Object contour detection is a task of extracting the shape created by the boundaries between objects in an image. Conventional methods limit the detection targets to specific categories, or miss-detect edges of patterns inside an object. We propose a new method to represent a contour image where the pixel value is the distance to the boundary. Contour detection becomes a regression problem that estimates this contour image. A deep convolutional network for contour estimation is combined with stereo vision to detect unspecified object contours. Furthermore, thanks to similar inference targets and common network structure, we propose a network that simultaneously estimates both contour and disparity with fully shared weights. As a result of experiments, the multi-tasking network drew a good precision-recall curve, and F-measure was about 0.833 for FlyingThings3D dataset. L1 loss of disparity estimation for the dataset was 2.571. This network reduces the amount of calculation and memory capacity by half, and accuracy drop compared to the dedicated networks is slight. Then we quantize both weights and activations of the network to 3-bit. We devise a dedicated hardware architecture for the quantized CNN and implement it on an FPGA. This circuit uses only internal memory to perform forward propagation calculations, that eliminates high-power external memory accesses. This circuit is a stall-free pixel-by-pixel pipeline, and performs 8 rows, 16 input channels, 16 output channels, 3 by 3 pixels convolution calculations in parallel. The convolution calculation performance at the operating frequency of 250 MHz is 9 TOPs/s.

We propose an attention mechanism in deep learning networks for gender recognition using the gaze distribution of human observers when they judge the gender of people in pedestrian images. Prevalent attention mechanisms spatially compute the correlation among values of all cells in an input feature map to calculate attention weights. If a large bias in the background of pedestrian images (e.g., test samples and training samples containing different backgrounds) is present, the attention weights learned using the prevalent attention mechanisms are affected by the bias, which in turn reduces the accuracy of gender recognition. To avoid this problem, we incorporate an attention mechanism called gaze-guided self-attention (GSA) that is inspired by human visual attention. Our method assigns spatially suitable attention weights to each input feature map using the gaze distribution of human observers. In particular, GSA yields promising results even when using training samples with the background bias. The results of experiments on publicly available datasets confirm that our GSA, using the gaze distribution, is more accurate in gender recognition than currently available attention-based methods in the case of background bias between training and test samples.

In this paper, we propose a novel Hybrid-Hierarchical spatial-aerial-Terrestrial Edge-Centric (H2TEC) for the space-air integrated Internet of Things (IoT) networks. (H2TEC) comprises unmanned aerial vehicles (UAVs) that act as mobile fog nodes to provide the required services for terminal nodes (TNs) in cooperation with the satellites. TNs in (H2TEC) offload their generated tasks to the UAVs for further processing. Due to the limited energy budget of TNs, a novel task allocation protocol, named TOP, is proposed to minimize the energy consumption of TNs while guaranteeing the outage probability and network reliability for which the transmission rate of TNs is optimized. TOP also takes advantage of the energy harvesting by which the low earth orbit satellites transfer energy to the UAVs when the remaining energy of the UAVs is below a predefined threshold. To this end, the harvested power of the UAVs is optimized alongside the corresponding harvesting time so that the UAVs can improve the network throughput via processing more bits. Numerical results reveal that TOP outperforms the baseline method in critical situations that more power is required to process the task. It is also found that even in such situations, the energy harvesting mechanism provided in the TOP yields a more efficient network throughput.

In this work, we consider a remote estimation system where a remote controller estimates the status of heterogeneous sensing devices with the information delivered over wireless channels. Status of heterogeneous devices changes at different speeds. With limited wireless resources, estimating as accurately as possible requires careful design of status update schemes. Status update schemes can be divided into two classes: centralized and decentralized. In centralized schemes, a central scheduler coordinates devices to avoid potential collisions. However, in decentralized schemes where each device updates on its own, update decisions can be made by using the current status which is unavailable in centralized schemes. The relation between these two schemes under the heterogeneous devices case is unclear, and thus we study these two schemes in terms of the mean square error (MSE) of the estimation. For centralized schemes, since the scheduler does not have the current status of each device, we study policies where the scheduling decisions are based on age of information (AoI), which measures the staleness of the status information held in the controller. The optimal scheduling policy is provided, along with the corresponding MSE. For decentralized schemes, we consider deviation-based policies with which only devices with estimation deviations larger than prescribed thresholds may update, and the others stay idle. We derive an approximation of the minimum MSE under the deviation-based policies and show that it is e/3 of the minimum MSE under the AoI-based policies. Simulation results further show that the actual minimum MSEs of these two policies are even closer than that shown by the approximation, which indicates that the cost of collision in the deviation-based policy cancels out the gain from exploiting status deviations.

Blockchain is a distributed ledger technology used for trading digital assets, such as cryptocurrency, and trail records that need to be audited by third parties. The use cases of blockchain are expanding beyond cryptocurrency management. In particular, the token economy, in which tokenized assets are exchanged across different blockchain ledgers, is gaining popularity. Cross-chain technologies such as atomic swap have emerged as security technologies to realize this new use case of blockchain. However, existing approaches of cross-chain technology have unresolved issues, such as application limitations on different blockchain platforms owing to the incompatibility of the communication interface and crypto algorithm and inability to handle a complex business logic such as the escrow trade. In this study, the ConnectionChain is proposed, which enables the execution of an extended smart contract using abstracted operation on interworking ledgers. Moreover, field experimental results using the system prototype are presented and explained.

Traffic congestion in road networks has been studied as the congestion game in game theory. In the existing work, the road usage by each agent was assumed to be static during the whole time horizon of the agent's travel, as in the classical congestion game. This assumption, however, should be reconsidered because each agent sequentially uses roads composing the route. In this paper, we propose a multi-agent distributed route selection scheme based on a gradient descent method considering the time-dependency among agents' road usage for vehicular networks. The proposed scheme first estimates the time-dependent flow on each road by considering the agents' probabilistic occupation under the first-in-first-out (FIFO) policy. Then, it calculates the optimal route choice probability of each route candidate using the gradient descent method and the estimated time-dependent flow. Each agent finally selects one route according to the optimal route choice probabilities. We first prove that the proposed scheme can exponentially converge to the steady-state at the convergence rate inversely proportional to the product of the number of agents and that of individual route candidates. Through simulations under a grid-like network and a real road network, we show that the proposed scheme can improve the actual travel time by 5.1% and 2.5% compared with the conventional static-flow based approach, respectively. In addition, we demonstrate that the proposed scheme is robust against incomplete information sharing among agents, which would be caused by its low penetration ratio or limited transmission range of wireless communications.

Heart rate measurement for mm-wave FMCW radar based on phase analysis comprises a variety of noise. Furthermore, because the breathing and heart frequencies are so close, the harmonic of the breathing signal interferes with the heart rate, and the band-pass filter cannot solve it. On the other hand, because heart rates vary from person to person, it is difficult to choose the basic function of WT (Wavelet Transform). To solve the aforementioned difficulties, we consider performing time-frequency domain analysis on human skin surface displacement data. The PA-LI (Phase Accumulation-Linear Interpolation) joint ICEEMDAN (Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) approach is proposed in this paper, which effectively enhances the signal's SNR, estimates the heart rate, and reconstructs the heartbeat signal. The experimental findings demonstrate that the proposed method can not only extract heartbeat signals with high SNR from the front direction, but it can also detect heart rate from other directions (e.g., back, left, oblique front, and ceiling).

There exists a great demand for automatic anomaly detection in industrial world. The anomaly has been defined as a group of samples that rarely or never appears. Given a type of products, one has to collect numerous samples and train an anomaly detector. When one diverts a model trained with old types of products with sufficient inventory to the new type, one can detect anomalies of the new type before a production line is established. However, because of the definition of the anomaly, a typical anomaly detector considers the new type of products anomalous even if it is consistent with the standard. Given the above practical demand, this study propose a novel problem setting, few-shot anomaly detection, where an anomaly detector trained in source domains is adapted to a small set of target samples without full retraining. Then, we tackle this problem using a hierarchical probabilistic model based on deep learning. Our empirical results on toy and real-world datasets demonstrate that the proposed model detects anomalies in a small set of target samples successfully.

The recommend system has been widely used in many web application areas such as e-commerce services. With the development of the recommend system, the HIN modeling method replaces the traditional bipartite graph modeling method to represent the recommend system. But several studies have already showed that recommend system is vulnerable to shilling attack (injecting attack). However, the effectiveness of how traditional shilling attack has rarely been studied directly in the HIN model. Moreover, no study has focused on how to enhance shilling attacks against HIN recommend system by using the high-level semantic information. This work analyzes the relationship between the high-level semantic information and the attacking effects in HIN recommend system. This work proves that attack results are proportional to the high-level semantic information. Therefore, we propose a heuristic attack method based on high-level semantic information, named Semantic Shilling Attack (SSA) on a HIN recommend system (HERec). This method injects a specific score into each selected item related to the target in semantics. It ensures transmitting the misleading information towards target items and normal users, and attempts to interfere with the effect of the recommend system. The experiment is dependent on two real-world datasets, and proves that the attacking effect is positively correlate with the number of meta-paths. The result shows that our method is more effective when compared with existing baseline algorithms.

Sharding is a solution to the blockchain scalability problem. A sharded blockchain divides consensus nodes (validators) into groups called shards and processes transactions separately to improve throughput and latency. In this paper, we analyze the rational behavior of users in account/balance model-based sharded blockchains and identify a phenomenon in which accounts (users' wallets and smart contracts) eventually get concentrated in a few shards, making shard loads unfair. This phenomenon leads to bad user experiences, such as delays in transaction inclusions and increased transaction fees. To solve this problem, we propose two load balancing methods in account/balance model-based sharded blockchains. Both methods perform load balancing by periodically reassigning accounts: in the first method, the blockchain protocol itself performs load balancing and in the second method, wallets perform load balancing. We discuss the pros and cons of the two protocols, and apply the protocols to the execution sharding in Ethereum 2.0, an existing sharding design. Further, we analyze by simulation how the protocols behave to confirm that we can observe smaller transaction delays and fees. As a result, we released the simulation program as “Shargri-La,” a simulator designed for general-purpose user behavior analysis on the execution sharding in Ethereum 2.0.

In the recent years, deep learning has achieved significant results in various areas of machine learning. Deep learning requires a huge amount of data to train a model, and data collection techniques such as web crawling have been developed. However, there is a risk that these data collection techniques may generate incorrect labels. If a deep learning model for image classification is trained on a dataset with noisy labels, the generalization performance significantly decreases. This problem is called Learning with Noisy Labels (LNL). One of the recent researches on LNL, called DivideMix [1], has successfully divided the dataset into samples with clean labels and ones with noisy labels by modeling loss distribution of all training samples with a two-component Mixture Gaussian model (GMM). Then it treats the divided dataset as labeled and unlabeled samples and trains the classification model in a semi-supervised manner. Since the selected samples have lower loss values and are easy to classify, training models are in a risk of overfitting to the simple pattern during training. To train the classification model without overfitting to the simple patterns, we propose to introduce consistency regularization on the selected samples by GMM. The consistency regularization perturbs input images and encourages model to outputs the same value to the perturbed images and the original images. The classification model simultaneously receives the samples selected as clean and their perturbed ones, and it achieves higher generalization performance with less overfitting to the selected samples. We evaluated our method with synthetically generated noisy labels on CIFAR-10 and CIFAR-100 and obtained results that are comparable or better than the state-of-the-art method.

In this paper, we propose rate adaptation mechanisms for robust and low-latency video transmissions exploiting multiple access points (Multi-AP) wireless local area networks (WLANs). The Multi-AP video transmissions employ link-level broadcast and packet-level forward error correction (FEC) in order to realize robust and low-latency video transmissions from a WLAN station (STA) to a gateway (GW). The PHY (physical layer) rate and FEC rate play a key role to control trade-off between the achieved reliability and airtime (i.e., occupancy period of the shared channel) for Multi-AP WLANs. In order to finely control this trade-off while improving the transmitted video quality, the proposed rate adaptation controls PHY rate and FEC rate to be employed for Multi-AP transmissions based on the link quality and frame format of conveyed video traffic. With computer simulations, we evaluate and investigate the effectiveness of the proposed rate adaptation in terms of packet delivery rate (PDR), airtime, delay, and peak signal to noise ratio (PSNR). Furthermore, the quality of video is assessed by using the traffic encoded/decoded by the actual video encoder/decoder. All these results show that the proposed rate adaptation controls trade-off between the reliability and airtime well while offering the high-quality and low-latency video transmissions.

The 2020 International Conference on Emerging Technologies for Communications (ICETC2020) was held online on December 2nd—4th, 2020, and 213 research papers were accepted and presented in each session. It is expected that the accepted papers will contribute to the development and extension of research in multiple research areas. In this survey paper, all accepted research papers are classified into four research areas: Physical & Fundamental, Communications, Network, and Information Technology & Application, and then research papers are classified into each research topic. For each research area and topic, this survey paper briefly introduces the presented technologies and methods.

In this paper, we propose a novel centralized control method to handle multi-radio and terminal connections in an 802.11ax wireless LAN (802.11ax) mixed environment. The proposed control method can improve the throughput by applying 802.11ax Spatial Reuse in an environment hosting different terminal standards and mixed terminal communication quality. We evaluate the proposed control method by computer simulations assuming environments with mixed terminal standards, mixed communication quality, and both.

Recently, segment routing, which is a modern forwarding mechanism, and Topology Independent Loop-free Alternate, which is an IP fast-reroute method using segment routing, have been proposed and have begun to be applied to real networks. When a failure occurs in a network, TI-LFA quickly restores packet forwarding without waiting for other nodes to update their routing tables. It does so by using segment routing to forward sections that may cause loops in the rerouting path. However, determining the segment routing sections has a high computational cost because it requires computation for each destination. This paper therefore proposes an algorithm to determine the egress node that is the exit of the segment routing section for all destination nodes with only three shortest-path tree calculations. The evaluation results of the proposed algorithm showed that the average tunnel lengths are at most 2.0 to 2.2 hops regardless of the size of the network. I also showed that the computational complexity of the proposed algorithm is O(Nlog N).

In this paper, we introduce our latest progress in the colloidal quantum dot enhanced color conversion layer for micro LEDs. Different methods of how to deploy colloidal quantum dots can be discussed and reviewed. The necessity of the using color conversion layer can be seen and color conversion efficiency of such layer can be calculated from the measured spectrum. A sub-pixel size of 5 micron of colloidal quantum dot pattern can be demonstrated in array format.

Liquid crystal (LC) device has high tunability with low power consumption and it is important not only in visible region but also in terahertz region. In this study, birefringence and absorption losses of hydrogen-bonded LC was estimated at 2.5 THz. Our results indicate that introduction of alkoxy chain to hydrogen-bonded LC is effective to increase birefringence in terahertz region. These results indicate that hydrogen-bonded LCs are a strong candidate for future terahertz devices because of their excellent properties in the terahertz region.

A mobile crowdsensing system (MCS) utilizes a crowd of users to collect large-scale data using their mobile devices efficiently. The collected data are usually linked with sensitive information, raising the concerns of user privacy leakage. To date, many approaches have been proposed to protect the users' privacy, with the majority relying on a centralized structure, which poses though attack and intrusion vulnerability. Some studies build a distributed platform exploiting a blockchain-type solution, which still requires a fully trusted third party (TTP) to manage a reliable reward distribution in the MCS. Spurred by the deficiencies of current methods, we propose a distributed user privacy protection structure that combines blockchain and a trusted execution environment (TEE). The proposed architecture successfully manages the users' privacy protection and an accurate reward distribution without requiring a TTP. This is because the encryption algorithms ensure data confidentiality and uncouple the correlation between the users' identity and the sensitive information in the collected data. Accordingly, the smart contract signature is used to manage the user deposit and verify the data. Extensive comparative experiments verify the efficiency and effectiveness of the proposed combined blockchain and TEE scheme.

In recent years, federated learning has attracted more and more attention as it could collaboratively train a global model without gathering the users' raw data. It has brought many challenges. In this paper, we proposed layer-based federated learning system with privacy preservation. We successfully reduced the communication cost by selecting several layers of the model to upload for global averaging and enhanced the privacy protection by applying local differential privacy. We evaluated our system in non independently and identically distributed scenario on three datasets. Compared with existing works, our solution achieved better performance in both model accuracy and training time.

In this letter, we derive a novel and accurate closed-form bit error rate (BER) approximation of the optical wireless communications (OWC) systems for the sub-carrier intensity modulation (SIM) employing binary phase-shift keying (BPSK) with multiple transmit and single receive apertures over strong atmospheric turbulence channels, which makes it possible to effectively investigate and predict the BER performance for various system configurations. Furthermore, we also derive a concise asymptotic BER formula to quantitatively evaluate the asymptotically achievable error performance (i.e., asymptotic diversity and combining gains) in the high signal-to-noise (SNR) regimes. Some numerical results are provided to corroborate the accuracy and effectiveness of our theoretical expressions.