This paper expands our previously proposed semi-blind uplink interference suppression scheme for multicell multiuser massive MIMO systems to support multi modulus signals. The original proposal applies the channel state information (CSI) aided blind adaptive array (BAA) interference suppression after the beamspace preprocessing and the decision feedback channel estimation (DFCE). BAA is based on the constant modulus algorithm (CMA) which can fully exploit the degree of freedom (DoF) of massive antenna arrays to suppress both inter-user interference (IUI) and inter-cell interference (ICI). Its effectiveness has been verified under the extensive pilot contamination constraint. Unfortunately, CMA basically works well only for constant envelope signals such as QPSK and thus the proposed scheme should be expanded to cover QAM signals for more general use. This paper proposes to apply the multi modulus algorithm (MMA) and the minimum mean square error weight derivation based on data-aided sample matrix inversion (MMSE-SMI). It can successfully realize interference suppression even with the use of multi-level envelope signals such as 16QAM with satisfactorily outage probability performance below the fifth percentile.

In this paper, we propose new generation methods of two-dimensional (2D) optical zero-correlation zone (ZCZ) sequences with the high peak autocorrelation amplitude. The 2D optical ZCZ sequence consists of a pair of a binary sequence which takes 1 or 0 and a bi-phase sequence which takes 1 or -1, and has a zero-correlation zone in the two-dimensional correlation function. Because of these properties, the 2D optical ZCZ sequence is suitable for optical code-division multiple access (OCDMA) system using an LED array having a plurality of light-emitting elements arranged in a lattice pattern. The OCDMA system using the 2D optical ZCZ sequence can be increased the data rate and can be suppressed interference by the light of adjacent LEDs. By using the proposed generation methods, we can improve the peak autocorrelation amplitude of the sequence. This means that the BER performance of the OCDMA system using the sequence can be improved.

This paper considers a distributed subgradient method for online optimization with event-triggered communication over multi-agent networks. At each step, each agent obtains a time-varying private convex cost function. To cooperatively minimize the global cost function, these agents need to communicate each other. The communication with neighbor agents is conducted by the event-triggered method that can reduce the number of communications. We demonstrate that the proposed online algorithm achieves a sublinear regret bound in a dynamic environment with slow dynamics.

Utilizing the enormous potential of quantum computers requires new and practical quantum algorithms. Motivated by the success of machine learning, we investigate the fusion of neural and quantum computing, and propose a learning method for a quantum neural network inspired by the Hebb rule. Based on an analogy between neuron-neuron interactions and qubit-qubit interactions, the proposed quantum learning rule successfully changes the coupling strengths between qubits according to training data. To evaluate the effectiveness and practical use of the method, we apply it to the memorization process of a neuro-inspired quantum associative memory model. Our numerical simulation results indicate that the proposed quantum versions of the Hebb and anti-Hebb rules improve the learning performance. Furthermore, we confirm that the probability of retrieving a target pattern from multiple learned patterns is sufficiently high.

We consider the bisimilarity control problem for partially observed nondeterministic discrete event systems with deterministic specifications. This problem requires us to synthesize a supervisor that achieves bisimulation equivalence of the supervised system and the deterministic specification under partial observation. We present necessary and sufficient conditions for the existence of such a deterministic supervisor and show that these conditions can be verified polynomially.

Inspired by an idea due to Levenshtein, we apply the low correlation zone constraint in the analysis of the weighted mean square aperiodic correlation. Then we derive a lower bound on the measure for quasi-complementary sequence sets with low correlation zone (LCZ-QCSS). We discuss the conditions of tightness for the proposed bound. It turns out that the proposed bound is tighter than Liu-Guan-Ng-Chen bound for LCZ-QCSS. We also derive a lower bound for QCSS, which improves the Liu-Guan-Mow bound in general.

The performance of packet processing applications is dependent on the memory access speed of network systems. Table lookup requires fast memory access and is one of the most common processes in various packet processing applications, which can be a dominant performance bottleneck. Therefore, in Network Function Virtualization (NFV)-aware environments, on-chip fast cache memories of a CPU of general-purpose hardware become critical to achieve high performance packet processing speeds of over tens of Gbps. Also, multiple types of applications and complex applications are executed in the same system simultaneously in carrier network systems, which require adequate cache memory capacities as well. In this paper, we propose a packet processing architecture that utilizes interleaved 3 Dimensional (3D)-stacked Dynamic Random Access Memory (DRAM) devices as off-chip Last Level Cache (LLC) in addition to several levels of dedicated cache memories of each CPU core. Entries of a lookup table are distributed in every bank and vault to utilize both bank interleaving and vault-level memory parallelism. Frequently accessed entries in 3D-stacked DRAM are also cached in on-chip dedicated cache memories of each CPU core. The evaluation results show that the proposed architecture reduces the memory access latency by 57%, and increases the throughput by 100% while reducing the blocking probability but about 10% compared to the architecture with shared on-chip LLC. These results indicate that 3D-stacked DRAM can be practical as off-chip LLC in parallel packet processing systems.

Identifying the infection sources in a network, including the sponsor of a network rumor, the servers that inject computer virus into a computer network, or the zero-patient in an infectious disease network, plays a critical role in limiting the damage caused by the infection. A two-source estimator is firstly constructed on basis of partitions of infection regions in this paper. Meanwhile, the two-source estimation problem is transformed into calculating the expectation of permitted permutations count which can be simplified to a single-source estimation problem under determined infection region. A heuristic algorithm is also proposed to promote the estimator to general graphs in a Breadth-First-Search (BFS) fashion. Experimental results are provided to verify the performance of our method and illustrate variations of error detection in different networks.

The partial-period autocorrelation of sequences is an important performance measure of communication systems employing them, but it is notoriously difficult to be analyzed. In this paper, we propose an algorithm to design unimodular sequences with low partial-period autocorrelations via directly minimizing the partial-period integrated sidelobe level (PISL). The proposed algorithm is inspired by the monotonic minimizer for integrated sidelobe level (MISL) algorithm. Then an acceleration scheme is considered to further accelerate the algorithms. Numerical experiments show that the proposed algorithm can effectively generate sequences with lower partial-period peak sidelobe level (PPSL) compared with the well-known Zadoff-Chu sequences.

In this study we propose a method to perform personal identification (PI) based on Electroencephalogram (EEG) signals, where the used network is named residual and multiscale spatio-temporal convolution neural network (RAMST-CNN). Combined with some popular techniques in deep learning, including residual learning (RL), multi-scale grouping convolution (MGC), global average pooling (GAP) and batch normalization (BN), RAMST-CNN has powerful spatio-temporal feature extraction ability as it achieves task-independence that avoids the complexity of selecting and extracting features manually. Experiments were carried out on multiple datasets, the results of which were compared with methods from other studies. The results show that the proposed method has a higher recognition accuracy even though the network it is based on is lightweight.

Impulsive noise (IN) is the most dominant factor degrading the performance of communication systems over powerlines. In order to improve performance of high-speed power line communication (PLC), this work focuses on mitigating burst IN effects based on compressive sensing (CS), and an adaptive burst IN mitigation method, namely combination of adaptive interleaver and permutation of null carriers is designed. First, the long burst IN is dispersed by an interleaver at the receiver and the characteristic of noise is estimated by the method of moment estimation, finally, the generated sparse noise is reconstructed by changing the number of null carriers(NNC) adaptively according to noise environment. In our simulations, the results show that the proposed IN mitigation technique is simple and effective for mitigating burst IN in PLC system, it shows the advantages to reduce the burst IN and to improve the overall system throughput. In addition, the performance of the proposed technique outpeformences other known nonlinear noise mitigation methods and CS methods.

In a smart home environment, sensors generate events whenever activities of residents are captured. However, due to some factors, abnormal events could be generated, which are technically reasonable but contradict to real-world activities. To detect abnormal events, a number of methods has been introduced, e.g., clustering-based or snapshot-based approaches. However, they have limitations to deal with complicated anomalies which occur with large number of events and blended within normal sensor readings. In this paper, we propose a novel method of detecting sensor anomalies under smart home environment by considering spatial correlation and dependable correlation between sensors. Initially, we pre-calculate these correlations of every pair of two sensors to discover their relations. Then, from periodic sensor readings, if it has any unmatched relations to the pre-computed ones, an anomaly is detected on the correlated sensor. Through extensive evaluations with real datasets, we show that the proposed method outperforms previous approaches with 20% improvement on detection rate and reasonably low false positive rate.

Recently, adaptive streaming over information centric network (ICN) has attracted attention. In adaptive streaming over ICN, the bitrate adaptation of the client often overestimates a bitrate for available bandwidth due to congestion because the client implicitly estimates congestion status from the content download procedures of ICN. As a result, streaming overestimated bitrate results in QoE degradation of clients such as cause of a stall time and frequent variation of the bitrate. In this paper, we propose a congestion-aware adaptive streaming over ICN combined with the explicit congestion notification (CAAS with ECN) to avoid QoE degradation. CAAS with ECN encourages explicit feedback of congestion detected in the router on the communication path, and introduces the upper band of the selectable bitrate (bitrate-cap) based on explicit feedback from the router to the bitrate adaptation of the clients. We evaluate the effectiveness of CAAS with ECN for client's QoE degradation due to congestion and behavior on the QoS metrics based on throughput. The simulation experiments show that the bitrate adjustment for all the clients improves QoE degradation and QoE fairness due to effective congestion avoidance.

The environment surrounding automotive technology is undergoing a major transformation. In particular, as technological innovation advances in new areas called “CASE” such as Connected, Autonomous/Automated, Shared, and Electric, various research activities are underway. However, this is an approach from the standpoint of the automobile centered, and when considering the development of a new automobile society, it is necessary to consider from the standpoint of “human centered,” who are users, too. Therefore, this paper proposes the possibility of technological innovation in the area of “Another CASE” such as Comfortable, Accessible, Safety, and Enjoy/Exciting, and introduces the contents of some interesting researches.

In this paper, we build a microscopic simulator of traffic flow in a three-modal transport society for pedestrians/slow vehicles/vehicles (P/SV/V) to evaluate a post P/V society. The simulator assumes that the SV includes bicycles and micro electric vehicles, whose speed is strictly and mechanically limited up to 30 km/h. In addition, this simulator adopts an SV overtaking model. Modal shifts caused by modal diversity requires new valuation indexes. The simulator has a significant feature of a traveler-based traffic demand simulation not a vehicle-based traffic demand simulation as well as new evaluation indexes. New assessment taking this situation into account is conducted and the results explain new aspects of traffic flow in a three-mode transport society.

Side-channel (SC) leakage from a cryptographic device chip is simulated as the dynamic current flowing out of the chip. When evaluating the simulated current, an evaluation by comparison with an actual measurement is essential; however, it is difficult to compare them directly. This is because a measured waveform is typically the output voltage of probe placed at the observation position outside the chip, and the actual dynamic current is modified by several transfer impedances. Therefore, in this paper, the probe voltage is converted into the dynamic current by using an EMC macro-model of a cryptographic device being evaluated. This paper shows that both the amplitude and the SC analysis (correlation power analysis and measurements to disclosure) results of the simulated dynamic current were evaluated appropriately by using the EMC macro-model. An evaluation confirms that the shape of the simulated current matches the measured one; moreover, the SC analysis results agreed with the measured ones well. On the basis of the results, it is confirmed that a register-transfer level (RTL) simulation of the dynamic current gives a reasonable estimation of SC traces.

C-C successive approximation register analog-to-digital converter (C-C SAR-ADC) is space-saving architecture compared to SAR-ADC with binary weighted capacitive digital-to-analog converter (CDAC). However, the accuracy of C-C SAR-ADC is degraded due to parasitic capacitance of floating nodes. This paper proposes an algorithm calibrating the non-linearity by γ-estimation to accurately estimate radix greater than 2 required to realize C-C SAR-ADC. Behavioral analyses show that the radix γ-estimation error become within 1.5, 0.4 and 0.1% in case of 8-, 10- and 12-bit resolution ADC, respectively. SPICE simulations show that the γ-estimation satisfies the requirement of 10-bit resolution C-C SAR-ADC. The C-C SAR-ADC using γ-estimation achieves 9.72bit of ENOB, 0.8/-0.5LSB and 0.5/-0.4LSB of DNL/INL.

This paper proposes a route calculation method for a bicycle navigation system that complies with traffic regulations. The extension of the node map and three kinds of route calculation methods are constructed and evaluated on the basis of travel times and system acceptability survey results. Our findings reveal the effectiveness of the proposed route calculation method and the acceptability of the bicycle navigation system that included the method.

In this paper, we investigate a model-free design of decentralized event-triggered mechanism for networked control systems (NCSs). The approach aims at simultaneously tuning the optimal parameters for the controller and the event-triggered condition, such that a prescribed cost function can be minimized. To achieve this goal, we employ the Bayesian optimization (BO), which is known to be an automatic tuning framework for finding the optimal solution to the black-box optimization problem. Thanks to its efficient search strategy for the global optimum, the BO allows us to design the event-triggered mechanism with relatively a small number of experimental evaluations. This is particularly suited for NCSs where network resources such as the limited life-time of battery powered devices are limited. Some simulation examples illustrate the effectiveness of the approach.

Many approaches enable teachers to digitalize students' answers and mark them on the computer. However, they are still limited for supporting marking descriptive mathematical answers that can best evaluate learners' understanding. This paper presents clustering of offline handwritten mathematical expressions (HMEs) to help teachers efficiently mark answers in the form of HMEs. In this work, we investigate a method of combining feature types from low-level directional features and multiple levels of recognition: bag-of-symbols, bag-of-relations, and bag-of-positions. Moreover, we propose a marking cost function to measure the marking effort. To show the effectiveness of our method, we used two datasets and another sampled from CROHME 2016 with synthesized patterns to prepare correct answers and incorrect answers for each question. In experiments, we employed the k-means++ algorithm for each level of features and considered their combination to produce better performance. The experiments show that the best combination of all the feature types can reduce the marking cost to about 0.6 by setting the number of answer clusters appropriately compared with the manual one-by-one marking.