pairing is caused by the round-off errors of the range-beat frequency and Doppler frequency, as well as their various combinations in the discrete frequency domain. We also investigated the effect of mismatch pairing on detection performance, and proposed a new approach to minimize this problem. First, we propose integer and half-integer frequency position-based pairing method during extraction of the range and Doppler frequencies in each ramp to increase detection accuracy. Second, we propose a window-based pairing method to identify the same target from range-Doppler frequencies extracted in the first step. We also find the appropriate window size to overcome pairing mismatch. Finally, we propose the method to obtain a higher accuracy of range and velocity by weighting the values determined in one window. To verify the detection performance of the proposed method by comparison with the typical method, simulations were conducted. Then, in a real field test using the developed radar prototype, the detection probability of the proposed algorithm showed more than 60% improvement in comparison with the conventional method.

FOX is a family of block ciphers published in 2004 and is famous for its provable security to cryptanalysis. In this paper, we present multiple 4-round impossible differentials and several new results of impossible differential attacks on 5,6,7-round FOX64 and 5-round FOX128 with the multiple differentials and the new early abort technique which shall reduce the data complexity and the time complexity respectively. In terms of the data complexity and the time complexity, our results are better than any of the previously known attacks.

A precise measurement of Cosmic Microwave Background (CMB) provides us rich information about the universe. In particular, its asymmetric polarization patterns, $B$-modes, are smoking gun signature of inflationary universe. Magnitude of the $B$-modes is order of 10,nK. Its measurement requires a high sensitive millimeter-wave telescope with a large number of superconducting detectors on its focal plane. Microwave Kinetic Inductance Detector (MKID) is appropriate detector for this purpose. MKID camera has been developed in cooperation of National Astronomical Observatory of Japan (NAOJ), Institute of Physical and Chemical Research (RIKEN), High Energy Accelerator Research Organization (KEK), and Okayama University. Our developments of MKID include: fabrication of high-quality superconducting film; optical components for a camera use; and readout electronics. For performance evaluation of total integrated system of our MKID camera, a calibration system was also developed. The system was incorporated in a 0.1 K dilution refrigerator with modulated polarization source. These developed technologies are applicable to other types of detectors.

This letter deals with the carrier frequency offsets (CFO) estimation problem for orthogonal frequency division multiple access (OFDMA) uplink systems. Combined with centro-symmetric (CS) trimmed autocorrelation matrix and weighting subspace projection, the proposed estimator has better estimate performance than MVDR, MUSIC, CS-MUSIC, and ESPRIT estimators, especially in relatively less of OFDMA blocks and low SNR situations. Simulation results are presented to verify the efficiency of the proposed estimator.

Detecting traffic anomalies is an indispensable component of overall security architecture. As Internet and traffic data with more sophisticated attacks grow exponentially, preserving security with signature-based traffic analyzers or analyzers that do not support massive traffic are not sufficient. In this paper, we propose a novel method based on combined sketch technique and S-transform analysis for detecting anomalies in massive traffic streams. The method does not require any prior knowledge such as attack patterns and models representing normal traffic behavior. To detect anomalies, we summarize the entropy of traffic data over time and maintain the summarized data in sketches. The entropy fluctuation of the traffic data aggregated to the same bucket is observed by S-transform to detect spectral changes referred to as anomalies in this work. We evaluated the performance of the method with real-world backbone traffic collected at the United States and Japan transit link in terms of both accuracy and false positive rates. We also explored the method parameters' influence on detection performance. Furthermore, we compared the performance of our method to S-transform-based and Wavelet-based methods. The results demonstrated that our method was capable of detecting anomalies and overcame both methods. We also found that our method was not sensitive to its parameter settings.

In this letter, we propose a simple framework for accelerating a state-of-the-art histogram-based weighted median filter at no expense. It is based on a process of determining the filter processing direction. The determination is achieved by measuring the local feature variation of input images. Through experiments with natural images, it is verified that, depending on input images, the filtering speed can be substantially increased by changing the filtering direction.

Recently, a next-generation heterodyne mixer detector---a hot electron bolometer (HEB) mixer employing a superconducting microbridge---has gradually opened up terahertz-band astronomy. The surrounding state-of-the-art technologies including fabrication processes, 4 K cryostats, cryogenic low-noise amplifiers, local oscillator sources, micromachining techniques, and spectrometers, as well as the HEB mixers, have played a valuable role in the development of super-low-noise heterodyne spectroscopy systems for the terahertz band. The current developmental status of terahertz-band HEB mixer receivers and their applications for spectroscopy and astronomy with ground-based, airborne, and satellite telescopes are presented.

Content caching networks like Information-Centric Networking (ICN) are beneficial to reduce the network traffic by storing content data on routers near to users. In ICN, it becomes an important issue to manage system resources, such as storage and network bandwidth, which are influenced by cache characteristics of each cache node. Meanwhile, cache aging techniques based on Time-To-Live (TTL) of content facilitate analyzing cache characteristics and can realize appropriate resource management by setting efficient TTLs. However, it is difficult to search for the efficient TTLs in a distributed cache system connected by multiple cache nodes. Therefore, we propose an adaptive control mechanism of the TTL value of content in distributed cache systems by using predictive models which can estimate the impact of the TTL values on network resources and cache performance. Furthermore, we show the effectiveness of the proposed mechanism.

Radio Frequency based Device-Free Localization (RFDFL) is an emerging localization technique without requirements of attaching any electronic device to a target. The target can be localized by means of measuring the shadowing of received signal strength caused by the target. However, the accuracy of RFDFL deteriorates seriously in environment with WiFi interference. State-of-the-art methods do not efficiently solve this problem. In this paper, we propose a dual-band method to improve the accuracy of RFDFL in environment without/with severe WiFi interference. We introduce an algorithm of fusing dual-band images in order to obtain an enhanced image inferring more precise location and propose a timestamp-based synchronization method to associate the dual-band images to ensure their one-one correspondence. With real-world experiments, we show that our method outperforms traditional single-band localization methods and improves the localization accuracy by up to 40.4% in real indoor environment with high WiFi interference.

This paper presents a downlink interference mitigation framework for two-tier heterogeneous networks, that consist of spectrum-sharing macrocells and femtocells*. This framework establishes cooperation between the two tiers through two algorithms, namely, the restricted waterfilling (RWF) algorithm and iterative reweighted least squares interference alignment (IRLS-IA) algorithm. The proposed framework models the macrocell-femtocell two-tier cellular system as an overlay cognitive radio system in which the macrocell system plays the role of the primary user (PU) while the femtocell networks play the role of the cognitive secondary users (SUs). Through the RWF algorithm, the macrocell basestation (MBS) cooperates with the femtocell basestations (FBSs) by releasing some of its eigenmodes to the FBSs to do their transmissions even if the traffic is heavy and the MBS's signal to noise power ratio (SNR) is high. Then, the FBSs are expected to achieve a near optimum sum rate through employing the IRLS-IA algorithm to mitigate both the co-tier and cross-tier interference at the femtocell users' (FUs) receivers. Simulation results show that the proposed IRLS-IA approach provides an improved sum rate for the femtocell users compared to the conventional IA techniques, such as the leakage minimization approach and the nuclear norm based rank constraint rank minimization approach. Additionally, the proposed framework involving both IRLS-IA and RWF algorithms provides an improved total system sum rate compared with the legacy approaches for the case of multiple femtocell networks.

A workflow net (WF-net for short) is a Petri net which represents a workflow. There are two important subclasses of WF-nets: extended free choice (EFC for short) and well-structured (WS for short). It is known that most actual workflows can be modeled as EFC WF-nets; and acyclic WS is a subclass of acyclic EFC but has more analysis methods. A sound acyclic EFC WF-net may be transformed to an acyclic WS WF-net without changing the observable behavior of the net. Such a transformation is called refactoring. In this paper, we tackled a problem, named acyclic EFC WF-net refactorizability problem, that decides whether a given sound acyclic EFC WF-net is refactorable to an acyclic WS WF-net. We gave two sufficient conditions on the problem, and constructed refactoring procedures based on the conditions. Furthermore, we applied the procedures to a sample workflow, and confirmed usefulness of the procedures for the enhancement of the readability and the analysis power of acyclic EFC WF-nets.

In recent 3GPP (3rd Generation Partnership Project) standardization meetings, D2D (Device-to-Device) discovery has been a major issue to support commercial/social services and public safety in disaster environment, and TDM (Time Division Multiplexing) based discovery channel structure is mainly considered to prevent mutual interference between D2D and cellular traffic. In this structure, D2D discovery among the same cell UEs (User Equipment) has no problem because they have the same timing source. However, LTE (Long Term Evolution) assumes an asynchronous network where two adjacent eNBs (evolved Node B) have a symbol-level timing offset. For that reason, asynchronous interference among discovery signals can appear in inter-cell D2D discovery. Therefore, channel re-use scheduling was studied previously in which neighboring cells do not use the same portion of the extended discovery channel and other non-neighboring cells re-use it. However, it still shows interference problems in small cell networks which cause substantial cellular traffic loss. Therefore, in this paper, we propose a novel discovery channel scheduling in which eNBs time-align their discovery channels from each other by sample-level. In the proposed scheme, serving eNB requests cell edge UEs to estimate NTD (Network Time Difference) between serving eNB and neighboring eNB. Then, considering multiple NTDs, eNB adjusts the sample position of its discovery channel based on a novel decision rule. We verify that the proposed scheme can match the discovery performance of a synchronous network with less cellular uplink loss.

In this paper, we present a new method for diagnosis of stochastic discrete event system. The method is based on anomaly detection for sequences. We call the method sequence profiling (SP). SP does not require any system models and any system-specific knowledge. The only information necessary for SP is event logs from the target system. Using event logs from the system in the normal situation, N-gram models are learned, where the N-gram model is used as approximation of the system behavior. Based on the N-gram model, the diagnoser estimates what kind of faults has occurred in the system, or may conclude that no faults occurs. Effectiveness of the proposed method is demonstrated by application to diagnosis of a multi-processor system.

Social recommendation systems that make use of the user's social information have recently attracted considerable attention. These recommendation approaches partly solve cold-start and data sparsity problems and significantly improve the performance of recommendation systems. The essence of social recommendation methods is to utilize the user's explicit social connections to improve recommendation results. However, this information is not always available in real-world recommender systems. In this paper, a solution to this problem of explicit social information unavailability is proposed. The existing user-item rating matrix is used to compute implicit social information, and then an ISRec (implicit social recommendation algorithm) which integrates this implicit social information and the user-item rating matrix for social recommendation is introduced. Experimental results show that our method performs much better than state-of-the-art approaches; moreover, complexity analysis indicates that our approach can be applied to very large datasets because it scales linearly with respect to the number of observations in the matrices.

Robust yet efficient techniques for detecting and tracking targets in infrared (IR) images are a significant component of automatic target recognition (ATR) systems. In our previous works, we have proposed infrared target detection and tracking systems based on sparse representation method. The proposed infrared target detection and tracking algorithms are based on sparse representation and Bayesian probabilistic techniques, respectively. In this paper, we adopt Naïve Bayes Nearest Neighbor (NBNN) that is an extremely simple, efficient algorithm that requires no training phase. State-of-the-art image classification techniques need a comprehensive learning and training step (e.g., using Boosting, SVM, etc.) In contrast, non-parametric Nearest Neighbor based image classifiers need no training time and they also have other more advantageous properties. Results of tracking in infrared sequences demonstrated that our algorithm is robust to illumination changes, and the tracking algorithm is found to be suitable for real-time tracking of a moving target in infrared sequences and its performance was quite good.

We proposed a hard-wired CPG hardware network to reproduce the gaits of four-legged animals. It should reproduce walking and bounding, and they should be switchable with each other by changing the value of only one voltage.

In this paper, we consider a decentralized failure diagnosis problem for discrete event systems. For a conjunctively codiagnosable system, there exists a conjunctive decentralized diagnoser that can detect the occurrence of any failure within a uniformly bounded number of steps. We present a method for synthesizing such a conjunctive decentralized diagnoser as an online diagnoser.

Recently, locality-constrained linear coding (LLC) as a coding strategy has attracted much attention, due to its better reconstruction than sparse coding and vector quantization. However, LLC ignores the weight information of codewords during the coding stage, and assumes that every selected base has same credibility, even if their weights are different. To further improve the discriminative power of LLC code, we propose a weighted LLC algorithm that considers the codeword weight information. Experiments on the KTH and UCF datasets show that the recognition system based on WLLC achieves better performance than that based on the classical LLC and VQ, and outperforms the recent classical systems.

Forty Thieves is a solitaire game with two 52-card decks. The object is to move all cards from ten tableau piles of four cards to eight foundations. Each foundation is built up by suit from ace to king of the same suit, and each tableau pile is built down by suit. You may move the top card from any tableau pile to a tableau or foundation pile, and from the stock to a foundation pile. We prove that the generalized version of Forty Thieves is NP-complete.

In this paper, we present a new algorithm for fast online motion segmentation with low time complexity. Feature points in each input frame of an image stream are represented as a spatial neighbor graph. Then, the affinities for each point pair on the graph, as edge weights, are computed through our effective motion analysis based on multi-temporal intervals. Finally, these points are optimally segmented by agglomerative hierarchical clustering combined with normalized modularity maximization. Through experiments on publicly available datasets, we show that the proposed method operates in real time with almost linear time complexity, producing segmentation results comparable with those of recent state-of-the-art methods.