A compression-function-based MAC function called FMAC was presented as well as a vector-input PRF called vFMAC in 2016. They were proven to be secure PRFs on the assumption that their compression function is a secure PRF against related-key attacks with respect to their non-cryptographic permutations in the single user setting. In this paper, it is shown that both FMAC and vFMAC are also secure PRFs in the multi-user setting on the same assumption as in the single user setting. These results imply that their security in the multi-user setting does not degrade with the number of the users and is as good as in the single user setting.

The potential transmission capacity of a standard single-mode fiber peaks at around 100Tb/s owing to fiber nonlinearity and the bandwidth limitation of amplifiers. As the last frontier of multiplexing, space-division multiplexing (SDM) has been studied intensively in recent years. Although there is still time to deploy such a novel fiber communication infrastructure; basic research on SDM has been carried out. Therefore, a comprehensive review is worthwhile at this time toward further practical investigations.

Socially aware networking is an emerging research field that aims to improve the current networking technologies and realize novel network services by applying social network analysis (SNA) techniques. Conducting socially aware networking studies requires knowledge of both SNA and communication networking, but it is not easy for communication networking researchers who are unfamiliar with SNA to obtain comprehensive knowledge of SNA due to its interdisciplinary nature. This paper therefore aims to fill the knowledge gap for networking researchers who are interested in socially aware networking but are not familiar with SNA. This paper surveys three types of important SNA techniques for socially aware networking: identification of influential nodes, link prediction, and community detection. Then, this paper introduces how SNA techniques are used in socially aware networking and discusses research trends in socially aware networking.

This paper proposes a radio propagation prediction method that uses point cloud data based on a hybrid of the ray-tracing (RT) method and an effective roughness (ER) model in urban environments for the fifth generation mobile communications system using high frequency bands. The proposed prediction method incorporates propagation characteristics that consider diffuse scattering from surface irregularities. The validity of the proposed method is confirmed by comparisons of measurement and prediction results gained from the proposed method and a conventional RT method based on power delay and angular profiles. From predictions based on the power delay and angular profiles, we find that the proposed method, assuming the roughness of σh=1mm, accurately predicts the propagation characteristics in the 20GHz band for urban line-of-sight environments. The prediction error for the delay spread is 2.1ns to 9.7ns in an urban environment.

The potential for using millimeter-wave (mmWave) frequencies in future 5G wireless cellular communication systems has motivated the study of large-scale antenna arrays to achieve highly directional beamforming. However, the conventional fully digital beamforming (DBF) methods which require one radio frequency (RF) chain per antenna element are not viable for large-scale antenna arrays due to the high cost and large power consumption of high frequency RF chain components. Hybrid precoding can significantly reduce the number of required RF chains and relieve the huge power consumption in mmWave massive multiple-input multiple-output (MIMO) systems, thus attracting much interests from academic and industry. In this paper, we consider the downlink communication of a massive multiuser MIMO (MU-MIMO) system in the mmWave channel, and propose a low complexity hybrid block diagonal geometric mean decomposition (BD-GMD) scheme. More specially, a joint transmit-receive (Tx-Rx) analog beamforming with large-scale arrays is proposed to improve channel gain, and then a low-dimensional BD-GMD approach is implemented at the equivalent baseband channel to mitigate the inter-user interference and equalize different data streams of each user. With the help of successive interference cancellation (SIC) at the receiver, we can decompose each user's MIMO channel into parallel sub-channels with identical higher SNRs/SINRs, thus equal-rate coding can be applied across the sub-channels of each user. Finally, simulation results verify that the proposed hybrid BD-GMD precoding scheme outperforms existing conventional fully digital and hybrid precoding schemes and is able to achieve much better BER performance.

In this paper, we propose a simple algorithm for detecting a vehicle trapped in flooded urban area by using quad-polarimetric SAR data. The four-component scattering power decomposition and phase difference of HH-VV co-pol ratio are effectively used in the proposed algorithm. Here we carry out polarimetric scattering measurement for a scaled vehicle model surrounded by two buildings model in an anechoic chamber, to acquire the quad-polarimetric SAR data. It is confirmed from the results of the image analysis for the measured SAR data that the proposed algorithm for vehicle detection works well even under severe environment where the vehicle is set in the shadow of the building and/or the alignment of the vehicle or the buildings is obliquely oriented to direction of the radar line of sight.

Forward converter is most suitable for low voltage and high current applications such as LEDs, battery chargers, EHV etc. The active clamp transformer reset technique offers many advantages over conventional single-ended reset techniques, including lower voltage stress on the main switch, the ability to switch at zero voltage and duty cycle operation above 50 percent. Several papers have compared the functional merits of the active clamp over the more extensively used RCD clamp, third winding and resonant reset techniques. This paper discusses about a center clamp technique with one common core reset circuit making it suitable for wide input voltage applications with extended duty cycle.

Dual-rotor machine (DRM) is a multiple input and output electromechanical device with two electrical and two mechanical ports which make it an optimal transmission system for hybrid electric vehicles. In attempt to boost its performance and efficiency, this work presents a dual-rotor permanent magnet (DR-PM) machine system used for continuously variable transmission (CVT) in HEVs. The proposed DR-PM machine is analyzed, and modeled in consideration of vehicle driving requirements. Considering energy conversion modes and torque transfer modes, operation conditions of the DR-PM machine system used for CVT are illustrated in detail. Integrated control model of the system is carried out, besides, intelligent speed ratio control strategy is designed by analyzing the dynamic coupling modes upon the integrated models to satisfy the performance requirements, reasonable energy-split between machine and engine, and optimal fuel economy. Experimental results confirm the validity of the mathematical model of the DR-PM machine system in the application of CVT, and the effectiveness of the intelligent speed ratio control strategy.

Asymmetrically designed polycrystalline silicon (poly-Si) thin film transistors (TFT) were fabricated and investigated to suppress kink effect and to improve electrical reliability. Asymmetric dual channel length poly-Si TFT (ADCL) shows the best reduction of kink and leakage currents. Technology computer-aided design simulation proves that ADCL can induce properly high voltage at floating node of the TFT at high drain-source voltage (VDS), which can mitigate the impact ionization and the degradation of the transconductance of the TFT showing high reliability under the hot carrier stress.

Encryption-then-Compression (EtC) systems have been proposed to securely transmit images through an untrusted channel provider. In this study, EtC systems were applied to social media like Twitter that carry out image manipulations. The block scrambling-based encryption schemes used in EtC systems were evaluated in terms of their robustness against image manipulation on social media. The aim was to investigate how five social networking service (SNS) providers, Facebook, Twitter, Google+, Tumblr and Flickr, manipulate images and to determine whether the encrypted images uploaded to SNS providers can avoid being distorted by such manipulations. In an experiment, encrypted and non-encrypted JPEG images were uploaded to various SNS providers. The results show that EtC systems are applicable to the five SNS providers.

Activity recognition from sensors is a classification problem over time-series data. Some research in the area utilize time and frequency domain handcrafted features that differ between datasets. Another categorically different approach is to use deep learning methods for feature learning. This paper explores a middle ground in which an off-the-shelf feature extractor is used to generate a large number of candidate time-domain features followed by a feature selector that was designed to reduce the bias toward specific classification techniques. Moreover, this paper advocates the use of features that are mostly insensitive to sensor orientation and show their applicability to the activity recognition problem. The proposed approach is evaluated using six different publicly available datasets collected under various conditions using different experimental protocols and shows comparable or higher accuracy than state-of-the-art methods on most datasets but usually using an order of magnitude fewer features.

Since cyber attacks such as cyberterrorism against Industrial Control Systems (ICSs) and cyber espionage against companies managing them have increased, the techniques to detect anomalies in early stages are required. To achieve the purpose, several studies have developed anomaly detection methods for ICSs. In particular, some techniques using packet flow regularity in industrial control networks have achieved high-accuracy detection of attacks disrupting the regularity, i.e. normal behaviour, of ICSs. However, these methods cannot identify scanning attacks employed in cyber espionage because the probing packets assimilate into a number of normal ones. For example, the malware called Havex is customised to clandestinely acquire information from targeting ICSs using general request packets. The techniques to detect such scanning attacks using widespread packets await further investigation. Therefore, the goal of this study was to examine high performance methods to identify anomalies even if elaborate packets to avoid alert systems were employed for attacks against industrial control networks. In this paper, a novel detection model for anomalous packets concealing behind normal traffic in industrial control networks was proposed. For the proposal of the sophisticated detection method, we took particular note of packet flow regularity and employed the Markov-chain model to detect anomalies. Moreover, we regarded not only original packets but similar ones to them as normal packets to reduce false alerts because it was indicated that an anomaly detection model using the Markov-chain suffers from the ample false positives affected by a number of normal, irregular packets, namely noise. To calculate the similarity between packets based on the packet flow regularity, a vector representation tool called word2vec was employed. Whilst word2vec is utilised for the culculation of word similarity in natural language processing tasks, we applied the technique to packets in ICSs to calculate packet similarity. As a result, the Markov-chain with word2vec model identified scanning packets assimulating into normal packets in higher performance than the conventional Markov-chain model. In conclusion, employing both packet flow regularity and packet similarity in industrial control networks contributes to improving the performance of anomaly detection in ICSs.

In this paper, we propose a novel algorithm called multi-projection ensemble discriminant clustering (MPEDC) for JPEG steganalysis. The scheme makes use of the optimal projection of linear discriminant analysis (LDA) algorithm to get more projection vectors by using the micro-rotation method. These vectors are similar to the optimal vector. MPEDC combines unsupervised K-means algorithm to make a comprehensive decision classification adaptively. The power of the proposed method is demonstrated on three steganographic methods with three feature extraction methods. Experimental results show that the accuracy can be improved using iterative discriminant classification.

In this study, transverse magnetic electromagnetic plane wave scatterings by rectangular cylinders have been analyzed by a high frequency asymptotic method. Scattering field can be generated by the equivalent electric and magnetic currents which are obtained approximately from the geometrical optics (GO) fields. Our formulation is found to be exactly the same with the physical optics (PO) for the conducting cylinders, and it can also be applicable for dielectric cylinders. Numerical calculations are made to compare the results with those by other methods, such as the geometrical theory of diffraction (GTD) and HFSS simulation. A good agreement has been observed to confirm the validity of our method.

Location sharing services have recently gained momentum over mobile online social networks (mOSNs), seeing the increasing popularity of GPS-capable mobile devices such as smart phones. Despite the convenience brought by location sharing, there comes severe privacy risks. Though many efforts have been made to protect user privacy during location sharing, many of them rely on the extensive deployment of trusted Cellular Towers (CTs) and some incur excessive time overhead. More importantly, little research so far can support complete privacy including location privacy, identity privacy and social relation privacy. We propose SAM, a new System Architecture for mOSNs, and P3S, a Privacy-Preserving Protocol based on SAM, to address the above issues for privacy-preserving location sharing over mOSNs. SAM and P3S differ from previous work in providing complete privacy for location sharing services over mOSNs. Theoretical analysis and extensive experimental results demonstrate the feasibility and efficiency of the proposed system and protocol.

For capacity expansion of the Quasi-Zenith Satellite System (QZSS) safety confirmation system, frame slotted ALOHA with flag method has previously been proposed as an access control scheme. While it is always able to communicate in an optimum state, its maximum channel efficiency is only 36.8%. In this paper, we propose adding a reservation channel (R-Ch) to the frame slotted ALOHA with flag method to increase the upper limit of the channel efficiency. With an R-Ch, collision due to random channel selection is decreased by selecting channels in multiple steps, and the channel efficiency is improved up to 84.0%. The time required for accommodating 3 million mobile terminals, each sending one message, when using the flag method only and the flag method with an R-Ch are compared. It is shown that the accommodating time can be reduced to less than half by adding an R-Ch to the flag method.

Device-to-device (D2D) communication in cellular networks is defined as direct communication between two mobile users without traversing the base station (BS) or core network. D2D communication can occur on the cellular frequencies (i.e., inband) or unlicensed spectrum (i.e., outband). A high capacity IEEE 802.11-based outband device-to-device communication system for cellular networks is introduced in this paper. Transmissions in device-to-device connections are managed using our proposed medium access control (MAC) protocol. In the proposed MAC protocol, backoff window size is adjusted dynamically considering the current network status and utilizing an appropriate transmission attempt rate. We have considered both cases that the request to send/clear to send (RTS/CTS) mechanism is and is not used in our protocol design. Describing mechanisms for guaranteeing quality of service (QoS) and enhancing reliability of the system is another part of our work. Moreover, performance of the system in the presence of channel impairments is investigated analytically and through simulations. Analytical and simulation results demonstrate that our proposed system has high throughput, and it can provide different levels of QoS for its users.

We improve the cycle time performance of EtherCAT networks with embedded Linux-based master by developing a Linux Ethernet driver optimized for EtherCAT operation. The Ethernet driver is developed to establish a direct interface between the master module and Ethernet controllers of embedded systems by removing the involvement of Linux network stack and the New API (NAPI) of standard Ethernet drivers. Consequently, it is achieved that the time-consuming memory copy operations are reduced and the process of EtherCAT frames is accelerated. In order to demonstrate the effect of the developed Ethernet driver, we set up EtherCAT networks composed of an embedded Linux-based master and commercial off-the-shelf slaves, and the experimental results confirm that the cycle time performance is significantly improved.

It is a hot issue that speeding up the network layers and decreasing the network parameters in convolutional neural networks (CNNs). In this paper, we propose a novel method, namely, symmetric decomposition of convolution kernels (SDKs). It symmetrically separates k×k convolution kernels into (k×1 and 1×k) or (1×k and k×1) kernels. We conduct the comparison experiments of the network models designed by SDKs on MNIST and CIFAR-10 datasets. Compared with the corresponding CNNs, we obtain good recognition performance, with 1.1×-1.5× speedup and more than 30% reduction of network parameters. The experimental results indicate our method is useful and effective for CNNs in practice, in terms of speedup performance and reduction of parameters.

Detecting anomalies, such as network failure or intentional attack in Internet, is a vital but challenging task. Although numerous techniques have been developed based on Internet traffic, detecting anomalies from the perspective of Internet topology structure is going to be possible because the anomaly detection of structured datasets based on complex network theory has become a focus of attention recently. In this paper, an anomaly detection method for the large-scale Internet topology is proposed to detect local structure crashes caused by the cascading failure. In order to quantify the dynamic changes of Internet topology, the network path changes coefficient (NPCC) is put forward which highlights the Internet abnormal state after it is attacked continuously. Furthermore, inspired by Fibonacci Sequence, we proposed the decision function that can determine whether the Internet is abnormal or not. That is the current Internet is abnormal if its NPCC is out of the normal domain calculated using the previous k NPCCs of Internet topology. Finally the new Internet anomaly detection method is tested against the topology data of three Internet anomaly events. The results show that the detection accuracy of all events are over 97%, the detection precision for three events are 90.24%, 83.33% and 66.67%, when k=36. According to the experimental values of index F1, larger values of k offer better detection performance. Meanwhile, our method has better performance for the anomaly behaviors caused by network failure than those caused by intentional attack. Compared with traditional anomaly detection methods, our work is more simple and powerful for the government or organization in items of detecting large-scale abnormal events.