With the development of network technology, next-generation networks must satisfy many new requirements for network functions and performance. The processing of overlong packet fields is one of the requirements and is also the basis for ID-based routing and content lookup, and packet field addition/deletion mechanisms. The current SDN switches do not provide good support for the processing of overlong fields. In this paper, we propose a series of optimization mechanisms for protocol-oblivious instructions, in which we address the problem of insufficient support for overlong data in existing SDN switches by extending the bit width of instructions and accelerating them using SIMD instruction sets. We also provide an intermediate representation of the protocol-oblivious instruction set to improve the efficiency of storing and reading instruction blocks, and further reduce the execution time of instruction blocks by preprocessing them. The experiments show that our approach improves the performance of overlong data processing by 56%. For instructions involving packet field addition and deletion, the improvement in performance reaches 455%. In normal forwarding scenarios, our solution reduces the packet forwarding latency by around 30%.

In this study, we have proposed a millimeter-wave (MMW) single-pixel imaging (SPI) system with a liquid-crystal (LC) mask cell. The LC cell functions as an electrically switchable mask based on the change in absorption properties, which depend on the orientation of the LC. We investigated the influence of noise on the measured and estimated data (reconstructed image). The proposed system exhibited moderate robustness against random noise (that were added) compared to raster scan-based and Hadamard matrix-based SPI systems. Finally, the results of some demonstrative experiments were introduced to ensure the applicability of the constructed MMW-SPI system, and steps for improving the reconstructed image quality were discussed.

The mean, median, and mode are usually calculated from univariate observations as the most basic representative values of a random variable. To measure the spread of the distribution, the standard deviation, interquartile range, and modal interval are also calculated. When we analyze continuous relations between a pair of random variables from bivariate observations, regression analysis is often used. By minimizing appropriate costs evaluating regression errors, we estimate the conditional mean, median, and mode. The conditional standard deviation can be estimated if the bivariate observations are obtained from a Gaussian process. Moreover, the conditional interquartile range can be calculated for various distributions by the quantile regression that estimates any conditional quantile (percentile). Meanwhile, the study of the modal interval regression is relatively new, and spline regression models, known as flexible models having the optimality on the smoothness for bivariate data, are not yet used. In this paper, we propose a modal interval regression method based on spline quantile regression. The proposed method consists of two steps. In the first step, we divide the bivariate observations into bins for one random variable, then detect the modal interval for the other random variable as the lower and upper quantiles in each bin. In the second step, we estimate the conditional modal interval by constructing both lower and upper quantile curves as spline functions. By using the spline quantile regression, the proposed method is widely applicable to various distributions and formulated as a convex optimization problem on the coefficient vectors of the lower and upper spline functions. Extensive experiments, including settings of the bin width, the smoothing parameter and weights in the cost function, show the effectiveness of the proposed modal interval regression in terms of accuracy and visual shape for synthetic data generated from various distributions. Experiments for real-world meteorological data also demonstrate a good performance of the proposed method.

Increased demand for DNS privacy has driven the creation of several encrypted DNS protocols, such as DNS over HTTPS (DoH), DNS over TLS (DoT), and DNS over QUIC (DoQ). Recently, DoT and DoH have been deployed by some vendors like Google and Cloudflare. This paper addresses privacy leakage in these three encrypted DNS protocols (especially DoQ) with different DNS recursive resolvers (Google, NextDNS, and Bind) and DNS proxy (AdGuard). More particularly, we investigate encrypted DNS traffic to determine whether the adversary can infer the category of websites users visit for this purpose. Through analyzing packet traces of three encrypted DNS protocols, we show that the classification performance of the websites (i.e., user's privacy leakage) is very high in terms of identifying 42 categories of the websites both in public (Google and NextDNS) and local (Bind) resolvers. By comparing the case with cache and without cache at the local resolver, we confirm that the caching effect is negligible as regards identification. We also show that discriminative features are mainly related to the inter-arrival time of packets for DNS resolving. Indeed, we confirm that the F1 score decreases largely by removing these features. We further investigate two possible countermeasures that could affect the inter-arrival time analysis in the local resolver: AdBlocker and DNS prefetch. However, there is no significant improvement in results with these countermeasures. These findings highlight that information leakage is still possible even in encrypted DNS traffic regardless of underlying protocols (i.e., HTTPS, TLS, QUIC).

This study proposes a new structure of a single-shunt rectifier circuit that can reduce circuit loss and improve efficiency over the conventional structure. The proposed structure can provide impedance matching to the measurement system (or receiving antenna) without the use of conventional matching circuits, such as stubs and tapers. The proposed structure can simultaneously perform full-wave rectification and impedance matching by placing a feeding point on the output filter's λ/4 transmission line. We use circuit simulation to compare the RF-DC conversion efficiency and circuit loss of the conventional and proposed structures. The simulation results show that the proposed structure has lower circuit loss and higher RF-DC conversion efficiency than the conventional structure. We fabricate the proposed rectifier circuit using a GaAs Schottky barrier diode. The simulation and measurement results show that the single-shunt rectifier circuit's proposed structure is capable of rectification and impedance matching. The fabricated rectifier circuit's RF-DC conversion efficiency reaches a maximum of 91.0%. This RF-DC conversion efficiency is a world record for 920-MHz band rectifier circuits.

Defect prediction approaches have been greatly contributing to software quality assurance activities such as code review or unit testing. Just-in-time defect prediction approaches are developed to predict whether a commit is a defect-inducing commit or not. Prior research has shown that commit-level prediction is not enough in terms of effort, and a defective commit may contain both defective and non-defective files. As the defect prediction community is promoting fine-grained granularity prediction approaches, we propose our novel class-level prediction, which is finer-grained than the file-level prediction, based on the files of the commits in this research. We designed our model for Python projects and tested it with ten open-source Python projects. We performed our experiment with two settings: setting with product metrics only and setting with product metrics plus commit information. Our investigation was conducted with three different classifiers and two validation strategies. We found that our model developed by random forest classifier performs the best, and commit information contributes significantly to the product metrics in 10-fold cross-validation. We also created a commit-based file-level prediction for the Python files which do not have the classes. The file-level model also showed a similar condition as the class-level model. However, the results showed a massive deviation in time-series validation for both levels and the challenge of predicting Python classes and files in a realistic scenario.

This paper proposes a novel approach to low complexity soft input decoding for lattice reduction-aided MIMO receivers. The proposed approach feeds a soft input decoder with soft signals made from hard decision signals generated by using a lattice reduction-aided linear detector. The soft signal is a weighted-sum of some candidate vectors that are near by the hard decision signal coming out from the lattice reduction-aided linear detector. This paper proposes a technique to adjust the weight adapt to the channel for the higher transmission performance. Furthermore, we propose to introduce a coefficient that is used for the weights in order to enhance the transmission performance. The transmission performance is evaluated in a 4×4 MIMO channel. When a linear MMSE filter or a serial interference canceller is used as the linear detector, the proposed technique achieves about 1.0dB better transmission performance at the BER of 10-5 than the decoder fed with the hard decision signals. In addition, the low computational complexity of the proposed technique is quantitatively evaluated.

Network-on-Chips (NoCs) are important components for scalable many-core processors. Because the performance of parallel applications is usually sensitive to the latency of NoCs, reducing it is a primary requirement. In this study, a compression router that hides the (de)compression-operation delay is proposed. The compression router (de)compresses the contents of the incoming packet before the switch arbitration is completed, thus shortening the packet length without latency penalty and reducing the network injection-and-ejection latency. Evaluation results show that the compression router improves up to 33% of the parallel application performance (conjugate gradients (CG), fast Fourier transform (FT), integer sort (IS), and traveling salesman problem (TSP)) and 63% of the effective network throughput by 1.8 compression ratio on NoC. The cost is an increase in router area and its energy consumption by 0.22mm2 and 1.6 times compared to the conventional virtual-channel router. Another finding is that off-loading the decompressor onto a network interface decreases the compression-router area by 57% at the expense of the moderate increase in communication latency.

In this paper, we propose an end-to-end two-branch feature attention network. The network is mainly used for single image dehazing. The network consists of two branches, we call it CAA-Net: 1) A U-NET network composed of different-level feature fusion based on attention (FEPA) structure and residual dense block (RDB). In order to make full use of all the hierarchical features of the image, we use RDB. RDB contains dense connected layers and local feature fusion with local residual learning. We also propose a structure which called FEPA.FEPA structure could retain the information of shallow layer and transfer it to the deep layer. FEPA is composed of serveral feature attention modules (FPA). FPA combines local residual learning with channel attention mechanism and pixel attention mechanism, and could extract features from different channels and image pixels. 2) A network composed of several different levels of FEPA structures. The network could make feature weights learn from FPA adaptively, and give more weight to important features. The final output result of CAA-Net is the combination of all branch prediction results. Experimental results show that the CAA-Net proposed by us surpasses the most advanced algorithms before for single image dehazing.

In this paper, we propose a combined use of transformed images and vision transformer (ViT) models transformed with a secret key. We show for the first time that models trained with plain images can be directly transformed to models trained with encrypted images on the basis of the ViT architecture, and the performance of the transformed models is the same as models trained with plain images when using test images encrypted with the key. In addition, the proposed scheme does not require any specially prepared data for training models or network modification, so it also allows us to easily update the secret key. In an experiment, the effectiveness of the proposed scheme is evaluated in terms of performance degradation and model protection performance in an image classification task on the CIFAR-10 dataset.

This paper presents a convex-analytic framework to learn sparse graphs from data. While our problem formulation is inspired by an extension of the graphical lasso using the so-called combinatorial graph Laplacian framework, a key difference is the use of a nonconvex alternative to the l1 norm to attain graphs with better interpretability. Specifically, we use the weakly-convex minimax concave penalty (the difference between the l1 norm and the Huber function) which is known to yield sparse solutions with lower estimation bias than l1 for regression problems. In our framework, the graph Laplacian is replaced in the optimization by a linear transform of the vector corresponding to its upper triangular part. Via a reformulation relying on Moreau's decomposition, we show that overall convexity is guaranteed by introducing a quadratic function to our cost function. The problem can be solved efficiently by the primal-dual splitting method, of which the admissible conditions for provable convergence are presented. Numerical examples show that the proposed method significantly outperforms the existing graph learning methods with reasonable computation time.

Construction of resilient Boolean functions in odd variables having strictly almost optimal (SAO) nonlinearity appears to be a rather difficult task in stream cipher and coding theory. In this paper, based on the modified High-Meets-Low technique, a general construction to obtain odd-variable SAO resilient Boolean functions without directly using PW functions or KY functions is presented. It is shown that the new class of functions possess higher resiliency order than the known functions while keeping higher SAO nonlinearity, and in addition the resiliency order increases rapidly with the variable number n.

Paragraph segmentation is a text segmentation task. Iikura et al. achieved excellent results on paragraph segmentation by introducing focal loss to Bidirectional Encoder Representations from Transformers. In this study, we investigated paragraph segmentation on Daily News and Novel datasets. Based on the approach proposed by Iikura et al., we used auxiliary loss to train the model to improve paragraph segmentation performance. Consequently, the average F1-score obtained by the approach of Iikura et al. was 0.6704 on the Daily News dataset, whereas that of our approach was 0.6801. Our approach thus improved the performance by approximately 1%. The performance improvement was also confirmed on the Novel dataset. Furthermore, the results of two-tailed paired t-tests indicated that there was a statistical significance between the performance of the two approaches.

This paper proposes a design of high-speed interconnection between optical modules and electrical modules via bonding-wires and coplanar waveguide transmission lines on printed circuit boards for 400 Gbps 4-channel optical communication systems. In order to broaden the interconnection bandwidth, interdigitated capacitors were integrated with GSG pads on chip for the first time. Simulation results indicate the reflection coefficient is below -10 dB from DC to 53 GHz and the insertion loss is below 1 dB from DC to 45 GHz. Both indicators show that the proposed interconnection structure can effectively satisfy the communication bandwidth requirements of 100-Gbps or even higher data-rate PAM4 signals.

This letter proposes a novel intelligent dynamic channel assignment (DCA) scheme with small-cells to improve the system performance for uplink machine-type communications (MTC) based on OFDMA-FDD. Outdoor MTC devices (OMDs) have serious interference from indoor MTC devices (IMDs) served by small-cell access points (SAPs) with frequency reuse. Thus, in the proposed DCA scheme, the macro base station (MBS) first measures the received signal strength from both OMDs and IMDs after setting the transmission power. Then, the MBS dynamically assigns subchannels to each SAP with consideration of strong interference from IMDs to the MBS. Through simulation results, it is shown that the proposed DCA scheme outperforms other schemes in terms of the capacity of OMDs and IMDs.

The prosperous Internet communication technologies have led to e-commerce in mobile computing and made Web of Things become popular. Electronic payment is the most important part of e-commerce, so many electronic payment schemes have been proposed. However, most of proposed schemes cannot give change. Based on proxy blind signatures, an e-cash payment system is proposed in this paper to solve this problem. This system can not only provide change divisibility through Web of Things, but also provide anonymity, verifiability, unforgeability and double-spending owner track.

Supersingular isogeny Diffie-Hellman (SIDH) is attractive for its relatively small public key size, but it is still unsatisfactory due to its efficiency, compared to other post-quantum proposals. In this paper, we focus on the performance of SIDH when the starting curve is E6 : y2 = x3 + 6x2 + x, which is fixed in Round-3 SIKE implementation. Inspired by previous works [1], [2], we present several tricks to accelerate key generation of SIDH and each process of SIKE. Our experimental results show that the performance of this work is at least 6.09% faster than that of the SIKE implementation, and we can further improve the performance when large storage is available.

Testing the radio frequency compatibility between Cn-band Satellite Navigation and Microwave Landing System (MLS) has included establishing a specific interference model and reporting the effect of such interference. This paper considers two interference scenarios according to the interfered system. By calculating the Power Flux Density (PFD) values, the interference for Cn-band satellite navigation downlink signal from several visible space stations on MLS service is evaluated. Simulation analysis of the interference for MLS DPSK-data word signal and scanning signal on Cn-band satellite navigation signal is based on the Spectral Separation Coefficient (SSC) and equivalent Carrier-to-Noise Ratio methodologies. Ground tests at a particular military airfield equipped with MLS ground stations were successfully carried out, and some measured data verified the theoretical and numerical results. This study will certainly benefit the design of Cn-band satellite navigation signals and guide the interoperability and compatibility research of Cn-band satellite navigation and MLS.

It has been widely recognized that in compressed sensing, many restricted isometry property (RIP) conditions can be easily obtained by using the null space property (NSP) with its null space constant (NSC) 0<θ≤1 to construct a contradicted method for sparse signal recovery. However, the traditional NSP with θ=1 will lead to conservative RIP conditions. In this paper, we extend the NSP with 0<θ<1 to a scale NSP, which uses a factor τ to scale down all vectors belonged to the Null space of a sensing matrix. Following the popular proof procedure and using the scale NSP, we establish more relaxed RIP conditions with the scale factor τ, which guarantee the bounded approximation recovery of all sparse signals in the bounded noisy through the constrained l1 minimization. An application verifies the advantages of the scale factor in the number of measurements.

This paper considers tracking of a non-cooperative emitter based on a single sensor. To this end, the direct target motion analysis (DTMA) approach, where the target state is straightforwardly achieved from the received signal, is exploited. In order to achieve observability, the sensor has to perform a maneuver relative to the emitter. By suitably building an approximated likelihood function, the unscented Kalman filter (UKF), which is able to work under high nonlinearity of the measurement model, is adopted to recursively estimate the target state. Besides, the posterior Cramér-Rao bound (PCRB) of DTMA, which can be used as performance benchmark, is also achieved. The effectiveness of proposed method is verified via simulation experiments.