N-Shift Regional Low Correlation (NS-RLC) sequences have the low values of the correlation function only in N-shift positions. Especially, N-Shift Regional Zero Correlation (NS-RZC) sequences have the zero values in N-shift positions. In this letter, the generation algorithm of N-shift RLC/RZC sequences derived from Three Low Correlation Zones (T-LCZ) sequence set and Three Zero Correlation Zones (T-ZCZ) sequence set is proposed. In order to highlight the relationship between these sequences, the corresponding theoretical bound is calculated and analyzed.

The properties of the surface-conduction electron-emitter display (SED) are mainly decided by the surface-conduction electron emitters (SCE), which are normally made from the expensive metal Pd. In this study, we propose to use metal Zn instead of Pd as the emitter material. Both the device electrode and ZnO thin film are deposited by a sputter, and the electron emitters (SCE) are formed by the electro-forming process. The electron emission characteristic is obtained and the luminescence is observed.

Machine reading comprehension with multi-hop reasoning always suffers from reasoning path breaking due to the lack of world knowledge, which always results in wrong answer detection. In this paper, we analyze what knowledge the previous work lacks, e.g., dependency relations and commonsense. Based on our analysis, we propose a Multi-dimensional Knowledge enhanced Graph Network, named MKGN, which exploits specific knowledge to repair the knowledge gap in reasoning process. Specifically, our approach incorporates not only entities and dependency relations through various graph neural networks, but also commonsense knowledge by a bidirectional attention mechanism, which aims to enhance representations of both question and contexts. Besides, to make the most of multi-dimensional knowledge, we investigate two kinds of fusion architectures, i.e., in the sequential and parallel manner. Experimental results on HotpotQA dataset demonstrate the effectiveness of our approach and verify that using multi-dimensional knowledge, especially dependency relations and commonsense, can indeed improve the reasoning process and contribute to correct answer detection.

Limited Random Sequence (LRS) is quite important for Analog-to-Information Converter (AIC) because it determines the random sampling scheme and the resultant performance. LRS is established with the elements of “0” and “1”. The “1” appears randomly in the segment of the sequence, so that the production of the original signal and LRS can be considered as the approximation of the random sampling of the original signal. The random sampling result can perfectly recover the signal with Compressive Sensing (CS) algorithm. In this paper, a high order LRS is proposed for the AIC design in Distributed Compressive Sensing (DCS), which has the following three typical features: 1) The high order LRS has the elements of integer which can indicate the index number of the sensor in DCS. 2) High order LRS can adapt to the sparsity variation of the original signal detected by each sensor. 3) Employing the AIC with high order LRS, the DCS algorithm can recover the signal with very low sampling rate, usually above 2 orders less than the traditional distributed sensors. In the paper, the scheme and the construction algorithm of high order LRS are proposed. The performance is evaluated with the application studies of the distributed sensor network and the camera picture correspondingly.

Lightweight single-layer slotted waveguide array antennas are fabricated using plastic materials with metal-plating. A plastic material that has good heat-radiation properties is investigated. Three types of antennas are fabricated by milling, using ABS resin, heat-radiating plastic, and aluminum alloy. In measurements, all three types of antennas are confirmed to have almost the same VSWR and gain in the 25 GHz frequency band.

Safety is the foremost requirement of avionics systems on aircraft. So far, avionics systems have evolved into an integrated system, i.e., integrated avionics system, and the derivative functions occur when the avionics systems are upgraded. However, the traditional safety analysis method is insufficient to be utilized in upgraded avionics systems due to these derivative functions. In this letter, a safety evaluation scheme is proposed to quantitatively evaluate the safety of the upgraded avionics systems. All the functions including the derivative functions can be traced and covered. Meanwhile, a set of safety issues based on different views is established to evaluate the safety capability from three layers, i.e., the mission layer, function layer and resource layer. The proposed scheme can be considered as an efficient scheme in the safety validation and verification in the upgraded avionics systems.

The adaptive predistorter and the negative feedback system are known as methods to compensate for the nonlinear distortion of a power amplifier. Although the feedback method is a simple technique, its instability impedes the capability of high-feedback gain to achieve a high-compensation effect. On the other hand, the predistorter requires a long time for convergence of the adaptive predistorters. In this paper, we propose a nonlinear distortion compensation method for a narrow-band signal. In this method, an adaptive predistorter and negative feedback are combined. In addition, to shorten the convergence time to minimize nonlinear distortion, a variable step-size (VS) method is also applied to the algorithm to determine the parameters of the adaptive predistorter. Using computer simulations, we show that the proposed scheme achieves both five times faster convergence speed than that of the predistorter and three times higher permissible delay time in the feedback amplifier than that of a negative feedback only amplifier.

Vehicular Ad hoc Network (VANET) is an important part of the Intelligent Transportation System (ITS). VANETs can realize communication between moving vehicles, infrastructures and other intelligent mobile terminals, which can greatly improve the road safety and traffic efficiency effectively. Existing studies of vehicular ad hoc network usually consider only one data transmission model, while the increasing density of traffic data sources means that the vehicular ad hoc network is evolving into Heterogeneous Vehicular Network (HetVNET) which needs hybrid data transmission scheme. Considering the Heterogeneous Vehicular Network, this paper presents a hybrid transmission MAC protocol including vehicle to vehicle communication (V2V) and vehicle to infrastructure communication (V2I/I2V). In this protocol, the data are identified according to timeliness, on the base of the traditional V2V and V2I/I2V communication. If the time-sensitive data (V2V data) fail in transmission, the node transmits the data to the base station and let the base station cooperatively transmit the data with higher priority. This transmission scheme uses the large transmission range of base station in an effective manner. In this paper, the queueing models of the vehicles and base station are analyzed respectively by one-dimensional and two-dimensional Markov Chain, and the expressions of throughput, packet drop rate and delay are also derived. The simulation results show that this MAC protocol can improve the transmission efficiency of V2V communication and reduce the delay of V2V data without losing the system performance.

At this stage, research in the field of Few-shot image classification (FSC) has made good progress, but there are still many difficulties in the field of Few-shot object detection (FSOD). Almost all of the current FSOD methods face catastrophic forgetting problems, which are manifested in that the accuracy of base class recognition will drop seriously when acquiring the ability to recognize Novel classes. And for many methods, the accuracy of the model will fall back as the class increases. To address this problem we propose a new memory-based method called Memorable Faster R-CNN (MemFRCN), which makes the model remember the categories it has already seen. Specifically, we propose a new tow-stage object detector consisting of a memory-based classifier (MemCla), a fully connected neural network classifier (FCC) and an adaptive fusion block (AdFus). The former stores the embedding vector of each category as memory, which enables the model to have memory capabilities to avoid catastrophic forgetting events. The final part fuses the outputs of FCC and MemCla, which can automatically adjust the fusion method of the model when the number of samples increases so that the model can achieve better performance under various conditions. Our method can perform well on unseen classes while maintaining the detection accuracy of seen classes. Experimental results demonstrate that our method outperforms other current methods on multiple benchmarks.

In this work, we introduce notions of quantum frequency arrangements consisting of quantum frequency squares, cubes, hypercubes and a notion of orthogonality between them. We also propose a notion of quantum mixed orthogonal array (QMOA). By using irredundant mixed orthogonal array proposed by Goyeneche et al. we can obtain k-uniform states of heterogeneous systems from quantum frequency arrangements and QMOAs. Furthermore, some examples are presented to illustrate our method.

The pilot symbols in the broadband Air-to-Ground (A/G) communications system, e.g., L-band Digital Aeronautical Communications System (L-DACS1), are expected to be also utilized for navigation. In order to identify the co-channel signals from different Ground Stations (GSs), the N-Shift Zero Correlation Zone (NS-ZCZ) sequences are employed for pilot sequences. The ideal correlation property of the proposed pilot sequence in ZCZ can maintain the signal with less co-channel interference. The simulation confirms that the more co-channel GSs are employed, the higher navigation accuracy can be achieved.

In this paper, we propose a direct digital RF transmitter with a 1-bit band-pass delta-sigma modulator (BP-DSM) that uses high order image components of the 7th Nyquist zone in Manchester coding for microwave and milimeter wave application. Compared to the conventional non-return-to-zero (NRZ) coding, in which the high order image components of 1-bit BP-DSM attenuate severely in the form of sinc function, the proposed 1-bit direct digital RF transmitter in Manchester code can improve the output power and signal-to-noise ratio (SNR) of the image components at specific (4n-1)th and (4n-2)th Nyquist Zone, which is confirmed by calculating of the power spectral density. Measurements are made to compare three types of 1-bit digital-to-analog converter (DAC) signal in output power and SNR; NRZ, 50% duty return-to-zero (RZ) and Manchester coding. By using 1 Vpp/8Gbps DAC output, 1-bit signals in Manchester coding show the highest output power of -20.3dBm and SNR of 40.3dB at 7th Nyquist Zone (26GHz) in CW condition. As a result, compared to NRZ and RZ coding, at 7th Nyquist zone, the output power is improved by 8.1dB and 6dB, respectively. Meanwhile, the SNR is improved by 7.6dB and 4.9dB, respectively. In 5Mbps-QPSK condition, 1-bit signals in Manchester code show the lowest error vector magnitude (EVM) of 2.4% and the highest adjacent channel leakage ratio (ACLR) of 38.2dB with the highest output power of -18.5dBm at 7th Nyquist Zone (26GHz), respectively, compared to the NRZ and 50% duty RZ coding. The measurement and simulation results of the image component of 1-bit signals at 7th Nyquist Zone (26GHz) are consistent with the calculation results.

Due to the high speed mobile environment, the aeronautical Cognitive Radio (CR) communications base on the channel with the time-variant stochastic non-continuous spectrum. The traditional fading channel models, such as Rayleigh, Rice, Nakagami-m and multipath channel models, can not describe the whole property of the channels of CR communications. In this letter, the statistical channel modeling scheme for aeronautical CR communications is proposed with a M/M/s(1) queuing model, which properly describes the random spectrum occupation of the primary users, i.e. aircrafts in aeronautical communications. The proposed channel model can be easily utilized in the channel simulation to testify the validity and efficiency of the aeronautical CR communications.

A narrowband interference (NBI) estimation and mitigation method based on compressive sensing (CS) for communication systems with repeated training sequences is investigated in this letter. The proposed CS-based differential measuring method is performed through the differential operation on the inter-block-interference-free regions of the received adjacent training sequences. The sparse NBI signal can be accurately recovered from a time-domain measurement vector of small size under the CS framework, without requiring channel information or dedicated resources. Theoretical analysis and simulation results show that the proposed method is robust to NBI under multi-path fading channels.

In this letter, we enhance Complementary Code Keying (CCK) modulation with Pseudo-Periodic Sequence. It has been proved that the new Pseudo-Periodic CCK modulation is more efficient and robust fighting against multi-path interference. In order to support our new scheme, we design and implement the corresponding simulation. The in-depth analysis of the reason why Pseudo-Periodic Sequence can do a favor to CCK is also presented and emphasized.

Large Area Synchronized (LAS)-CDMA, actually composed of LA codes and pulse compressing LS codes, has been proposed as a most promising scheme in 3G and 4G wireless communications. LS codes are famous for the Zero Correlation Zone (ZCZ) in the auto-correlation and cross-correlation functions, which endows the codes with the capability to perfectly reduce the Multiple Access Interference (MAI) and Inter Symbol Interference (ISI) if the maximum transmission delay is less than the length of ZCZ. In this letter, we provide a general and systematic method to construct LS codes from the set of complete complementary sequences. Our method is much more general than the ordinary LS construction method revealed previously.

Rotate magnetic field can be used for ranging, especially the environment where electronic filed suffers a deep fading and attenuation, such as drilling underground. However, magnetic field is still affected by the ferromagnetic materials, e.g., oil casing pipe. The measurement error is not endurable for single measurement. In this paper, the Geometric Predicted Unscented Kalman Filtering (GP-UKF) algorithm is developed for rotate magnetic ranging system underground. With GP-UKF, the Root Mean Square Error (RMSE) can be suppressed. It is really important in a long range detection by magnetic field, i.e., more than 50 meters.

Convolutional neural network (CNN) based vehicle re-identificatioin (ReID) inevitably has many disadvantages, such as information loss caused by downsampling operation. Therefore we propose a vision transformer (Vit) based vehicle ReID method to solve this problem. To improve the feature representation of vision transformer and make full use of additional vehicle information, the following methods are presented. (I) We propose a Quadratic Split Architecture (QSA) to learn both global and local features. More precisely, we split an image into many patches as “global part” and further split them into smaller sub-patches as “local part”. Features of both global and local part will be aggregated to enhance the representation ability. (II) The Auxiliary Information Embedding (AIE) is proposed to improve the robustness of the model by plugging a learnable camera/viewpoint embedding into Vit. Experimental results on several benchmarks indicate that our method is superior to many advanced vehicle ReID methods.

In this letter, a path dynamics assessment asynchronous advantage actor-critic scheduling algorithm (PDAA3C) is proposed to solve the MPTCP scheduling problem by using deep reinforcement learning Actor-Critic framework. The algorithm picks out the optimal transmitting path faster by multi-core asynchronous updating and also guarantee the network fairness. Compared with the existing algorithms, the proposed algorithm achieves 8.6% throughput gain over RLDS algorithm, and approaches the theoretic upper bound in the NS3 simulation.

Trellis coded modulation (TCM) concept is applied to the mode constellation points of orbital angular momentum (OAM) modulation. OAM modulation considers the multiple OAM modes as additional constellation points and maps a first part of a block of information bits to the transmitting OAM modes. Therefore, spatial multiplexing gains are retained and spectral efficiency is boosted. The second part of the block of information bits is mapped to a complex symbol using conventional digital modulation schemes. At any particular time instant, only one OAM mode is active. The receiver estimates the transmitted symbol and the active OAM mode, then uses the two estimates to retrieve the original block of data bits. Simulation reveals that with the TCM employed both for the OAM constellation points and the signal constellation points, a considerable bit error rate (BER) gain can be obtained under all turbulence conditions, compared with that of the no coding scheme.