In this letter, we discuss the ambiguity function of interleaved sequences. Furthermore, using the Guassian sum and choosing binary m-sequences as column sequences, we investigate the property of a binary sequence set given by Zhou, Tang, Gong (IEEE Trans. Inf. Theory, 54(9), 2008), which has low ambiguity property in a large region. Those sequences could be used in radar systems.

This paper presents a deep network based on morphological filters for Gaussian denoising. The morphological filters can be applied with only addition, max, and min functions and require few computational resources. Therefore, the proposed network is suitable for implementation using a small microprocessor. Each layer of the proposed network consists of a top-hat transform, which extracts small peaks and valleys of noise components from the input image. Noise components are iteratively reduced in each layer by subtracting the noise components from the input image. In this paper, the extensions of opening and closing are introduced as linear combinations of the morphological filters for the top-hat transform of this deep network. Multiplications are only required for the linear combination of the morphological filters in the proposed network. Because almost all parameters of the network are structuring elements of the morphological filters, the feature maps and parameters can be represented in short bit-length integer form, which is suitable for implementation with single instructions, multiple data (SIMD) instructions. Denoising examples show that the proposed network obtains denoising results comparable to those of BM3D [1] without linear convolutions and with approximately one tenth the number of parameters of a full-scale deep convolutional neural network [2]. Moreover, the computational time of the proposed method using SIMD instructions of a microprocessor is also presented.

Reducing the power consumption of capsule endoscopy is essential for its further development. We introduce K-SVD dictionary learning to design a dictionary for sparse coding, and improve reconstruction accuracy of capsule endoscopic images captured using compressed sensing. At a compression ratio of 20%, the proposed method improves image quality by approximately 4.4 dB for the peak signal-to-noise ratio.

High-altitude platform stations (HAPSs) are recognized as a promising technology for coverage extension in the sixth generation (6G) mobile communications and beyond. The purpose of this study is to develop a HAPS system with a coverage radius of 100km and high capacity by focusing on the following two aspects: array antenna structure and user selection. HAPS systems must jointly use massive multiple-input multiple-output (mMIMO) and multiuser MIMO techniques to increase their capacity. However, the coverage achieved by a conventional planar array antenna is limited to a circular area with a radius of only tens of kilometers. A conventional semi-orthogonal user selection (SUS) scheme based on the orthogonality of channel vectors achieves high capacity, but it has high complexity. First, this paper proposes a cylindrical mMIMO system to achieve an ultra-wide coverage radius of 100km and high capacity. Second, this paper presents a novel angle-based user selection (AUS) scheme, where a user selection problem is formulated as a maximization of the minimum angular difference between users over all user groups. Finally, a low-complexity suboptimal algorithm (SA) for AUS is also proposed. Assuming an area with a 100km radius, simulation results demonstrate that the proposed cylindrical mMIMO system improves the signal-to-interference-plus-noise ratio by approx. 12dB at the boundary of the area, and it achieves approx. 1.5 times higher capacity than the conventional mMIMO which uses a planar array antenna. In addition, the results show that the proposed AUS scheme improves the lower percentiles in the system capacity distribution compared with SUS and basic random user selection. Furthermore, the computational complexity of the proposed SA is in the order of only 1/4000 that of SUS.

OpenFlow is a widely adopted implementation of the Software-Defined Networking (SDN) architecture. Since conventional network monitoring systems are unable to cope with OpenFlow networks, researchers have developed various monitoring systems tailored for OpenFlow networks. However, these existing systems either rely on a specific controller framework or an API, both of which are not part of the OpenFlow specification, and thus limit their applicability. This article proposes a transparent and low-overhead monitoring system for OpenFlow networks, referred to as Opimon. Opimon monitors the network topology, switch statistics, and flow tables in an OpenFlow network and visualizes the result through a web interface in real-time. Opimon monitors a network by interposing a proxy between the controller and switches and intercepting every OpenFlow message exchanged. This design allows Opimon to be compatible with any OpenFlow switch or controller. We tested the functionalities of Opimon on a virtual network built using Mininet and a large-scale international OpenFlow testbed (PRAGMA-ENT). Furthermore, we measured the performance overhead incurred by Opimon and demonstrated that the overhead in terms of latency and throughput was less than 3% and 5%, respectively.

Binary code similarity comparison methods are mainly used to find bugs in software, to detect software plagiarism, and to reduce the workload during malware analysis. In this paper, we propose a method to compare the binary code similarity of each function by using a combination of Control Flow Graphs (CFGs) and disassembled instruction sequences contained in each function, and to detect a function with high similarity to a specified function. One of the challenges in performing similarity comparisons is that different compile-time optimizations and different architectures produce different binary code. The main units for comparing code are instructions, basic blocks and functions. The challenge of functions is that they have a graph structure in which basic blocks are combined, making it relatively difficult to derive similarity. However, analysis tools such as IDA, display the disassembled instruction sequence in function units. Detecting similarity on a function basis has the advantage of facilitating simplified understanding by analysts. To solve the aforementioned challenges, we use machine learning methods in the field of natural language processing. In this field, there is a Transformer model, as of 2017, that updates each record for various language processing tasks, and as of 2021, Transformer is the basis for BERT, which updates each record for language processing tasks. There is also a method called node2vec, which uses machine learning techniques to capture the features of each node from the graph structure. In this paper, we propose SIBYL, a combination of Transformer and node2vec. In SIBYL, a method called Triplet-Loss is used during learning so that similar items are brought closer and dissimilar items are moved away. To evaluate SIBYL, we created a new dataset using open-source software widely used in the real world, and conducted training and evaluation experiments using the dataset. In the evaluation experiments, we evaluated the similarity of binary codes across different architectures using evaluation indices such as Rank1 and MRR. The experimental results showed that SIBYL outperforms existing research. We believe that this is due to the fact that machine learning has been able to capture the features of the graph structure and the order of instructions on a function-by-function basis. The results of these experiments are presented in detail, followed by a discussion and conclusion.

This paper proposes a methodology for designing broadband class B/J power amplifier based on a mirrored lowpass filter matching structure. According to this filter theory, the impedance of this design method is mainly related to the cutoff frequency. Series inductors and shunt capacitors filter out high frequencies. The change of input impedance with frequency is small in the passband. Which can suppress higher harmonics and expand bandwidth. In order to confirm the validity of the design method, a broadband high-efficiency power amplifier in the 1.3 - 3.9GHz band is designed and fabricated. Measurement results show that the output power is greater than 40.5dBm, drain efficiency is 61.2% - 70.8% and the gain is greater than 10dB.

To sustain and expand the agricultural economy even as its workforce shrinks, the efficiency of farm operations must be improved. One key to efficiency improvement is completely unmanned driving of farm machines, which requires stable monitoring and control of machines from remote sites, a safety system to ensure safe autonomous driving even without manual operations, and precise positioning in not only small farm fields but also wider areas. As possible solutions for those issues, we have developed technologies of wireless network quality prediction, an end-to-end overlay network, machine vision for safety and positioning, network cooperated vehicle control and autonomous tractor control and conducted experiments in actual field environments. Experimental results show that: 1) remote monitoring and control can be seamlessly continued even when connection between the tractor and the remote site needs to be switched across different wireless networks during autonomous driving; 2) the safety of the autonomous driving can automatically be ensured by detecting both the existence of people in front of the unmanned tractor and disturbance of network quality affecting remote monitoring operation; and 3) the unmanned tractor can continue precise autonomous driving even when precise positioning by satellite systems cannot be performed.

Linear codes with a few-weight have important applications in combinatorial design, strongly regular graphs and cryptography. In this paper, we first construct a class of Boolean functions with at most five-valued Walsh spectra, and determine their spectrum distribution. Then, we derive two classes of linear codes with at most six-weight from the new functions. Meanwhile, the length, dimension and weight distributions of the codes are obtained. Results show that both of the new codes are minimal and among them, one is wide minimal code and the other is a narrow minimal code and thus can be used to design secret sharing scheme with good access structures. Finally, some Magma programs are used to verify the correctness of our results.

In this paper, for improving the robustness of D2D-based SNS by avoiding the cascading failure, we propose an autonomous decentralized friendship management called virtual temporal friendship creation. In our proposed virtual temporal friendship creation, some virtual temporal friendships are created among users based on an optimization problem to improve the robustness although these friendships cannot be used to perform the message exchange in SNS. We investigate the impact of creating a new friendship on the node resilience for the optimization problem. Then we consider an autonomous decentralized algorithm based on the obtained results for the optimization problem of virtual temporal friendship creation. We evaluate the performance of the virtual temporal friendship creation with simulation and investigate the effectiveness of this method by comparing with the performance of a method with meta-heuristic algorithm. From numerical examples, we show that the virtual temporal friendship creation can improve the robustness quickly in an autonomous and decentralized way.

A method to generate color palettes from images is proposed. Here, deep neural networks (DNN) are utilized in order to consider human perception. Two aspects of human perception are considered; one is attention to image, and the other is human preference for colors. This method first extracts N regions with dominant color categories from the image considering human attention. Here, N is the number of colors in a color palette. Then, the representative color is obtained from each region considering the human preference for color. Two deep neural-net systems are adopted here, one is for estimating the image area which attracts human attention, and the other is for estimating human preferable colors from image regions to obtain representative colors. The former is trained with target images obtained by an eye tracker, and the latter is trained with dataset of color selection by human. Objective and subjective evaluation is performed to show high performance of the proposed system compared with conventional methods.

The narrow bandwidth limitation of 300-3400Hz on the public switching telephone network results in speech quality deterioration. In this paper, we propose an artificial bandwidth extension approach that reconstructs the missing lower bandwidth of 50-300Hz using sinusoidal synthesis based on the first formant location. Sinusoidal synthesis generates sinusoidal waves with a harmonic structure. The proposed method detects the fundamental frequency using an autocorrelation method based on YIN algorithm, where a threshold processing avoids the false fundamental frequency detection on unvoiced sounds. The amplitude of the sinusoidal waves is calculated in the time domain from the weighted energy of 300-600Hz. In this case, since the first formant location corresponds to the first peak of the spectral envelope, we reconstruct the harmonic structure to avoid attenuating and overemphasizing by increasing the weight when the first formant location is lower, and vice versa. Consequently, the subjective and objective evaluations show that the proposed method reduces the speech quality difference between the original speech signal and the bandwidth extended speech signal.

Estimating the spatial parameters of the signals by using the effective data of a single snapshot is essential in the field of reconnaissance and confrontation. Major drawback of existing algorithms is that its constructed covariance matrix has a great degree of rank loss. The performance of existing algorithms gets degraded with low signal-to-noise ratio. In this paper, a three-parallel linear array based algorithm is proposed to achieve two-dimensional direction of arrival estimates in a single snapshot scenario. The key points of the proposed algorithm are: 1) construct three pseudo matrices with full rank and no rank loss by using the single snapshot data from the received signal model; 2) by using the rotation relation between pseudo matrices, the matched 2D-DOA is obtained with an efficient parameter matching method. Main objective of this work is on improving the angle estimation accuracy and reducing the loss of degree of freedom in single snapshot 2D-DOA estimation.

Computer networks tend to be subjected to the proliferation of mobile demands, therefore it poses a great challenge to guarantee the quality of network service. For real-time systems, the QoS performance bound analysis for the complex network topology and background traffic in modern networks is often difficult. Network calculus, nevertheless, converts a complex non-linear network system into an analyzable linear system to accomplish more accurate delay bound analysis. The existing network environment contains complex network resource allocation schemes, and delay bound analysis is generally pessimistic, hence it is essential to modify the analysis model to improve the bound accuracy. In this paper, the main research approach is to obtain the measurement results of an actual network by building a measurement environment and the corresponding theoretical results by network calculus. A comparison between measurement data and theoretical results is made for the purpose of clarifying the scheme of bandwidth scheduling. The measurement results and theoretical analysis results are verified and corrected, in order to propose an accurate per-flow end-to-end delay bound analytic model for a large-scale scheduling network. On this basis, the instructional significance of the analysis results for the engineering construction is discussed.

Through the concept of network slicing, a single physical network infrastructure can be split into multiple logically-independent Network Slices (NS), each of which is customized for the needs of its respective individual user or industrial vertical. In the beyond 5G (B5G) system, this customization can be done for many targeted services, including, but not limited to, 5G use cases and beyond 5G. The network slices should be optimized and customized to stitch a suitable environment for targeted industrial services and verticals. This paper proposes a novel Quality of Service (QoS) framework that optimizes and customizes the network slices to ensure the service level agreement (SLA) in terms of end-to-end reliability, delay, and bandwidth communication. The proposed framework makes use of network softwarization technologies, including software-defined networking (SDN) and network function virtualization (NFV), to preserve the SLA and ensure elasticity in managing the NS. This paper also mathematically models the end-to-end network by considering three parts: radio access network (RAN), transport network (TN), and core network (CN). The network is modeled in an abstract manner based on these three parts. Finally, we develop a prototype system to implement these algorithms using the open network operating system (ONOS) as a SDN controller. Simulations are conducted using the Mininet simulator. The results show that our QoS framework and the proposed resource allocation algorithms can effectively schedule network resources for various NS types and provide reliable E2E QoS services to end-users.

Cloud-based Global Navigation Satellite Systems (CB-GNSS) positioning architecture that offloads part of GNSS positioning computation to cloud/edge infrastructure has been studied as an architecture that adds valued functions via the network. The merits of CB-GNSS positioning are that it can take advantage of the abundant computing resources on the cloud/edge to add unique functions to the positioning calculation and reduce the cost of GNSS receiver terminals. An issue in GNSS positioning is the degradation in positioning accuracy in unideal reception environments where open space is limited and some satellite signals are blocked. To resolve this issue, we propose a satellite selection algorithm that effectively removes the multipath components of blocked satellite signals, which are the main cause of drop in positioning accuracy. We build a Proof of Concept (PoC) test environment of CB-GNSS positioning architecture implementing the proposed satellite selection algorithm and conduct experiments to verify its positioning performance in unideal static and dynamic conditions. For static long-term positioning in a multipath signal reception environment, we found that CB-GNSS positioning with the proposed algorithm enables a low-end GNSS receiver terminal to match the positioning performance comparable to high-end GNSS receiver terminals in terms of the FIX rate. In an autonomous tractor driving experiment on a farm road crossing a windbreak, we succeeded in controlling the tractor's autonomous movement by maintaining highly precise positioning even in the windbreak. These results indicates that the proposed satellite selection algorithm achieves high positioning performance even in poor satellite signal reception environments.

For beyond 5G era, in network function virtualization (NFV) environments, service chaining can be utilized to provide the flexible network infrastructures needed to support the creation of various application services. In this paper, we propose a dynamic service chain construction based on model predictive control (MPC) to utilize network resources. In the proposed method, the number of data packets in the buffer at each node is modeled as a dynamical system for MPC. Then, we formulate an optimization problem with the predicted amount of traffic injecting into each service chain from users for the dynamical system. In the optimization problem, the transmission route of each service chain, the node where each VNF is placed, and the amount of resources for each VNF are determined simultaneously by using MPC so that the amount of resources allocated to VNFs and the number of VNF migrations are minimized. In addition, the performance of data transmission is also controlled by considering the maximum amount of data packets stored in buffers. The performance of the proposed method is evaluated by simulation, and the effectiveness of the proposed method with different parameter values is investigated.

To reduce the complexity of direct radio frequency (RF) undersampling real-time spectrum monitoring in wireless Internet of Things (IoT) bands (920MHz, 2.4GHz, and 5 GHz bands), a design method of sampling frequencies is proposed in this paper. The Direct RF Undersampling receiver architecture enables the use of ADC with sampling clock lower frequency than receiving RF signal, but it needs RF signal identification signal processing from folded spectrums with multiple sampling clock frequencies. The proposed design method allows fewer sampling frequencies to be used than the conventional design method for continuous frequency range (D.C. to 5GHz-band). The proposed method reduced 2 sampling frequencies in wireless IoT bands case compared with the continuous range. The design result using the proposed method is verified by measurement.

Heterogeneous drone swarms are large hybrid drone clusters in which multiple drones with different wireless protocols are interconnected by some translator drones called GWs. Nowadays, because inexpensive drones, such as toy drones, have become widely used in society, the technology for constructing huge drone swarms is attracting more and more attention. In this paper, we propose an autonomous GW mobility control algorithm for establishing stabilized and low-delay communication among heterogeneous clusters, assuming that only GWs are controllable and relocatable to ensure the flexible operationality of drone swarms. Our proposed algorithm is composed of two independent sub algorithms - the Link Stabilizer and the Path Optimizer. The Stabilizer maintains the neighbor links and consists of two schemes: the neighbor clustering based on relative velocities and the GW velocity calculation using a kinetic model. The Optimizer creates a shortcut to reduce the end-to-end delay for newly established communication by relocating the GW dynamically. We also propose a conceptual protocol design to implement this algorithm into real-world drone swarms in a distributed manner. Computer simulation reveals that the Stabilizer improved the connection stability for all three mobility models even under the high node mobility, and the Optimizer reduced the communication delay by the optimal shortcut formation under any conditions of the experiments and its performance is comparable to the performance upper limit obtained by the brute-force searching.