Background: Cognitive impairment is a major health issue particularly with the increasing aging population. There are around 47.5 million dementia cases globally. Traffic air pollution issue is a chief environmental p...Background: Cognitive impairment is a major health issue particularly with the increasing aging population. There are around 47.5 million dementia cases globally. Traffic air pollution issue is a chief environmental problem principally in the mega cities such as Cairo. Methodology: In a Cross sectional and comparative research study the study subjects recruited involved 200 individuals, categorized into two research groups: 100 from Cairo’s elderly home residents and 100 from EL-Gharbaya’s elderly home residents. Results: Statistical linear regression analysis revealed that fine particulate matter, carbon monoxide, and nitric oxide have a statistically significant impact on cognitive function (p values Conclusions: Traffic related air pollutants were strongly associated with cognitive impairment within elderly population in geriatric home residents in Egypt. Regarding to statistically significant difference in concentration of traffic related air pollutants between urban and rural areas, urban areas were more polluted than rural areas.展开更多
In this paper, we propose a mechanism named modified backoff （MB） mechanism to decrease the channel idle time in IEEE 802.11 distributed coordination function （DCF）. In the noisy channel, when signal-to-noise rat...In this paper, we propose a mechanism named modified backoff （MB） mechanism to decrease the channel idle time in IEEE 802.11 distributed coordination function （DCF）. In the noisy channel, when signal-to-noise ratio （SNR） is low, applying this mechanism in DCF greatly improves the throughput and lowers the channel idle time. This paper presents an analytical model for the performance study of IEEE 802.11 MB-DCF for nonsaturated heterogeneous traffic in the presence of transmission errors. First, we introduce the MB-DCF and compare its performance to IEEE 802.11 DCF with binary exponential backoff （BEB）. The IEEE 802.11 DCF with BEB mechanism suffers from more channel idle time under low SNR. The MB-DCF ensures high throughput and low packet delay by reducing the channel idle time under the low traffic in the network. However, to the best of the authors＇ knowledge, there are no previous works that enhance the performance of the DCF under imperfect wireless channel. We show through analysis that the proposed mechanism greatly outperforms the original IEEE 802.11 DCF in the imperfect channel condition. The effectiveness of physical and link layer parameters on throughput performance is explored. We also present a throughput investigation of the heterogeneous traffic for different radio conditions.展开更多
现有目标检测算法对背景复杂下小交通标志的检测效果并不理想。为此,提出了一种基于归一化通道注意力机制YOLOv7的交通标志检测算法(YOLOv7 based on normalized channel attention mechanism,YOLOv7-NCAM)。为了使YOLOv7-NCAM模型具有...现有目标检测算法对背景复杂下小交通标志的检测效果并不理想。为此,提出了一种基于归一化通道注意力机制YOLOv7的交通标志检测算法(YOLOv7 based on normalized channel attention mechanism,YOLOv7-NCAM)。为了使YOLOv7-NCAM模型具有像素级建模能力,提高它对小目标交通标志特征的提取能力,YOLOv7-NCAM算法使用FReLU激活函数构建了DBF和CBF两种卷积层,并用它们来组建模型的Backbone模块和Neck模块;提出一种归一化通道注意力机制(normalized channel attention mechanism,NCAM)并加入Head模块中。通过与整体网络一起训练,得到归一化(batch normalization,BN)缩放因子,利用缩放因子算出各个通道的权重因子,提升网络对交通标志特征的表达能力,从而使YOLOv7-NCAM网络模型能够集中关注检测目标交通标志。通过在CCTSDB-2021交通标志检测数据集上的测试,与YOLOv7网络模型对比结果表明,YOLOv7-NCAM算法对背景复杂下小交通标志的检测各项指标均有明显提高:准确率(precision,P)达到91.5%,比原网络高出9.5个百分点;召回率(recall,R)达到85.9%,比原网络高出5.7个百分点;均值平均精度(mean average precision,mAP)达到了91.4%,比原网络高出4.7个百分点。与现有的交通标志检测算法相比,YOLOv7-NCAM算法的检测准确率也有提高,且检测速度48.3 FPS,能满足实时需求。展开更多
交通异常事件检测是智能交通系统中的关键任务,但现有目标检测算法在该领域的应用尚存在技术瓶颈,针对检测精度不足、模型对复杂场景的适应性差以及缺乏高质量的公开数据集等问题,提出了一种改进的YOLOv6模型,旨在提高交通异常事件(如...交通异常事件检测是智能交通系统中的关键任务,但现有目标检测算法在该领域的应用尚存在技术瓶颈,针对检测精度不足、模型对复杂场景的适应性差以及缺乏高质量的公开数据集等问题,提出了一种改进的YOLOv6模型,旨在提高交通异常事件(如车辆碰撞、单车冲撞和车辆起火)检测的准确性和性能。先将原YOLOv6模型中的损失函数替换为CIoU损失函数,以增强模型的定位精度,后引入CBAM注意力机制,以提高模型对关键特征的关注度,再采用自动混合精度训练策略优化训练过程,最后为了验证改进效果,通过游戏引擎Grand Theft Auto V生成数据集,并对其进行标注,涵盖3类交通异常事件。试验结果表明:1)提出的改进YOLOv6模型在交通异常事件的检测任务中可获得87.2%的平均检测精度,在各项指标上表现优异;2)召回率AR较次优模型提高2.1%,IoU阈值为0.5时,平均精度mAP高出2.6%;IoU阈值为0.5至0.95时,mAP增长3.7%;3)车辆相撞、单车相撞和车辆起火烧毁的精度分别达到79.9%、37.6%和65.6%,均优于次优模型,验证了改进方法的有效性。展开更多
文摘Background: Cognitive impairment is a major health issue particularly with the increasing aging population. There are around 47.5 million dementia cases globally. Traffic air pollution issue is a chief environmental problem principally in the mega cities such as Cairo. Methodology: In a Cross sectional and comparative research study the study subjects recruited involved 200 individuals, categorized into two research groups: 100 from Cairo’s elderly home residents and 100 from EL-Gharbaya’s elderly home residents. Results: Statistical linear regression analysis revealed that fine particulate matter, carbon monoxide, and nitric oxide have a statistically significant impact on cognitive function (p values Conclusions: Traffic related air pollutants were strongly associated with cognitive impairment within elderly population in geriatric home residents in Egypt. Regarding to statistically significant difference in concentration of traffic related air pollutants between urban and rural areas, urban areas were more polluted than rural areas.
文摘In this paper, we propose a mechanism named modified backoff （MB） mechanism to decrease the channel idle time in IEEE 802.11 distributed coordination function （DCF）. In the noisy channel, when signal-to-noise ratio （SNR） is low, applying this mechanism in DCF greatly improves the throughput and lowers the channel idle time. This paper presents an analytical model for the performance study of IEEE 802.11 MB-DCF for nonsaturated heterogeneous traffic in the presence of transmission errors. First, we introduce the MB-DCF and compare its performance to IEEE 802.11 DCF with binary exponential backoff （BEB）. The IEEE 802.11 DCF with BEB mechanism suffers from more channel idle time under low SNR. The MB-DCF ensures high throughput and low packet delay by reducing the channel idle time under the low traffic in the network. However, to the best of the authors＇ knowledge, there are no previous works that enhance the performance of the DCF under imperfect wireless channel. We show through analysis that the proposed mechanism greatly outperforms the original IEEE 802.11 DCF in the imperfect channel condition. The effectiveness of physical and link layer parameters on throughput performance is explored. We also present a throughput investigation of the heterogeneous traffic for different radio conditions.
文摘现有目标检测算法对背景复杂下小交通标志的检测效果并不理想。为此,提出了一种基于归一化通道注意力机制YOLOv7的交通标志检测算法(YOLOv7 based on normalized channel attention mechanism,YOLOv7-NCAM)。为了使YOLOv7-NCAM模型具有像素级建模能力,提高它对小目标交通标志特征的提取能力,YOLOv7-NCAM算法使用FReLU激活函数构建了DBF和CBF两种卷积层,并用它们来组建模型的Backbone模块和Neck模块;提出一种归一化通道注意力机制(normalized channel attention mechanism,NCAM)并加入Head模块中。通过与整体网络一起训练,得到归一化(batch normalization,BN)缩放因子,利用缩放因子算出各个通道的权重因子,提升网络对交通标志特征的表达能力,从而使YOLOv7-NCAM网络模型能够集中关注检测目标交通标志。通过在CCTSDB-2021交通标志检测数据集上的测试,与YOLOv7网络模型对比结果表明,YOLOv7-NCAM算法对背景复杂下小交通标志的检测各项指标均有明显提高:准确率(precision,P)达到91.5%,比原网络高出9.5个百分点;召回率(recall,R)达到85.9%,比原网络高出5.7个百分点;均值平均精度(mean average precision,mAP)达到了91.4%,比原网络高出4.7个百分点。与现有的交通标志检测算法相比,YOLOv7-NCAM算法的检测准确率也有提高,且检测速度48.3 FPS,能满足实时需求。
文摘交通异常事件检测是智能交通系统中的关键任务,但现有目标检测算法在该领域的应用尚存在技术瓶颈,针对检测精度不足、模型对复杂场景的适应性差以及缺乏高质量的公开数据集等问题,提出了一种改进的YOLOv6模型,旨在提高交通异常事件(如车辆碰撞、单车冲撞和车辆起火)检测的准确性和性能。先将原YOLOv6模型中的损失函数替换为CIoU损失函数,以增强模型的定位精度,后引入CBAM注意力机制,以提高模型对关键特征的关注度,再采用自动混合精度训练策略优化训练过程,最后为了验证改进效果,通过游戏引擎Grand Theft Auto V生成数据集,并对其进行标注,涵盖3类交通异常事件。试验结果表明:1)提出的改进YOLOv6模型在交通异常事件的检测任务中可获得87.2%的平均检测精度,在各项指标上表现优异;2)召回率AR较次优模型提高2.1%,IoU阈值为0.5时,平均精度mAP高出2.6%;IoU阈值为0.5至0.95时,mAP增长3.7%;3)车辆相撞、单车相撞和车辆起火烧毁的精度分别达到79.9%、37.6%和65.6%,均优于次优模型,验证了改进方法的有效性。
