Compressive sensing enables quite lower sampling rate compared with Nyquist sampling. As long as the signal is sparsity in some basis, the random sampling with CS can be employed. In order to make CS applied in the practice, the Analog to Information Converter (AIC) should be involved. Based on the Limited Random Sequence (LRS) modulation, the AIC with LRS can be designed with high performance according to the fixed sparsity. However, if the sparsity of the signal varies with time, the original AIC with LRS is not efficient. In this paper, the adaptive AIC which adapts its scheme of LRS according to the variation of the sparsity is proposed and the prototype system is designed. Due to the adaption of the AIC with the scheme of LRS, the sampling rate can be further reduced. The simulation results confirm the performance of the proposed adaptive AIC scheme. The prototype system can successfully fulfil the random sampling and adapt to the variation of sparsity, which verify and consolidate the validity and feasibility for the future implementation of adaptive AIC on chip.

In this paper, we present a set of sequence pairs which produce zero correlation windows not only in the middle part of the sum of aperiodic correlation functions, but also in the two terminal parts. We name it "Ear Windows." In approximately synchronous CDMA communication system, this set of sequences is able to completely remove the inter-symbol interference (ISI) and multi-user interference (MUI) caused by the multi-path effect if the maximum delay is shorter than the length of the "Ear windows." In addition, it is also feasible in M-ary modulation. The inter-code interference will be mitigated drastically.

Autonomous Underwater Vehicle (AUV) can be utilized to directly measure the geomagnetic map in deep sea. The traditional map interpolation algorithms based on sampling continuation above the sea level yield low resolution and accuracy, which restricts the applications such as the deep sea geomagnetic positioning, navigation, searching and surveillance, etc. In this letter, we propose a Three-Dimensional (3D) Compressive Sensing (CS) algorithm in terms of the real trajectory of AUV which can be optimized with the required accuracy. The geomagnetic map recovered with the CS algorithm shows high precision compared with traditional interpolation schemes, by which the magnetic positioning accuracy can be greatly improved.

Let G11 (resp., G12) be the ternary Golay code of length 11 (resp., 12). In this letter, we investigate the separating redundancies of G11 and G12. In particular, we determine the values of sl(G11) for l = 1, 3, 4 and sl(G12) for l = 1, 4, 5, where sl(G11) (resp., sl(G12)) is the l-th separating redundancy of G11 (resp., G12). We also provide lower and upper bounds on s2(G11), s2(G12), and s3(G12).

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.

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.

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.

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.

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.

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.

In recent years, many variants of key point based image descriptors have been designed for the image matching, and they have achieved remarkable performances. However, to some images, local features appear to be inapplicable. Since theses images usually have many local changes around key points compared with a normal image, we define this special image category as the image with local changes (IL). An IL pair (ILP) refers to an image pair which contains a normal image and its IL. ILP usually loses local visual similarities between two images while still holding global visual similarity. When an IL is given as a query image, the purpose of this work is to match the corresponding ILP in a large scale image set. As a solution, we use a compressed HOG feature descriptor to extract global visual similarity. For the nearest neighbor search problem, we propose random projection indexed KD-tree forests (rKDFs) to match ILP efficiently instead of exhaustive linear search. rKDFs is built with large scale low-dimensional KD-trees. Each KD-tree is built in a random projection indexed subspace and contributes to the final result equally through a voting mechanism. We evaluated our method by a benchmark which contains 35,000 candidate images and 5,000 query images. The results show that our method is efficient for solving local-changes invariant image matching problems.

The success of heuristic search in AI planning largely depends on the design of the heuristic. On the other hand, previous experience contains potential domain information that can assist the planning process. In this context, we have studied dynamic macro-based heuristic planning through action relationship analysis. We present an approach for analyzing the action relationship and design an algorithm that learns macros in solved cases. We then propose a dynamic macro-based heuristic that appropriately reuses the macros rather than immediately assigning them to domains. The above ideas are incorporated into a working planning system called Dynamic Macro-based Fast Forward planner. Finally, we evaluate our method in a series of experiments. Our method effectively optimizes planning since it reduces the result length by an average of 10% relative to the FF, in a time-economic manner. The efficiency is especially improved when invoking an action consumes time.