Internet of things(IoT)is used in various fields such as smart cities,smart home,manufacturing industries,and healthcare.Its application in healthcare has many advantages and disadvantages.One of its most common proto...Internet of things(IoT)is used in various fields such as smart cities,smart home,manufacturing industries,and healthcare.Its application in healthcare has many advantages and disadvantages.One of its most common protocols is Message Queue Telemetry Transport(MQTT).MQTT protocol works as a publisher/subscriber which is suitable for IoT devices with limited power.One of the drawbacks of MQTT is that it is easy to manipulate.The default security provided by MQTT during user authentication,through username and password,does not provide any type of data encryption,to ensure confidentiality or integrity.This paper focuses on the security of IoT healthcare over the MQTT protocol,through the implementation of lightweight generating and key exchange algorithms.The research contribution of this paper is twofold.The first one is to implement a lightweight generating and key exchange algorithm for MQTT protocol,with the key length of 64 bits through OMNET++simulation.The second one is to obtain lower power consumption from some existing algorithms.Moreover,the power consumption through using the proposed algorithm is 0.78%,1.16%,and 1.93% of power for 256 bits,512 bits,and 1024 respectively.On the other hand,the power consumption without using the encryption is 0.25%,0.51%,and 1.03% for the same three payloads length.展开更多
This paper proposes SW-YOLO(StarNet Weighted-Conv YOLO),a lightweight human pose estimation network for edge devices.Current mainstream pose estimation algorithms are computationally inefficient and have poor feature ...This paper proposes SW-YOLO(StarNet Weighted-Conv YOLO),a lightweight human pose estimation network for edge devices.Current mainstream pose estimation algorithms are computationally inefficient and have poor feature capture capabilities for complex poses and occlusion scenarios.This work introduces a lightweight backbone architecture that integrates WConv(Weighted Convolution)and StarNet modules to address these issues.Leveraging StarNet’s superior capabilities in multi-level feature fusion and long-range dependency modeling,this architecture enhances the model’s spatial perception of human joint structures and contextual information integration.These improvements significantly enhance robustness in complex scenarios involving occlusion and deformation.Additionally,the introduction of WConv convolution operations,based on weight recalibration and receptive field optimization,dynamically adjusts feature importance during convolution.This reduces redundant computations while maintaining or enhancing feature representation capabilities at an extremely low computational cost.Consequently,SW-YOLO substantially reduces model complexity and inference latency while preserving high accuracy,significantly outperforming existing lightweight networks.展开更多
近年来,由于基于图像识别的粉尘检测方法不存在安装和检测范围局限性等问题,因此得到了充分重视和发展,但现有方法实时性和准确性仍需提升。为此,提出了一种基于改进YOLOv5算法的粉尘图像检测方法。首先,对现有YOLOv5算法主干网络以及N...近年来,由于基于图像识别的粉尘检测方法不存在安装和检测范围局限性等问题,因此得到了充分重视和发展,但现有方法实时性和准确性仍需提升。为此,提出了一种基于改进YOLOv5算法的粉尘图像检测方法。首先,对现有YOLOv5算法主干网络以及Neck网络进行改进,将轻量化网络GhostNet替换原有主干网络,以降低网络参数,再输出3个特征层;然后,针对主干网络输出的3个特征层,施加注意力机制CA,增加网络精度;最后,设计消融实验和对比实验验证改进算法的有效性。结果表明:改进算法的平均检测精度mAP(mean Average Precision)能达到92.11%,检测速度达37帧/s。展开更多
文摘Internet of things(IoT)is used in various fields such as smart cities,smart home,manufacturing industries,and healthcare.Its application in healthcare has many advantages and disadvantages.One of its most common protocols is Message Queue Telemetry Transport(MQTT).MQTT protocol works as a publisher/subscriber which is suitable for IoT devices with limited power.One of the drawbacks of MQTT is that it is easy to manipulate.The default security provided by MQTT during user authentication,through username and password,does not provide any type of data encryption,to ensure confidentiality or integrity.This paper focuses on the security of IoT healthcare over the MQTT protocol,through the implementation of lightweight generating and key exchange algorithms.The research contribution of this paper is twofold.The first one is to implement a lightweight generating and key exchange algorithm for MQTT protocol,with the key length of 64 bits through OMNET++simulation.The second one is to obtain lower power consumption from some existing algorithms.Moreover,the power consumption through using the proposed algorithm is 0.78%,1.16%,and 1.93% of power for 256 bits,512 bits,and 1024 respectively.On the other hand,the power consumption without using the encryption is 0.25%,0.51%,and 1.03% for the same three payloads length.
文摘This paper proposes SW-YOLO(StarNet Weighted-Conv YOLO),a lightweight human pose estimation network for edge devices.Current mainstream pose estimation algorithms are computationally inefficient and have poor feature capture capabilities for complex poses and occlusion scenarios.This work introduces a lightweight backbone architecture that integrates WConv(Weighted Convolution)and StarNet modules to address these issues.Leveraging StarNet’s superior capabilities in multi-level feature fusion and long-range dependency modeling,this architecture enhances the model’s spatial perception of human joint structures and contextual information integration.These improvements significantly enhance robustness in complex scenarios involving occlusion and deformation.Additionally,the introduction of WConv convolution operations,based on weight recalibration and receptive field optimization,dynamically adjusts feature importance during convolution.This reduces redundant computations while maintaining or enhancing feature representation capabilities at an extremely low computational cost.Consequently,SW-YOLO substantially reduces model complexity and inference latency while preserving high accuracy,significantly outperforming existing lightweight networks.
文摘近年来,由于基于图像识别的粉尘检测方法不存在安装和检测范围局限性等问题,因此得到了充分重视和发展,但现有方法实时性和准确性仍需提升。为此,提出了一种基于改进YOLOv5算法的粉尘图像检测方法。首先,对现有YOLOv5算法主干网络以及Neck网络进行改进,将轻量化网络GhostNet替换原有主干网络,以降低网络参数,再输出3个特征层;然后,针对主干网络输出的3个特征层,施加注意力机制CA,增加网络精度;最后,设计消融实验和对比实验验证改进算法的有效性。结果表明:改进算法的平均检测精度mAP(mean Average Precision)能达到92.11%,检测速度达37帧/s。
