For flight simulators, it is crucial to create three-dimensional terrain using clear remote sensing images. However, due to haze and other contributing variables, the obtained remote sensing images typically have low contrast and blurry features. In order to build a flight simulator visual system, we propose a deep learning-based dehaze model for remote sensing images dehazing. An encoder-decoder architecture is proposed that consists of a multiscale fusion module and a gated large kernel convolutional attention module. This architecture can fuse multi-resolution global and local semantic features and can adaptively extract image features under complex terrain. The experimental results demonstrate that, with good generality and application, the model outperforms existing comparison techniques and achieves high-confidence dehazing in remote sensing images with a variety of haze concentrations, multi-complex terrains, and multi-spatial resolutions.

Automatic detection of prohibited items is vital in helping security staff be more efficient while improving the public safety index. However, prohibited item detection within X-ray security inspection images is limited by various factors, including the imbalance distribution of categories, diversity of prohibited item scales, and overlap between items. In this paper, we propose to leverage the Poisson blending algorithm with the Canny edge operator to alleviate the imbalance distribution of categories maximally in the X-ray images dataset. Based on this, we improve the cascade network to deal with the other two difficulties. To address the prohibited scale diversity problem, we propose the Re-BiFPN feature fusion method, which includes a coordinate attention atrous spatial pyramid pooling (CA-ASPP) module and a recursive connection. The CA-ASPP module can implicitly extract direction-aware and position-aware information from the feature map. The recursive connection feeds the CA-ASPP module processed multi-scale feature map to the bottom-up backbone layer for further multi-scale feature extraction. In addition, a Rep-CIoU loss function is designed to address the overlapping problem in X-ray images. Extensive experimental results demonstrate that our method can successfully identify ten types of prohibited items, such as Knives, Scissors, Pressure, etc. and achieves 83.4% of mAP, which is 3.8% superior to the original cascade network. Moreover, our method outperforms other mainstream methods by a significant margin.

Deep learning techniques are used to transform the style of images and produce diverse images. In the text style transformation field, many previous studies attempted to generate stylized text using deep learning networks. However, to achieve multiple style transformations for text images, the methods proposed in previous studies require learning multiple networks or cannot be guided by style images. Thus, in this study we focused on multistyle transformation of text images using style images to guide the generation of results. We propose a multiple-style transformation network for text style transfer, which we refer to as the Multi-Style Shape Matching GAN (Multi-Style SMGAN). The proposed method generates multiple styles of text images using a single model by training the model only once, and allows users to control the text style according to style images. The proposed method implements conditions to the network such that all styles can be distinguished effectively in the network, and the generation of each styled text can be controlled according to these conditions. The proposed network is optimized such that the conditional information can be transmitted effectively throughout the network. The proposed method was evaluated experimentally on a large number of text images, and the results show that the trained model can generate multiple-style text in realtime according to the style image. In addition, the results of a user survey study indicate that the proposed method produces higher quality results compared to existing methods.

In this note, we consider the problem of finding a step-by-step transformation between two longest increasing subsequences in a sequence, namely LONGEST INCREASING SUBSEQUENCE RECONFIGURATION. We give a polynomial-time algorithm for deciding whether there is a reconfiguration sequence between two longest increasing subsequences in a sequence. This implies that INDEPENDENT SET RECONFIGURATION and TOKEN SLIDING are polynomial-time solvable on permutation graphs, provided that the input two independent sets are largest among all independent sets in the input graph. We also consider a special case, where the underlying permutation graph of an input sequence is bipartite. In this case, we give a polynomial-time algorithm for finding a shortest reconfiguration sequence (if it exists).

We introduce the Hexagonal Convolutional Neural Network (HCNN), a modified version of CNN that is robust against rotation. HCNN utilizes a hexagonal kernel and a multi-block structure that enjoys more degrees of rotation information sharing than standard convolution layers. Our structure is easy to use and does not affect the original tissue structure of the network. We achieve the complete rotational invariance on the recognition task of simple pattern images and demonstrate better performance on the recognition task of the rotated MNIST images, synthetic biomarker images and microscopic cell images than past methods, where the robustness to rotation matters.

For any m strings of total length n, we propose an O(mn log n)-time, O(n)-space algorithm that finds a maximal common subsequence of all the strings, in the sense that inserting any character in it no longer yields a common subsequence of them. Such a common subsequence could be treated as indicating a nontrivial common structure we could find in the strings since it is NP-hard to find any longest common subsequence of the strings.

Machine learning (ML) has been used for various tasks in network operations in recent years. However, since the scale of networks has grown and the amount of data generated has increased, it has been increasingly difficult for network operators to conduct their tasks with a single server using ML. Thus, ML with edge-cloud cooperation has been attracting attention for efficiently processing and analyzing a large amount of data. In the edge-cloud cooperation setting, although transmission latency, bandwidth congestion, and accuracy of tasks using ML depend on the load balance of processing data with edge servers and a cloud server in edge-cloud cooperation, the relationship is too complex to estimate. In this paper, we focus on monitoring anomalous traffic as an example of ML tasks for network operations and formulate transmission latency, bandwidth congestion, and the accuracy of the task with edge-cloud cooperation considering the ratio of the amount of data preprocessed in edge servers to that in a cloud server. Moreover, we formulate an optimization problem under constraints for transmission latency and bandwidth congestion to select the proper ratio by using our formulation. By solving our optimization problem, the optimal load balance between edge servers and a cloud server can be selected, and the accuracy of anomalous traffic monitoring can be estimated. Our formulation and optimization framework can be used for other ML tasks by considering the generating distribution of data and the type of an ML model. In accordance with our formulation, we simulated the optimal load balance of edge-cloud cooperation in a topology that mimicked a Japanese network and conducted an anomalous traffic detection experiment by using real traffic data to compare the estimated accuracy based on our formulation and the actual accuracy based on the experiment.

We have developed and evaluated a prototype micro-pump for a new form of medication that is driven by a chemical reaction. The chemical reaction between citric acid and sodium bicarbonate produces carbon dioxide, the pressure of which pushes the medication out. This micropump is smaller in size than conventional diaphragm-type micropumps and is suitable for swallowing.

Memory-efficient Internet Protocol (IP) lookup with high speed is essential to achieve link-speed packet forwarding in IP routers. The rapid growth of Internet traffic and the development of optical link technologies have made IP lookup a major performance bottleneck in core routers. In this paper, we propose a new IP route lookup architecture based on hardware called Prefix-Route Trie (PR-Trie), which supports both IPv4 and IPv6 addresses. In PR-Trie, we develop a novel structure called Overlapping Hybrid Trie (OHT) to perform fast longest-prefix-matching (LPM) based on Multibit-Trie (MT), and a hash-based level matching query used to achieve only one off-chip memory access per lookup. In addition, the proposed PR-Trie also supports fast incremental updates. Since the memory complexity in MT-based IP lookup schemes depends on the level-partitioning solution and the data structure used, we develop an optimization algorithm called Bitmap-based Prefix Partitioning Optimization (BP2O). The proposed BP2O is based on a heuristic search using Ant Colony Optimization (ACO) algorithms to optimize memory efficiency. Experimental results using real-life routing tables prove that our proposal has superior memory efficiency. Theoretical performance analyses show that PR-Trie outperforms the classical Trie-based IP lookup algorithms.

We study a new transistor-level side-channel leakage caused by charges trapped in between stacked transistors namely residual electric charges (RECs). Building leakage models is important in designing countermeasures against side-channel attacks (SCAs). The conventional work showed that even a transistor-level leakage is measurable with a local electromagnetic measurement. One example is the current-path leak [1], [2]: an attacker can distinguish the number of transistors in the current path activated during a signal transition. Addressing this issue, Sugawara et al. proposed to use a mirror circuit that has the same number of transistors on its possible current paths. We show that this countermeasure is insufficient by showing a new transistor-level leakage, caused by RECs, not covered in the previous work. RECs can carry the history of the gate's state over multiple clock cycles and changes the gate's electrical behavior. We experimentally verify that RECs cause exploitable side-channel leakage. We also propose a countermeasure against REC leaks and designed advanced encryption standard-128 (AES-128) circuits using IO-masked dual-rail read-only memory with a 180-nm complementary metal-oxide-semiconductor (CMOS) process. We compared the resilience of our AES-128 circuits against EMA attacks with and without our countermeasure and investigated an RECs' effect on physically unclonable functions (PUFs). We further extend RECs to physically unclonable function. We demonstrate that RECs affect the performance of arbiter and ring-oscillator PUFs through experiments using our custom chips fabricated with 180- and 40-nm CMOS processes*.

The issue of copying values or references has historically been studied for managing memory objects, especially in distributed systems. In this paper, we explore a new topic on copying values v.s. references, for memory page compaction on virtualized systems. Memory page compaction moves target physical pages to a contiguous memory region at the operating system kernel level to create huge pages. Memory virtualization provides an opportunity to perform memory page compaction by copying the references of the physical pages. That is, instead of copying pages' values, we can move guest physical pages by changing the mappings of guest-physical to machine-physical pages. The goal of this paper is a quantitative comparison between value- and reference-based memory page compaction. To do so, we developed a software mechanism that achieves memory page compaction by appropriately updating the references of guest-physical pages. We prototyped the mechanism on Linux 4.19.29 and the experimental results show that the prototype's page compaction is up to 78% faster and achieves up to 17% higher performance on the memory-intensive real-world applications as compared to the default value-copy compaction scheme.

In this paper, we propose a new rate-based congestion control method for Named Data Networking (NDN) using additive increase multiplicative decrease (AIMD) and explicit rate notification. In the proposed method, routers notify a corresponding consumer of bottleneck bandwidth by use of Data packets, in a relatively long interval. In addition, routers monitor outgoing faces using the leaky bucket mechanism. When congestion is detected, the routers report this to corresponding consumers using negative-acknowledgment (NACK) packets. A consumer sets its Interest sending rate to the reported rate when a new value is reported. In addition, the consumer adjusts the sending rate to be around the reported rate based on the AIMD mechanism at Data/NACK packet reception. Computer simulations show that the proposed method achieves a high throughput performance and max-min fairness thanks to the effective congestion avoidance.

The road space rationing (RSR) method regulates a period in which a user group can make telephone calls in order to decrease the call attempt rate and induce calling parties to shorten their calls during disaster congestion. This paper investigates what settings of this indirect control induce more self-restraint and how the settings change calling parties' behavior using experimental psychology. Our experiments revealed that the length of the regulated period differently affected calling parties' behavior (call duration and call attempt rate) and indicated that the 60-min RSR method (i.e., 10 six-min periods) is the most effective setting against disaster congestion.

For recent datacenter networks, RDMA (Remote Direct Memory Access) can ease the overhead of the TCP/IP protocol suite. The RoCEv2 (RDMA over Converged Ethernet version 2) standard enables RDMA on widely deployed Ethernet technology. RoCEv2 leverages priority-based flow control (PFC) for realizing the lossless environment required by RDMA. However, PFC is well-known to have the technical weakness of head-of-line blocking. Congestion control for RDMA is a very hot research topic for datacenter networks. In this paper, we propose a novel congestion control algorithm for RoCEv2, TIDD (Timer-based Increase and Delay-based Decrease). TIDD basically combines the timer-based increase of DCQCN and delay-based decrease of TIMELY. Extensive simulation results show that TIDD satisfies the high throughput and low latency required for datacenter networks.

For a graph G=(V,E), finding a set of disjoint edges that do not share any vertices is called a matching problem, and finding the maximum matching is a fundamental problem in the theory of distributed graph algorithms. Although local algorithms for the approximate maximum matching problem have been widely studied, exact algorithms have not been much studied. In fact, no exact maximum matching algorithm that is faster than the trivial upper bound of O(n2) rounds is known for general instances. In this paper, we propose a randomized $O(s_{max}^{3/2})$-round algorithm in the CONGEST model, where smax is the size of maximum matching. This is the first exact maximum matching algorithm in o(n2) rounds for general instances in the CONGEST model. The key technical ingredient of our result is a distributed algorithms of finding an augmenting path in O(smax) rounds, which is based on a novel technique of constructing a sparse certificate of augmenting paths, which is a subgraph of the input graph preserving at least one augmenting path. To establish a highly parallel construction of sparse certificates, we also propose a new characterization of sparse certificates, which might also be of independent interest.

We have utilized single-pixel imaging and deep-learning to solve the privacy-preserving problem in gesture recognition for interactive display. Silhouette images of hand gestures were acquired by use of a display panel as an illumination. Reconstructions of gesture images have been performed by numerical experiments on single-pixel imaging by changing the number of illumination mask patterns. For the training and the image restoration with deep learning, we prepared reconstructed data with 250 and 500 illuminations as datasets. For each of the 250 and 500 illuminations, we prepared 9000 datasets in which original images and reconstructed data were paired. Of these data, 8500 data were used for training a neural network (6800 data for training and 1700 data for validation), and 500 data were used to evaluate the accuracy of image restoration. Our neural network, based on U-net, was able to restore images close to the original images even from reconstructed data with greatly reduced number of illuminations, which is 1/40 of the single-pixel imaging without deep learning. Compared restoration accuracy between cases using shadowgraph (black on white background) and negative-positive reversed images (white on black background) as silhouette image, the accuracy of the restored image was lower for negative-positive-reversed images when the number of illuminations was small. Moreover, we found that the restoration accuracy decreased in the order of rock, scissor, and paper. Shadowgraph is suitable for gesture silhouette, and it is necessary to prepare training data and construct neural networks, to avoid the restoration accuracy between gestures when further reducing the number of illuminations.

Recently, information centric network (ICN) has attracted attention because cached content delivery from router's cache storage improves quality of service (QoS) by reducing redundant traffic. Then, adaptive video streaming is applied to ICN to improve client's quality of experience (QoE). However, in the previous approaches for the cache control, the router implicitly caches the content requested by a user for the other users who may request the same content subsequently. As a result, these approaches are not able to use the cache effectively to improve client's QoE because the cached contents are not always requested by the other users. In addition, since the previous cache control does not consider network congestion state, the adaptive bitrate (ABR) algorithm works incorrectly and causes congestion, and then QoE degrades due to unnecessary congestion. In this paper, we propose an explicit cache placement notification for congestion-aware adaptive video streaming over ICN (CASwECPN) to mitigate congestion. CASwECPN encourages explicit feedback according to the congestion detection in the router on the communication path. While congestion is detected, the router caches the requested content to its cache storage and explicitly notifies the client that the requested content is cached (explicit cache placement and notification) to mitigate congestion quickly. Then the client retrieve the explicitly cached content in the router detecting congestion according to the general procedures of ICN. The simulation experiments show that CASwECPN improves both QoS and client's QoE in adaptive video streaming that adjusts the bitrate adaptively every video segment download. As a result, CASwECPN effectively uses router's cache storage as compared to the conventional cache control policies.

The frequency of uterine peristalsis is closely related to the success rate of pregnancy. An ultrasonic imaging is almost always employed for the measure of the frequency. The physician subjectively evaluates the frequency from the ultrasound image by the naked eyes. This paper aims to measure the frequency of uterine peristalsis from the ultrasound image. The ultrasound image consists of relative amounts in the brightness, and the contour of the uterine is not clear. It was not possible to measure the frequency by using the inter-frame difference and optical flow, which are the representative methods of motion detection, since uterine peristaltic movement is too small to apply them. This paper proposes a measurement method of the frequency of the uterine peristalsis from the ultrasound image in the implantation phase. First, traces of uterine peristalsis are semi-automatically done from the images with location-axis and time-axis. Second, frequency analysis of the uterine peristalsis is done by Fourier transform for 3 minutes. As a result, the frequency of uterine peristalsis was known as the frequency with the dominant frequency ingredient with maximum value among the frequency spectrums. Thereby, we evaluate the number of the frequency of uterine peristalsis quantitatively from the ultrasound image. Finally, the success rate of pregnancy is calculated from the frequency based on Fuzzy logic. This enabled us to evaluate the success rate of pregnancy by measuring the uterine peristalsis from the ultrasound image.

Hyperspectral images (HSIs) are generally susceptible to various noise, such as Gaussian and stripe noise. Recently, numerous denoising algorithms have been proposed to recover the HSIs. However, those approaches cannot use spectral information efficiently and suffer from the weakness of stripe noise removal. Here, we propose a tensor decomposition method with two different constraints to remove the mixed noise from HSIs. For a HSI cube, we first employ the tensor singular value decomposition (t-SVD) to effectively preserve the low-rank information of HSIs. Considering the continuity property of HSIs spectra, we design a simple smoothness constraint by using Tikhonov regularization for tensor decomposition to enhance the denoising performance. Moreover, we also design a new unidirectional total variation (TV) constraint to filter the stripe noise from HSIs. This strategy will achieve better performance for preserving images details than original TV models. The developed method is evaluated on both synthetic and real noisy HSIs, and shows the favorable results.

In this paper, we used the data set of domain names Global Top 1M provided by Alexa to analyze the effectiveness of Fallback in ECN. For the same test server, we first negotiate a connection with Not-ECN-Capable, and then negotiate a connection with ECN-Capable, if the sender does not receive the response to ECN-Capable negotiation from the receiver by the end of retransmission timeout, it will enter the Fallback state, and switch to negotiating a connection with Not-ECN-Capable. By extracting the header fields of the TCP/IP packets, we confirmed that in most regions, connectivity will be slightly improved after Fallback is enabled and Fallback has a positive effect on the total time of the whole access process. Meanwhile, we provided the updated information about the characteristics related to ECN with Fallback in different regions by considering the geographical region distribution of all targeted servers.