Embedded systems have been widely used. In addition, embedded systems have been gradually complicated. It is important to ensure the safety for embedded software by software model checking. We have developed a verification system for verifying embedded assembly programs. It generates exact Kripke structure by exhaustively and dynamically simulating assembly programs, and simultaneously verify it by model checking. In addition, we have introduced undefined values to reduce the number of states in order to avoid the state space explosion.

Crowd modeling and simulation is an active research field that has drawn increasing attention from industry, academia and government recently. In this paper, we present a generic data-driven approach to generate crowd behaviors that can match the video data. The proposed approach is a bi-layer model to simulate crowd behaviors in pedestrian traffic in terms of exclusion statistics, parallel dynamics and social psychology. The bottom layer models the microscopic collision avoidance behaviors, while the top one focuses on the macroscopic pedestrian behaviors. To validate its effectiveness, the approach is applied to generate collective behaviors and re-create scenarios in the Informatics Forum, the main building of the School of Informatics at the University of Edinburgh. The simulation results demonstrate that the proposed approach is able to generate desirable crowd behaviors and offer promising prediction performance.

To drastically increase the splitting ratio of extended-reach (40km span) time- and wavelength-division multiplexed passive optical networks (WDM/TDM-PONs), we modify the gain control scheme of our automatic gain controlled semiconductor optical amplifiers (AGC-SOAs) that were developed to support upstream transmission in long-reach systems. While the original AGC-SOAs are located outside the central office (CO) as repeaters, the new AGC-SOAs are located inside the CO and connected to each branch of an optical splitter in the CO. This arrangement has the potential to greatly reduce the costs of CO-sited equipment as they are shared by many more users if the new gain control scheme works properly even when the input optical powers are low. We develop a prototype and experimentally confirm its effectiveness in increasing the splitting ratio of extended-reach systems to 512.

In the topologies for interconnected nodes, it is desirable to have a low degree and a small diameter. For the same number of nodes, a dual-cube topology has almost half the degree compared to a hypercube while increasing the diameter by just one. Hence, it is a promising topology for interconnection networks of massively parallel systems. We propose here a stochastic fault-tolerant routing algorithm to find a non-faulty path from a source node to a destination node in a dual-cube.

This paper presents a capacitor-loaded 4x4 planar loop array for three-dimensional near-field beamforming of magnetic resonance wireless power transfer (WPT). This planar loop array provides three important functions: beamforming, selective power transfer, and the ability to work alignment free with the receiver. These functions are realized by adjusting the capacitance of each loop. The optimal capacitance of each loop that corresponds to the three functions can be found using a genetic algorithm (GA); the three functions were verified by comparing simulations and measurements at a frequency of 6.78MHz. Finally, the beamforming mechanism of a near-field loop array was investigated using the relationship between the current magnitude and the resonance frequency of each loop, resulting in the findings that the magnitude and the resonance frequency are correlated. This focused current of the specified loop creates a strong magnetic field in front of that loop, resulting in near-field beamforming.

This paper proposes a proportional fair-based joint optimization method for user association and the bandwidth ratio of protected radio resources exclusively used by pico base stations (BSs) for inter-cell interference coordination (ICIC) in heterogeneous networks where low transmission-power pico BSs overlay a high transmission-power macro BS. The proposed method employs an iterative algorithm, in which the user association process for a given bandwidth ratio of protected radio resources and the bandwidth ratio control of protected radio resources for a given user association are applied alternately and repeatedly up to convergence. For user association, we use our previously reported decentralized iterative user association method based on the feedback information of each individual user assisted by a small amount of broadcast information from the respective BSs. Based on numerical results, we show that the proposed method adaptively achieves optimal user association and bandwidth ratio control of protected radio resources, which maximizes the geometric mean user throughput within the macrocell coverage area. The system throughput of the proposed method is compared to that for conventional approaches to show the performance gain.

Index generation functions model content-addressable memory, and are useful in virus detectors and routers. Linear decompositions yield simpler circuits that realize index generation functions. This paper proposes a balanced decision tree based heuristic to efficiently design linear decompositions for index generation functions. The proposed heuristic finds a good linear decomposition of an index generation function by using appropriate cost functions and a constraint to construct a balanced tree. Since the proposed heuristic is fast and requires a small amount of memory, it is applicable even to large index generation functions that cannot be solved in a reasonable time by existing heuristics. This paper shows time and space complexities of the proposed heuristic, and experimental results using some large examples to show its efficiency.

The growing demand for high-speed data communication has continued to meet the need for ever-increasing I/O bandwidth in recent VLSI systems. However, signal integrity issues, such as intersymbol interference (ISI) and reflections, make the channel band-limited at high-speed data rates. We propose high-speed data transmission techniques for VLSI systems using Tomlinson-Harashima precoding (THP). Because THP can eliminate ISI by inverting the characteristics of channels with limited peak and average power at the transmitter, it is suitable for implementing advanced low-voltage and high-speed VLSI systems. This paper presents a novel double-rate THP equalization technique especially intended for multi-valued data transmission to further improve THP performance. Simulation and measurement results show that the proposed THP equalization with a double sampling rate can enhance the data transition time and, therefore, improve the eye opening.

Ultra-wideband millimeter wave radars significantly enhance the capabilities of three-dimensional (3D) imaging sensors, making them suitable for short-range surveillance and security purposes. For such applications, developed the range point migration (RPM) method, which achieves highly accurate surface extraction by using a range-point focusing scheme. However, this method is inaccurate and incurs great computation cost for complicated-shape targets with many reflection points, such as the human body. As an essential solution to this problem, we introduce herein a range-point clustering algorithm that exploits, the RPM feature. Results from numerical simulations assuming 140-GHz millimeter wavelength radar verify that the proposed method achieves remarkably accurate 3D imaging without sacrificing computational efficiency.

We developed a novel, proof-of-concept side-channel attack framework called RouteDetector, which identifies a route for a train trip by simply reading smart device sensors: an accelerometer, magnetometer, and gyroscope. All these sensors are commonly used by many apps without requiring any permissions. The key technical components of RouteDetector can be summarized as follows. First, by applying a machine-learning technique to the data collected from sensors, RouteDetector detects the activity of a user, i.e., “walking,” “in moving vehicle,” or “other.” Next, it extracts departure/arrival times of vehicles from the sequence of the detected human activities. Finally, by correlating the detected departure/arrival times of the vehicle with timetables/route maps collected from all the railway companies in the rider's country, it identifies potential routes that can be used for a trip. We demonstrate that the strategy is feasible through field experiments and extensive simulation experiments using timetables and route maps for 9,090 railway stations of 172 railway companies.

An enormous number of malware samples pose a major threat to our networked society. Antivirus software and intrusion detection systems are widely implemented on the hosts and networks as fundamental countermeasures. However, they may fail to detect evasive malware. Thus, setting a high priority for new varieties of malware is necessary to conduct in-depth analyses and take preventive measures. In this paper, we present a traffic model for malware that can classify network behaviors of malware and identify new varieties of malware. Our model comprises malware-specific features and general traffic features that are extracted from packet traces obtained from a dynamic analysis of the malware. We apply a clustering analysis to generate a classifier and evaluate our proposed model using large-scale live malware samples. The results of our experiment demonstrate the effectiveness of our model in finding new varieties of malware.

For network researchers and practitioners, active measurement, in which probe packets are injected into a network, is a powerful tool to measure end-to-end delay. It is, however, suffers the intrusiveness problem, where the load of the probe traffic itself affects the network QoS. In this paper, we first demonstrate that there exists a fundamental accuracy bound of the conventional active measurement of delay. Second, to transcend that bound, we propose INTrusiveness-aware ESTimation (INTEST), an approach that compensates for the delays produced by probe packets in wired networks. Simulations of M/M/1 and MMPP/M/1 show that INTEST enables a more accurate estimation of end-to-end delay than conventional methods. Furthermore, we extend INTEST for multi-hop networks by using timestamps or multi-flow probes.

Midori128 is a lightweight block cipher proposed at ASIACRYPT 2015 to achieve low energy consumption per bit. Currently, the best published impossible differential attack on Midori128 covers 10 rounds without the pre-whitening key. By exploiting the special structure of the S-boxes and the binary linear transformation layer in Midori128, we present impossible differential distinguishers that cover 7 full rounds including the mix column operations. Then, we exploit four of these distinguishers to launch multiple impossible differential attack against 11 rounds of the cipher with the pre-whitening and post-whitening keys.

Massive multiple-input multiple-output (MIMO) technology is one of the key enablers in the fifth generation mobile communications (5G), in order to accommodate growing traffic demands and to utilize higher super high frequency (SHF) and extremely high frequency (EHF) bands. In the paper, we propose a novel transmit precoding named “nonlinear block multi-diagonalization (NL-BMD) precoding” for multiuser MIMO (MU-MIMO) downlink toward 5G. Our NL-BMD precoding strategy is composed of two essential techniques: block multi-diagonalization (BMD) and adjacent inter-user interference pre-cancellation (IUI-PC). First, as an extension of the conventional block diagonalization (BD) method, the linear BMD precoder for the desired user is computed to incorporate a predetermined number of interfering users, in order to ensure extra degrees of freedom at the transmit array even after null steering. Additionally, adjacent IUI-PC, as a nonlinear operation, is introduced to manage the residual interference partially allowed in BMD computation, with effectively-reduced numerical complexity. It is revealed through computer simulations that the proposed NL-BMD precoding yields up to 67% performance improvement in average sum-rate spectral efficiency and enables large-capacity transmission regardless of the user distribution, compared with the conventional BD precoding.

This paper proposes Deep Neural Network (DNN)-based Voice Conversion (VC) using input-to-output highway networks. VC is a speech synthesis technique that converts input features into output speech parameters, and DNN-based acoustic models for VC are used to estimate the output speech parameters from the input speech parameters. Given that the input and output are often in the same domain (e.g., cepstrum) in VC, this paper proposes a VC using highway networks connected from the input to output. The acoustic models predict the weighted spectral differentials between the input and output spectral parameters. The architecture not only alleviates over-smoothing effects that degrade speech quality, but also effectively represents the characteristics of spectral parameters. The experimental results demonstrate that the proposed architecture outperforms Feed-Forward neural networks in terms of the speech quality and speaker individuality of the converted speech.

Many-core architecture is becoming an attractive design choice in high-end embedded systems design. There are, however, many important design issues, and load balancing is one of them. In this work, we take the approach of diffusive load balancing which enables autonomic load distribution in many-core systems. We improve the existing schemes by adding the concept of simulated annealing for more effective load distribution. The modified scheme is also capable of managing a situation of non-uniform granularity of task loading, which the existing ones cannot. In addition, the suggested scheme is extended to be able to handle dependencies existing in task graphs where tasks have communications between each other. As experiments, we tried various existing schemes as well as the proposed one to map synthetic applications and real world applications on a many-core architecture with 21 cores and 4 memory tiles. For the applications without communications, the experiments show that the proposed scheme gives the best results in terms of peak load and standard deviation. For real applications such as mp3 decoder and h.263 encoder which have communications between tasks, we show the effectiveness of our communication-aware scheme for load balancing in terms of throughput.

The increasing demand on scalability and reusability of system-on-chip design as well as the decoupling between computation and communication has motivated the growth of the Network-on-Chip (NoC) paradigm in the last decade. In NoC-based systems, the computational resources (i.e. IPs) communicate with each other using a network infrastructure. Many works have focused on the development of NoC architectures and routing mechanisms, while the interfacing between network and associated IPs also needs to be considered. In this paper, we present a novel efficient AXI (AMBA eXtensible Interface) compliant network adapter for NoC architectures, which is named an AXI-NoC adapter. The proposed network adapter achieves high communication throughput of 20.8Gbits/s and consumes 4.14mW at the operating frequency of 650MHz. It has a low area footprint (952 gates, approximate to 2,793µm2 with CMOS 45nm technology) thanks to its effective hybrid micro-architectures and with zero latency thanks to the proposed mux-selection method.

A group signature allows any group member to anonymously sign a message. One of the important issues is an efficient membership revocation. The scheme proposed by Libert et al. has achieved O(1) signature and membership certificate size, O(1) signing and verification times, and O(log N) public key size, where N is the total number of members. However the Revocation List (RL) data is large, due to O(R) signatures in RL, where R is the number of revoked members. The scheme proposed by Nakanishi et al. achieved a compact RL of O(R/T) signatures for any integer T. However, this scheme increases membership certificate size by O(T). In this paper, we extend the scheme proposed by Libert et al., by reducing the RL size to O(R/T) using a vector commitment to compress the revocation entries, while O(1) membership certificate size remains.

Nonnegative matrix factorization (NMF) is one of the most popular machine learning tools for speech enhancement. The supervised NMF-based speech enhancement is accomplished by updating iteratively with the prior knowledge of the clean speech and noise spectra bases. However, in many real-world scenarios, it is not always possible for conducting any prior training. The traditional semi-supervised NMF (SNMF) version overcomes this shortcoming while the performance degrades. In this letter, without any prior knowledge of the speech and noise, we present an improved semi-supervised NMF-based speech enhancement algorithm combining techniques of NMF and robust principal component analysis (RPCA). In this approach, fixed speech bases are obtained from the training samples chosen from public dateset offline. The noise samples used for noise bases training, instead of characterizing a priori as usual, can be obtained via RPCA algorithm on the fly. This letter also conducts a study on the assumption whether the time length of the estimated noise samples may have an effect on the performance of the algorithm. Three metrics, including PESQ, SDR and SNR are applied to evaluate the performance of the algorithms by making experiments on TIMIT with 20 noise types at various signal-to-noise ratio levels. Extensive experimental results demonstrate the superiority of the proposed algorithm over the competing speech enhancement algorithm.

An transfer learning method, called Kernel Canonical Correlation Analysis plus (KCCA+), is proposed for heterogeneous Cross-company defect prediction. Combining the kernel method and transfer learning techniques, this method improves the performance of the predictor with more adaptive ability in nonlinearly separable scenarios. Experiments validate its effectiveness.