This paper proposes a novel application of coded apertures (CAs) for visual information hiding. CA is one of the representative computational photography techniques, in which a patterned mask is attached to a camera as an alternative to a conventional circular aperture. With image processing in the post-processing phase, various functions such as omnifocal image capturing and depth estimation can be performed. In general, a watermark embedded as high-frequency components is difficult to extract if captured outside the focal length, and defocus blur occurs. Installation of a CA into the camera is a simple solution to mitigate the difficulty, and several attempts are conducted to make a better design for stable extraction. On the contrary, our motivation is to design a specific CA as well as an information hiding scheme; the secret information can only be decoded if an image with hidden information is captured with the key aperture at a certain distance outside the focus range. The proposed technique designs the key aperture patterns and information hiding scheme through evolutionary multi-objective optimization so as to minimize the decryption error of a hidden image when using the key aperture while minimizing the accuracy when using other apertures. During the optimization process, solution candidates, i.e., key aperture patterns and information hiding schemes, are evaluated on actual devices to account for disturbances that cannot be considered in optical simulations. Experimental results have shown that decoding can be performed with the designed key aperture and similar ones, that decrypted image quality deteriorates as the similarity between the key and the aperture used for decryption decreases, and that the proposed information hiding technique works on actual devices.

In this paper, we explore privacy-preserving techniques in federated learning, including those can be used with both neural networks and decision trees. We begin by identifying how information can be leaked in federated learning, after which we present methods to address this issue by introducing two privacy-preserving frameworks that encompass many existing privacy-preserving federated learning (PPFL) systems. Through experiments with publicly available financial, medical, and Internet of Things datasets, we demonstrate the effectiveness of privacy-preserving federated learning and its potential to develop highly accurate, secure, and privacy-preserving machine learning systems in real-world scenarios. The findings highlight the importance of considering privacy in the design and implementation of federated learning systems and suggest that privacy-preserving techniques are essential in enabling the development of effective and practical machine learning systems.

In this paper, we investigate optimal design of millimeter-wave (mmWave) multiuser line-of-sight multiple-input-multiple-output (LOS MIMO) systems using hybrid arrays of subarrays based on hybrid block diagonalization (BD) precoding and combining scheme. By introducing a general 3D geometric channel model, the optimal subarray separation products of the transmitter and receiver for maximizing sum-rate is designed in terms of two regular configurations of adjacent subarrays and interleaved subarrays for different users, respectively. We analyze the sensitivity of the optimal design parameters on performance in terms of a deviation factor, and derive expressions for the eigenvalues of the multiuser equivalent LOS MIMO channel matrix, which are also valid for non-optimal design. Simulation results show that the interleaved subarrays can support longer distance communication than the adjacent subarrays given the appropriate fixed subarray deployment.

Device-to-device (D2D) communication allows user terminals to directly communicate with each other without the need for any base stations (BSs). Since the D2D communication underlaying a cellular system shares frequency channels with BSs, co-channel interference may occur. Successive interference cancellation (SIC), which is also called the serial interference canceler, detects and subtracts user signals from received signals in descending order of received power, can cope with the above interference and has already been applied to fog nodes that manage communications among machine-to-machine (M2M) devices besides direct communications with BSs. When differences among received power levels of user signals are negligible, however, SIC cannot work well and thus causes degradation in bit error rate (BER) performance. To solve such a problem, this paper proposes to apply parallel interference cancellation (PIC), which can simultaneously detect both desired and interfering signals under the maximum likelihood criterion and can maintain good BER performance even when power level differences among users are small. When channel coding is employed, however, SIC can be superior to PIC in terms of BER under some channel conditions. Considering the superiority, this paper also proposes to select the proper cancellation scheme and modulation and coding scheme (MCS) that can maximize the throughput of D2D under a constraint of BER, in which the canceler selection is referred to as adaptive interference cancellation. Computer simulations show that PIC outperforms SIC under almost all channel conditions and thus the adaptive selection from PIC and SIC can achieve a marginal gain over PIC, while PIC can achieve 10% higher average system throughput than that of SIC. As for transmission delay time, it is demonstrated that the adaptive selection and PIC can shorten the delay time more than any other schemes, although the fog node causes the delay time of 1ms at least.

The application of millimeter wave (mmWave) directional transmission technology in high-speed railway (HSR) scenarios helps to achieve the goal of multiple gigabit data rates with low latency. However, due to the high mobility of trains, the traditional initial access (IA) scheme with high time consumption is difficult to guarantee the effectiveness of the beam alignment. In addition, the high path loss at the coverage edge of the millimeter wave remote radio unit (mmW-RRU) will also bring great challenges to the stability of IA performance. Fortunately, the train trajectory in HSR scenarios is periodic and regular. Moreover, the cell-free network helps to improve the system coverage performance. Based on these observations, this paper proposes an efficient IA scheme based on location and history information in cell-free networks, where the train can flexibly select a set of mmW-RRUs according to the received signal quality. We specifically analyze the collaborative IA process based on the exhaustive search and based on location and history information, derive expressions for IA success probability and delay, and perform the numerical analysis. The results show that the proposed scheme can significantly reduce the IA delay and effectively improve the stability of IA success probability.

Cross-domain data fusion is becoming a key driver in the growth of numerous and diverse applications in the Internet of Things (IoT) era. We have proposed the concept of a new information platform, Geo-Centric Information Platform (GCIP), that enables IoT data fusion based on geolocation, i.e., produces spatio-temporal content (STC), and then provides the STC to users. In this environment, users cannot know in advance “when,” “where,” or “what type” of STC is being generated because the type and timing of STC generation vary dynamically with the diversity of IoT data generated in each geographical area. This makes it difficult to directly search for a specific STC requested by the user using the content identifier (domain name of URI or content name). To solve this problem, a new content discovery method that does not directly specify content identifiers is needed while taking into account (1) spatial and (2) temporal constraints. In our previous study, we proposed a content discovery method that considers only spatial constraints and did not consider temporal constraints. This paper proposes a new content discovery method that matches user requests with content metadata (topic) characteristics while taking into account spatial and temporal constraints. Simulation results show that the proposed method successfully discovers appropriate STC in response to a user request.

Compressed sensing is a rapidly growing research field in signal and image processing, machine learning, statistics, and systems control. In this survey paper, we provide a review of the theoretical foundations of compressed sensing and present state-of-the-art algorithms for solving the corresponding optimization problems. Additionally, we discuss several practical applications of compressed sensing, such as group testing, sparse system identification, and sparse feedback gain design, and demonstrate their effectiveness through Python programs. This survey paper aims to contribute to the advancement of compressed sensing research and its practical applications in various scientific disciplines.

In the beyond 5G and 6G networks, the number of connected devices and their types will greatly increase including not only user devices such as smartphones but also the Internet of Things (IoT). Moreover, Non-terrestrial networks (NTN) introduce dynamic changes in the types of connected devices as base stations or access points are moving objects. Therefore, continuous network capacity design is required to fulfill the network requirements of each device. However, continuous optimization of network capacity design for each device within a short time span becomes difficult because of the heavy calculation amount. We introduce device types as groups of devices whose traffic characteristics resemble and optimize network capacity per device type for efficient network capacity design. This paper proposes a method to classify device types by analyzing only encrypted traffic behavior without using payload and packets of specific protocols. In the first stage, general device types, such as IoT and non-IoT, are classified by analyzing packet header statistics using machine learning. Then, in the second stage, connected devices classified as IoT in the first stage are classified into IoT device types, by analyzing a time series of traffic behavior using deep learning. We demonstrate that the proposed method classifies device types by analyzing traffic datasets and outperforms the existing IoT-only device classification methods in terms of the number of types and the accuracy. In addition, the proposed model performs comparable as a state-of-the-art model of traffic classification, ResNet 1D model. The proposed method is suitable to grasp device types in terms of traffic characteristics toward efficient network capacity design in networks where massive devices for various services are connected and the connected devices continuously change.

To establish a network service in network functions virtualization (NFV) networks, the orchestrator addresses the challenge of service chaining and virtual network function placement (SC-VNFP) by mapping virtual network functions (VNFs) and virtual links onto physical nodes and links. Unlike traditional networks, network operators in NFV networks must contend with both hardware and software failures in order to ensure resilient network services, as NFV networks consist of physical nodes and software-based VNFs. To guarantee network service quality in NFV networks, the existing work has proposed an approach for the SC-VNFP problem that considers VNF diversity and redundancy. VNF diversity splits a single VNF into multiple lightweight replica instances that possess the same functionality as the original VNF, which are then executed in a distributed manner. VNF redundancy, on the other hand, deploys backup instances with standby mode on physical nodes to prepare for potential VNF failures. However, the existing approach does not adequately consider the tradeoff between resource efficiency and service availability in the context of VNF diversity and redundancy. In this paper, we formulate the SC-VNFP problem with VNF diversity and redundancy as a two-step integer linear program (ILP) that adjusts the balance between service availability and resource efficiency. Through numerical experiments, we demonstrate the fundamental characteristics of the proposed ILP, including the tradeoff between resource efficiency and service availability.

Ising machines can find optimum or quasi-optimum solutions of combinatorial optimization problems efficiently and effectively. The graph coloring problem, which is one of the difficult combinatorial optimization problems, is to assign a color to each vertex of a graph such that no two vertices connected by an edge have the same color. Although methods to map the graph coloring problem onto the Ising model or quadratic unconstrained binary optimization (QUBO) model are proposed, none of them considers minimizing the number of colors. In addition, there is no Ising-machine-based method considering additional constraints in order to apply to practical problems. In this paper, we propose a mapping method of the graph coloring problem including minimizing the number of colors and additional constraints to the QUBO model. As well as the constraint terms for the graph coloring problem, we firstly propose an objective function term that can minimize the number of colors so that the number of used spins cannot increase exponentially. Secondly, we propose two additional constraint terms: One is that specific vertices have to be colored with specified colors; The other is that specific colors cannot be used more than the number of times given in advance. We theoretically prove that, if the energy of the proposed QUBO mapping is minimized, all the constraints are satisfied and the objective function is minimized. The result of the experiment using an Ising machine showed that the proposed method reduces the number of used colors by up to 75.1% on average compared to the existing baseline method when additional constraints are not considered. Considering the additional constraints, the proposed method can effectively find feasible solutions satisfying all the constraints.

Ising machines can find optimum or quasi-optimum solutions of combinatorial optimization problems efficiently and effectively. It is known that, when a good initial solution is given to an Ising machine, we can finally obtain a solution closer to the optimal solution. However, several Ising machines cannot directly accept an initial solution due to its computational nature. In this paper, we propose a method to give quasi-initial solutions into Ising machines that cannot directly accept them. The proposed method gives the positive or negative external magnetic field coefficients (magnetic field controlling term) based on the initial solutions and obtains a solution by using an Ising machine. Then, the magnetic field controlling term is re-calculated every time an Ising machine repeats the annealing process, and hence the solution is repeatedly improved on the basis of the previously obtained solution. The proposed method is applied to the capacitated vehicle routing problem with an additional constraint (constrained CVRP) and the max-cut problem. Experimental results show that the total path distance is reduced by 5.78% on average compared to the initial solution in the constrained CVRP and the sum of cut-edge weight is increased by 1.25% on average in the max-cut problem.

In recent years, with the wide spread of the Internet of Things (IoT) devices, security issues for hardware devices have been increasing, where detecting their anomalous behaviors becomes quite important. One of the effective methods for detecting anomalous behaviors of IoT devices is to utilize consumed energy and operation duration time extracted from their power waveforms. However, the existing methods do not consider the shape of time-series data and cannot distinguish between power waveforms with similar consumed energy and duration time but different shapes. In this paper, we propose a method for detecting anomalous behaviors based on the shape of time-series data by incorporating a shape-based distance (SBD) measure. The proposed method first obtains the entire power waveform of the target IoT device and extracts several application power waveforms. After that, we give the invariances to them, and we can effectively obtain the SBD between every two application power waveforms. Based on the SBD values, the local outlier factor (LOF) method can finally distinguish between normal application behaviors and anomalous application behaviors. Experimental results demonstrate that the proposed method successfully detects anomalous application behaviors, while the existing state-of-the-art method fails to detect them.

This letter provides an efficient massive multiple-input multiple-output (MIMO) detector based on quasi-newton methods to speed up the convergence performance under realistic scenarios, such as high user load and spatially correlated channels. The proposed method leverages the information of the Hessian matrix by merging Barzilai-Borwein method and Limited Memory-BFGS method. In addition, an efficient initial solution based on constellation mapping is proposed. The simulation results demonstrate that the proposed method diminishes performance loss to 0.7dB at the bit-error-rate of 10-2 at 128×32 antenna configuration with low complexity, which surpasses the state-of-the-art (SOTA) algorithms.

Statistical wafer-level characteristic variation modeling offers an attractive method for reducing the measurement cost in large-scale integrated (LSI) circuit testing while maintaining test quality. In this method, the performance of unmeasured LSI circuits fabricated on a wafer is statistically predicted based on a few measured LSI circuits. Conventional statistical methods model spatially smooth variations in the wafers. However, actual wafers can exhibit discontinuous variations that are systematically caused by the manufacturing environment, such as shot dependence. In this paper, we propose a modeling method that considers discontinuous variations in wafer characteristics by applying the knowledge of manufacturing engineers to a model estimated using Gaussian process regression. In the proposed method, the process variation is decomposed into systematic discontinuous and global components to improve estimation accuracy. An evaluation performed using an industrial production test dataset indicates that the proposed method effectively reduces the estimation error for an entire wafer by over 36% compared with conventional methods.

This paper introduces an inverter-based true random number generator (I-TRNG). It uses a single CMOS inverter to amplify thermal noise multiple times. An adaptive calibration mechanism based on clock tuning provides robust operation across a wide range of supply voltage 0.5∼1.1V and temperature -40∼140°C. An 8-bit Von-Neumann post-processing circuit (VN8W) is implemented for maximum raw entropy extraction. In a 130nm CMOS technology, the I-TRNG entropy source only occupies 635μm2 and consumes 0.016pJ/raw-bit at 0.6V. The I-TRNG occupies 13406μm2, including the entropy source, adaptive calibration circuit, and post-processing circuit. The minimum energy consumption of the I-TRNG is 1.38pJ/bit at 0.5V, while passing all NIST 800-22 and 800-90B tests. Moreover, an equivalent 15-year life at 0.7V, 25°C is confirmed by an accelerated NBTI aging test.

Seen from the Internet Service Provider (ISP) side, network traffic monitoring is an indispensable part during network service provisioning, which facilitates maintaining the security and reliability of the communication networks. Among the numerous traffic conditions, we should pay extra attention to traffic anomaly, which significantly affects the network performance. With the advancement of Machine Learning (ML), data-driven traffic anomaly detection algorithms have established high reputation due to the high accuracy and generality. However, they are faced with challenges on inefficient traffic feature extraction and high computational complexity, especially when taking the evolving property of traffic process into consideration. In this paper, we proposed an online learning framework for traffic anomaly detection by embracing Gaussian Process (GP) and Sparse Representation (SR) in two steps: 1). To extract traffic features from past records, and better understand these features, we adopt GP with a special kernel, i.e., mixture of Gaussian in the spectral domain, which makes it possible to more accurately model the network traffic for improving the performance of traffic anomaly detection. 2). To combat noise and modeling error, observing the inherent self-similarity and periodicity properties of network traffic, we manually design a feature vector, based on which SR is adopted to perform robust binary classification. Finally, we demonstrate the superiority of the proposed framework in terms of detection accuracy through simulation.

Convolutional Neural Networks (CNNs) and Transformers have achieved remarkable performance in detection and classification tasks. Nevertheless, their feature extraction cannot consider both local and global information, so the detection and classification performance can be further improved. In addition, more and more deep learning networks are designed as more and more complex, and the amount of computation and storage space required is also significantly increased. This paper proposes a combination of CNN and transformer, and designs a local feature enhancement module and global context modeling module to enhance the cascade network. While the local feature enhancement module increases the range of feature extraction, the global context modeling is used to capture the feature maps' global information. To decrease the model complexity, a shared sublayer is designed to realize the sharing of weight parameters between the adjacent convolutional layers or cross convolutional layers, thereby reducing the number of convolutional weight parameters. Moreover, to effectively improve the detection performance of neural networks without increasing network parameters, the optimal transport assignment approach is proposed to resolve the problem of label assignment. The classification loss and regression loss are the summations of the cost between the demander and supplier. The experiment results demonstrate that the proposed Combination of CNN and Transformer with Shared Sublayer (CCTSS) performs better than the state-of-the-art methods in various datasets and applications.

In this paper, we investigate the evolution of an optical network architecture and discuss the future direction of research on optical network design and control. We review existing research on optical network design and control and present some open challenges. One of the important open challenges lies in multilayer resource optimization including IT and optical network resources. We propose an adaptive joint optimization method of IT resources and optical spectrum under time-varying traffic demand in optical networks while avoiding an increase in operation cost. We formulate the problem as mixed integer linear programming and then quantitatively evaluate the trade-off relationship between the optimality of reconfiguration and operation cost. We demonstrate that we can achieve sufficient network performance through the adaptive joint optimization while suppressing an increase in operation cost.

The novel optical path routing architecture named flexible waveband routing networks is reviewed in this paper. The nodes adopt a two-stage path routing scheme where wavelength selective switches (WSSs) bundle optical paths and form a small number of path groups and then optical switches without wavelength selectivity route these groups to desired outputs. Substantial hardware scale reduction can be achieved as the scheme enables us to use small scale WSSs, and even more, share a WSS by multiple input cores/fibers through the use of spatially-joint-switching. Furthermore, path groups distributed over multiple bands can be switched by these optical switches and thus the adaptation to multi-band transmission is straightforward. Network-wide numerical simulations and transmission experiments that assume multi-band transmission demonstrate the validity of flexible waveband routing.

To cope with the drastic increase in traffic, space division multiplexing elastic optical networks (SDM-EONs) have been investigated. In multicore fiber environments that realize SDM-EONs, crosstalk (XT) occurs between optical paths transmitted in the same frequency slots of adjacent cores, and the quality of the optical paths is degraded by the mutual influence of XT. To solve this problem, we propose a core and spectrum assignment method that introduces the concept of prohibited frequency slots to protect the degraded optical paths. First-fit-based spectrum resource allocation algorithms, including our previous study, have the problem that only some frequency slots are used at low loads, and XT occurs even though sufficient frequency slots are available. In this study, we propose a core and spectrum assignment method that introduces the concepts of “adjacency criterion” and “XT budget” to suppress XT at low and middle loads without worsening the path blocking rate at high loads. We demonstrate the effectiveness of the proposed method in terms of the path blocking rate using computer simulations.