脊髓损伤通常会导致四肢瘫痪的患者限制其活动能力。轮椅对于患者来说可能是一个很好的主张，但大多数人可以手动操作，也可以借助操纵杆操作的电动机。但是，这需要使用手，使其不适合四肢瘫痪的患者。另一方面，即使受到脑损伤的人，控制眼动的运动也保留了。监视眼睛中的运动可能是为轮椅生成控制信号的有用工具。本文是通过试图控制模仿轮椅的机器人来转换从眼睛转换为有意义的信号的一种方法。总体系统具有成本效益，并使用简单的图像处理和模式识别来控制机器人。开发了一种Android应用，在实际情况下，患者的援助可以使用该应用程序，以更加完善轮椅。

目前，大多数社会机器人通过传感器与周围环境和人类相互作用，这些传感器是机器人的组成部分，这限制了传感器，人机相互作用和互换性的可用性。在许多应用中需要一种适合许多机器人的可穿戴传感器衣服。本文介绍了一个经济实惠的可穿戴传感器背心，以及带有物联网（物联网）的开源软件架构，用于社会人形机器人。背心由触摸，温度，手势，距离，视觉传感器和无线通信模块组成。 IOT功能允许机器人与人类和互联网一起与人类交互。设计的体系结构适用于任何具有通用图形处理单元（GPGPU），I2C / SPI总线，Internet连接和机器人操作系统（ROS）的任何社交机器人。此架构的模块化设计使开发人员能够轻松地添加/删除/更新复杂行为。所提出的软件架构提供IOT技术，GPGPU节点，I2C和SPI总线管理器，视听交互节点（语音到文本，文本到语音和图像理解），以及行为节点和其他节点之间的隔离。所提出的IOT解决方案包括机器人中的相关节点，RESTful Web服务和用户界面。我们使用HTTP协议作为与Internet的社会机器人双向通信的手段。开发人员可以在C，C ++和Python编程语言中轻松编辑或添加节点。我们的架构可用于为社会人形机器人设计更复杂的行为。

每当一个人听到污染的时候，比不是不是，他们思想的第一个想法是空气污染。全球最介绍和讨论的污染之一是我们水体中不可生物降解的废物引起的。在印度的情况下，河流和湖泊表面有很多塑料废物。 Ganga River是10家河流之一，占90％的塑料，最终在大海中，由于这种废物，湖泊有主要的纳拉纳斯和湖泊的主要案例。这限制了清洁水源，导致水源的主要耗尽。从2001年到2012年，在海德拉巴市，3245公顷的湖泊消散了。在新德里南部的平均水平，水将九英尺抬起。因此，清洁这些本地水体和河流至关重要。我们的宗旨是开发一种水面清洁机床，该机床部署在岸上。机器人将在途中检测垃圾斑块并收集垃圾，从而使水体清洁。该解决方案采用监测机制，以便在发现任何人被发现污染水体时提醒当局。开发了一种通过使用太阳能来为系统供电的可持续系统。计算机视觉算法用于检测水面上的垃圾。该垃圾由机器人收集，并在指定位置处置。除了清洁水体外，还借助虚拟击剑算法实施了预防措施，如果有人试图污染水处理，则会提醒当局。部署Web应用程序和移动应用程序以分别检查机器人和岸上监控的运动。这种完整的解决方案涉及水护理所需的预防性和治疗措施。

本地或无功导航对于在室内环境中运行的自主移动机器人至关重要。计算机视觉等技术需要显着的计算能力，这增加了成本。同样，使用基本方法使机器人容易受到不一致行为的影响。本文旨在开发一种机器人，通过使用机器学习来预测基于来自四个超声波传感器的距离输入的最佳障碍物避免移动，这些机器人余额，这些机器人通过战略安装在前面，前左，前右侧和背部的四个超声波传感器的距离输入机器人。底层硬件由Arduino Uno和Raspberry Pi 3b组成。首先在机器人收集的数据上培训机器学习模型。然后，Arduino不断调查传感器并计算距离值，并且在避免批判性的情况下，由Arduino制造合适的机动。在其他情况下，使用USB连接将传感器数据发送到覆盆子PI，并且机器学习模型产生最佳导航的移动，这将相应地发送到Arduino以进行驱动电机。该系统安装在2 WD机器人机箱上，并在杂乱的室内环境中进行测试，结果最令人印象深刻。

使用物理互动设备（如小鼠和键盘）阻碍了自然主义的人机相互作用，并增加了大流行期间表面接触的可能性。现有的手势识别系统不具备用户身份验证，使其不可靠。当前手势识别技术中的静态手势会引入较长的适应周期并降低用户兼容性。我们的技术非常重视用户识别和安全。我们使用有意义且相关的手势进行任务操作，从而获得更好的用户体验。本文旨在设计一个强大的，具有面部验证的手势识别系统，该系统利用图形用户界面，主要通过用户识别和授权专注于安全性。面部模型使用MTCNN和FACENET来验证用户，而我们的LSTM-CNN体系结构进行手势识别，并以五类的手势获得了95％的精度。通过我们的研究开发的原型已成功执行了上下文依赖性任务，例如保存，打印，控制视频播放器操作和退出以及无上下文的操作系统任务，例如睡眠，关闭和直观地解锁。我们的应用程序和数据集可作为开源。

尽管环境条件不利，现代农业仍在不断发展以增加产量。一种有希望的方法是“温室种植”，为耕种的植物提供了微气候，以克服不利的气候。然而，大型的温室在整个复合物中都会形成不均匀的微气候，需要高度的人类监督。我们建议部署农业机器人，以在温室中创造和维持积极的生态条件，从而降低人工成本并增加产量。该原型将包含两个主要系统，即导航系统和数据分析系统。导航系统将由Arduino控制，并且将使用ESP8266微芯片处理数据分析。用于测量温室参数的许多传感器将安装在机器人上。它将遵循预定义的路径，同时在检查点进行读数。微芯片将从传感器收集和处理数据，传输到云，并向执行器发出命令。将定期测量土壤和气候参数，例如温度，湿度，光强度，土壤水分，pH值。当参数不在指定的范围内时，农业机器人将采取纠正措施，例如打击器/加热器，开始灌溉等。如果需要外部干预，例如肥料，则将相应地指示。在大规模的温室中部署这样的农业机器人来监测和控制小气候，可以降低人工成本，同时提高生产率。尽管有初始成本，但它可以通过提供灵活性，低功耗和易于管理来提供高度投资回报，以帮助温室提高水效率，提供均匀分散和受控的阳光强度，温度和湿度。

Drowsiness on the road is a widespread problem with fatal consequences; thus, a multitude of systems and techniques have been proposed. Among existing methods, Ghoddoosian et al. utilized temporal blinking patterns to detect early signs of drowsiness, but their algorithm was tested only on a powerful desktop computer, which is not practical to apply in a moving vehicle setting. In this paper, we propose an efficient platform to run Ghoddosian's algorithm, detail the performance tests we ran to determine this platform, and explain our threshold optimization logic. After considering the Jetson Nano and Beelink (Mini PC), we concluded that the Mini PC is the most efficient and practical to run our embedded system in a vehicle. To determine this, we ran communication speed tests and evaluated total processing times for inference operations. Based on our experiments, the average total processing time to run the drowsiness detection model was 94.27 ms for Jetson Nano and 22.73 ms for the Beelink (Mini PC). Considering the portability and power efficiency of each device, along with the processing time results, the Beelink (Mini PC) was determined to be most suitable. Also, we propose a threshold optimization algorithm, which determines whether the driver is drowsy or alert based on the trade-off between the sensitivity and specificity of the drowsiness detection model. Our study will serve as a crucial next step for drowsiness detection research and its application in vehicles. Through our experiment, we have determinend a favorable platform that can run drowsiness detection algorithms in real-time and can be used as a foundation to further advance drowsiness detection research. In doing so, we have bridged the gap between an existing embedded system and its actual implementation in vehicles to bring drowsiness technology a step closer to prevalent real-life implementation.

The Age-of-Information (AoI) metric has been widely studied in the theoretical communication networks and queuing systems literature. However, experimental evaluation of its applicability to complex real-world time-sensitive systems is largely lacking. In this work, we develop, implement, and evaluate an AoI-based application layer middleware that enables the customization of WiFi networks to the needs of time-sensitive applications. By controlling the storage and flow of information in the underlying WiFi network, our middleware can: (i) prevent packet collisions; (ii) discard stale packets that are no longer useful; and (iii) dynamically prioritize the transmission of the most relevant information. To demonstrate the benefits of our middleware, we implement a mobility tracking application using a swarm of UAVs communicating with a central controller via WiFi. Our experimental results show that, when compared to WiFi-UDP/WiFi-TCP, the middleware can improve information freshness by a factor of 109x/48x and tracking accuracy by a factor of 4x/6x, respectively. Most importantly, our results also show that the performance gains of our approach increase as the system scales and/or the traffic load increases.

为了简化图书馆管理的过程，已经采用了许多技术，但其中大多数专注于库存管理。在发行和返回图书馆的发行和返回图书馆的领域，几乎没有任何自动化进展。在大学和学校中，宿舍经常忘记及时将发行的书籍返回图书馆。为了解决上述问题并确保及时提交已发行的书籍，这项工作开发了一个解决这些复杂性的书籍机器人。该机器人可以从A点到B点通勤，扫描并验证QR码和条形码。该机器人将具有一定的有效载荷能力来携带书籍。 QR码和条形码扫描将由PI摄像头，OpenCV和Raspberry Pi启用，从而使书籍交换安全。机器人的探测器操作将通过Blynk应用程序手动控制。本文着重于如何减少人类干预，并在机器人的帮助下自动化图书馆管理系统的问题。

Filming sport videos from an aerial view has always been a hard and an expensive task to achieve, especially in sports that require a wide open area for its normal development or the ones that put in danger human safety. Recently, a new solution arose for aerial filming based on the use of Unmanned Aerial Vehicles (UAVs), which is substantially cheaper than traditional aerial filming solutions that require conventional aircrafts like helicopters or complex structures for wide mobility. In this paper, we describe the design process followed for building a customized UAV suitable for sports aerial filming. The process includes the requirements definition, technical sizing and selection of mechanical, hardware and software technologies, as well as the whole integration and operation settings. One of the goals is to develop technologies allowing to build low cost UAVs and to manage them for a wide range of usage scenarios while achieving high levels of flexibility and automation. This work also shows some technical issues found during the development of the UAV as well as the solutions implemented.

In recent decades, several assistive technologies for visually impaired and blind (VIB) people have been developed to improve their ability to navigate independently and safely. At the same time, simultaneous localization and mapping (SLAM) techniques have become sufficiently robust and efficient to be adopted in the development of assistive technologies. In this paper, we first report the results of an anonymous survey conducted with VIB people to understand their experience and needs; we focus on digital assistive technologies that help them with indoor and outdoor navigation. Then, we present a literature review of assistive technologies based on SLAM. We discuss proposed approaches and indicate their pros and cons. We conclude by presenting future opportunities and challenges in this domain.

自动化驾驶系统（广告）开辟了汽车行业的新领域，为未来的运输提供了更高的效率和舒适体验的新可能性。然而，在恶劣天气条件下的自主驾驶已经存在，使自动车辆（AVS）长时间保持自主车辆（AVS）或更高的自主权。本文评估了天气在分析和统计方式中为广告传感器带来的影响和挑战，并对恶劣天气条件进行了解决方案。彻底报道了关于对每种天气的感知增强的最先进技术。外部辅助解决方案如V2X技术，当前可用的数据集，模拟器和天气腔室的实验设施中的天气条件覆盖范围明显。通过指出各种主要天气问题，自主驾驶场目前正在面临，近年来审查硬件和计算机科学解决方案，这项调查概述了在不利的天气驾驶条件方面的障碍和方向的障碍和方向。

With the advancement in computing and robotics, it is necessary to develop fluent and intuitive methods for interacting with digital systems, augmented/virtual reality (AR/VR) interfaces, and physical robotic systems. Hand motion recognition is widely used to enable these interactions. Hand configuration classification and MCP joint angle detection is important for a comprehensive reconstruction of hand motion. sEMG and other technologies have been used for the detection of hand motions. Forearm ultrasound images provide a musculoskeletal visualization that can be used to understand hand motion. Recent work has shown that these ultrasound images can be classified using machine learning to estimate discrete hand configurations. Estimating both hand configuration and MCP joint angles based on forearm ultrasound has not been addressed in the literature. In this paper, we propose a CNN based deep learning pipeline for predicting the MCP joint angles. The results for the hand configuration classification were compared by using different machine learning algorithms. SVC with different kernels, MLP, and the proposed CNN have been used to classify the ultrasound images into 11 hand configurations based on activities of daily living. Forearm ultrasound images were acquired from 6 subjects instructed to move their hands according to predefined hand configurations. Motion capture data was acquired to get the finger angles corresponding to the hand movements at different speeds. Average classification accuracy of 82.7% for the proposed CNN and over 80% for SVC for different kernels was observed on a subset of the dataset. An average RMSE of 7.35 degrees was obtained between the predicted and the true MCP joint angles. A low latency (6.25 - 9.1 Hz) pipeline has been proposed for estimating both MCP joint angles and hand configuration aimed at real-time control of human-machine interfaces.

根据世界卫生组织的数据，估计视觉障碍会影响全球约22亿人。目前，视力障碍必须依靠导航辅助工具来替代其视觉感，例如基于白色的甘蔗或GPS（全球定位系统）导航，两者都无法在室内工作。白色的甘蔗不能用于确定用户在房间内的位置，而GPS通常可以在室内失去连接，并且不提供方向信息，这两种方法都不适合室内使用。因此，这项研究试图开发3D成像解决方案，该解决方案能够通过复杂的室内环境实现非接触式导航。与以前的方法相比，该设备可以查明用户的位置和方向，同时仅需要53.1％的内存，并且处理速度更快125％。该设备还可以比以前的最新模型检测到60.2％的障碍，同时仅需要41％的内存和处理速度260％。在与人类参与者进行测试时，该设备允许与环境障碍物的碰撞减少94.5％，并允许步行速度提高48.3％，这表明我的设备可以使视力受损更安全，更快地导航。总而言之，这项研究表明了一个基于3D的导航系统，用于视力障碍。该方法可以由多种移动低功率设备（例如手机）使用，以确保所有人都可以使用这项研究。

折射率是最常见的眼睛障碍，是可更正视觉障碍的关键原因，造成了美国近80％的视觉障碍。可以使用多种方法诊断折射误差，包括主观折射，视网膜镜检查和自动磨蚀器。尽管主观折射是黄金标准，但它需要患者的合作，因此不适合婴儿，幼儿和发育迟缓的成年人。视网膜镜检查是一种客观折射方法，不需要患者的任何输入。但是，视网膜镜检查需要镜头套件和训练有素的检查员，这限制了其用于大规模筛查的使用。在这项工作中，我们通过将智能手机连接到视网膜镜和录制视网膜镜视频与患者戴着定制的纸框架来自动化自动化。我们开发了一个视频处理管道，该管道将视网膜视频视为输入，并根据我们提出的视网膜镜检查数学模型的扩展来估算净屈光度错误。我们的系统减轻了对镜头套件的需求，可以由未经培训的检查员进行。在一项185只眼睛的临床试验中，我们的灵敏度为91.0％，特异性为74.0％。此外，与主观折射测量相比，我们方法的平均绝对误差为0.75 $ \ pm $ 0.67D。我们的结果表明，我们的方法有可能用作现实世界中医疗设置中的基于视网膜镜检查的折射率筛选工具。

This paper proposes the use of an event camera as a component of a vision system that enables counting of fast-moving objects - in this case, falling corn grains. These type of cameras transmit information about the change in brightness of individual pixels and are characterised by low latency, no motion blur, correct operation in different lighting conditions, as well as very low power consumption. The proposed counting algorithm processes events in real time. The operation of the solution was demonstrated on a stand consisting of a chute with a vibrating feeder, which allowed the number of grains falling to be adjusted. The objective of the control system with a PID controller was to maintain a constant average number of falling objects. The proposed solution was subjected to a series of tests to determine the correctness of the developed method operation. On their basis, the validity of using an event camera to count small, fast-moving objects and the associated wide range of potential industrial applications can be confirmed.

自动检测飞行无人机是一个关键问题，其存在（特别是未经授权）可以造成风险的情况或损害安全性。在这里，我们设计和评估了多传感器无人机检测系统。结合常见的摄像机和麦克风传感器，我们探索了热红外摄像机的使用，指出是一种可行且有希望的解决方案，在相关文献中几乎没有解决。我们的解决方案还集成了鱼眼相机，以监视天空的更大部分，并将其他摄像机转向感兴趣的对象。传感溶液与ADS-B接收器，GPS接收器和雷达模块相辅相成，尽管由于其有限的检测范围，后者未包含在我们的最终部署中。即使此处使用的摄像机的分辨率较低，热摄像机也被证明是与摄像机一样好的可行解决方案。我们作品的另外两个新颖性是创建一个新的公共数据集的多传感器注释数据，该数据与现有的类别相比扩大了类的数量，以及对探测器性能的研究作为传感器到传感器的函数的研究目标距离。还探索了传感器融合，表明可以以这种方式使系统更强大，从而减轻对单个传感器的虚假检测

在这项工作中，我们通过混合现实（MR）应用中的视频传球来探讨自幻想的创建。我们介绍了我们的端到端系统，包括：在商业头部安装显示器（HMD）上进行自定义MR视频通行证实现，我们基于深度学习的实时egpocentric身体细分算法以及我们优化的卸载体系结构，以交流使用HMD分割服务器。为了验证这项技术，我们设计了一种身临其境的VR体验，用户必须在活跃的火山火山口中穿过狭窄的瓷砖路径。这项研究是在三个身体表示条件下进行的：虚拟手，带有颜色的全身分割的视频传递以及深度学习全身分割的视频通行。这种身临其境的经历由30名女性和28名男性进行。据我们所知，这是首次旨在评估基于视频的自我avatar的用户研究，以代表用户在MR场景中。结果表明，不同身体表示在存在方面没有显着差异，虚拟手和全身表示之间的某些实施方案中等改善。视觉质量结果表明，就整个身体感知和整体分割质量而言，深入学习算法的结果更好。我们提供了一些关于使用基于视频的自我幻想的讨论，以及对评估方法的一些思考。提出的E2E解决方案处于最新技术状态的边界，因此在达到成熟之前仍有改进的空间。但是，该溶液是新型MR分布式溶液的关键起点。

大脑电脑接口（BCI）系统通过无肌肉活动的直接测量来支持通信。需要验证大脑电脑界面系统，以严重残疾人的真实世界使用的长期研究，并必须实施其普遍传播的有效和可行的模型。最后，必须提高BCI性能的日常和时刻瞬间可靠性，以便接近自然肌肉的功能的可靠性。本次审查讨论了BCI系统的结构和功能，阐明了术语集成和进度，并且还基于用于BCI系统的侵入性记录技术的当前可用性来识别和阐述该领域的机遇。

The term ``neuromorphic'' refers to systems that are closely resembling the architecture and/or the dynamics of biological neural networks. Typical examples are novel computer chips designed to mimic the architecture of a biological brain, or sensors that get inspiration from, e.g., the visual or olfactory systems in insects and mammals to acquire information about the environment. This approach is not without ambition as it promises to enable engineered devices able to reproduce the level of performance observed in biological organisms -- the main immediate advantage being the efficient use of scarce resources, which translates into low power requirements. The emphasis on low power and energy efficiency of neuromorphic devices is a perfect match for space applications. Spacecraft -- especially miniaturized ones -- have strict energy constraints as they need to operate in an environment which is scarce with resources and extremely hostile. In this work we present an overview of early attempts made to study a neuromorphic approach in a space context at the European Space Agency's (ESA) Advanced Concepts Team (ACT).