Non-orthogonal multiple access (NOMA) is a promising multiple access scheme for further improving the spectrum efficiency compared to orthogonal multiple access (OMA) in the 5th Generation (5G) mobile communication systems. As inter-user interference cancellers for NOMA, two kinds of receiver structures are considered. One is the reduced complexity-maximum likelihood receiver (R-ML) and the other is the codeword level interference canceller (CWIC). In this paper, we show that the R-ML is superior to the CWIC in terms of scheduling flexibility. In addition, we propose a link to system (L2S) mapping scheme for the R-ML to conduct a system level evaluation, and show that the proposed scheme accurately predicts the block error rate (BLER) performance of the R-ML. The proposed L2S mapping scheme also demonstrates that the system level throughput performance of the R-ML is higher than that for the CWIC thanks to the scheduling flexibility.

Quantum steganography is to send secret quantum information through a quantum channel, such that an unauthorized user will not be aware of the existence of secret data. The depolarizing channel can hide quantum information by disguising it as channel errors of a quantum error-correcting code. We improve the efficiency of quantum steganography with noisy depolarizing channels, by modifying the twirling procedure and adding quantum teleportation. The proposed scheme not only meets the requirements of quantum steganography but also has higher efficiency.

This paper presents the design of a second-order and a fourth-order bandpass filter (BPF) for 60 GHz millimeter-wave applications in 0.18 µm CMOS technology. The proposed on-chip BPFs employ the folded open loop structure designed on pattern ground shields. The adoption of a folded structure and utilization of multiple transmission zeros in the stopband permit the compact size and high selectivity for the BPF. Moreover, the pattern ground shields obviously slow down the guided waves which enable further reduction in the physical length of the resonator, and this, in turn, results in improvement of the insertion losses. A very good agreement between the electromagnetic (EM) simulations and measurement results has been achieved. As a result, the second-order BPF has the center frequency of 57.5 GHz, insertion loss of 2.77 dB, bandwidth of 14 GHz, return loss less than 27.5 dB and chip size of 650 µm810 µm (including bonding pads) while the fourth-order BPF has the center frequency of 57 GHz, insertion loss of 3.06 dB, bandwidth of 12 GHz, return loss less than 30 dB with chip size of 905 µm810 µm (including bonding pads).

We investigate the problem of joint frequency and power allocation in wireless mesh networks, using a self-pricing game based solution. In traditional pricing game models, the price factor is determined from the global information of the network, which causes heavy communication overhead. To overcome this problem, we propose a self-pricing game model, in which the price factor is determined by the distributed access points processing their individual information; moreover, it is implemented in an autonomous and distributed fashion. The existence and the efficiency of Nash equilibrium (NE) of the proposed game are studied. It is shown that the proposed game based solution achieves near cooperative network throughput while it reduces the communication overhead significantly. Also, a forcing convergence algorithm is proposed to counter the vibration of channel selection. Simulation results verify the effectiveness and robustness of the proposed scheme.

With the scaling of technology, nanoscale CMOS integrated circuits are becoming more sensitive to single event double node upsets induced by charge sharing. A novel highly robust hardened latch design is presented that is fully resilient to single event double node upsets and single node upsets. The proposed latch employs multiple redundant C-elements to form a dual interlocked structure in which the redundant C-elements can bring the affected nodes back to the correct states regardless of the energy of the striking particle. Detailed HSPICE results confirm that the proposed latch features complete resilience to double node upsets and achieves an improved trade-off in terms of robustness, area, delay and power in comparison with previous latches. Extensive Monte Carlo simulations validate the proposed latch features as less sensitive to process, supply voltage and temperature variations.

We propose a retrieval method using scale invariant visual phrases (SIVPs). Our method encodes spatial information into the SIVPs which capture translation, rotation and scale invariance, and employs the SIVPs to determine the spatial correspondences between query image and database image. To compute the spatial correspondences efficiently, the SIVPs are introduced into the inverted index, and SIVP verification is investigated to refine the candidate images returned from inverted index. Experimental results demonstrate that our method improves the retrieval accuracy while increasing the retrieval efficiency.

We propose a novel probing scheme capable of discovering shared bottlenecks among multiple paths between two multihomed hosts simultaneously, without any specific help from the network routers, and a subsequent grouping approach for partitioning these paths into groups. Simulation results show that the probing and grouping have an excellent performance under different network conditions.

In this paper, we identify the tradeoff between security and reliability in the amplify-and-forward (AF) distributed beamforming (DBF) cooperative network with K untrusted relays. In particular, we derive the closed-form expressions for the connection outage probability (COP), the secrecy outage probability (SOP), the tradeoff relationship, and the secrecy throughput. Analytical and simulation results demonstrate that increasing K leads to the enhancement of the reliability performance, but the degradation of the security performance. This tradeoff also means that there exists an optimal K maximizing the secrecy throughput.

The selection of mutation strategy greatly affects the performance of differential evolution algorithm (DE). For different types of optimization problems, different mutation strategies should be selected. How to choose a suitable mutation strategy for different problems is a challenging task. To deal with this challenge, this paper proposes a novel DE algorithm based on local fitness landscape, called FLIDE. In the proposed method, fitness landscape information is obtained to guide the selection of mutation operators. In this way, different problems can be solved with proper evolutionary mechanisms. Moreover, a population adjustment method is used to balance the search ability and population diversity. On one hand, the diversity of the population in the early stage is enhanced with a relative large population. One the other hand, the computational cost is reduced in the later stage with a relative small population. The evolutionary information is utilized as much as possible to guide the search direction. The proposed method is compared with five popular algorithms on 30 test functions with different characteristics. Experimental results show that the proposed FLIDE is more effective on problems with high dimensions.

When ultra-high-frequency (UHF) method is applied in partial discharge (PD) detection for GIS, the propagation process and rules of electromagnetic (EM) wave need to be understood clearly for conducting diagnosis and assessment about the real insulation status. The preceding researches are mainly concerning about the radial component of the UHF signal, but the propagation of the signal components in axial and radial directions and that perpendicular to the radial direction of the GIS tank are rarely considered. So in this paper, for a 252,kV GIS with T-shaped structure (TS), the propagation and attenuation of PD-induced EM wave in different circumferential angles and directions are investigated profoundly in time and frequency domain based on Finite Difference Time Domain (FDTD) method. The attenuation rules of the peak to peak value (Vpp) and cumulative energy are concluded. By comparing the results of straight branch and T branch, the influence of T-shaped structure over the propagation of different signal components are summarized. Moreover, the new circumferential and axial location methods proposed in the previous work are verified to be still applicable. This paper discusses the propagation mechanism of UHF signal in T-shaped tank, which provides some referential significance towards the utilization of UHF technique and better implementation of PD detection.

With exponentially increasing power densities due to technology scaling and ever increasing demand for performance, chip temperature has become an important issue that limits the performance of computer systems. Typically, it is essential to use a set of on-chip thermal sensors to monitor temperatures during the runtime. The runtime thermal measurements are then employed by dynamic thermal management techniques to manage chip performance appropriately. In this paper, we propose an inverse distance weighting method based on a dynamic Voronoi diagram for the reconstruction of full thermal characterization of integrated circuits with non-uniform thermal sensor placements. Firstly we utilize the proposed method to transform the non-uniformly spaced samples to virtual uniformly spaced data. Then we apply three classical interpolation algorithms to reconstruct the full thermal signals in the uniformly spaced samples mode. To evaluate the effectiveness of our method, we develop an experiment for reconstructing full thermal status of a 16-core processor. Experimental results show that the proposed method significantly outperforms spectral analysis techniques, and can obtain full thermal characterization with an average absolute error of 1.72% using 9 thermal sensors per core.

In Natural Language Understanding, intent detection and slot filling have been widely used to understand user queries. However, current methods tend to rely on single words and sentences to understand complex semantic concepts, and can only consider local information within the sentence. Therefore, they usually cannot capture long-distance dependencies well and are prone to problems where complex intentions in sentences are difficult to recognize. In order to solve the problem of long-distance dependency of the model, this paper uses ConceptNet as an external knowledge source and introduces its extensive semantic information into the multi-intent detection and slot filling model. Specifically, for a certain sentence, based on confidence scores and semantic relationships, the most relevant conceptual knowledge is selected to equip the sentence, and a concept context map with rich information is constructed. Then, the multi-head graph attention mechanism is used to strengthen context correlation and improve the semantic understanding ability of the model. The experimental results indicate that the model has significantly improved performance compared to other models on the MixATIS and MixSNIPS multi-intent datasets.

Chinese spelling correction (CSC) models detect and correct a text typo based on the misspelled character and its context. Recently, Bert-based models have dominated the research of Chinese spelling correction. However, these methods only focus on the semantic information of the text during the pretraining stage, neglecting the learning of correcting spelling errors. Moreover, when multiple incorrect characters are in the text, the context introduces noisy information, making it difficult for the model to accurately detect the positions of the incorrect characters, leading to false corrections. To address these limitations, we apply the multimodal pre-trained language model ChineseBert to the task of spelling correction. We propose a self-distillation learning-based pretraining strategy, where a confusion set is used to construct text containing erroneous characters, allowing the model to jointly learns how to understand language and correct spelling errors. Additionally, we introduce a single-channel masking mechanism to mitigate the noise caused by the incorrect characters. This mechanism masks the semantic encoding channel while preserving the phonetic and glyph encoding channels, reducing the noise introduced by incorrect characters during the prediction process. Finally, experiments are conducted on widely used benchmarks. Our model achieves superior performance against state-of-the-art methods by a remarkable gain.

Reinforcement learning has been used to adaptive service composition. However, traditional algorithms are not suitable for large-scale service composition. Based on Q-Learning algorithm, a multi-task oriented algorithm named multi-Q learning is proposed to realize subtask-assistance strategy for large-scale and adaptive service composition. Differ from previous studies that focus on one task, we take the relationship between multiple service composition tasks into account. We decompose complex service composition task into multiple subtasks according to the graph theory. Different tasks with the same subtasks can assist each other to improve their learning speed. The results of experiments show that our algorithm could obtain faster learning speed obviously than traditional Q-learning algorithm. Compared with multi-agent Q-learning, our algorithm also has faster convergence speed. Moreover, for all involved service composition tasks that have the same subtasks between each other, our algorithm can improve their speed of learning optimal policy simultaneously in real-time.

Bloom Filter is a bit array (a one-dimensional storage structure) that provides a compact representation for a set of data, which can be used to answer the membership query in an efficient manner with a small number of false positives. It has a lot of applications in many areas. In this paper, we extend the design of Bloom Filter by using a multi-dimensional matrix to replace the one-dimensional structure with three different implementations, namely OFFF, WOFF, FFF. We refer the extended Bloom Filter as Feng Filter. We show the false positive rates of our method. We compare the false positive rate of OFFF with that of the traditional one-dimensional Bloom Filter and show that under certain condition, OFFF has a lower false positive rate. Traditional Bloom Filter can be regarded as a special case of our Feng Filter.

6DoF pose estimation from a monocular RGB image is a challenging but fundamental task. The methods based on unit direction vector-field representation and Hough voting strategy achieved state-of-the-art performance. Nevertheless, they apply the smooth l1 loss to learn the two elements of the unit vector separately, resulting in which is not taken into account that the prior distance between the pixel and the keypoint. While the positioning error is significantly affected by the prior distance. In this work, we propose a Prior Distance Augmented Loss (PDAL) to exploit the prior distance for more accurate vector-field representation. Furthermore, we propose a lightweight channel-level attention module for adaptive feature fusion. Embedding this Adaptive Fusion Attention Module (AFAM) into the U-Net, we build an Attention Voting Network to further improve the performance of our method. We conduct extensive experiments to demonstrate the effectiveness and performance improvement of our methods on the LINEMOD, OCCLUSION and YCB-Video datasets. Our experiments show that the proposed methods bring significant performance gains and outperform state-of-the-art RGB-based methods without any post-refinement.

This paper proposes an improved closed-form method for moving source localization using time difference of arrival (TDOA), frequency difference of arrival (FDOA) and differential Doppler rate measurements. After linearizing the measurement equations by introducing three additional parameters, a rough estimate is obtained by using the weighted least-square (WLS) estimator. To further refine the estimate, the relationship between additional parameters and source location is utilized. The proposed method gives a final closed-form solution without iteration or the extra mathematics operations used in existing methods by employing the basic idea of WLS processing. Numerical examples show that the proposed method exhibits better robustness and performance compared with several existing methods.

Many recent studies have focused on leveraging rich information types to increase useful information for improving fault localization effectiveness. However, they rarely investigate the impact of information richness on fault localization to give guidance on how to enrich information for improving localization effectiveness. This paper presents the first systematic study to fill this void. Our study chooses four representative information types and investigates the relationship between their richness and the localization effectiveness. The results show that information richness related to frequency execution count involves a high risk of degrading the localization effectiveness, and backward slice is effective in improving localization effectiveness.

This letter improves two adaptive steganographic methods in Refs. [5], [6], which utilize the remainders of two consecutive pixels to record the information of secret data. Through analysis, we point out that they perform mistakenly under some conditions, and the recipient cannot extract the secret data exactly. We correct these by enlarging the adjusting range of the remainders of two consecutive pixels within the block in the embedding procedure. Furthermore, the readjusting phase in Ref. [6] is improved by allowing every two-pixel block to be fully modified, and then the sender can select the best choice that introduces the smallest embedding distortion. Experimental results show that the improved method not only extracts secret data exactly but also reduces the embedding distortion.

In this letter, we focus on the spectrum sharing cognitive radio system, wherein a single-input multi-output cognitive fading channel is considered. Subject to the joint average interference constraint and peak interference constraint at the primary receiver, the outage capacity of the cognitive channel involving joint beamforming and power control is analyzed. We derive the optimal beamforming and power control strategy and deduce the closed-form expression for the outage capacity under Rayleigh fading model, the functional regions of two kinds of interference constraints are discussed as well. Furthermore, considering zero-outage transmission, we investigate the delay-limited capacity and introduce a new concept called the zero-outage average interference wall. Extensive simulations corroborate our theoretical results.