Nyquist folding receiver (NYFR) is a novel reconnaissance receiving architecture and it can realize wideband receiving with small amount of equipment. As a tradeoff of non-cooperative wideband receiving, the NYFR output will add an unknown key parameter that is called Nyquist zone (NZ) index. In this letter, we concentrate on the NZ index estimation of the NYFR output. Focusing on the basic pulse radar signals, the constant frequency signal, the binary phase coded signal and the linear frequency modulation signal are considered. The matching component function is proposed to estimate the NZ indexes of the NYFR outputs without the prior information of the signal modulation type. In addition, the relations between the matching component function and the parameters of the NYFR are discussed. Simulation results demonstrate the efficacy of the proposed method.

Permission warnings and privacy policy enforcement are widely used to inform mobile app users of privacy threats. These mechanisms disclose information about use of privacy-sensitive resources such as user location or contact list. However, it has been reported that very few users pay attention to these mechanisms during installation. Instead, a user may focus on a more user-friendly source of information: text description, which is written by a developer who has an incentive to attract user attention. When a user searches for an app in a marketplace, his/her query keywords are generally searched on text descriptions of mobile apps. Then, users review the search results, often by reading the text descriptions; i.e., text descriptions are associated with user expectation. Given these observations, this paper aims to address the following research question: What are the primary reasons that text descriptions of mobile apps fail to refer to the use of privacy-sensitive resources? To answer the research question, we performed empirical large-scale study using a huge volume of apps with our ACODE (Analyzing COde and DEscription) framework, which combines static code analysis and text analysis. We developed light-weight techniques so that we can handle hundred of thousands of distinct text descriptions. We note that our text analysis technique does not require manually labeled descriptions; hence, it enables us to conduct a large-scale measurement study without requiring expensive labeling tasks. Our analysis of 210,000 apps, including free and paid, and multilingual text descriptions collected from official and third-party Android marketplaces revealed four primary factors that are associated with the inconsistencies between text descriptions and the use of privacy-sensitive resources: (1) existence of app building services/frameworks that tend to add API permissions/code unnecessarily, (2) existence of prolific developers who publish many applications that unnecessarily install permissions and code, (3) existence of secondary functions that tend to be unmentioned, and (4) existence of third-party libraries that access to the privacy-sensitive resources. We believe that these findings will be useful for improving users' awareness of privacy on mobile software distribution platforms.

Automatic game strategy data acquisition is important for the realization of the professional strategy analysis systems by providing evaluation values such as the team status and the efficacy of plays. The key factor that influences the performance of the strategy data acquisition in volleyball game is the unknown player roles. Player role means the position with game meaning of each player in the team formation, such as the setter, attacker and blocker. The unknown player role makes individual player unreliable and loses the contribution of each player in the strategy analysis. This paper proposes a court-divisional team motion feature and a player performance curve to deal with the unknown player roles in strategy data acquisition. Firstly, the court-divisional team motion feature is proposed for the team tactical status detection. This feature reduces the influence of individual player information by summing up the ball relative motion density of all the players in divided court area, which corresponds to the different plays. Secondly, the player performance curves are proposed for the efficacy variables acquisition in attack play. The player roles candidates are detected by three features that represent the entire process of a player starting to rush (or jump) to the ball and hit the ball: the ball relative distance, ball approach motion and the attack motion feature. With the 3D ball trajectories and multiple players' positions tracked from multi-view volleyball game videos, the experimental detection rate of each team status (attack, defense-ready, offense-ready and offense status) are 75.2%, 84.2%, 79.7% and 81.6%. And for the attack efficacy variables acquisition, the average precision of the set zone, the number of available attackers, the attack tempo and the number of blockers are 100%, 100%, 97.8%, and 100%, which achieve 8.3% average improvement compared with manual acquisition.

A simple and novel multiple-symbol differential detection (MSDD) scheme is proposed for IEEE 802.15.4 binary phase shift keying (BPSK) receivers. The detection is initiated by estimating and compensating the carrier frequency offset (CFO) effect in the chip sample of interest. With these new statistics, the decisions are jointly made by allowing the observation window length to be longer than two bit intervals. Simulation results demonstrate that detection reliability of the IEEE 802.15.4 BPSK receivers is significantly improved. Namely, at packet error rate (PER) of 1×10-3, the signal-to-noise ratio (SNR) gap between ideal coherent detection (perfect carrier reference phase and no CFO) with differential decoding and conventional optimal single differential coherent detection (SDCD) is filled by 2.1dB when the observation window length is set to 6bit intervals. Then, the benefit that less energy consumed by retransmissions is successfully achieved.

The great increase of the digital communications, where the technological society depends on applications, devices and networks, the security problems motivate different researches for providing algorithms and systems resistant to attacks, and these lasts need of services of confidentiality, authentication, integrity, etc. This paper proposes the hardware implementation of an steganographic/cryptographic algorithm, which is based on the DWT (Discrete Wavelet Transform) and the AES (Advanced Encryption Standard) cipher algorithm in CBC mode. The proposed scheme takes advantage of a double-security ciphertext, which makes difficult to identify and decipher it. The hardware architecture reports a high efficiency (182.2 bps/slice and 85.2 bps/LUT) and low hardware resources consumption (867 slices and 1853 LUTs), where several parallel implementations can improve the throughout (0.162 Mbps) for processing large amounts of data.

In this letter, two new families of quaternary sequences with low four-level or five-level autocorrelation are constructed based on generalized cyclotomy over Z2p. These quaternary sequences are balanced and the maximal absolute value of the out-of-phase autocorrelation is 4.

The aim of this work is to develop a method for the simultaneous analysis of multiple groups and their members based on hierarchical tensor manifold modeling. The method is particularly designed to analyze multiple teams, such as sports teams and business teams. The proposed method represents members' data using a nonlinear manifold for each team, and then these manifolds are further modeled using another nonlinear manifold in the model space. For this purpose, the method estimates the role of each member in the team, and discovers correspondences between members that play similar roles in different teams. The proposed method was applied to basketball league data, and it demonstrated the ability of knowledge discovery from players' statistics. We also demonstrated that the method could be used as a general tool for multi-level multi-group analysis by applying it to marketing data.

Hazes with various properties spread widely across flat areas with depth continuities and corner areas with depth discontinuities. Removing haze from a single hazy image is difficult due to its ill-posed nature. To solve this problem, this study proposes a modified hybrid median filter that performs a median filter to preserve the edges of flat areas and a hybrid median filter to preserve depth discontinuity corners. Recovered scene radiance, which is obtained by removing hazy particles, restores image visibility using adaptive nonlinear curves for dynamic range expansion. Using comparative studies and quantitative evaluations, this study shows that the proposed method achieves similar or better results than those of other state-of-the-art methods.

In this paper we present proofs for the new biases in RC4 which were experimentally found and listed out (without theoretical justifications and proofs) in a paper by Vanhoef et al. in USENIX 2015. Their purpose was to exploit the vulnerabilities of RC4 in TLS using the set of new biases found by them. We also show (and prove) new results on couple of very strong biases residing in the joint distribution of three consecutive output bytes of the RC4 stream cipher. These biases provides completely new distinguisher for RC4 taking roughly O(224) samples to distinguish streams of RC4 from a uniformly random stream. We also provide a list of new results with proofs relating to some conditional biases in the keystreams of the RC4 stream cipher.

A fusion framework between CNN and RNN is proposed dedicatedly for air-writing recognition. By modeling the air-writing using both spatial and temporal features, the proposed network can learn more information than existing techniques. Performance of the proposed network is evaluated by using the alphabet and numeric datasets in the public database namely the 6DMG. Average accuracy of the proposed fusion network outperforms other techniques, i.e. 99.25% and 99.83% are observed in the alphabet gesture and the numeric gesture, respectively. Simplified structure of RNN is also proposed, which can attain about two folds speed-up of ordinary BLSTM network. It is also confirmed that only the distance between consecutive sampling points is enough to attain high recognition performance.

The demand for and the scope of connected services have rapidly grown and developed in many industries such as electronic appliances, robotics, and industry automation. In the automotive field, including connected vehicles, different types of connected services have become available and they provide convenience and comfort with users while yielding new business opportunities. With the advent of connected vehicles, the threat of cyber attacks has become a serious issue and protection methods against these attacks are urgently needed to provide safe and secure connected services. From 2017, attack methods have become more sophisticated through different attack surfaces attached to navigation systems and telematics modules, and security requirements to circumvent such attacks have begun to be established. Individual threats have been addressed previously; however, there are few reports that provide an overview of cyber security related to connected vehicles. This paper gives our perspective on cyber security for connected vehicles based on a survey of recent studies related to vehicle security. To introduce these studies, the environment surrounding connected vehicles is classified into three categories: inside the vehicle, communications between the back-end systems and vehicles, and the back-end systems. In each category, this paper introduces recent trends in cyber attacks and the protection requirements that should be developed for connected services. We show that the overall security covering the three categories must be considered because the security of the vehicle is jeopardized even if one item in the categories is not covered. We believe that this paper will further contribute to development of all service systems related to connected vehicles including autonomous vehicles and to the investigation into cyber security against these attacks.

Mining software repositories allow software practitioners to improve the quality of software systems and to support maintenance based on historical data. Such data is scattered across autonomous and heterogeneous information sources, such as version control, bug tracking and build automation systems. Despite having many tools to track and measure the data originated from such repositories, software practitioners often suffer from a scarcity of the techniques necessary to dynamically leverage software repositories to fulfill their complex information needs. For example, answering a question such as “What is the number of commits between two successful builds?” requires tiresome manual inspection of multiple repositories. As a solution, this paper presents a conceptual framework and a proof of concept visual query interface to satisfy distinct software quality related information needs of software practitioners. The data originated from repositories is integrated and analyzed to perform systematic investigations, which helps to uncover hidden relationships between software quality and trends of software evolution. This approach has several significant benefits such as the ability to perform real-time analyses, the ability to combine data from various software repositories and generate queries dynamically. The framework evaluated with 31 subjects by using a series of questions categorized into three software evolution scenarios. The evaluation results evidently show that our framework surpasses the state of the art tools in terms of correctness, time and usability.

Security facilities such as firewall system and IDS/IPS (Intrusion Detection System/Intrusion Prevention System) have become fundamental solutions against cyber threats. With the rapid change of cyber attack tactics, detail investigations like DPI (Deep Packet Inspection) and SPI (Stateful Packet Inspection) for incoming traffic become necessary while they also cause the decrease of network throughput. In this paper, we propose an SDN (Software Defined Network) - based proactive firewall system in collaboration with domain name resolution to solve the problem. The system consists of two firewall units (lightweight and normal) and a proper one will be assigned for checking the client of incoming traffic by the collaboration of SDN controller and internal authoritative DNS server. The internal authoritative DNS server obtains the client IP address using EDNS (Extension Mechanisms for DNS) Client Subnet Option from the external DNS full resolver during the name resolution stage and notifies the client IP address to the SDN controller. By checking the client IP address on the whitelist and blacklist, the SDN controller assigns a proper firewall unit for investigating the incoming traffic from the client. Consequently, the incoming traffic from a trusted client will be directed to the lightweight firewall unit while from others to the normal firewall unit. As a result, the incoming traffic can be distributed properly to the firewall units and the congestion can be mitigated. We implemented a prototype system and evaluated its performance in a local experimental network. Based on the results, we confirmed that the prototype system presented expected features and acceptable performance when there was no flooding attack. We also confirmed that the prototype system showed better performance than conventional firewall system under ICMP flooding attack.

A novel secure spatial modulation (SM) scheme based on dynamic multi-parameter weighted-type fractional Fourier transform (WFRFT), abbreviated as SMW, is proposed. Each legitimate transmitter runs WFRFT on the spatially modulated super symbols before transmit antennas, the parameters of which are dynamically updated using the transmitting bits. Each legitimate receiver runs inverse WFRFT to demodulate the received signals, the parameters of which are also dynamically generated using the recovered bits with the same updating strategies as the transmitter. The dynamic update strategies of WFRFT parameters are designed. As a passive eavesdropper is ignorant of the initial WFRFT parameters and the dynamic update strategies, which are indicated by the transmitted bits, it cannot recover the original information, thereby guaranteeing the communication security between legitimate transmitter and receiver. Besides, we formulate the maximum likelihood (ML) detector and analyze the secrecy capacity and the upper bound of BER. Simulations demonstrate that the proposed SMW scheme can achieve a high level of secrecy capacity and maintain legitimate receiver's low BER performance while deteriorating the eavesdropper's BER.

In this letter, we consider the harvested-energy fairness problem in cognitive multicast systems with simultaneous wireless information and power transfer. In the cognitive multicast system, a cognitive transmitter with multi-antenna sends the same information to cognitive users in the presence of licensed users, and cognitive users can decode information and harvest energy with a power-splitting structure. The harvested-energy fairness problem is formulated and solved by using two proposed algorithms, which are based on semidefinite relaxation with majorization-minimization method, and sequential parametric convex approximation with feasible point pursuit technique, respectively. Finally, the performances of the proposed solutions and baseline schemes are verified by simulation results.

Sparse code multiple access (SCMA) based on the message passing algorithm (MPA) for multiuser detection is a competitive non-orthogonal multiple access technique for fifth-generation wireless communication networks Among the existing multiuser detection schemes for uplink (UP) SCMA systems, the serial MPA (S-MPA) scheme, where messages are updated sequentially, generally converges faster than the conventional MPA (C-MPA) scheme, where all messages are updated in a parallel manner. In this paper, the optimization of message scheduling in the S-MPA scheme is proposed. Firstly, some statistical results for the probability density function (PDF) of the received signal are obtained at various signal-to-noise ratios (SNR) by using the Monte Carlo method. Then, based on the non-orthogonal property of SCMA, the data mapping relationship between resource nodes and user nodes is comprehensively analyzed. A partial codeword transmission of S-MPA (PCTS-MPA) with threshold decision scheme of PDF is proposed and verified. Simulations show that the proposed PCTS-MPA not only reduces the complexity of MPA without changing the bit error ratio (BER), but also has a faster convergence than S-MPA, especially at high SNR values.

A significant portion of computational resources of embedded systems for visual detection is dedicated to feature extraction, and this severely affects the detection accuracy and processing performance of the system. To solve this problem, we propose a feature descriptor based on histograms of oriented gradients (HOG) consisting of simple linear algebra that can extract equivalent information to the conventional HOG feature descriptor at a low computational cost. In an evaluation, a leading-edge detection algorithm with this decomposed vector HOG (DV-HOG) achieved equivalent or better detection accuracy compared with conventional HOG feature descriptors. A hardware implementation of DV-HOG occupies approximately 14.2 times smaller cell area than that of a conventional HOG implementation.

In this paper, we propose a secure near-field communication (NFC) for smartphones by combining acoustic and vibrational communication. In our hybrid system, a transmitter transmits an encrypted message and encryption key from a loudspeaker and vibration motor, respectively. While the sound emitted from the loudspeaker propagates through the air, the vibration emitted by the vibration motor propagates through the body of smartphones. Hence, only receivers touching the transmitter can receive both the encrypted message and the key, resulting in secure communication. We designed a software modulator and demodulator suitable for the vibrational communication by using return-to-zero (RZ) code. Then we established a hybrid communication system by combining acoustic and vibrational communication modems, and evaluated its performance in experiments. The results indicate that our hybrid system achieved a secure (among physically contacted devices) and fast (800kbps) NFC for smartphones.

We propose the cube-based perceptual encryption (C-PE), which consists of cube scrambling, cube rotation, cube negative/positive transformation, and cube color component shuffling, and describe its application to the encryption-then-compression (ETC) system of Motion JPEG (MJPEG). Especially, cube rotation replaces the blocks in the original frames with ones in not only the other frames but also the depth-wise cube sides (spatiotemporal sides) unlike conventional block-based perceptual encryption (B-PE). Since it makes intra-block observation more difficult and prevents unauthorized decryption from only a single frame, it is more robust than B-PE against attack methods without any decryption key. However, because the encrypted frames including the blocks from the spatiotemporal sides affect the MJPEG compression performance slightly, we also devise a version of C-PE with no spatiotemporal sides (NSS-C-PE) that hardly affects compression performance. C-PE makes the encrypted video sequence robust against the only single frame-based algorithmic brute force (ABF) attack with only 21 cubes. The experimental results show the compression efficiency and encryption robustness of the C-PE/NSS-C-PE-based ETC system. C-PE-based ETC system shows mixed results depending on videos, whereas NSS-C-PE-based ETC system shows that the BD-PSNR can be suppressed to about -0.03dB not depending on videos.

Adaptive noise cancellation using adaptive filters is a known method for removing noise that interferes with signal measurements. The adaptive noise canceller performs filtering based on the current situation through a windowing process. The shape of the window function determines the tracking performance of the adaptive noise canceller with respect to the fluctuation of the property of the unknown system that noise (reference signal) passes. However, the shape of the window function in the field of adaptive filtering has not yet been considered in detail. This study mathematically treats the effect of the window function on the adaptive noise canceller and proposes an optimization method for the window function in situations where offline processing can be performed, such as biomedical signal measurements. We also demonstrate the validity of the optimized window function through numerical experiments.