近日，埃及通讯与信息技术部（MCIT）发布了2019年信息与通信技术（ICT）发展年鉴，对过去一年ICT领域的有关数据、重要事件、国际合作及未来计划进行了系统梳理。 年鉴中显示，埃及ICT产业在2019年实现快速增长，全年创造产值934亿埃镑（约合60亿美元），较2018年增长16.6%；占GDP比重由2018年的3.5%上升至4%。该领域全年新增初创型企业约1500家，中小企业投资增长23%。在吸引ICT初创企业投资方面，埃及在中东和北非地区排名第2。 人员培训方面，2019年共有约1.3万人接受专业培训（2018年为4000人），计划到2020年培训人数增长至2万人，2021年底增至2.5万人。 基础设施方面，过去一年埃及融资约16亿美元用于提高网络运行速度。2019年全国平均网速达到20兆/秒（2018年6月仅为5.7兆/秒）；成立“国家信息通讯技术服务质量控制与监测中心”，对移动运营商服务质量进行定期评估。 数字转型方面，打造“数字城市”，推动塞得港（Port Said）完成公共服务数字化转型；建设“数字政府”公共服务平台（涉及国有财产管理、交通信息、司法公证、法律诉讼、卫生健康、住房登记、农业管理等多方面），提高政府透明度和管理效率。 启动人工智能战略，主要包括：建设人工智能应用中心，探索如何利用人工智能技术满足国家发展目标（主要目标领域包括卫生、农业、环境、水资源、经济规划与预测、语言处理）；参加国际人工智能会议，与外国政府机构和私营企业合作开展能力建设和应用开发活动；组建国家人工智能理事会，负责制定国家人工智能发展战略；向非盟和阿盟分别提交建立人工智能工作组的建议，推动形成共同立场并代表本地区参加国际组织有关活动。 创新创业培育方面，依托技术创新创业中心（TIEC），通过与国内外机构（包括华为）合作，组织一系列创新创业竞赛、培训和企业对接活动。 国际合作方面，MCIT组织埃及机构和企业参加国际会议、论坛、研讨会；与非洲、阿拉伯国家，以及欧盟（及其成员国）、美国、俄罗斯、日本、韩国、印度等国家和国际电信联盟（ITU）、世界知识产权组织、UNESCO、OECD等国际组织建立了合作机制。 2019年对华合作方面，埃及参加了4月在北京举行的第二届“一带一路国际合作论坛”及相关活动，期间MCIT与工业和信息化部签署合作谅解备忘录（为期5年），就加强投资、市场、技术开发等达成一致意见；5月，工信部、中国信息与通讯技术研究院、中国电子技术标准化研究所派团访问埃及；8月，MCIT赴中国参加“金砖国家未来网络创新论坛”，期间访问了中国电信和哈尔滨工业大学，就加强人才培训交换了意见。 此外，埃及信息技术产业发展署（ITIDA）与九次方大数据签署合作协议（在埃及成立分公司），与科大讯飞就人工智能培训、语音识别软件开发等内容签署合作协议；埃及电信与中国电信、华为、中兴也签署了多项合作文件。 年鉴长达339页，分为“电信与邮政服务基础设施”“数字转型”“人员能力建设”“数字融合”“创新创业”“产业发展”和“国际关系”7个大项（32个小项），是了解埃及ICT领域发展成就及动向的综合性参考材料。
工业互联网是新一代信息技术与工业经济深度融合的全新工业生态、关键基础设施和新型应用模式，推动生产力跃升，促进生产关系变革。在国家政策指导和有关各方共同努力下，我国工业互联网正由理念倡导加速走向落地深耕阶段，对经济社会发展的带动作用日益彰显。科学研判工业互联网产业经济发展态势，对企业运营、行业发展和政府决策具有重要的参考意义。在工业互联网产业联盟多家成员单位的支持下，在充分征求各方专家意见的基础上，中国信通院组织编写了《报告》，并希望通过《报告》的发布，更好地支撑政府决策、引导产业界加快工业互联网创新发展，为“两个强国”建设和经济高质量发展贡献力量。目录如下： 一、工业互联网产业体系及核算方法 （一）产业体系 （二）核算框架 （三）核算方法 二、工业互联网产业经济总体态势 （一）工业互联网产业经济发展迅猛 （二）工业互联网产业经济结构优化 三、工业互联网核心产业发展情况 （一）工业数字化装备产业快速增长 （二）工业互联自动化产业平稳推进 （三）工业互联网网络产业高速发展 （四）工业互联网安全产业潜力巨大 （五）工业互联网平台与工业软件产业前景广阔 四、工业互联网融合带动的经济影响 （一）工业互联网加速向一二三产渗透 （二）工业互联网对第二产业带动作用最显著 五、未来发展建议 附件一：核算方法说明 附件二：数据来源说明 附件三：缺失数据处理模型
Groningen University has been granted a € 600,000 subsidy to develop a fully automated workforce incorporating small drones to help greenhouse growers control photosynthesis and pests better and make more precise yield predictions. The so-called Smart Industry subsidy from the Dutch organisation for scientific research NWO, was granted to the SMART-AGENTS project led by Dr. Kerstin Bunte. NWO facilitates excellent, curiosity-driven disciplinary, interdisciplinary and multidisciplinary research. In the Smart Industry program, scientists and companies work together on projects at the intersection of big data, smart industry and the creative industry. The aim is to conduct research into developing symbiotic networks of collaborative agents (drones) as a solution for logistics and agriculture/horticulture. Challenges in autonomous solutions According to the team of the SMART-AGENTS project, agricultural, horticultural and logistical (warehouses) environments are facing challenges when it comes to autonomous solutions. The sheer scale of greenhouse production for instance, makes pest control, monitoring photosynthetic efficiency and making accurate yield predictions difficult, costly and hazardous. They say there is a continuously growing demand for ‘mobile agents’ that can perform various tasks. In particular multi-platform Cyber-Physical-Systems (CPSs), in which various agents contribute to a network that solves multiple goals simultaneously. Network of small autonomous drones For this, the researchers want to develop a fully automated system with a collaborative network of small autonomous drones. With their equipment and capabilities, these drones contribute to solving key tasks. The researchers focus on the development of biomimetic micro sensors and the integration of multiple sensors for robust navigation and control in dynamic ‘living’ environments. Also if these are dark and hazy.
Agricultural robotics is nowadays a complex, challenging, and exciting research topic. Some agricultural environments present harsh conditions to robotics operability. In the case of steep slope vineyards, there are several challenges: terrain irregularities, characteristics of illumination, and inaccuracy/unavailability of signals emitted by the Global Navigation Satellite System (GNSS). Under these conditions, robotics navigation becomes a challenging task. To perform these tasks safely and accurately, the extraction of reliable features or landmarks from the surrounding environment is crucial. This work intends to solve this issue, performing accurate, cheap, and fast landmark extraction in steep slope vineyard context. To do so, we used a single camera and an Edge Tensor Processing Unit (TPU) provided by Google’s USB Accelerator as a small, high-performance, and low power unit suitable for image classification, object detection, and semantic segmentation. The proposed approach performs object detection using Deep Learning (DL)-based Neural Network (NN) models on this device to detect vine trunks. To train the models, Transfer Learning (TL) is used on several pre-trained versions of MobileNet V1 and MobileNet V2. A benchmark between the two models and the different pre-trained versions is performed. The models are pre-trained in a built in-house dataset, that is publicly available containing 336 different images with approximately 1,600 annotated vine trunks. There are considered two vineyards, one using camera images with the conventional infrared filter and others with an infrablue filter. Results show that this configuration allows a fast vine trunk detection, with MobileNet V2 being the most accurate retrained detector, achieving an overall Average Precision of 52.98%;. We briefly compare the proposed approach with the state-of-the-art Tiny YOLO-V3 running on Jetson TX2, showing the outperformance of the adopted system in this work. Additionally, it is also shown that the proposed detectors are suitable for the Localization and Mapping problems.
Current harvesting robots have limited performance, due to the unstructured and dynamic nature of both the target crops and their environment. Efforts to date focus on improving sensing and robotic systems. This paper presents a parallel approach, to 'design' the crop and its environment to best fit the robot, similar to robotic integration in industrial robot deployments. A systematic methodology to select and modify the crop 'design' (crop and environment) to improve robotic harvesting is presented. We define crop-dependent robotic features for successful harvesting (e.g., visibility, reachability), from which associated crop features are identified (e.g., crop density, internode length). Methods to influence the crop features are derived (e.g., cultivation practices, climate control) along with a methodological approach to evaluate the proposed designs. A case study of crop 'design' for robotic sweet pepper harvesting is presented, with statistical analyses of influential parameters. Since comparison of the multitude of existing crops and possible modifications is impossible due to complexity and time limitations, a sequential field experimental setup is planned. Experiments over three years, 10 cultivars, two climate control conditions, two cultivation techniques and two artificial illumination types were performed. Results showed how modifying the crop effects the crops characteristics influencing robotic harvesting by increased visibility and reachability. The systematic crop 'design' approach also led to robot design recommendations. The presented 'engineering' the crop 'design' framework highlights the importance of close synergy between crop and robot design achieved by strong collaboration between robotic and agronomy experts resulting in improved robotic harvesting performance.
[学术文献] Hybrid Signal-Processing Method Based on Neural Network for Prediction of NO3, K, Ca, and Mg Ions in Hydroponic Solutions Using an Array of Ion-Selective Electrodes 进入全文
In closed hydroponics, fast and continuous measurement of individual nutrient concentrations is necessary to improve water- and nutrient-use efficiencies and crop production. Ion-selective electrodes (ISEs) could be one of the most attractive tools for hydroponic applications. However, signal drifts over time and interferences from other ions present in hydroponic solutions make it difficult to use the ISEs in hydroponic solutions. In this study, hybrid signal processing combining a two-point normalization (TPN) method for the effective compensation of the drifts and a back propagation artificial neural network (ANN) algorithm for the interpretation of the interferences was developed. In addition, the ANN-based approach for the prediction of Mg concentration which had no feasible ISE was conducted by interpreting the signals from a sensor array consisting of electrical conductivity (EC) and ion-selective electrodes (NO3, K, and Ca). From the application test using 8 samples from real greenhouses, the hybrid method based on a combination of the TPN and ANN methods showed relatively low root mean square errors of 47.2, 13.2, and 18.9 mg.L-1 with coefficients of variation (CVs) below 10% for NO3, K, and Ca, respectively, compared to those obtained by separate use of the two methods. Furthermore, the Mg prediction results with a root mean square error (RMSE) of 14.6 mg.L-1 over the range of 10-60 mg.L-1 showed potential as an approximate diagnostic tool to measure Mg in hydroponic solutions. These results demonstrate that the hybrid method can improve the accuracy and feasibility of ISEs in hydroponic applications.