In this paper, we propose an access control method with a secret key for object detection models for the first time so that unauthorized users without a secret key cannot benefit from the performance of trained models. The method enables us not only to provide a high detection performance to authorized users but to also degrade the performance for unauthorized users. The use of transformed images was proposed for the access control of image classification models, but these images cannot be used for object detection models due to performance degradation. Accordingly, in this paper, selected feature maps are encrypted with a secret key for training and testing models, instead of input images. In an experiment, the protected models allowed authorized users to obtain almost the same performance as that of non-protected models but also with robustness against unauthorized access without a key.

Recently, the face hallucination method based on deep learning understands the mapping between low-resolution (LR) and high-resolution (HR) facial patterns by exploring the priors of facial structure. However, how to maintain the face structure consistency after the reconstruction of face images at different scales is still a challenging problem. In this letter, we propose a novel multi-scale structure prior learning (MSPL) for face hallucination. First, we propose a multi-scale structure prior block (MSPB). Considering the loss of high-frequency information in the LR space, we mainly process the input image in three different scale ascending dimensional spaces, and map the image to the high dimensional space to extract multi-scale structural prior information. Then the size of feature maps is recovered by downsampling, and finally the multi-scale information is fused to restore the feature channels. On this basis, we propose a local detail attention module (LDAM) to focus on the local texture information of faces. We conduct extensive face hallucination reconstruction experiments on a public face dataset (LFW) to verify the effectiveness of our method.

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.

For a long time, the compensation effect of hearing aid is mainly evaluated subjectively, and there are fewer studies of objective evaluation. Furthermore, a pure speech signal is generally required as a reference in the existing objective evaluation methods, which restricts the practicality in a real-world environment. Therefore, this paper presents a non-intrusive speech quality evaluation method for hearing aid, which combines the audiogram and weighted frequency information. The proposed model mainly includes an audiogram information extraction network, a frequency information extraction network, and a quality score mapping network. The audiogram is the input of the audiogram information extraction network, which helps the system capture the information related to hearing loss. In addition, the low-frequency bands of speech contain loudness information and the medium and high-frequency components contribute to semantic comprehension. The information of two frequency bands is input to the frequency information extraction network to obtain time-frequency information. When obtaining the high-level features of different frequency bands and audiograms, they are fused into two groups of tensors that distinguish the information of different frequency bands and used as the input of the attention layer to calculate the corresponding weight distribution. Finally, a dense layer is employed to predict the score of speech quality. The experimental results show that it is reasonable to combine the audiogram and the weight of the information from two frequency bands, which can effectively realize the evaluation of the speech quality of the hearing aid.

Reducing the number of link crossings in a network drawn on the plane such as a wiring board is a well-known problem, and especially the calculation of the minimum number of such crossings: this is the crossing number problem. It has been shown that finding a general solution to the crossing number problem is NP-hard. So, this problem is addressed for particular classes of graphs and this is also our approach in this paper. More precisely, we focus hereinafter on the torus topology. First, we discuss an upper bound on cr(T(2, k)) the number of crossings in a 2-dimensional k-ary torus T(2, k) where k ≥ 2: the result cr(T(2, k)) ≤ k(k - 2) and the given constructive proof lay foundations for the rest of the paper. Second, we extend this discussion to derive an upper bound on the crossing number of a 3-dimensional k-ary torus: cr(T(3, k)) ≤ 2k4 - k3 - 4k2 - 2⌈k/2⌉⌊k/2⌋(k - (k mod 2)) is obtained. Third, an upper bound on the crossing number of an n-dimensional k-ary torus is derived from the previously established results, with the order of this upper bound additionally established for more clarity: cr(T(n, k)) is O(n2k2n-2) when n ≥ k and O(nk2n-1) otherwise.

In this letter, we consider a global stabilization problem for a class of feedforward systems by an event-triggered control. This is an extended work of [10] in a way that there are uncertain feedforward nonlinearity and time-varying input delay in the system. First, we show that the considered system is globally asymptotically stabilized by a proposed event-triggered controller with a gain-scaling factor. Then, we also show that the interexecution times can be enlarged by adjusting a gain-scaling factor. A simulation example is given for illustration.

Locally repairable codes have attracted lots of interest in Distributed Storage Systems. If a symbol of a code can be repaired respectively by t disjoint groups of other symbols, each groups has size at most r, we say that the code symbol has (r, t)-locality. In this paper, we employ parity-check matrix to construct information single-parity (r, t)-locality LRCs. All our codes attain the Singleton-like bound of LRCs where each repair group contains a single parity symbol and thus are optimal.

To improve the performance of underwater acoustic communications, this letter proposes a polar coding scheme with adaptive channel equalization, which can reduce the amount of feedback information. Furthermore, a hybrid automatic repeat request (HARQ) mechanism is provided to mitigate the impact of estimation errors. Simulation results show that the proposed scheme outperforms the turbo equalization in bit error rate. Computational complexity analysis is also provided for comparison.

A method of radar-based contactless vital-sign sensing and electrocardiogram (ECG) signal reconstruction using deep learning is proposed. A radar system is an effective tool for contactless vital-sign sensing because it can measure a small displacement of the body surface without contact. However, most of the conventional methods have limited evaluation indices and measurement conditions. A method of measuring body-surface-displacement signals by using frequency-modulated continuous-wave (FMCW) radar and reconstructing ECG signals using a convolutional neural network (CNN) is proposed. This study conducted two experiments. First, we trained a model using the data obtained from six subjects breathing in a seated condition. Second, we added sine wave noise to the data and trained the model again. The proposed model is evaluated with a correlation coefficient between the reconstructed and actual ECG signal. The results of first experiment show that their ECG signals are successfully reconstructed by using the proposed method. That of second experiment show that the proposed method can reconstruct signal waveforms even in an environment with low signal-to-noise ratio (SNR).

It is of great significance to design a stable and reliable routing protocol for Vehicular Ad Hoc Networks (VANETs) that adopt Vehicle to Vehicle (V2V) communications in the face of frequent network topology changes. In this paper, we propose a hybrid routing algorithm, RCRIQ, based on improved Q-learning. For an established cluster structure, the cluster head is used to select the gateway vehicle according to the gateway utility function to expand the communication range of the cluster further. During the link construction stage, an improved Q-learning algorithm is adopted. The corresponding neighbor vehicle is chosen according to the maximum Q value in the neighbor list. The heuristic algorithm selects the next-hop by the maximum heuristic function value when selecting the next-hop neighbor node. The above two strategies are comprehensively evaluated to determine the next hop. This way ensures the optimal selection of the next hop in terms of reachability and other communication parameters. Simulation experiments show that the algorithm proposed in this article has better performance in terms of routing stability, throughput, and communication delay in the urban traffic scene.

This paper proposes a migration model for distributed server allocation. In distributed server allocation, each user is assigned to a server to minimize the communication delay. In the conventional model, a user cannot migrate to another server to avoid instability. We develop a model where each user can migrate to another server while receiving services. We formulate the proposed model as an integer linear programming problem. We prove that the considered problem is NP-complete. We introduce a heuristic algorithm. Numerical result shows that the proposed model reduces the average communication delay by 59% compared to the conventional model at most.

We have proposed integrated waveguide structure suitable for mid- and near- infrared light propagation using Si and CaF2 heterostructures on Si substrate. Using a fabrication process based on etching, lithography and crystal growth techniques, we have formed a slab-waveguide structure with a current injection mechanism on a SOI substrate, which would be a key component for Si/CaF2 quantum cascade lasers and other optical integrated systems. The propagation of light at a wavelength of 1.55 µm through a Si/CaF2 waveguide structure have been demonstrated for the first time using a structure with a Si/CaF2 multilayered core with 610-nm-thick, waveguide width of 970 nm, which satisfies single-mode condition in the horizontal direction within a tolerance of fabrication accuracy. The waveguide loss for transverse magnetic (TM) mode has been evaluated to be 51.4 cm-1. The cause of the loss was discussed by estimating the edge roughness scattering and free carrier absorption, which suggests further reduction of the loss would be possible.

This paper describes a novel low-latency 4K 60 fps HEVC (high efficiency video coding)/H.265 multi-channel encoding system with content-aware bitrate control for live streaming. Adaptive bitrate (ABR) streaming techniques, such as MPEG-DASH (dynamic adaptive streaming over HTTP) and HLS (HTTP live streaming), spread widely on Internet video streaming. Live content has increased with the expansion of streaming services, which has led to demands for traffic reduction and low latency. To reduce network traffic, we propose content-aware dynamic and seamless bitrate control that supports multi-channel real-time encoding for ABR, including 4K 60 fps video. Our method further supports chunked packaging transfer to provide low-latency streaming. We adopt a hybrid architecture consisting of hardware and software processing. The system consists of multiple 4K HEVC encoder LSIs that each LSI can encode 4K 60 fps or up to high-definition (HD) ×4 videos efficiently with the proposed bitrate control method. The software takes the packaging process according to the various streaming protocol. Experimental results indicate that our method reduces encoding bitrates obtained with constant bitrate encoding by as much as 56.7%, and the streaming latency over MPEG-DASH is 1.77 seconds.

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.

Constructing accurate similarity graph is an important process in graph-based clustering. However, traditional methods have three drawbacks, such as the inaccuracy of the similarity graph, the vulnerability to noise and outliers, and the need for additional discretization process. In order to eliminate these limitations, an entropy regularized unsupervised clustering based on maximum correntropy criterion and adaptive neighbors (ERMCC) is proposed. 1) Combining information entropy and adaptive neighbors to solve the trivial similarity distributions. And we introduce l0-norm and spectral embedding to construct similarity graph with sparsity and strong segmentation ability. 2) Reducing the negative impact of non-Gaussian noise by reconstructing the error using correntropy. 3) The prediction label vector is directly obtained by calculating the sparse strongly connected components of the similarity graph Z, which avoids additional discretization process. Experiments are conducted on six typical datasets and the results showed the effectiveness of the method.

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 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.

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.

The multi-FPGA system known as, the Flow-in-Cloud (FiC) system, is composed of mid-range FPGAs that are directly interconnected by high-speed serial links. FiC is currently being developed as a server for multi-access edge computing (MEC), which is one of the core technologies of 5G. Because the applications of MEC are sometimes timing-critical, a static time division multiplexing (STDM) network has been used on FiC. However, the STDM network exhibits the disadvantage of decreasing link utilization, especially under light traffic. To solve this problem, we propose a hybrid router that combines packet switching for low-priority communication and STDM for high-priority communication. In our hybrid network, the packet switching uses slots that are unused by the STDM; therefore, best-effort communication by packet switching and QoS guarantee communication by the STDM can be used simultaneously. Furthermore, to improve each link utilization under a low network traffic load, we propose a dynamic communication switching algorithm. In our algorithm, each router monitors the network load metrics, and according to the metrics, timing-critical tasks select the STDM according to the metrics only when congestion occurs. This can achieve both QoS guarantee and efficient utilization of each link with a small resource overhead. In our evaluation, the dynamic algorithm was up to 24.6% faster on the execution time with a high network load compared to the packet switching on a real multi-FPGA system with 24 boards.

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.