人类活动识别（Har）是一个正在进行的研究主题。它具有医疗支持，体育，健身，社交网络，人机界面，高级护理，娱乐，监控以及列表的应用。传统上，电脑视觉方法用于Har，它具有许多问题，例如保密或隐私，环境因素的影响，流动性，更高的运行成本，闭塞等。最近出现了使用传感器，尤其是惯性传感器的新趋势。使用传感器数据作为传统计算机视觉算法的替代方案存在若干优点。在文献中记录了计算机视觉算法的许多局限，包括利用传感器数据的深度神经网络（DNN）和机器学习（ML）方法的研究。我们使用智能手机的惯性传感器数据检查并分析了人类活动识别的不同机器学习和深度学习方法。为了确定哪种方法最适合此应用。

Driving through pothole infested roads is a life hazard and economically costly. The experience is even worse for motorists using the pothole filled road for the first time. Pothole-filled road networks have been associated with severe traffic jam especially during peak times of the day. Besides not being fuel consumption friendly and being time wasting, traffic jams often lead to increased carbon emissions as well as noise pollution. Moreover, the risk of fatal accidents has also been strongly associated with potholes among other road network factors. Discovering potholes prior to using a particular road is therefore of significant importance. This work presents a successful demonstration of sensor-based pothole mapping agent that captures both the pothole's depth as well as its location coordinates, parameters that are then used to generate a pothole map for the agent's entire journey. The map can thus be shared with all motorists intending to use the same route.

Recurrent neural networks are capable of learning the dynamics of an unknown nonlinear system purely from input-output measurements. However, the resulting models do not provide any stability guarantees on the input-output mapping. In this work, we represent a recurrent neural network as a linear time-invariant system with nonlinear disturbances. By introducing constraints on the parameters, we can guarantee finite gain stability and incremental finite gain stability. We apply this identification method to learn the motion of a four-degrees-of-freedom ship that is moving in open water and compare it against other purely learning-based approaches with unconstrained parameters. Our analysis shows that the constrained recurrent neural network has a lower prediction accuracy on the test set, but it achieves comparable results on an out-of-distribution set and respects stability conditions.

Accurate recognition of food items along with quality assessment is of paramount importance in the agricultural industry. Such automated systems can speed up the wheel of the food processing sector and save tons of manual labor. In this connection, the recent advancement of Deep learning-based architectures has introduced a wide variety of solutions offering remarkable performance in several classification tasks. In this work, we have exploited the concept of Densely Connected Convolutional Neural Networks (DenseNets) for fruit quality assessment. The feature propagation towards the deeper layers has enabled the network to tackle the vanishing gradient problems and ensured the reuse of features to learn meaningful insights. Evaluating on a dataset of 19,526 images containing six fruits having three quality grades for each, the proposed pipeline achieved a remarkable accuracy of 99.67%. The robustness of the model was further tested for fruit classification and quality assessment tasks where the model produced a similar performance, which makes it suitable for real-life applications.

Generative models have been very successful over the years and have received significant attention for synthetic data generation. As deep learning models are getting more and more complex, they require large amounts of data to perform accurately. In medical image analysis, such generative models play a crucial role as the available data is limited due to challenges related to data privacy, lack of data diversity, or uneven data distributions. In this paper, we present a method to generate brain tumor MRI images using generative adversarial networks. We have utilized StyleGAN2 with ADA methodology to generate high-quality brain MRI with tumors while using a significantly smaller amount of training data when compared to the existing approaches. We use three pre-trained models for transfer learning. Results demonstrate that the proposed method can learn the distributions of brain tumors. Furthermore, the model can generate high-quality synthetic brain MRI with a tumor that can limit the small sample size issues. The approach can addresses the limited data availability by generating realistic-looking brain MRI with tumors. The code is available at: ~\url{https://github.com/rizwanqureshi123/Brain-Tumor-Synthetic-Data}.

In this study, we propose a lung nodule detection scheme which fully incorporates the clinic workflow of radiologists. Particularly, we exploit Bi-Directional Maximum intensity projection (MIP) images of various thicknesses (i.e., 3, 5 and 10mm) along with a 3D patch of CT scan, consisting of 10 adjacent slices to feed into self-distillation-based Multi-Encoders Network (MEDS-Net). The proposed architecture first condenses 3D patch input to three channels by using a dense block which consists of dense units which effectively examine the nodule presence from 2D axial slices. This condensed information, along with the forward and backward MIP images, is fed to three different encoders to learn the most meaningful representation, which is forwarded into the decoded block at various levels. At the decoder block, we employ a self-distillation mechanism by connecting the distillation block, which contains five lung nodule detectors. It helps to expedite the convergence and improves the learning ability of the proposed architecture. Finally, the proposed scheme reduces the false positives by complementing the main detector with auxiliary detectors. The proposed scheme has been rigorously evaluated on 888 scans of LUNA16 dataset and obtained a CPM score of 93.6\%. The results demonstrate that incorporating of bi-direction MIP images enables MEDS-Net to effectively distinguish nodules from surroundings which help to achieve the sensitivity of 91.5% and 92.8% with false positives rate of 0.25 and 0.5 per scan, respectively.

创伤后应激障碍（PTSD）是一种长期衰弱的精神状况，是针对灾难性生活事件（例如军事战斗，性侵犯和自然灾害）而发展的。 PTSD的特征是过去的创伤事件，侵入性思想，噩梦，过度维护和睡眠障碍的闪回，所有这些都会影响一个人的生活，并导致相当大的社会，职业和人际关系障碍。 PTSD的诊断是由医学专业人员使用精神障碍诊断和统计手册（DSM）中定义的PTSD症状的自我评估问卷进行的。在本文中，这是我们第一次收集，注释并为公共发行准备了一个新的视频数据库，用于自动PTSD诊断，在野生数据集中称为PTSD。该数据库在采集条件下表现出“自然”和巨大的差异，面部表达，照明，聚焦，分辨率，年龄，性别，种族，遮挡和背景。除了描述数据集集合的详细信息外，我们还提供了评估野生数据集中PTSD的基于计算机视觉和机器学习方法的基准。此外，我们建议并评估基于深度学习的PTSD检测方法。提出的方法显示出非常有希望的结果。有兴趣的研究人员可以从：http：//www.lissi.fr/ptsd-dataset/下载PTSD-in-wild数据集的副本

事件传感是生物启发的飞行指导和控制系统中的主要组成部分。我们探讨了事件摄像机在腹侧着陆期间与表面进行时间接触（TTC）的用法。这是通过估计差异（逆TTC）的差异来实现的，即径向光流的速率，是从着陆期间产生的事件流。我们的核心贡献是针对基于事件的差异估计的一种新颖的对比度最大化公式，以及一种分支和结合算法，可准确地最大化对比度并找到最佳的差异值。进行GPU加速度以加快全球算法。另一个贡献是一个新的数据集，其中包含来自腹面着陆的真实事件流，该数据集用于测试和基准我们的方法。由于全局优化，与其他启发式差异估计器或基于事件的光流方法相比，我们的算法更有能力恢复真正的分歧。随着GPU加速，我们的方法还可以实现竞争性的运行时间。

使用合成数据训练的深层模型需要适应域的适应性，以弥合模拟环境和目标环境之间的差距。最新的域适应方法通常需要来自目标域的足够数量（未标记的）数据。但是，当目标域是极端环境（例如空间）时，这种需求很难满足。在本文中，我们的目标问题是接近卫星姿势估计，从实际的会合任务中获取卫星的图像是昂贵的。我们证明，事件传感提供了一种有希望的解决方案，可以在Stark照明差异下从模拟到目标域。我们的主要贡献是一种基于事件的卫星姿势估计技术，纯粹是对合成事件数据进行培训的，该数据具有基本数据增强，以提高针对实际（嘈杂）事件传感器的鲁棒性。基础我们的方法是一个具有仔细校准的地面真相的新型数据集，其中包括通过在剧烈的照明条件下在实验室中模拟卫星集合场景获得的真实事件数据。数据集上的结果表明，我们基于事件的卫星姿势估计方法仅在没有适应的情况下接受合成数据训练，可以有效地概括为目标域。

基于学习的MRI翻译涉及一个合成模型，该模型将源对比度映射到目标对比图像上。多机构合作是跨广泛数据集培训合​​成模型的关键，但是集中式培训涉及隐私风险。联合学习（FL）是一个协作框架，相反，采用分散培训，以避免共享成像数据并减轻隐私问题。但是，成像数据的分布中固有的异质性可能会损害训练的模型。一方面，即使对于具有固定源目标配置的常见翻译任务，图像分布的隐式变化也很明显。相反，当规定具有不同源目标配置的不同翻译任务时，在站点内和跨站点内会出现明确的变化。为了提高针对域转移的可靠性，我们在这里介绍了MRI合成的第一种个性化FL方法（PFLSYNTH）。 PFLSYNTH基于配备映射器的对抗模型，该映射器会产生特定于单个站点和源目标对比的潜伏期。它利用新颖的个性化阻滞了基于这些潜伏期的发电机跨发电机图的统计和加权。为了进一步促进位点特异性，在发电机的下游层上采用了部分模型聚集，而上游层则保留在本地。因此，PFLSYNTH可以培训统一的合成模型，该模型可以可靠地跨越多个站点和翻译任务。在多站点数据集上进行的全面实验清楚地证明了PFLSHNTH在多对比度MRI合成中对先前联合方法的增强性能。