基于DCNv2和Transformer Decoder的隧道衬砌裂缝高效检测模型研究

为解决因衬砌裂缝性状随机、分布密集、标注框分辨率低所导致的现有模型识别精度低、检测速度慢及参数量庞大等问题,以第2版可变形卷积网络(DCNv2)和端到端变换器解码器(Transformer Decoder)为基础对YOLOv8网络框架进行改进,提出了面向衬砌裂缝的检测模型DTD-YOLOv8。首先,通过引入DCNv2对YOLOv8主干卷积网络C2f进行融合以实现模型对裂缝形变特征的准确快速感知,同时采用Transformer Decoder对YOLOv8检测头进行替换以实现端到端框架内完整目标检测流程,从而消除因Anchor-free处理模式所带来的计算消耗。采用自建裂缝数据集对SSD,Faster-RCNN,RT-DETR,YOLOv3,YOLOv5,YOLOv8和DTD-YOLOv8的7种检测模型进行对比验证。结果表明:改进模型F1分数和mAP@50值分别为87.05%和89.58%;其中F1分数相较其他6种模型分别提高了14.16%,7.68%,1.55%,41.36%,8.20%和7.40%;mAP@50分别提高了28.84%,15.47%,1.33%,47.65%,10.14%和10.84%。改进模型参数量仅为RT-DETR的三分之一,检测单张图片的速度为16.01 ms,FPS为65.46帧每秒,对比其他模型检测速度得到提升。该模型在面向运营隧道裂缝检测任务需求时能够表现出高效的性能。

Parallel Implementation of the CCSDS Turbo Decoder on GPU

This paper presents a software turbo decoder on graphics processing units(GPU).Unlike previous works,the proposed decoding architecture for turbo codes mainly focuses on the Consultative Committee for Space Data Systems(CCSDS)standard.However,the information frame lengths of the CCSDS turbo codes are not suitable for flexible sub-frame parallelism design.To mitigate this issue,we propose a padding method that inserts several bits before the information frame header.To obtain low-latency performance and high resource utilization,two-level intra-frame parallelisms and an efficient data structure are considered.The presented Max-Log-Map decoder can be adopted to decode the Long Term Evolution(LTE)turbo codes with only small modifications.The proposed CCSDS turbo decoder at 10 iterations on NVIDIA RTX3070 achieves about 150 Mbps and 50Mbps throughputs for the code rates 1/6 and 1/2,respectively.

Quantized Decoders that Maximize Mutual Information for Polar Codes

In this paper,we innovatively associate the mutual information with the frame error rate(FER)performance and propose novel quantized decoders for polar codes.Based on the optimal quantizer of binary-input discrete memoryless channels(BDMCs),the proposed decoders quantize the virtual subchannels of polar codes to maximize mutual information(MMI)between source bits and quantized symbols.The nested structure of polar codes ensures that the MMI quantization can be implemented stage by stage.Simulation results show that the proposed MMI decoders with 4 quantization bits outperform the existing nonuniform quantized decoders that minimize mean-squared error(MMSE)with 4 quantization bits,and yield even better performance than uniform MMI quantized decoders with 5 quantization bits.Furthermore,the proposed 5-bit quantized MMI decoders approach the floating-point decoders with negligible performance loss.

Improved List Sphere Decoder for Multiple Antenna Systems

An improved list sphere decoder （ILSD） is proposed based on the conventional list sphere decoder （LSD） and the reduced- complexity maximum likelihood sphere-decoding algorithm. Unlike the conventional LSD with fixed initial radius, the ILSD adopts an adaptive radius to accelerate the list cdnstruction. Characterized by low-complexity and radius-insensitivity, the proposed algorithm makes iterative joint detection and decoding more realizable in multiple-antenna systems. Simulation results show that computational savings of ILSD over LSD are more apparent with more transmit antennas or larger constellations, and with no performance degradation. Because the complexity of the ILSD algorithm almost keeps invariant with the increasing of initial radius, the BER performance can be improved by selecting a sufficiently large radius.

A Concatenated ML Decoder for ST/SFBC-OFDM Systems in Double Selective Fading Channels

This paper presented a concatenated maximum-likelihood (ML) decoder for space-time/space-frequency block coded orthogonal frequency diversion multiplexing (ST/SFBC-OFDM) systems in double selective fading channels. The proposed decoder first detects space-time or space-frequency codeword elements separately. Then, according to the coarsely estimated codeword elements, the ML decoding is performed in a smaller constellation element set to searching final codeword. It is proved that the proposed decoder has optimal performances if and only if subchannels are constant during a codeword interval. The simulation results show that the performances of proposed decoder is close to that of the optimal ML decoder in severe Doppler and delay spread channels. However, the complexity of proposed decoder is much lower than that of the optimal ML decoder.

Comparison and Design of Decoder in B3G Mobile Communication System

Turbo code has been shown to have ability to achieve performance that is close to Shannon limit. It has been adopted by various commercial communication systems. Both universal mobile telecommunications system (UMTS) TDD and FDD have also employed turbo code as the error correction coding scheme. It outperforms convolutional code in large block size, but because of its time delay, it is often only used in the non-real-time service. In this paper, we discuss the encoder and decoder structure of turbo code in B3G mobile communication System. In addition, various decoding techniques, such as the Log-MAP, Max-log-MAP and SOVA algorithm for non-real-time service are deduced and compared. The performance results of decoder and algorithms in different configurations are also shown.

PERFORMANCE ANALYSIS AND SIMULATION OF OCDMA SYSTEM BASED ON FBGs ENCODER/DECODER

The encoding/decoding scheme based on Fiber Bragg Grating (FBG) for Optical Code Division Multiple Access (OCDMA) system is analyzed and the whole process from transmitting end to receiving end is researched in detail. The mathematical mode including signal transmission, summing, receiving and recovering are established respectively. One of the main sources of Bit Error Rate (BER) of OCDMA system based on FBGs is the unevenness of signal power spectrum, which leads to the chip powers unequal with each other. The Signal to Interfere Ratio (SIR) and BER performance of the system are studied and simulated at the case with uneven distribution of chips' powers.

Unifying Convolution and Transformer Decoder for Textile Fiber Identification

At present,convolutional neural networks(CNNs)and transformers surpass humans in many situations(such as face recognition and object classification),but do not work well in identifying fibers in textile surface images.Hence,this paper proposes an architecture named FiberCT which takes advantages of the feature extraction capability of CNNs and the long-range modeling capability of transformer decoders to adaptively extract multiple types of fiber features.Firstly,the convolution module extracts fiber features from the input textile surface images.Secondly,these features are sent into the transformer decoder module where label embeddings are compared with the features of each type of fibers through multi-head cross-attention and the desired features are pooled adaptively.Finally,an asymmetric loss further purifies the extracted fiber representations.Experiments show that FiberCT can more effectively extract the representations of various types of fibers and improve fiber identification accuracy than state-of-the-art multi-label classification approaches.

Cooperative Relaying in a Three User Downlink NOMA System Using Dynamic Power Allocation

Non-orthogonal multiple access(NOMA)represents the latest addition to the array of multiple access techniques,enabling simultaneous servicing of multiple users within a singular resource block in terms of time,frequency,and code.A typical NOMA configuration comprises a base station along with proximate and distant users.The proximity users experience more favorable channel conditions in contrast to distant users,resulting in a compromised performance for the latter due to the less favorable channel conditions.When cooperative communication is integrated with NOMA,the overall system performance,including spectral efficiency and capacity,is further elevated.This study introduces a cooperative NOMA setup in the downlink,involving three users,and employs dynamic power allocation(DPA).Within this framework,User 2 acts as a relay,functioning under the decode-and-forward protocol,forwarding signals to both User 1 and User 3.This arrangement aims to bolster the performance of the user positioned farthest from the base station,who is adversely affected by weaker channel conditions.Theoretical and simulation outcomes reveal enhancements within the system’s performance.

改进YOLOv8的烟盒缺陷检测算法

近年来,人们对于烟盒包装质量的要求越来越高,在现代化的生产中,烟盒的生产速度大幅度提升,生产设备也都实现了智能化。但是,对于烟盒的表面质量检测仍然采用人工的方式进行。针对人工烟盒表面缺陷检测容易发生漏检、错检等问题,提出了一种基于改进YOLOv8的烟盒缺陷检测算法。首先在YOLOv8的颈部网络引入了GD机制,提高了模型对于不同层级间的信息融合能力;其次加入了尺度序列特征融合模块,增强了网络对不同尺度信息的提取能力;最后使用RT-DETR的Decoder替换YOLOv8的头部网络,这使得网络模型无需依赖于复杂的NMS后处理步骤,大大简化了检测流程并提高了效率。实验结果表明:改进后的算法模型在自制的烟盒缺陷数据集上与YOLOv8算法相比检测精度提升到了94.6%,检测速度达到了121.4 FPS。并且与其他目标检测算法相比,改进后的算法在检测精度和检测速度方面有一定的优势,更适合应用在烟厂对烟盒表面质量的检测。

Area optimization of parallel Chien search architecture for Reed-Solomon(255,239) decoder

A global optimization algorithm （GOA） for parallel Chien search circuit in Reed-Solomon （RS） （255,239） decoder is presented. By finding out the common modulo 2 additions within groups of Galois field （GF） multipliers and pre-computing the common items, the GOA can reduce the number of XOR gates efficiently and thus reduce the circuit area. Different from other local optimization algorithms, the GOA is a global one. When there are more than one maximum matches at a time, the best match choice in the GOA has the least impact on the final result by only choosing the pair with the smallest relational value instead of choosing a pair randomly. The results show that the area of parallel Chien search circuits can be reduced by 51% compared to the direct implementation when the group-based GOA is used for GF multipliers and by 26% if applying the GOA to GF multipliers separately. This optimization scheme can be widely used in general parallel architecture in which many GF multipliers are involved.

A Total Dose Radiation Hardened PDSOI CMOS 3-Line to 8-Line Decoder

The first domestic total dose hardened 2μm partially depleted silicon-on-insulator （PDSOI） CMOS 3-line to 8- line decoder fabricated in SIMOX is demonstrated. The radiation performance is characterized by transistor threshold voltage shifts,circuit static leakage currents,and I-V curves as a function of total dose up to 3× 10^5rad（Si）. The worst case threshold voltage shifts of the front channels are less than 20mV for nMOS transistors at 3 × 10^5rad（Si） and follow-up irradiation and less than 70mV for the pMOS transistors. Furthermore, no significant radiation induced leakage currents and functional degeneration are observed.

An Asynchronous Implementation of Add-Compare-Select Processor for Communication Systems

A novel asynchronous ACS(add-compare-select) processor for Viterbi decoder is described.It is controlled by local handshake signals instead of the globe clock.The circuits of asynchronous adder unit,asynchronous comparator unit,and asynchronous selector unit are proposed.A full-custom design of asynchronous 4-bit ACS processor is fabricated in CSMC-HJ 0.6μm CMOS 2P2M mixed-mode process.At a supply voltage of 5V,when it operates at 20MHz,the power consumption is 75.5mW.The processor has no dynamic power consumption when it awaits an opportunity in sleep mode.The results of performance test of asynchronous 4-bit ACS processor show that the average case response time 19.18ns is only 82% of the worst-case response time 23.37ns.Compared with the synchronous 4-bit ACS processor in power consumption and performance by simulation,it reveals that the asynchronous ACS processor has some advantages than the synchronous one.

A Denoiser for Correlated Noise Channel Decoding: Gated-Neural Network

This letter proposes a sliced-gated-convolutional neural network with belief propagation(SGCNN-BP) architecture for decoding long codes under correlated noise. The basic idea of SGCNNBP is using Neural Networks(NN) to transform the correlated noise into white noise, setting up the optimal condition for a standard BP decoder that takes the output from the NN. A gate-controlled neuron is used to regulate information flow and an optional operation—slicing is adopted to reduce parameters and lower training complexity. Simulation results show that SGCNN-BP has much better performance(with the largest gap being 5dB improvement) than a single BP decoder and achieves a nearly 1dB improvement compared to Fully Convolutional Networks(FCN).

A Video Captioning Method by Semantic Topic-Guided Generation

In the video captioning methods based on an encoder-decoder,limited visual features are extracted by an encoder,and a natural sentence of the video content is generated using a decoder.However,this kind ofmethod is dependent on a single video input source and few visual labels,and there is a problem with semantic alignment between video contents and generated natural sentences,which are not suitable for accurately comprehending and describing the video contents.To address this issue,this paper proposes a video captioning method by semantic topic-guided generation.First,a 3D convolutional neural network is utilized to extract the spatiotemporal features of videos during the encoding.Then,the semantic topics of video data are extracted using the visual labels retrieved from similar video data.In the decoding,a decoder is constructed by combining a novel Enhance-TopK sampling algorithm with a Generative Pre-trained Transformer-2 deep neural network,which decreases the influence of“deviation”in the semantic mapping process between videos and texts by jointly decoding a baseline and semantic topics of video contents.During this process,the designed Enhance-TopK sampling algorithm can alleviate a long-tail problem by dynamically adjusting the probability distribution of the predicted words.Finally,the experiments are conducted on two publicly used Microsoft Research Video Description andMicrosoft Research-Video to Text datasets.The experimental results demonstrate that the proposed method outperforms several state-of-art approaches.Specifically,the performance indicators Bilingual Evaluation Understudy,Metric for Evaluation of Translation with Explicit Ordering,Recall Oriented Understudy for Gisting Evaluation-longest common subsequence,and Consensus-based Image Description Evaluation of the proposed method are improved by 1.2%,0.1%,0.3%,and 2.4% on the Microsoft Research Video Description dataset,and 0.1%,1.0%,0.1%,and 2.8% on the Microsoft Research-Video to Text dataset,respectively,compared with the existing video captioning methods.As a result,the proposed method can generate video captioning that is more closely aligned with human natural language expression habits.

Improved Segmented Belief Propagation List Decoding for Polar Codes with Bit-Flipping

Belief propagation list(BPL) decoding for polar codes has attracted more attention due to its inherent parallel nature. However, a large gap still exists with CRC-aided SCL(CA-SCL) decoding.In this work, an improved segmented belief propagation list decoding based on bit flipping(SBPL-BF) is proposed. On the one hand, the proposed algorithm makes use of the cooperative characteristic in BPL decoding such that the codeword is decoded in different BP decoders. Based on this characteristic, the unreliable bits for flipping could be split into multiple subblocks and could be flipped in different decoders simultaneously. On the other hand, a more flexible and effective processing strategy for the priori information of the unfrozen bits that do not need to be flipped is designed to improve the decoding convergence. In addition, this is the first proposal in BPL decoding which jointly optimizes the bit flipping of the information bits and the code bits. In particular, for bit flipping of the code bits, a H-matrix aided bit-flipping algorithm is designed to enhance the accuracy in identifying erroneous code bits. The simulation results show that the proposed algorithm significantly improves the errorcorrection performance of BPL decoding for medium and long codes. It is more than 0.25 d B better than the state-of-the-art BPL decoding at a block error rate(BLER) of 10^(-5), and outperforms CA-SCL decoding in the low signal-to-noise(SNR) region for(1024, 0.5)polar codes.

A highly reliable encoding and decoding communication framework based on semantic information

Increasing research has focused on semantic communication,the goal of which is to convey accurately the meaning instead of transmitting symbols from the sender to the receiver.In this paper,we design a novel encoding and decoding semantic communication framework,which adopts the semantic information and the contextual correlations between items to optimize the performance of a communication system over various channels.On the sender side,the average semantic loss caused by the wrong detection is defined,and a semantic source encoding strategy is developed to minimize the average semantic loss.To further improve communication reliability,a decoding strategy that utilizes the semantic and the context information to recover messages is proposed in the receiver.Extensive simulation results validate the superior performance of our strategies over state-of-the-art semantic coding and decoding policies on different communication channels.

基于信道短化和圆球译码的水声通信迭代序列检测

The demand for high-data-rate underwater acoustic communications(UACs)in marine development is increasing;however,severe multipaths make demodulation a challenge.The decision feedback equalizer(DFE)is one of the most popular equalizers in UAC;however,it is not the optimal algorithm.Although maximum likelihood sequence estimation(MLSE)is the optimal algorithm,its complexity increases exponentially with the number of channel taps,making it challenging to apply to UAC.Therefore,this paper proposes a complexity-reduced MLSE to improve the bit error rate(BER)performance in multipath channels.In the proposed algorithm,the original channel is first shortened using a channel-shortening method,and several dominant channel taps are selected for MLSE.Subsequently,sphere decoding(SD)is performed in the following MLSE.Iterations are applied to eliminate inter-symbol interference caused by weak channel taps.The simulation and sea experiment demonstrate the superiority of the proposed algorithm.The simulation results show that channel shortening combined with SD can drastically reduce computational complexity,and iterative SD performs better than DFE based on recursive least squares(RLS-DFE),DFE based on improved proportionate normalized least mean squares(IPNLMS-DFE),and channel estimation-based DFE(CE-DFE).Moreover,the sea experimental results at Zhairuoshan Island in Zhoushan show that the proposed receiver scheme has improved BER performance over RLSDFE,IPNLMS-DFE,and CE-DFE.Compared with the RLS-DFE,the BER,after five iterations,is reduced from 0.0076 to 0.0037 in the 8–12 k Hz band and from 0.1516 to 0.1145 in the 13–17 k Hz band at a distance of 2000 m.Thus,the proposed algorithm makes it possible to apply MLSE in UAC in practical scenarios.

Low complexity iterative differential decoding algorithm for multiband UWB communication systems

Due to not requiring channel state information （CSI） at both the transmitter and the receiver, noncoherent ultra-wideband （UWB） incurs a performance penalty of approximately 3 dB in the required signal to noise ratio （SNR） compared to the coherent case. To overcome the gap, an effective differential encoding and decoding scheme for multiband UWB systems is proposed. The proposed scheme employs the parallel concatenation of two recursive differential unitary space-frequency encoders at the transmitter. At the receiver, two component decoders iteratively decode information bits by interchanging soft metric values between each other. To reduce the computation complexity, a decoding algorithm which only uses transition probability to calculate the log likelihood ratios （LLRs） for the decoded bits is given. Simulation results show that the proposed scheme can dramatically outperform the conventional differential and even coherent detection at high SNR with a few iterations.

The"Decoding Zhonghua"International Conference on Dialogue among Civilisations Held in Beijing

The"Decoding Zhonghua"International Conference on Dialogue among Civilisations,hosted by China International Public Relations Association,China Ethnic News and Academy of Contemporary China and World Studies was held in Beijing on January 17th.With the theme"Pursing Harmonious Coexistence of Civilisations through Dialogue".