Named Data Networking (NDN) uses name to indicate content mechanism to divide content, and uses content names for routing and addressing. However, the traditional network devices that support the TCP/IP protocol stack and location-centric communication mechanisms cannot support functions such as in-network storage and multicast distribution of NDN effectively. The performance of NDN routers designed for specific functional platforms is limited, and it is difficult to deploy on a large scale, so the NDN network can only be implemented by software. With the development of data plane languages such as Programmable Protocol-Independent Packet Processors (P4), the practical deployment of NDN becomes achievable. To ensure efficient data distribution in the network, this paper proposes a protocol-independent multicast method according to each binary bit. The P4 language is used to define a bit vector in the data packet intrinsic metadata field, which is used to mark the requested port. When the requested content is returned, the routing node will check which port has requested the content according to the bit vector recorded in the register, and multicast the Data packet. The experimental results show that bitwise multicast technology can eliminate the number of flow tables distributed compared with the dynamic multicast group technology, and reduce the content response delay by 57% compared to unicast transmission technology.

Recently, robust speech recognition for real-world applications has attracted much attention. This paper proposes a robust speech recognition method based on the teacher-student learning framework for domain adaptation. In particular, the student network will be trained based on a novel optimization criterion defined by the encoder outputs of both teacher and student networks rather than the final output posterior probabilities, which aims to make the noisy audio map to the same embedding space as clean audio, so that the student network is adaptive in the noise domain. Comparative experiments demonstrate that the proposed method obtained good robustness against noise.

To the best of our knowledge, there have been very few work on computational algorithms for the core or its variants in MC-nets games. One exception is the work by [Hirayama, et.al., 2014], where a constraint generation algorithm has been proposed to compute a payoff vector belonging to the least core. In this paper, we generalize this algorithm into the one for finding a payoff vector belonging to ϵ-core with pre-specified bound guarantee. The underlying idea behind this algorithm is basically the same as the previous one, but one key contribution is to give a clearer view on the pricing problem leading to the development of our new general algorithm. We showed that this new algorithm was correct and never be trapped in an infinite loop. Furthermore, we empirically demonstrated that this algorithm really presented a trade-off between solution quality and computational costs on some benchmark instances.

Wireless sensor networks provide long-term monitoring of the environment, but sensors are powered by small batteries. Using a mobile charger (MC) to replenish energy of sensors is one promising solution to prolong their usage time. Many approaches have been developed to find the MC's moving path, and they assume that sensors have a fixed sensing rate (SR) and prefer to fully charge sensors. In practice, sensors can adaptively adjust their SRs to meet application demands or save energy. Besides, due to the fully charging policy, some sensors with low energy may take long to wait for the MC's service. Thus, the paper formulates a path and charge (P&C) problem, which asks how to dispatch the MC to visit sensors with adaptive SRs and decide their charging time, such that both survivability and throughput of sensors can be maximized. Then, we propose an efficient P&C scheduling (EPCS) algorithm, which builds the shortest path to visit each sensor. To make the MC fast move to charge the sensors near death, some sensors with enough energy are excluded from the path. Moreover, EPCS adopts a floating charging mechanism based on the ratio of workable sensors and their energy depletion. Simulation results verify that EPCS can significantly improve the survivability and throughput of sensors.

Order-preserving encryption using the hypergeomatric probability distribution leaks about the half bits of a plaintext and the distance between two arbitrary plaintexts. To solve these problems, Popa et al. proposed a mutable order-preserving encoding. This is a keyless encoding scheme that adopts an order-preserving index locating the corresponding ciphertext via tree-based data structures. Unfortunately, it has the following shortcomings. First, the frequency of the ciphertexts reveals that of the plaintexts. Second, the indices are highly correlated to the corresponding plaintexts. For these reasons, statistical cryptanalysis may identify the encrypted fields using public information. To overcome these limitations, we propose a multi-tree approach to the mutable order-preserving encoding. The cost of interactions increases by the increased number of trees, but the proposed scheme mitigates the distribution leakage of plaintexts and also reduces the problematic correlation to plaintexts.

This brief presents A 16/32Gb/s dual-mode transmitter including a linearity calibration loop to maintain amplitude linearity of the SST driver. Linearity detection and corresponding master-slave power supply circuits are designed to implement the proposed architecture. The proposed transmitter is manufactured in a 22nm FD-SOI process. The linearity calibration loop reduces the peak INL errors of the transmitter by 50%, and the RLM rises from 92.4% to 98.5% when the transmitter is in PAM4 mode. The chip area of the transmitter is 0.067mm2, while the proposed linearity enhanced part is 0.05×0.02mm2 and the total power consumption is 64.6mW with a 1.1V power supply. The linearity calibration loop can be detached from the circuit without consuming extra power.

Machine-to-Machine (M2M) communication plays a pivotal role in the evolution of Internet of Things (IoT). Cellular networks are considered to be a key enabler for M2M communications, which are originally designed mainly for Human-to-Human (H2H) communications. The introduction of M2M users will cause a series of problems to traditional H2H users, i.e., interference between various traffic. Resource allocation is an effective solution to these problems. In this paper, we consider a shared resource block (RB) and power allocation in an H2H/M2M coexistence scenario, where M2M users are subdivided into delay-tolerant and delay-sensitive types. We first model the RB-power allocation problem as maximization of capacity under Quality-of-Service (QoS) constraints of different types of traffic. Then, a learning framework is introduced, wherein a complex agent is built from simpler subagents, which provides the basis for distributed deployment scheme. Further, we proposed distributed Q-learning based autonomous RB-power allocation algorithm (DQ-ARPA), which enables the machine type network gateways (MTCG) as agents to learn the wireless environment and choose the RB-power autonomously to maximize M2M pairs' capacity while ensuring the QoS requirements of critical services. Simulation results indicates that with an appropriate reward design, our proposed scheme succeeds in reducing the impact of delay-tolerant machine type users on critical services in terms of SINR thresholds and outage ratios.

Although intervention practices like mindfulness meditation have proven effective in treating psychosis, there is no clarity on the mechanism of information propagation in the brain. In this study, we formulated a network optimization problem and searched for the optimal solution using Digital Annealer developed by Fujitsu Ltd. This is inspired by quantum computing and is effective in solving large-scale combinatorial optimization problems to find the information propagation pathway in the brain that contributes to the realization of mindfulness. Specifically, we defined the optimal network state as the state of the brain network that is considered to be associated with the mindfulness state. We formulated the problem into two network optimization problems — the minimum vertex-cover problem and the maximum-flow problem — to search for the information propagation pathway that is important for realizing the state. In the minimum vertex-cover problem, we aimed to identify brain regions that are important for the realization of the mindfulness state, and identified eight regions, including four that were suggested to be consistent with previous studies. We formulated the problem as a maximum-flow problem to identify the information propagation pathways in the brain that contribute to the activation of these four identified regions. As a result, approximately 30% of the connections in the brain network structure of this study were identified, and the pathway with the highest flow rate was considered to characterize the bottom-up emotion regulation during mindfulness. The findings of this study could be useful for more direct interventions in the context of mindfulness, which are being investigated by neurofeedback and other methods. This is because existing studies have not clarified the information propagation pathways that contribute to the realization of the brain network states that characterize mindfulness states. In addition, this approach may be useful as a methodology to identify information propagation pathways in the brain that contribute to the realization of higher-order human cognitive activities, such as mindfulness, within large-scale brain networks.

This paper shows structural optimal design of optical waveguide components utilizing an efficient 3D frequency-domain and 2D time-domain beam propagation method (BPM) with an alternating direction implicit (ADI) scheme. Usual optimal design procedure is based on iteration of numerical simulation, and total computational cost of the optimal design mainly depends on the efficiency of numerical analysis method. Since the system matrices are tridiagonal in the ADI-based BPM, efficient analysis and optimal design are available. Shape and topology optimal design shown in this paper is based on optimization of density distribution and sensitivity analysis to the density parameters. Computational methods of the sensitivity are shown in the case of using the 3D semi-vectorial and 2D time-domain BPM based on ADI scheme. The validity of this design approach is shown by design of optical waveguide components: mode converters, and a polarization beam splitter.

In order to increase communication capacity, the use of millimeter-wave and terahertz-wave bands are being actively explored. Non-radiative dielectric waveguide known as NRD guide is one of promising platform of millimeter-wave integrated circuits thanks to its non-radiative and low loss nature. In order to develop millimeter wave circuits with various functions, various circuit components have to be efficiently designed to meet requirements from application side. In this paper, for efficient design of NRD guide devices, we develop a topology optimal design technique based on function-expansion-method which can express arbitrary structure with arbitrary geometric topology. In the present approach, recently developed two-dimensional full-vectorial finite element method (2D-FVFEM) for NRD guide devices is employed to improve computational efficiency and several evolutionary approaches, which do not require appropriate initial structure depending on a given design problem, are used to optimize design variables, thus, NRD guide devices having desired functions are efficiently obtained without requiring designer's special knowledge.

When confirming the ACLR (adjacent channel leakage power ratio), which are representative indicators of distortion in the design of PA (power amplifier), it is well known how to calculate the AM-AM/PM characteristics of PA, input time series data of modulated signals, and analyze the output by Fourier analysis. In 5G (5th generation) mobile phones, not only QPSK (quadrature phase shift keying) modulation but also 16QAM (quadrature modulation), 64QAM, and 256QAM are becoming more multivalued as modulation signals. In addition, the modulation band may exceed 100MHz, and the amount of time series data increases, and the increase in calculation time becomes a problem. In order to shorten the calculation time, calculating the total amount of distortion generated by PA from the probability density of the modulation signal and the AM (amplitude modulation)-AM/PM (phase modulation) characteristics of PA is considered. For the AM-AM characteristics of PA, in this paper, IMD3 (inter modulation distortion 3) obtained from probability density and IMD3 by Fourier analysis, which are often used so long, are compared. As a result, it was confirmed that the result of probability density analysis is similar to that of Fourier analysis, when the nonlinearity is somewhat small. In addition, the agreement between the proposed method and the conventional method was confirmed with an error of about 2.0dB of ACLR using the modulation waves with a bandwidth of 5MHz, RB (resource block) being 25, and QPSK modulation.

Person images captured by surveillance cameras in real scenes often have low resolution (LR), which suffers from severe degradation in recognition performance when matched with pre-stocked high-resolution (HR) images. There are existing methods which typically employ super-resolution (SR) techniques to address the resolution discrepancy problem in person re-identification (re-ID). However, SR techniques are intended to enhance the human eye visual fidelity of images without caring about the recovery of pedestrian identity information. To cope with this challenge, we propose an orthogonal depth feature decomposition network. And we decompose pedestrian features into resolution-related features and identity-related features who are orthogonal to each other, from which we design the identity-preserving loss and resolution-invariant loss to ensure the recovery of pedestrian identity information. When compared with the SOTA method, experiments on the MLR-CUHK03 and MLR-VIPeR datasets demonstrate the superiority of our method.

With the popularity of smart devices, mobile crowdsensing, in which the crowdsensing platform gathers useful data from users of smart devices, e.g., smartphones, has become a prevalent paradigm. Various incentive mechanisms have been extensively adopted for the crowdsensing platform to incentivize users of smart devices to offer sensing data. Existing works have concentrated on rewarding smart-device users for their short term effort to provide data without considering the long-term factors of smart-device users and the quality of data. Our previous work has considered the quality of data of smart-device users by incorporating the long-term reputation of smart-device users. However, our previous work only considered a quality maximization problem with budget constraints on one location. In this paper, multiple locations are considered. Stackelberg game is utilized to solve a two-stage optimization problem. In the first stage, the crowdsensing platform allocates the budget to different locations and sets price as incentives for users to maximize the total data quality. In the second stage, the users make efforts to provide data to maximize its utility. Extensive numerical simulations are conducted to evaluate proposed algorithm.

This paper introduces the working principle of continuous wave (CW) illuminator and selects the test space by developing the wave impedance selection algorithm for the CW illuminator. For the vertical polarization and the horizontal polarization of CW illuminator, the law of wave impedance distribution after loading is analyzed and the influence of loading distribution on test space selection is studied. The selection principle of wave impedance based on incident field or total field at the monitoring point is analyzed.

In [2], Choi et al. proposed an identity-based password-authenticated key exchange (iPAKE) protocol using the Boneh-Franklin IBE scheme, and its generic construction (UKAM-PiE) that was standardized in ISO/IEC 11770-4/AMD 1. In this paper, we show that the iPAKE and UKAM-PiE protocols are insecure against passive/active attacks by a malicious PKG (Private Key Generator) where the malicious PKG can find out all clients' passwords by just eavesdropping on the communications, and the PKG can share a session key with any client by impersonating the server. Then, we propose a strengthened PAKE (for short, SPAIBE) protocol with IBE, which prevents such a malicious PKG's passive/active attacks. Also, we formally prove the security of the SPAIBE protocol in the random oracle model and compare relevant PAKE protocols in terms of efficiency, number of passes, and security against a malicious PKG.

Towards realization of a noise-induced synchronization in a natural environment, an experimental study is carried out using the Van der Pol oscillator circuit. We focus on acoustic sounds as a potential source of noise that may exist in nature. To mimic such a natural environment, white noise sounds were generated from a loud speaker and recorded into microphone signals. These signals were then injected into the oscillator circuits. We show that the oscillator circuits spontaneously give rise to synchronized dynamics when the microphone signals are highly correlated with each other. As the correlation among the input microphone signals is decreased, the level of synchrony is lowered monotonously, implying that the input correlation is the key determinant for the noise-induced synchronization. Our study provides an experimental basis for synchronizing clocks in distributed sensor networks as well as other engineering devices in natural environment.

In a 1-out-of-n oblivious signature scheme, a user provides a set of messages to a signer for signatures but he/she can only obtain a valid signature for a specific message chosen from the message set. There are two security requirements for 1-out-of-n oblivious signature. The first is ambiguity, which requires that the signer is not aware which message among the set is signed. The other one is unforgeability which requires that the user is not able to derive any other valid signature for any messages beyond the one that he/she has chosen. In this paper, we provide a generic construction of 1-out-of-n oblivious signature. Our generic construction consists of two building blocks, a commitment scheme and a standard signature scheme. Our construction is round efficient since it only asks one interaction (i.e., two rounds) between the user and signer. Meanwhile, in our construction, the ambiguity of the 1-out-of-n oblivious signature scheme is based on the hiding property of the underlying commitment, while the unforgeability is based on the binding property of the underlying commitment scheme and the unforgeability of the underlying signature scheme. Moreover, our construction can also enjoy strong unforgeability as long as the underlying building blocks have strong binding property and strong unforgeability respectively. Given the fact that commitment and digital signature are well-studied topics in cryptography and numerous concrete schemes have been proposed in the standard model, our generic construction immediately yields a bunch of instantiations in the standard model based on well-known assumptions, including not only traditional assumptions like Decision Diffie-Hellman (DDH), Decision Composite Residue (DCR), etc., but also some post-quantum assumption like Learning with Errors (LWE). As far as we know, our construction admits the first 1-out-of-n oblivious signature schemes based on the standard model.

The Euclidean projection operation is the most complex and time-consuming of the alternating direction method of multipliers (ADMM) decoding algorithms, resulting in a large number of resources when deployed on hardware platforms. We propose a simplified line segment projection algorithm (SLSA) and present the hardware design and the quantization scheme of the SLSA. In simulation results, the proposed SLSA module has a better performance than the original algorithm with the same fixed bitwidths due to the centrosymmetric structure of SLSA. Furthermore, the proposed SLSA module with a simpler structure without hypercube projection can reduce time consuming by up to 72.2% and reduce hardware resource usage by more than 87% compared to other Euclidean projection modules in the experiments.

In the Artificial Intelligence for IT Operations scenarios, KPI (Key Performance Indicator) is a very important operation and maintenance monitoring indicator, and research on KPI anomaly detection has also become a hot spot in recent years. Aiming at the problems of low detection efficiency and insufficient representation learning of existing methods, this paper proposes a fast clustering-based KPI anomaly detection method HCE-DWL. This paper firstly adopts the combination of hierarchical agglomerative clustering (HAC) and deep assignment based on CNN-Embedding (CE) to perform cluster analysis (that is HCE) on KPI data, so as to improve the clustering efficiency of KPI data, and then separately the centroid of each KPI cluster and its Transformed Outlier Scores (TOS) are given weights, and finally they are put into the LightGBM model for detection (the Double Weight LightGBM model, referred to as DWL). Through comparative experimental analysis, it is proved that the algorithm can effectively improve the efficiency and accuracy of KPI anomaly detection.

With the rapid deployment of the Internet of Things, where various devices are connected to communication networks, remote driving applications for Unmanned Vehicles (UVs) are attracting attention. In addition to automobiles, autonomous driving technology is expected to be applied to various types of equipment, such as small vehicles equipped with surveillance cameras to monitor building internally and externally, autonomous vehicles that deliver office supplies, and wheelchairs. When a UV is remotely controlled, the control accuracy deteriorates due to transmission delay and jitter. The accuracy must be kept high to realize UV control system by a cloud server. In this study, we investigate the effectiveness of Digital Twin Computing (DTC) for path tracking control of a UV. We show the results of simulations that use transmission delay values measured on the Internet with some cloud servers. Through the results, we quantitatively clarify that application of DTC improves control accuracy on path tracking control. We also clarify that application of jitter buffer, which absorbs the transmission delay fluctuation, can further improve the accuracy.