Vulnerable objects detection for autonomous driving: A review

Object detection performed by Autonomous Vehicles (AV)s is a crucial operation that comes ahead of various autonomous driving tasks, such as object tracking, trajectories estimation, and collision avoidance. Dynamic road elements (pedestrians, cyclists, vehicles) impose a greater challenge due to their continuously changing location and behaviour. This paper presents a comprehensive review of the state-of-the-art object detection technologies focusing on both the sensory systems and algorithms used. It begins with a brief introduction on the autonomous driving operations and challenges. Then, different sensory systems employed on existing AVs are elaborated while illustrating their advantages, limitations and applications. Also, sensory systems employed by different research are reviewed. Moreover, due to the significant role Deep Neural Networks (DNN)s are playing in object detection tasks, different DNN-based networks are also highlighted. Afterwards, previous research on dynamic objects detection performed by AVs are reviewed in tabular forms. Finally, a conclusion summarizes the outcomes of the review and suggests future work towards the development of vehicles with higher automation levels.     

Carbon-doped porous hollow alpha-Fe2O3 microtubules controlled by absorbent cotton bio-template to detect acetic acid vapor

To overcome the scarcity and costliness of noble metal dopants, carbon (C) doping, as a low-cost alternative, was achieved by absorbent cotton when the alpha (α)-Fe₂O₃ microtubules were synthesized with a facile hydrothermal method and necessary calcination. The absorbent cotton not only provided carbon source but controlled the microtubular morphology of α-Fe₂O₃. Meanwhile, for comparison, a pure α-Fe₂O₃ nanoparticles sample was also prepared without using absorbent cotton. Numerous techniques were employed to characterize the element composition and microstructures. The consequences demonstrated that carbon had been successfully incorporated into the porous hollow α-Fe₂O₃ microtubules composed of many nanoparticles. Compared with the α-Fe₂O₃ nanoparticles, the carbon-doped α-Fe₂O₃ microtubules possessed the unique morphology, large specific surface area and pore size, and abundant oxygen vacancies (O_V). To reveal the function of the carbon-doped α-Fe₂O₃ microtubules and α-Fe₂O₃ nanoparticles, two chemical gas sensors were manufactured and researched systematically. Forasmuch as those advantages mentioned above, the sensor based on the carbon-doped α-Fe₂O₃ microtubules exhibited better gas sensing properties to acetic acid vapor at a lower optimal operating temperature of 260 °C, such as higher response value, shorter response and recovery time, good repeatability, and stability. And thence the carbon-doped α-Fe₂O₃ microtubules product could be considered as an excellent acetic acid vapor sensor in the future. In addition, the possible grown mechanism and gas sensing mechanism of the carbon-doped α-Fe₂O₃ microtubules were discussed in detail. The work provides a new strategy to improve the gas sensing performance of α-Fe₂O₃ material.     

Smart “Sticky Note” for strain and temperature sensing using few-layer graphene from exfoliation in red spinach solution

The synthesis of few-layer graphene from graphite typically uses N, N methyl pyrrolidone (NMP) or dimethylformamide (DMF) due to the strong affinity of both solvents for graphite. However, NMP and DMF are known as carcinogens and a long-time exposure to these substances may subject users to potential risk of major health issue later. Therefore, a replacement with dispersing solvent that is not only harmless but also able to exfoliate graphite at an excellent concentration yield must be outlined for a sustainable mass-production of graphene. In this work, we have successfully exfoliated graphite to few-layer graphene with a recorded yield concentration of up to 0.75°mg/ml (2.5°h) just by using extracted red spinach/water mixture as an exfoliating medium. The prepared graphene was found to possess less structural defect (I_D/I_G: 0.5) and high C/O ratio (6.8) and can be used further as an electrical conductive ink for smart “Sticky Note” sensor. The fabricated device was able to detect strain and temperature with gauge factor and temperature coefficient resistance of 23.5 and −32.14 $\times$ 10^-4°Ω/°;C, respectively. We believe that this study would be useful for the preparation of environmental-friendly graphene that is not only strain and thermally sensitive but also producible at low -cost.     

Preparation and temperature-resistance characteristics of novel dense SiAlCN ceramics

The compact green bodies, prepared via a novel solid-liquid mixing method of precursors, were successfully pyrolyzed to obtain the dense bulk SiAlCN ceramics at 1000 °C. It can be seen from their SEM that they have uniform and dense microstructure, indicating that this method can be used to prepare bulk ceramics. In order to verify that they can be used as sensor heads, their temperature-resistance characteristics and repeatability were tested. The results show that the conductive mechanism belongs to Arrhenius's Tailed-State and Extended-State in the temperature range of 500–650 °C and 650–930 °C, respectively. And it shows that SiAlCN ceramics can be used as the sensor heads for high-temperature sensors.     

基于改进正弦余弦算法的无线传感器节点部署优化

为了提高无线传感器网络（WSN）的性能，提出了一种基于改进正弦余弦算法（ESCA）的节点部署优化方法。首先，引入双曲正弦调节因子和动态余弦波权重系数，以平衡算法的全局探索与局部开发能力；然后，提出了一种基于拉普拉斯和高斯分布的变异策略，避免算法陷入局部最优。对于基准函数的优化实验结果表明，ESCA相比引力搜索算法、鲸鱼优化算法、基本正弦余弦算法（SCA）及其改进算法具有更高的收敛精度和收敛速度。最后，将ESCA应用于WSN节点部署优化，结果表明其优化覆盖率相比改进粒子群优化算法、外推人工蜂群算法、改进灰狼优化算法和自适应混沌量子粒子群算法分别提高了1.55个百分点、7.72个百分点、2.99个百分点和7.63个百分点，用更少节点便可达到相同目标精度。     

低开销负载均衡的改进型TORA协议

针对无线传感器网络拓扑快速变化、链路易拥塞断开，存在路由开销大、负载不均衡、网络生命周期短等问题，提出低开销负载均衡的改进型TORA（TORA-p）。通过定义网络变化率参数，自适应调整互联网封装协议（IMEP）中OBM包的最大重发次数，减少不必要的路由维护开销；同时，结合MAC层信息，定义链路可用性优化选路方式，自动平衡网络负载，避免关键路径和节点负载过重而过早死亡和断开。使用truetime2.0平台设定特定的仿真环境，对TORA-p算法的性能进行了详细比较分析，结果表明：TORA-p均衡了网络负载，提升了网络生命周期，同时也减少了路由开销和延时，展现出了较好的数据传输能力。     

WSNs中规避攻击者的源位置隐私保护路由协议

为了保证无线传感器网络（Wireless Sensor Networks，WSNs）中的源节点位置隐私安全，同时实现安全性能和网络能耗的均衡，提出了WSNs中规避攻击者的源节点位置隐私路由协议，该协议假设节点具有检测攻击者的能力，通过发出危险警告消息使路由路径上的节点采取路由改变策略，使攻击者无法回溯到源节点，延长了源节点保持位置隐私的安全时间。理论分析和仿真实验表明，该协议在消耗较少的通信开销的情况下保证了源节点位置的高度隐私。     

Design optimization of semiconductor piezoresistors with Schottky diode contacts

A modeling theory is developed to predict the performance of piezoresistors which incorporate Schottky diode electrical contacts. This new theory allows the design of high performance gauges which can be fabricated using Non-Lithographically-Based Microfabrication (NLBM) techniques. These semiconductor piezoresistors can be designed in customizable sizes and fabricated in parallel in order to integrate position sensing into MEMS flexural positioners. Customizable sensing for nanopositioning platforms will enable advances in a range of nano-scale fabrication and metrology applications. A semiconductor piezoresistor with Schottky diode contacts was fabricated and attached to a titanium flexure. This device is shown to match predicted electrical performance within about 8% and to show a gauge factor of 116, within 2% of the predicted value. Optimized performance limits for Schottky diode semiconductor piezoresistors are identified to be about 127 dB full noise dynamic range for a quarter bridge over a 10 kHz sensor bandwidth on a 600 μm width titanium flexure, making them ideal for sensing on meso-/micro-scale flexural positioners. Methods are suggested for achieving the performance limits indicated above and the impact of these methods on the sensor dynamic range are studied.     

Fault handling in large water networks with online dictionary learning

Fault detection and isolation in water distribution networks is an active topic due to the nonlinearities of flow propagation and recent increases in data availability due to sensor deployment. Here, we propose an efficient two-step data driven alternative: first, we perform sensor placement taking the network topology into account; second, we use incoming sensor data to build a network model through online dictionary learning. Online learning is fast and allows tackling large networks as it processes small batches of signals at a time. This brings the benefit of continuous integration of new data into the existing network model, either in the beginning for training or in production when new data samples are gathered. The proposed algorithms show good performance in our simulations on both small and large-scale networks.     

Mining urban data (part A)

Modern cities are flooded with data. New information sources like public transport and wearable devices provide opportunities for novel applications that will improve citizens׳ quality of life. From a data science perspective, data emerging from smart cities give rise to a lot of challenges that constitute a new inter-disciplinary field of research. This article introduces the first part of a special issue on the topic ‘Mining Urban Data’ published in the journal Information Systems.