Since ISAR is widely applied in many occasions and provides high resolution images of the target, ISAR countermeasures are attracting more and more attention. Most of the present methods of deception jamming are not suitable for engineering realization due to the heavy computation load or the large calculation delay. Deception jamming against ISAR requires large computation resource and real-time performance algorithms. Many studies on false target jamming assume that the jammer is able to receive the target echo or transmit the jamming signal to the real target, which is sometimes not possible. How to impose the target property onto the intercepted radar signal is critical to a deception jammer. This paper proposes a jamming algorithm based on parallel convolution and one-bit quantization. The algorithm is able to produce a single false target on ISAR image by the jammer itself. The requirement for computation resource is within the capabilities of current digital signal processors such as FPGA or DSP. The method processes the samples of radar signal in parallel and generates the jamming signal at the rate of ADC data, solving the problem that the real-time performance is not satisfied when the input data rate for convolution is far higher than the clock frequency of FPGA. In order to reduce the computation load of convolution, one-bit quantization is utilized. The complex multiplication is implemented by logical resources, which significantly reduces the consumption of FPGA multipliers. The parallel convolution jamming signal, whose date rate exceeds the FPGA clock rate, is introduced and analyzed in detail. In theory, the bandwidth of jamming signal can be half of the sampling frequency of high speed ADC, making the proposed jamming algorithm able to counter ultra-wideband ISAR signals. The performance and validity of the proposed method are verified by simulations. This jamming method is real-time and capable of producing a false target of large size at the low cost of FPGA device.

In lossy image/video encoding, there is a compromise between the number of bits and the extent of distortion. Optimizing the allocation of bits to different sources, such as frames or blocks, can improve the encoding performance. In intra-frame encoding, due to the dependency among macro blocks (MBs) introduced by intra prediction, the optimization of bit allocation to the MBs usually has high complexity. So far, no practical optimal bit allocation methods for intra-frame encoding exist, and the commonly used method for intra-frame encoding is the fixed-QP method. We suggest that the QP selection inside an image/a frame can be optimized by aiming at the constant perceptual quality (CPQ). We proposed an iteration-based bit allocation scheme for H.264/AVC intra-frame encoding, in which all the local areas (which is defined by a group of MBs (GOMBs) in this paper) in the frame are encoded to have approximately the same perceptual quality. The SSIM index is used to measure the perceptual quality of the GOMBs. The experimental results show that the encoding performance on intra-frames can be improved greatly by the proposed method compared with the fixed-QP method. Furthermore, we show that the optimization on the intra-frame can bring benefits to the whole sequence encoding, since a better reference frame can improve the encoding of the subsequent frames. The proposed method has acceptable encoding complexity for offline applications.

This paper proposes an enhanced affinity graph (EA-graph) for image segmentation. Firstly, the original image is over-segmented to obtain several sets of superpixels with different scales, and the color and texture features of the superpixels are extracted. Then, the similarity relationship between neighborhood superpixels is used to construct the local affinity graph. Meanwhile, the global affinity graph is obtained by sparse reconstruction among all superpixels. The local affinity graph and global affinity graph are superimposed to obtain an enhanced affinity graph for eliminating the influences of noise and isolated regions in the image. Finally, a bipartite graph is introduced to express the affiliation between pixels and superpixels, and segmentation is performed using a spectral clustering algorithm. Experimental results on the Berkeley segmentation database demonstrate that our method achieves significantly better performance compared to state-of-the-art algorithms.

Smart contracts are becoming more and more popular in financial scenarios like medical insurance. Rather than traditional schemes, using smart contracts as a medium is a better choice for both participants, as it is fairer, more reliable, more efficient, and enables real-time payment. However, medical insurance contracts need to input the patient's condition information as the judgment logic to trigger subsequent execution. Since the blockchain is a closed network, it lacks a secure network environment for data interaction with the outside world. The Data feed aims to provide the service of the on-chain and off-chain data interaction. Existing researches on the data feed has solved the security problems on it effectively, such as Town Crier, TLS-N and they have also taken into account the privacy-preserving problems. However, these schemes cannot actually protect privacy because when the ciphertext data is executed by the contract, privacy information can still be inferred by analyzing the transaction results, since states of the contract are publicly visible. In this paper, based on zero-knowledge proof and Hawk technology, a on-and-off-chain complete smart contract data feed privacy-preserving scheme is proposed. In order to present our scheme more intuitively, we combined the medical insurance compensation case to implement it, which is called MIPDF. In our MIPDF, the patient and the insurance company are parties involved in the contract, and the hospital is the data provider of data feed. The patient's medical data is sent to the smart contract under the umbrella of the zero-knowledge proof signature scheme. The smart contract verifies the proof and calculates the insurance premium based on the judgment logic. Meanwhile, we use Hawk technology to ensure the privacy of on-chain contract execution, so that no information will be disclosed due to the result of contract execution. We give a general description of our scheme within the Universal Composability (UC) framework. We experiment and evaluate MIPDF on Ethereum for in-depth analysis. The results show that our scheme can securely and efficiently support the functions of medical insurance and achieve complete privacy-preserving.

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.

Chinese Spelling Correction (CSC) is an important natural language processing task. Existing methods for CSC mostly utilize BERT models, which select a character from a candidate list to correct errors in the sentence. World knowledge refers to structured information and relationships spanning a wide range of domains and subjects, while definition knowledge pertains to textual explanations or descriptions of specific words or concepts. Both forms of knowledge have the potential to enhance a model’s ability to comprehend contextual nuances. As BERT lacks sufficient guidance from world knowledge for error correction and existing models overlook the rich definition knowledge in Chinese dictionaries, the performance of spelling correction models is somewhat compromised. To address these issues, within the world knowledge network, this study injects world knowledge from knowledge graphs into the model to assist in correcting spelling errors caused by a lack of world knowledge. Additionally, the definition knowledge network in this model improves the error correction capability by utilizing the definitions from the Chinese dictionary through a comparative learning approach. Experimental results on the SIGHAN benchmark dataset validate the effectiveness of our approach.

Chinese spelling correction is a foundational task in natural language processing that aims to detect and correct spelling errors in text. Most spelling corrections in Chinese used multimodal information to model the relationship between incorrect and correct characters. However, feature information mismatch occured during fusion result from the different sources of features, causing the importance relationships between different modalities to be ignored, which in turn restricted the model from learning in an efficient manner. To this end, this paper proposes a multimodal language model-based Chinese spelling corrector, named as MISpeller. The method, based on ChineseBERT as the basic model, allows the comprehensive capture and fusion of character semantic information, phonetic information and graphic information in a single model without the need to construct additional neural networks, and realises the phenomenon of unequal fusion of multi-feature information. In addition, in order to solve the overcorrection issues, the replication mechanism is further introduced, and the replication factor is used as the dynamic weight to efficiently fuse the multimodal information. The model is able to control the proportion of original characters and predicted characters according to different input texts, and it can learn more specifically where errors occur. Experiments conducted on the SIGHAN benchmark show that the proposed model achieves the state-of-the-art performance of the F1 score at the correction level by an average of 4.36%, which validates the effectiveness of the model.

Federated cloud, as a promising technology, can improve the computing capacity for autonomous driving in the vehicle-road-cloud collaborative system. However, the allocation of federated clouds should consider the environmental changes based on the real-time impact of vehicle terminal location. To improve computational efficiency while ensuring the effectiveness of federated clouds, this paper proposes a one-sided matching reverse auction based on the federated clouds (OSFC) method for scheduling autonomous driving sensors in a vehicle-road-cloud collaborative environment. This method dynamically allocates communication resources according to the actual situation of the vehicle terminals in real time. Numerical simulations show that our proposed OSFC method significantly improves computational efficiency while ensuring the effectiveness of federated clouds compared with state-of-the-art work.

With the widespread popularity of a large number of social platforms, an increasing number of new words gradually appear. However, such new words have made some NLP tasks like word segmentation more challenging. Therefore, new word detection is always an important and tough task in NLP. This paper aims to extract new words using the BiLSTM+CRF model which added some features selected by us. These features include word length, part of speech (POS), contextual entropy and degree of word coagulation. Comparing to the traditional new word detection methods, our method can use both the features extracted by the model and the features we select to find new words. Experimental results demonstrate that our model can perform better compared to the benchmark models.

The line-based method has been one of the most commonly-used methods of hardware implementation of two-dimensional (2D) discrete wavelet transform (DWT). However, data buffer is required between the row DWT processor and the column DWT processor to solve the data flow mismatch, which increases the on-chip memory size and the output latency. Since the incompatible data flow is induced from the intrinsic property of adopted lifting-based algorithm, a decomposed lifting algorithm (DLA) is presented by rearranging the data path of lifting steps to ensure that image data is processed in raster scan manner in row processor and column processor. Theoretical analysis indicates that the precision issue of DLA outperforms other lifting-based algorithms in terms of round-off noise and internal word-length. A memory-efficient and high-performance line-based architecture is proposed based on DLA without the implementation of data buffer. For an N M image, only 2N internal memory is required for 5/3 filter and 4N of that is required for 9/7 filter to perform 2D DWT, where N and M indicate the width and height of an image. Compared with related 2D DWT architectures, the size of on-chip memory is reduced significantly under the same arithmetic cost, memory bandwidth and timing constraint. This design was implemented in SMIC 0.18 µm CMOS logic fabrication with 32 kbits dual-port RAM and 20 K equivalent 2-input NAND gates in a 1.00 mm 1.00 mm die, which can process 512 512 image under 100 MHz.

In this paper, a balance operation Transimpedance Amplifier (TIA) with low-noise has been implemented for optical receivers in 130 nm SiGe BiCMOS Technology, in which the optimal tradeoff emitter current density and the location of high-frequency noise corner were analyzed for acquiring low-noise performance. The Auto-Zero Feedback Loop (AZFL) without introducing unnecessary noises at input of the TIA, the tail current sink with high symmetries and the balance operation TIA with the shared output of Operational Amplifier (OpAmp) in AZFL were designed to keep balanced operation for the TIA. Moreover, cascode and shunt-feedback were also employed to expanding bandwidth and decreasing input referred noise. Besides, the formula for calculating high-frequency noise corner in Heterojunction Bipolar Transistor (HBT) TIA with shunt-feedback was derived. The electrical measurement was performed to validate the notions described in this work, appearing 9.6 pA/√Hz of input referred noise current Power Spectral Density (PSD), balance operation (VIN1=896mV, VIN2=896mV, VOUT1=1.978V, VOUT2=1.979V), bandwidth of 32GHz, overall transimpedance gain of 68.6dBΩ, a total 117mW power consumption and chip area of 484µm × 486µm.

In this letter, we study the R-D properties of independent sources based on MSE and SSIM, and compare the bit allocation performance under the MINAVE and MINMAX criteria in video encoding. The results show that MINMAX has similar results in terms of average distortion with MINAVE by using SSIM, which illustrates the consistency between these two criteria in independent perceptual video coding. Further more, MINMAX results in lower quality fluctuation, which shows its advantage for perceptual video coding.

The bit-error-rate (BER) performance predicted by the semi-analytical evolution technique proposed by Li Ping et al. becomes inaccurate for parallel concatenated coded interleave-division multiple-access (PCC-IDMA) systems. To solve this problem, we develop a novel evolution technique of such systems. Numerical results show that the predicted performance agrees well with the simulation results, and that this technique is useful for system optimization.

Named Data Networking (NDN) is a proposed future Internet architecture that shifts the fundamental abstraction of the network from host-to-host communication to request-response for named, signed data-an information dissemination focused approach. This paper describes a general design for receiver-driven, real-time streaming data (RTSD) applications over the current NDN implementation that aims to take advantage of the architecture's unique affordances. It is based on experimental development and testing of running code for real-time video conferencing, a positional tracking system for interactive multimedia, and a distributed control system for live performance. The design includes initial approaches to minimizing latency, managing buffer size and Interest retransmission, and adapting retrieval to maximize bandwidth and control congestion. Initial implementations of these approaches are evaluated for functionality and performance results, and the potential for future research in this area, and improved performance as new features of the architecture become available, is discussed.

Security, flexibility, and scalability are critical to the success of wireless communications. Wireless networks with movable base stations combine the advantages of mobile ad hoc networks and wireless LAN to achieve these goals. Hierarchical mobile wireless network (HMWN) is proposed for supporting movable base stations. In such a system, mobile hosts are organized into hierarchical groups. The group agents serve as a distributed trust entity. A secure packet forwarding algorithm and an authentication and key exchange protocol are developed to protect the network infrastructure. A roaming support mechanism and the associated mutual authentication protocol are proposed to secure the foreign group and the mobile host when it roams within the network. The computation overhead of secure packet forwarding and roaming support algorithms is studied via experiments. The results demonstrate that these two security mechanisms only require, respectively, less than 2% and 0.2% to 5% CPU time in a low-end 700 MHz PC.

In 3GPP (3rd Generation Partnership Project) LTE (Long Term Evolution) systems, when HARQ (Hybrid Automatic Repeat request) retransmission is invoked, the data at the transmitter are retransmitted randomly or sequentially regardless of their relationship to the wrongly decoded data. Such practice is inefficient since precious transmission resources will be spent to retransmit data that may be of no use in error correction at the receiver. This paper proposes an incremental redundancy HARQ scheme based on Error Position Estimating Coding (ePec) and LDPC (Low Density Parity Check Code) channel coding, which is called ePec-LDPC HARQ. The proposal is able to feedback the wrongly decoded code blocks within a specific MAC (Media Access Control) PDU (Protocol Data Unit) from the receiver. The transmitter gets the feedback information and then performs targeted retransmission. That is, only the data related to the wrongly decoded code blocks are retransmitted, which can improve the retransmission efficiency and thus reduce the retransmission overload. An enhanced incremental redundancy LDPC coding approach, called EIR-LDPC, together with a physical layer framing method, is developed to implement ePec-LDPC HARQ. Performance evaluations show that ePec-LDPC HARQ reduces the overall transmission resources by 15% compared to a conventional LDPC HARQ scheme. Moreover, the average retransmission times of each MAC PDU and the transmission delay are also reduced considerably.

We study the transmission techniques in orthogonal frequency division multiplexing (OFDM) systems with imperfect channel state information at the transmitter (CSIT). We focus on the issue of utilizing the available CSIT by a single forward error control (FEC) code. We first analyze the system performance for the ideal coding case. We then develop a simple but efficient scheme for the practical coding case, which is based on joint FEC coding and linear precoding at the transmitter and iterative linear minimum-mean-square-error (LMMSE) detection at the receiver. Numerical results show that significant performances gains can be achieved by the proposed scheme.

Train-borne video captured from the camera installed in the front or back of the train has been used for railway environment surveillance, including missing communication units and bolts on the track, broken fences, unpredictable objects falling into the rail area or hanging on wires on the top of rails. Moreover, the track condition can be perceived visually from the video by observing and analyzing the train-swaying arising from the track irregularity. However, it's a time-consuming and labor-intensive work to examine the whole large scale video up to dozens of hours frequently. In this paper, we propose a simple and effective method to detect the train-swaying quickly and automatically. We first generate the long rail track panorama (RTP) by stitching the stripes cut from the video frames, and then extract track profile to perform the unevenness detection algorithm on the RTP. The experimental results show that RTP, the compact video representation, can fast examine the visual train-swaying information for track condition perceiving, on which we detect the irregular spots with 92.86% recall and 82.98% precision in only 2 minutes computation from the video close to 1 hour.

Automatic disassembly of railway fasteners is of great significance for improving the efficiency of replacing rails. The accurate positioning of fastener is the key factor to realize automatic disassembling. However, most of the existing literature mainly focuses on fastener region positioning and the literature on accurate positioning of fasteners is scarce. Therefore, this paper constructed a visual inspection system for accurate positioning of fastener (VISP). At first, VISP acquires railway image by image acquisition subsystem, and then the subimage of fastener can be obtained by coarse-to-fine method. Subsequently, the accurate positioning of fasteners can be completed by three steps, including contrast enhancement, binarization and spike region extraction. The validity and robustness of the VISP were verified by vast experiments. The results show that VISP has competitive performance for accurate positioning of fasteners. The single positioning time is about 260ms, and the average positioning accuracy is above 90%. Thus, it is with theoretical interest and potential industrial application.