With the rapid development of low altitude economic industry,low altitude adhoc network technology has been getting more and more intensive attention.In the adhoc network protocol designed in this paper,the convolutio...With the rapid development of low altitude economic industry,low altitude adhoc network technology has been getting more and more intensive attention.In the adhoc network protocol designed in this paper,the convolutional code used is(3,1,7),and the design of a low power Viterbi decoder adapted to multi-rate variations is proposed.In the traditional Viterbi decoding method,the high complexity of path metric(PM)accumulation and Euclidean distance computation leads to the problems of low efficiency and large storage resources in the decoder.In this paper,an improved add compare select(ACS)algorithm,a generalized formula for branch metric(BM)based on Manhattan distance,and a method to reduce the accumulated PM for different Viterbi decoders are put forward.A simulation environment based on Vivado and Matlab to verify the accuracy and effectiveness of the proposed Viterbi decoder is also established.The experimental results show that the total power consumption is reduced by 15.58%while the decoding accuracy of the Viterbi decoder is guaranteed,which meets the design requirements of a low power Viterbi decoder.展开更多
Skin cancer is the most prevalent cancer globally,primarily due to extensive exposure to Ultraviolet(UV)radiation.Early identification of skin cancer enhances the likelihood of effective treatment,as delays may lead t...Skin cancer is the most prevalent cancer globally,primarily due to extensive exposure to Ultraviolet(UV)radiation.Early identification of skin cancer enhances the likelihood of effective treatment,as delays may lead to severe tumor advancement.This study proposes a novel hybrid deep learning strategy to address the complex issue of skin cancer diagnosis,with an architecture that integrates a Vision Transformer,a bespoke convolutional neural network(CNN),and an Xception module.They were evaluated using two benchmark datasets,HAM10000 and Skin Cancer ISIC.On the HAM10000,the model achieves a precision of 95.46%,an accuracy of 96.74%,a recall of 96.27%,specificity of 96.00%and an F1-Score of 95.86%.It obtains an accuracy of 93.19%,a precision of 93.25%,a recall of 92.80%,a specificity of 92.89%and an F1-Score of 93.19%on the Skin Cancer ISIC dataset.The findings demonstrate that the model that was proposed is robust and trustworthy when it comes to the classification of skin lesions.In addition,the utilization of Explainable AI techniques,such as Grad-CAM visualizations,assists in highlighting the most significant lesion areas that have an impact on the decisions that are made by the model.展开更多
A modified Benes network is proposed to be used as an optimal shuffle network in worldwide interoperability for microwave access （WiMAX） low density parity check （LDPC） decoders, When the size of the input is not ...A modified Benes network is proposed to be used as an optimal shuffle network in worldwide interoperability for microwave access （WiMAX） low density parity check （LDPC） decoders, When the size of the input is not a power of two, the modified Benes network can achieve the most optimal performance. This modified Benes network is non-blocking and can perform any sorts of permutations, so it can support 19 modes specified in the WiMAX system. Furthermore, an efficient algorithm to generate the control signals for all the 2 × 2 switches in this network is derived, which can reduce the hardware complexity and overall latency of the modified Benes network. Synthesis results show that the proposed control signal generator can save 25.4% chip area and the overall network latency can be reduced by 36. 2%.展开更多
业务流程合规性检查可以帮助企业及早发现潜在问题,保证业务流程的正常运行和安全性。提出一种基于改进BERT(Bidirectional Encoder Representations from Transformers)和轻量化卷积神经网络(CNN)的业务流程合规性检查方法。首先,根据...业务流程合规性检查可以帮助企业及早发现潜在问题,保证业务流程的正常运行和安全性。提出一种基于改进BERT(Bidirectional Encoder Representations from Transformers)和轻量化卷积神经网络(CNN)的业务流程合规性检查方法。首先,根据历史事件日志中的轨迹提取轨迹前缀,构造带拟合情况标记的数据集;其次,使用融合相对上下文关系的BERT模型完成轨迹特征向量的表示;最后,使用轻量化CNN模型构建合规性检查分类器,完成在线业务流程合规性检查,有效提高合规性检查的准确率。在5个真实事件日志数据集上进行实验,结果表明,该方法相比Word2Vec+CNN模型、Transformer模型、BERT分类模型在准确率方面有较大提升,且与传统BERT+CNN相比,所提方法的准确率最高可提升2.61%。展开更多
基金Supported by the National Natural Science Foundation of China(No.62103257).
文摘With the rapid development of low altitude economic industry,low altitude adhoc network technology has been getting more and more intensive attention.In the adhoc network protocol designed in this paper,the convolutional code used is(3,1,7),and the design of a low power Viterbi decoder adapted to multi-rate variations is proposed.In the traditional Viterbi decoding method,the high complexity of path metric(PM)accumulation and Euclidean distance computation leads to the problems of low efficiency and large storage resources in the decoder.In this paper,an improved add compare select(ACS)algorithm,a generalized formula for branch metric(BM)based on Manhattan distance,and a method to reduce the accumulated PM for different Viterbi decoders are put forward.A simulation environment based on Vivado and Matlab to verify the accuracy and effectiveness of the proposed Viterbi decoder is also established.The experimental results show that the total power consumption is reduced by 15.58%while the decoding accuracy of the Viterbi decoder is guaranteed,which meets the design requirements of a low power Viterbi decoder.
文摘Skin cancer is the most prevalent cancer globally,primarily due to extensive exposure to Ultraviolet(UV)radiation.Early identification of skin cancer enhances the likelihood of effective treatment,as delays may lead to severe tumor advancement.This study proposes a novel hybrid deep learning strategy to address the complex issue of skin cancer diagnosis,with an architecture that integrates a Vision Transformer,a bespoke convolutional neural network(CNN),and an Xception module.They were evaluated using two benchmark datasets,HAM10000 and Skin Cancer ISIC.On the HAM10000,the model achieves a precision of 95.46%,an accuracy of 96.74%,a recall of 96.27%,specificity of 96.00%and an F1-Score of 95.86%.It obtains an accuracy of 93.19%,a precision of 93.25%,a recall of 92.80%,a specificity of 92.89%and an F1-Score of 93.19%on the Skin Cancer ISIC dataset.The findings demonstrate that the model that was proposed is robust and trustworthy when it comes to the classification of skin lesions.In addition,the utilization of Explainable AI techniques,such as Grad-CAM visualizations,assists in highlighting the most significant lesion areas that have an impact on the decisions that are made by the model.
基金The National Natural Science Foundation of China(No.60871079)
文摘A modified Benes network is proposed to be used as an optimal shuffle network in worldwide interoperability for microwave access （WiMAX） low density parity check （LDPC） decoders, When the size of the input is not a power of two, the modified Benes network can achieve the most optimal performance. This modified Benes network is non-blocking and can perform any sorts of permutations, so it can support 19 modes specified in the WiMAX system. Furthermore, an efficient algorithm to generate the control signals for all the 2 × 2 switches in this network is derived, which can reduce the hardware complexity and overall latency of the modified Benes network. Synthesis results show that the proposed control signal generator can save 25.4% chip area and the overall network latency can be reduced by 36. 2%.
文摘业务流程合规性检查可以帮助企业及早发现潜在问题,保证业务流程的正常运行和安全性。提出一种基于改进BERT(Bidirectional Encoder Representations from Transformers)和轻量化卷积神经网络(CNN)的业务流程合规性检查方法。首先,根据历史事件日志中的轨迹提取轨迹前缀,构造带拟合情况标记的数据集;其次,使用融合相对上下文关系的BERT模型完成轨迹特征向量的表示;最后,使用轻量化CNN模型构建合规性检查分类器,完成在线业务流程合规性检查,有效提高合规性检查的准确率。在5个真实事件日志数据集上进行实验,结果表明,该方法相比Word2Vec+CNN模型、Transformer模型、BERT分类模型在准确率方面有较大提升,且与传统BERT+CNN相比,所提方法的准确率最高可提升2.61%。
