This paper proposes coded modulation for physical layer network coding in multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM) bi-directional wireless relay systems where precoding is applied. The proposed coded modulation enables the relays to decode the received signals, which improves the transmission performance. Soft input decoding for the proposed coded modulation is proposed. Furthermore, we propose two precoder weight optimization techniques, called “per subcarrier weight optimization” and “total weight optimization”. This paper shows a precoder configuration based on the optimization with the lattice reduction or the sorted QR-decomposition. The performance of the proposed network coding is evaluated by computer simulation in a MIMO-OFDM two-hop wireless relay system with the 16 quadrature amplitude modulation (QAM) or the 256QAM. The proposed coded modulation attains a coding gain of about 2dB at the BER of 10-4. The total weight optimization achieves about 1dB better BER performance than the other at the BER of 10-4.

New boundary integral equations are proposed for two-port slab waveguides which satisfy single mode condition. The boundary integral equations are combined with the orthogonality of guided mode and non-guided field. They are solved by the standard boundary element method with no use of mode expansion technique. Reflection and transmission coefficients of guided mode are directly determined by the boundary element method. To validate the proposed method, step waveguides for TE wave incidence and triangular rib waveguides for TM wave incidence are investigated by numerical calculations.

Graphics processing units (GPUs) are highly efficient architectures for parallel stencil code; however, the small device (i.e., GPU) memory capacity (several tens of GBs) necessitates the use of out-of-core computation to process excess data. Great programming effort is needed to manually implement efficient out-of-core stencil code. To relieve such programming burdens, directive-based frameworks emerged, such as the pipelined accelerator (PACC); however, they usually lack specific optimizations to reduce data transfer. In this paper, we extend PACC with two data-centric optimizations to address data transfer problems. The first is a direct-mapping scheme that eliminates host (i.e., CPU) buffers, which intermediate between the original data and device buffers. The second is a region-sharing scheme that significantly reduces host-to-device data transfer. The extended PACC was applied to an acoustic wave propagator, automatically extending the length of original serial code 2.3-fold to obtain the out-of-core code. Experimental results revealed that on a Tesla V100 GPU, the generated code ran 41.0, 22.1, and 3.6 times as fast as implementations based on Open Multi-Processing (OpenMP), Unified Memory, and the previous PACC, respectively. The generated code also demonstrated usefulness with small datasets that fit in the device capacity, running 1.3 times as fast as an in-core implementation.

We propose an optimal power allocation scheme that maximizes the transmission rate of device-to-device (D2D) communications underlaying a cellular system based on orthogonal frequency division multiplexing (OFDM). The proposed algorithm first calculates the maximum allowed transmission power of a D2D transmitter to restrict the interference caused to a cellular link that share the same OFDM subchannels with the D2D link. Then, with a constraint on the maximum transmit power, an optimization of water-filling type is performed to find the optimal transmit power allocation across subchannels and within each subchannel. The performance of the proposed power allocation scheme is evaluated in terms of the average achievable rate of the D2D link.

The notion of the signal-to-noise ratio (SNR), proposed by Guilley, et al. in 2004, is a property that attempts to characterize the resilience of (n, m)-functions F=(f1,...,fm) (cryptographic S-boxes) against differential power analysis. But how to study the signal-to-noise ratio for a Boolean function still appears to be an important direction. In this paper, we give a tight upper and tight lower bounds on SNR for any (balanced) Boolean function. We also deduce some tight upper bounds on SNR for balanced Boolean function satisfying propagation criterion. Moreover, we obtain a SNR relationship between an n-variable Boolean function and two (n-1)-variable decomposition functions. Meanwhile, we give SNR(f⊞g) and SNR(f⊡g) for any balanced Boolean functions f, g. Finally, we give a lower bound on SNR(F), which determined by SNR(fi) (1≤i≤m), for (n, m)-function F=(f1,f2,…,fm).

This paper summarizes our recent studies on architecture, photonic integration, system validation and networking performance analysis of a flexible low-latency interconnect optical network switch (Flex-LIONS) for datacenter and high-performance computing (HPC) applications. Flex-LIONS leverages the all-to-all wavelength routing property in arrayed waveguide grating routers (AWGRs) combined with microring resonator (MRR)-based add/drop filtering and multi-wavelength spatial switching to enable topology and bandwidth reconfigurability to adapt the interconnection to different traffic profiles. By exploiting the multiple free spectral ranges of AWGRs, it is also possible to provide reconfiguration while maintaining minimum-diameter all-to-all interconnectivity. We report experimental results on the design, fabrication, and system testing of 8×8 silicon photonic (SiPh) Flex-LIONS chips demonstrating error-free all-to-all communication and reconfiguration exploiting different free spectral ranges (FSR0 and FSR1, respectively). After reconfiguration in FSR1, the bandwidth between the selected pair of nodes is increased from 50Gb/s to 125Gb/s while an all interconnectivity at 25Gb/s is maintained using FSR0. Finally, we investigate the use of Flex-LIONS in two different networking scenarios. First, networking simulations for a 256-node datacenter inter-rack communication scenario show the potential latency and energy benefits when using Flex-LIONS for optical reconfiguration based on different traffic profiles (a legacy fat-tree architecture is used for comparison). Second, we demonstrate the benefits of leveraging two FSRs in an 8-node 64-core computing system to provide reconfiguration for the hotspot nodes while maintaining minimum-diameter all-to-all interconnectivity.

We developed a polarization-independent and reserved-band-less complementary spectral inverted optical phase conjugation (CSI-OPC) device using dual-band difference frequency generation based on highly efficient periodically poled LiNbO3 waveguide technologies. To examine the nonlinearity mitigation in a long-haul transmission using a large number of OPCs, we installed a CSI-OPC device in the middle of a pure silica core fiber-based recirculating loop transmission line with a length of 320km. First, we examined the fiber-input power tolerance after 5,120-km and 6,400-km transmission using 22.5-Gbaud PDM-16QAM 10-channel DWDM signals and found a Q-factor improvement of over 1.3dB along with enhanced power tolerance thanks to mitigating the fiber nonlinearity. We then demonstrated transmission distance extension using the CSI-OPC device. The use of multiple CSI-OPCs enables an obvious performance improvements attained by extending the transmission distance from 6,400km to 8,960km, which corresponds to applying the CSI-OPC device 28 times. Moreover, there was no Q-factor degradation for the link in a linear regime after applying the CSI-OPC device more than 16 times. These results demonstrate that the CSI-OPC device can improve the nonlinear tolerance of PDM-16QAM signals without an excess penalty.

In this paper, we propose a user-assisted QoS control scheme that utilizes media adaptive buffering to enhance QoE of audiovisual and haptic IP communications. The scheme consists of two modes: a manual mode and an automatic mode. It enables users to switch between these two modes according to their inclinations. We compare four QoS control schemes: the manual mode only, the automatic mode only, the switching scheme starting with the manual mode, and the switching scheme starting with the automatic mode. We assess the effects of the four schemes, user attributes, and tasks on QoE through a subjective experiment which provides information on users' behavior in addition to QoE scores. As a result of the experiment, we show that the user-assisted QoS control scheme can enhance QoE. Furthermore, we notice that the proper QoS control scheme depends on user attributes and tasks.

Current stereo matching methods benefit a lot from the precise stereo estimation with Convolutional Neural Networks (CNNs). Nevertheless, patch-based siamese networks rely on the implicit assumption of constant depth within a window, which does not hold for slanted surfaces. Existing methods for handling slanted patches focus on post-processing. In contrast, we propose a novel module for matching cost networks to overcome this bias. Slanted objects appear horizontally stretched between stereo pairs, suggesting that the feature extraction in the horizontal direction should be different from that in the vertical direction. To tackle this distortion, we utilize asymmetric convolutions in our proposed module. Experimental results show that the proposed module in matching cost networks can achieve higher accuracy with fewer parameters compared to conventional methods.

Link prediction, the computational problem of determining whether there is a link between two objects, is important in machine learning and data mining. Feature-based link prediction, in which the feature vectors of the two objects are given, is of particular interest because it can also be used for various identification-related problems. Although the factorization machine and the higher-order factorization machine (HOFM) are widely used for feature-based link prediction, they use feature combinations not only across the two objects but also from the same object. Feature combinations from the same object are irrelevant to major link prediction problems such as predicting identity because using them increases computational cost and degrades accuracy. In this paper, we present novel models that use higher-order feature combinations only across the two objects. Since there were no algorithms for efficiently computing higher-order feature combinations only across two objects, we derive one by leveraging reported and newly obtained results of calculating the ANOVA kernel. We present an efficient coordinate descent algorithm for proposed models. We also improve the effectiveness of the existing one for the HOFM. Furthermore, we extend proposed models to a deep neural network. Experimental results demonstrated the effectiveness of our proposed models.

Previously, dynamic multi-hop shortcut (DMHS) communications to improve packet delivery rate and reduce end-to-end transmission delay was proposed. In this letter, we theoretically derive the end-to-end throughput of DMHS considering the retransmission at each node for a simple network model without considering collision. Moreover, we show the validity of the derived expression using computer simulations, and we clarify the effect of various parameters on the throughput using DMHS.

Android occupies a very large market share in the field of mobile devices, and quantities of applications are created everyday allowing users to easily use them. However, privacy leaks on Android terminals may result in serious losses to businesses and individuals. Current permission model cannot effectively prevent privacy data leakage. In this paper, we find a way to protect privacy data on Android terminals from the perspective of privacy information propagation by porting the concept of contextual integrity to the realm of privacy protection. We propose a computational model of contextual integrity suiting for Android platform and design a privacy protection system based on the model. The system consists of an online phase and offline phase; the main function of online phase is to computing the value of distribution norm and making privacy decisions, while the main function of offline phase is to create a classification model that can calculate the value of the appropriateness norm. Based on the 6 million permission requests records along with 2.3 million runtime contextual records collected by dynamic analysis, we build the system and verify its feasibility. Experiment shows that the accuracy of offline classifier reaches up to 0.94. The experiment of the overall system feasibility illustrates that 70% location data requests, 84% phone data requests and 46% storage requests etc., violate the contextual integrity.

This letter studies the physical layer security of an unmanned aerial vehicle (UAV)-enabled multicasting system, where a UAV serves as a mobile transmitter to send a common confidential message to a group of legitimate users under the existence of multiple eavesdroppers. The worst situation in which each eavesdropper can wiretap all legitimate users is considered. We seek to maximize the average secrecy rate by jointly optimizing the UAV's transmit power and trajectory over a given flight period. The resulting optimization problem is nonconvex and intractable to solve. To circumvent the nonconvexity, we propose an iterative algorithm to approximate the solution based on the alternating optimization and successive convex approximation methods. Simulation results validate the convergence and effectiveness of our proposed algorithm.

Knowledge graph embedding aims to embed entities and relations of multi-relational data in low dimensional vector spaces. Knowledge graphs are useful for numerous artificial intelligence (AI) applications. However, they (KGs) are far from completeness and hence KG embedding models have quickly gained massive attention. Nevertheless, the state-of-the-art KG embedding models ignore the category specific projection of entities and the impact of entity types in relational aspect. For example, the entity “Washington” could belong to the person or location category depending on its appearance in a specific relation. In a KG, an entity usually holds many type properties. It leads us to a very interesting question: are all the type properties of an entity are meaningful for a specific relation? In this paper, we propose a KG embedding model TPRC that leverages entity-type properties in the relational context. To show the effectiveness of our model, we apply our idea to the TransE, TransR and TransD. Our approach outperforms other state-of-the-art approaches as TransE, TransD, DistMult and ComplEx. Another, important observation is: introducing entity type properties in the relational context can improve the performances of the original translation distance based models.

In this article, we propose a method called “continuous noise masking (cNM)” that allows eliminating residual buzziness in a continuous vocoder, i.e. of which all parameters are continuous and offers a simple and flexible speech analysis and synthesis system. Traditional parametric vocoders generally show a perceptible deterioration in the quality of the synthesized speech due to different processing algorithms. Furthermore, an inaccurate noise resynthesis (e.g. in breathiness or hoarseness) is also considered to be one of the main underlying causes of performance degradation, leading to noisy transients and temporal discontinuity in the synthesized speech. To overcome these issues, a new cNM is developed based on the phase distortion deviation in order to reduce the perceptual effect of the residual noise, allowing a proper reconstruction of noise characteristics, and model better the creaky voice segments that may happen in natural speech. To this end, the cNM is designed to keep only voice components under a condition of the cNM threshold while discarding others. We evaluate the proposed approach and compare with state-of-the-art vocoders using objective and subjective listening tests. Experimental results show that the proposed method can reduce the effect of residual noise and can reach the quality of other sophisticated approaches like STRAIGHT and log domain pulse model (PML).

A lumped element kinetic inductance detector (LeKID) relying on a superconducting resonator is a promising candidate for sensing high energy particles such as neutrinos, X-rays, gamma-rays, alpha particles, and the particles found in the dark matter owing to its large-format capability and high sensitivity. To develop a high energy camera, we formulated design rules based on the experimental results from niobium (Nb)-based LeKIDs at 1 K irradiated with alpha-particles of 5.49 MeV. We defined the design rules using the electromagnetic simulations for minimizing the crosstalk. The neighboring pixels were fixed at 150 µm with a frequency separation of 250 MHz from each other to reduce the crosstalk signal as low as the amplifier-limited noise level. We examined the characteristics of the Nb-based resonators, where the signal decay time was controlled in the range of 0.5-50 µs by changing the designed quality factor of the detectors. The amplifier noise was observed to restrict the performance of our device, as expected. We improved the energy resolution by reducing the filling factor of inductor lines. The best energy resolution of 26 for the alpha particle of 5.49 MeV was observed in our device.

As smartphones and IoT devices become widespread, probabilistic event streams, which are continuous analysis results of sensing data, have received a lot of attention. One of the applications of probabilistic event streams is monitoring of time series events based on regular expressions. That is, we describe a monitoring query such as “Has the tracked object moved from RoomA to RoomB in the past 30 minutes?” by using a regular expression, and then check whether corresponding events occur in a probabilistic event stream with a sliding window. Although we proposed the fundamental monitoring method of time series events in our previous work, three problems remain: 1) it is based on an unusual assumption about slide size of a sliding window, 2) the grammar of pattern queries did not include “negation”, and 3) it was not optimized for multiple monitoring queries. In this paper, we propose several techniques to solve the above problems. First, we remove the assumption about slide size, and propose adaptive slicing of sliding windows for efficient probability calculation. Second, we calculate the occurrence probability of a negation pattern by using an inverted DFA. Finally, we propose the merge of multiple DFAs based on disjunction to process multiple queries efficiently. Experimental results using real and synthetic datasets demonstrate effectiveness of our approach.

This letter investigates the secure transmission improvement scheme for indoor visible light communications (VLC) by using the protected zone. Firstly, the system model is established. For the input signal, the non-negativity and the dimmable average optical intensity constraint are considered. Based on the system model, the secrecy capacity for VLC without considering the protected zone is obtained. After that, the protected zone is determined, and the construction of the protected zone is also provided. Finally, the secrecy capacity for VLC with the protected zone is derived. Numerical results show that the secure performance of VLC improves dramatically by employing the protected zone.

In the coming smart-home era, more and more household electrical appliances are generating more and more sensor data and transmitting them over the home networks, which are often connected to Internet through Point-to-Point Protocol over Ethernet (PPPoE) for desirable authentication and accounting. However, according to our knowledge, high-speed commercial home PPPoE router is still absent for a home network environment. In this paper, we first introduce and evaluate our programmable platform FLARE-DPDK for ease of programming network functions. Then we introduce our effort to build a compact 10Gbps software FLARE PPPoE router on a commercial mini-PC. In our implementation, the control plane is implemented with Linux PPPoE software for authentication-like signaling control. The data plane is implemented over FLARE-DPDK platform, where we get packets from physical network interfaces directly bypassing Linux kernel and distribute packets to multiple CPU cores for data processing in parallel. We verify our software PPPoE router in both lab and production network environment. The experimental results show that our FLARE software PPPoE router can achieve much higher throughput than a commercial PPPoE router tested in a production environment.

In small cell deployments, the combined usage of user association and inter-cell interference coordination (ICIC) is inevitable. This paper investigates the joint optimization of user association and ICIC in the downlink. We first formulate the joint optimization problem as a utility maximization problem. We then employ the logarithmic utility function known as the proportional fair criteria. The optimum user association and the ICIC are derived by solving a convex optimization problem based on the average spectral efficiencies of all users. We propose an iterative algorithm to obtain the optimum solution to this problem. We evaluate the performance of the proposed algorithm for the small cell deployments and shows that the proposed algorithm works well. We also compare the performance of the proposed algorithm based on utility maximization user association with the CRE, and show the superiority of the utility maximization. Furthermore, we show that intra-tier ICIC and inter-tier ICIC can effectively improve the throughput performance according to the conditions. It is also shown that the combined usage of inter-tier ICIC and intra-tier ICIC enhances the throughput performance compared to schemes employing either the inter- or intra-tier ICIC scheme.