SPHINCS+ is a state-of-the-art post-quantum hash-based signature that is a candidate for the NIST post-quantum cryptography standard. For a target bit security, SPHINCS+ supports many different tradeoffs between the signature size and the signing speed. SPHINCS+ provides 6 parameter sets: 3 parameter sets for size optimization and 3 parameter sets for speed optimization. We propose new parameter sets with better performance. Specifically, SPHINCS+ implementations with our parameter sets are up to 26.5% faster with slightly shorter signature sizes.

Source retrieval is the primary task of plagiarism detection. It searches the documents that may be the sources of plagiarism to a suspicious document. The state-of-the-art approaches usually rely on the classical information retrieval models, such as the probability model or vector space model, to get the plagiarism sources. However, the goal of source retrieval is to obtain the source documents that contain the plagiarism parts of the suspicious document, rather than to rank the documents relevant to the whole suspicious document. To model the “partial matching” between documents, this paper proposes a Partial Matching Convolution Neural Network (PMCNN) for source retrieval. In detail, PMCNN exploits a sequential convolution neural network to extract the plagiarism patterns of contiguous text segments. The experimental results on PAN 2013 and PAN 2014 plagiarism source retrieval corpus show that PMCNN boosts the performance of source retrieval significantly, outperforming other state-of-the-art document models.

As NAND flash-based storage has been settled, a flash translation layer (FTL) has been in charge of mapping data addresses on NAND flash memory. Many FTLs implemented various mapping schemes, but the amount of mapping data depends on the mapping level. However, the FTL should contemplate mapping consistency irrespective of how much mapping data dwell in the storage. Furthermore, the recovery cost by the inconsistency needs to be considered for a faster storage reboot time. This letter proposes a novel method that enhances the consistency for a page-mapping level FTL running a legacy logging policy. Moreover, the recovery cost of page mappings also decreases. The novel method is to adopt a virtually-shrunk segment and deactivate page-mapping logs by assembling and storing the segments. This segment scheme already gave embedded NAND flash-based storage enhance its response time in our previous study. In addition to that improved result, this novel plan maximizes the page-mapping consistency, therefore improves the recovery cost compared with the legacy page-mapping FTL.

This paper proposes a novel radio access scheme that uses duplicated transmission via multiple frequency channels to achieve mission critical Internet of Things (IoT) services requiring highly reliable wireless communications; the interference constraints that yield the required reliability are revealed. To achieve mission critical IoT services by wireless communication, it is necessary to improve reliability in addition to satisfying the required transmission delay time. Reliability is defined as the packet arrival rate without exceeding the desired transmission delay time. Traffic of the own system and interference from the other systems using the same frequency channel such as unlicensed bands degrades the reliability. One solution is the frequency/time diversity technique. However, these techniques may not achieve the required reliability because of the time taken to achieve the correct reception. This paper proposes a novel scheme that transmits duplicate packets utilizing multiple wireless interfaces over multiple frequency channels. It also proposes a suppressed duplicate transmission (SDT) scheme, which prevents the wastage of radio resources. The proposed scheme achieves the same reliable performance as the conventional scheme but has higher tolerance against interference than retransmission. We evaluate the relationship between the reliability and the occupation time ratio where the interference occupation time ratio is defined as the usage ratio of the frequency resources occupied by the other systems. We reveal the upper bound of the interference occupation time ratio for each frequency channel, which is needed if channel selection control is to achieve the required reliability.

This paper reports the evolution and application potential of image sensors with high-speed brightness gradient sensors. We propose an adaptive exposure time control method using the apparent motion estimated by this sensor, and evaluate results for the change in illuminance and global / local motion.

In this study, a novel decoupling method using parasitic elements (PEs) connected by a bridge line (BL) for two planar inverted-F antennas (PIFAs) is proposed. The proposed method is developed from a well-known decoupling method that uses a BL to directly connect antenna elements. When antenna elements are connected directly by a BL, strong mutual coupling can be reduced, but the resonant frequency shifts to a different frequency. Hence, to shift the resonant frequency toward the desired frequency, the original size of the antenna elements must be adjusted. This is disadvantageous if the method is applied in cases where the design conditions render it difficult to connect the antennas directly or adjust the original antenna size. Therefore, to easily reduce mutual coupling in such a case, a decoupling method that does not require both connecting antennas directly and adjusting the original antenna size is necessitated. This study demonstrates that using PEs connected by a BL reduces the mutual coupling from -6.6 to -14.1dB, and that the resonant frequency is maintained at the desired frequency (2.0GHz) without having to adjust the original PIFAs size. In addition, impedance matching can be adjusted to the desired frequency, resulting in an improved total antenna efficiency from 77.4% to 94.6%. This method is expected to be a simple and effective approach for reducing the mutual coupling between larger numbers of PIFA elements in the future.

To date, many studies have employed clustering for the classification of unlabeled data. Deep separate clustering applies several deep learning models to conventional clustering algorithms to more clearly separate the distribution of the clusters. In this paper, we employ a convolutional autoencoder to learn the features of input images. Following this, k-means clustering is conducted using the encoded layer features learned by the convolutional autoencoder. A center loss function is then added to aggregate the data points into clusters to increase the intra-cluster homogeneity. Finally, we calculate and increase the inter-cluster separability. We combine all loss functions into a single global objective function. Our new deep clustering method surpasses the performance of existing clustering approaches when compared in experiments under the same conditions.

This paper reports a single dimensional mode based multiplexer / de-multiplexer using the slab waveguide to realize high modes multiplexing and high integration in the non-MIMO (multi-in multi-out) multimode transmission system. A sufficient mode crosstalk of -20 dB was obtained by selecting suitable parameters of the spacing between the connecting positions of each arrayed waveguide Di, the radius slab waveguide R0 and lateral V-parameter.

Currently, the IEEE 802.11n wireless local-area network (WLAN) has been extensively deployed world-wide. For the efficient channel assignment to access-points (APs) from the limited number of partially overlapping channels (POCs) at 2.4GHz band, we have studied the throughput drop estimation model for concurrently communicating links using the channel bonding (CB). However, non-CB links should be used in dense WLANs, since the CB links often reduce the transmission capacity due to high interferences from other links. In this paper, we examine the throughput drop estimation model for concurrently communicating links without using the CB in 802.11n WLAN, and its application to the POC assignment to the APs. First, we verify the model accuracy through experiments in two network fields. The results show that the average error is 9.946% and 6.285% for the high and low interference case respectively. Then, we verify the effectiveness of the POC assignment to the APs using the model through simulations and experiments. The results show that the model improves the smallest throughput of a host by 22.195% and the total throughput of all the hosts by 22.196% on average in simulations for three large topologies, and the total throughput by 12.89% on average in experiments for two small topologies.

This paper proposes a parallel peak cancellation (PC) process for the computational complexity-efficient algorithm called PC with a channel-null constraint (PCCNC) in the adaptive peak-to-average power ratio (PAPR) reduction method using the null space in a multiple-input multiple-output (MIMO) channel for MIMO-orthogonal frequency division multiplexing (OFDM) signals. By simultaneously adding multiple PC signals to the time-domain transmission signal vector, the required number of iterations of the iterative algorithm is effectively reduced along with the PAPR. We implement a constraint in which the PC signal is transmitted only to the null space in the MIMO channel by beamforming (BF). By doing so the data streams do not experience interference from the PC signal on the receiver side. Since the fast Fourier transform (FFT) and inverse FFT (IFFT) operations at each iteration are not required unlike the previous algorithm and thanks to the newly introduced parallel processing approach, the enhanced PCCNC algorithm reduces the required total computational complexity and number of iterations compared to the previous algorithms while achieving the same throughput-vs.-PAPR performance.

In Internet applications, when users search for information, the search engines invariably return some invalid webpages that do not contain valid information. These invalid webpages interfere with the users' access to useful information, affect the efficiency of users' information query and occupy Internet resources. Accurate and fast filtering of invalid webpages can purify the Internet environment and provide convenience for netizens. This paper proposes an invalid webpage filtering model (HAIF) based on deep learning and hierarchical attention mechanism. HAIF improves the semantic and sequence information representation of webpage text by concatenating lexical-level embeddings and paragraph-level embeddings. HAIF introduces hierarchical attention mechanism to optimize the extraction of text sequence features and webpage tag features. Among them, the local-level attention layer optimizes the local information in the plain text. By concatenating the input embeddings and the feature matrix after local-level attention calculation, it enriches the representation of information. The tag-level attention layer introduces webpage structural feature information on the attention calculation of different HTML tags, so that HAIF is better applicable to the Internet resource field. In order to evaluate the effectiveness of HAIF in filtering invalid pages, we conducted various experiments. Experimental results demonstrate that, compared with other baseline models, HAIF has improved to various degrees on various evaluation criteria.

In recent years, checking sleep quality has become essential from a healthcare perspective. In this paper, we propose a respiratory rate (RR) monitoring system that can be used in the bedroom without wearing any sensor devices directly. To develop the system, passive radio-frequency identification (RFID) tags are introduced and attached to a blanket, instead of attaching them to the human body. The received signal strength indicator (RSSI) and phase values of the passive RFID tags are continuously obtained using an RFID reader through antennas located at the bedside. The RSSI and phase values change depending on the respiration of the person wearing the blanket. Thus, we can estimate the RR using these values. After providing an overview of the proposed system, the RR estimation flow is explained in detail. The processing flow includes noise elimination and irregular breathing period estimation methods. The evaluation demonstrates that the proposed system can estimate the RR and respiratory status without considering the user's body posture, body type, gender, or change in the RR.

We have proposed a waveguide to microstrip line transition, which perpendicularly connects one waveguide into two microstrip lines. It consists of only a waveguide and a dielectric substrate with copper foils. A backshort waveguide for typical transitions is not needed. Additionally, the transition does not require via holes on the substrate. These innovations simplify the structure and the manufacturing process. We assume that our transition and antennas are co-located on the substrate. We reduced the undesirable radiation from the transition so as not to contaminate the desirable radiation pattern. In this paper, we address output phase of our transition. Since the transition has two MSL output ports connecting to different radiation elements, the phase error between two dividing signals leads to beam shift in the radiation pattern. Unfortunately, misalignment of etching pattern between copper layers of the substrate is unavoidable. The structural asymmetry causes the phase error. In order to tolerate the misalignment, we propose to add a pair of open stubs to the transition. We show that the structure drastically stabilizes the output phase. Though the stubs create some extra radiation, we confirm that the impact is not significant. Moreover, we fabricate and measure a prototype antenna that uses the transition. In the case of with stubs, the radiation pattern is unchanged even if the misalignment is severe.

This work presents a DNN accelerator architecture specifically designed for performing efficient inference on compressed and sparse DNN models. Leveraging the data sparsity, a runtime processing scheme is proposed to deal with the encoded weights and activations directly in the compressed domain without decompressing. Furthermore, a new data flow is proposed to facilitate the reusage of input activations across the fully-connected (FC) layers. The proposed design is implemented and verified using the Xilinx Virtex-7 FPGA. Experimental results show it achieves 1.99×, 1.95× faster and 20.38×, 3.04× more energy efficient than CPU and mGPU platforms, respectively, running AlexNet.

This paper proposes an optimization model under uncertain traffic demands to design the backup network to minimize the total capacity of a backup network to protect the primary network from multiple link failures, where the probability of link failure is specified. The hose uncertainty is adopted to express uncertain traffic demands. The probabilistic survivability guarantee is provided by determining both primary and backup network routing, simultaneously. Robust optimization is introduced to provide probabilistic survivability guarantees for different link capacities in the primary network model under the hose uncertainty. Robust optimization in the proposed model handles two uncertain items: uncertain failed primary link with different capacities and uncertain traffic demands. We formulate an optimization problem for the proposed model. Since it is difficult to directly solve it, we introduce a heuristic approach for the proposed model. By using the heuristic approach, we investigate how the probability of link failure affects both primary and backup network routing. Numerical results show that the proposed model yields a backup network with lower total capacity requirements than the conventional model for the link failure probabilities examined in this paper. The results indicate that the proposed model reduces the total capacity of the backup network compared to the conventional model under the hose uncertainty. The proposed model shares more effectively the backup resources to protect primary links by determining routing in both primary and backup networks.

This paper presents a method to optimize 2-D sparse array configurations along with a technique to interpolate holes to accurately estimate the direction of arrival (DOA). Conventional 2-D sparse arrays are often defined using a closed-form representation and have the property that they can create hole-free difference co-arrays that can estimate DOAs of incident signals that outnumber the physical elements. However, this property restricts the array configuration to a limited structure and results in a significant mutual coupling effect between consecutive sensors. In this paper, we introduce an optimization-based method for designing 2-D sparse arrays that enhances flexibility of array configuration as well as DOA estimation accuracy. We also propose a method to interpolate holes in 2-D co-arrays by nuclear norm minimization (NNM) that permits holes and to extend array aperture to further enhance DOA estimation accuracy. The performance of the proposed optimum arrays is evaluated through numerical examples.

Z-complementary pairs (ZCPs) were proposed by Fan et al. to make up for the scarcity of Golay complementary pairs. A ZCP of odd length N is called Z-optimal if its zero correlation zone width can achieve the maximum value (N + 1)/2. In this letter, inserting three elements to a GCP of length L, or deleting a point of a GCP of length L, we propose two constructions of Z-optimal ZCPs with length L + 3 and L - 1, where L=2α 10β 26γ, α ≥ 1, β ≥ 0, γ ≥ 0 are integers. The proposed constructions generate ZCPs with new lengths which cannot be produced by earlier ones.

Deep learning is playing an increasingly important role in signal processing field due to its excellent performance on many inference problems. Parametric bilinear generalized approximate message passing (P-BiG-AMP) is a new approximate message passing based approach to a general class of structure-matrix bilinear estimation problems. In this letter, we propose a novel feed-forward neural network architecture to realize P-BiG-AMP methodology with deep learning for the inference problem of compressive sensing under matrix uncertainty. Linear transforms utilized in the recovery process and parameters involved in the input and output channels of measurement are jointly learned from training data. Simulation results show that the trained P-BiG-AMP network can achieve higher reconstruction performance than the P-BiG-AMP algorithm with parameters tuned via the expectation-maximization method.

With the development and spread of Internet of Things (IoT) technology, various kinds of data are now being generated from IoT devices. Some data generated from IoT devices depend on geographical location and time, and we refer to them as spatio-temporal data (STD). Since the “locally produced and consumed” paradigm of STD use is effective for location-dependent applications, the authors have previously proposed a vehicle-based STD retention system. However, in low vehicle density environments, the data retention becomes difficult due to the decrease in the number of data transmissions in this method. In this paper, we propose a new data transmission control method for data retention in the low vehicle density environments.

Channel modeling is a vital step in designing transceivers for wireless implant communication systems due to the extremely challenging environment of the human body. In this paper, the in-to-on body path loss and group delay were first analyzed using an electric dipole and a current loop in the 10-60MHz human body communication band. A path loss model was derived using finite difference time domain (FDTD) simulation and an anatomical human body model. As a result, it was found that the path loss increases with distance in an exponent of 5.6 for dipole and 3.9 for loop, and the group delay variation is within 1ns for both dipole and loop which suggests a flat phase response. Moreover, the electric and magnetic field distributions revealed that the magnetic field components dominate in-body signal transmission in this frequency band. Based on the analysis results of the implant channel, the link budget was analyzed. An experiment on a prototype transceiver was also performed to validate the path loss model and bit error rate (BER) performance. The experimentally derived path loss exponent was between the electric dipole path loss exponent and the current loop path loss exponent, and the BER measurement showed the feasibility of 20Mbps implant communication up to a body depth of at least 15cm.