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Optical microfiber-based refractometer, which relies on modal interference operating near the dispersion turning point (DTP), has attracted a high level of research interest. The ultrahigh sensitivity can be achieved by mitigating the group effective refractive index difference between the modes in the coupling. Through modifying the microfiber diameter, the group effective refractive index difference can be modified to zero which is defined as DTP. However, all the DTP-based sensors report low fabrication tolerance and narrow operation bandwidth of probing wavelength for the high-sensitivity region where the refractive index (RI) sensitivities higher than 104 nm/RIU can be achieved, which extremely limits the practical applications. To address the challenge, we manipulate and optimize the mode dispersion by using a tapered specially-designed single stress-applying fiber (SSAF). By introducing the stress-applying part (SAP) into the cladding, the wavelength sensitivity of the group effective RI difference can be significantly reduced through the flexibly engineerable control over the material index. In this way, the operation bandwidth is dramatically broadened for 500 nm in comparison with that of conventional microfiber with the same diameter based on DTP, indicating a broad range of optional probing wavelength and high fabrication tolerance. We experimentally verify the ultrahigh sensitivity and broadband operation around the DTP using an SSAF-based microfiber with a waist diameter of about 2.3 μm. The maximum RI sensitivity of 30563 nm/RIU is obtained.

We propose a compact assemble-easy cost-effective vector bending sensor consisting of a short section of single stress-applying fiber (SSAF) spliced between two standard single-mode fibers (SMFs). The SSAF is specially designed by introducing a stress-applying part (SAP) into the cladding to achieve the asymmetrical geometry. Because of the mode field mismatch between the SMF and the SSAF, the cladding mode is effectively excited, which is sensitive to the bending and has orientation-dependence. Due to the large effective refractive index difference between the core and cladding mode, a compact dual-mode interferometer can be achieved with a thousands-micron-long structure. When the fiber is bent, the mode fields and the effective refractive indices will be modified, and accordingly, the direction-dependent interference spectral shift is observed. Owing to the well-designed SSAF structure, the cost-effective vector bending sensor can be easily realized by a simple sandwich structure without extra complex alignment procedures or fiber grating inscription. We experimentally verify the vector bending property. The maximum sensitivity of 2.04 nm/m−1 is obtained, and the experimental results match well with the simulations. Our proposed device is compact, easy to assemble, and cost-effective, which is promising in the field of vector bending measurement.

The radial pulse is considered as a fundamental indicator for the diagnosis of cardiovascular diseases, which can guide therapeutic decisions in complex clinical situations. We designed and fabricated a real-time low-cost wearable pulse monitoring system based on a graphene-coated fiber to detect the radial pulse. The graphene-coated fiber, acting as sensing component, had excellent conductance property and high sensitivity. However, the amplitude of the pulse is subtle, always masked by noise. As a consequence, a diminutive signal conditioning unit integrated with a low-noise instrument amplifier was developed to enhance the signals. Furtherly, we proposed a newly multistage filter method to attenuate the noise synchronously. The signal noise ratio was improved by at least 27 dB, compared with a traditional measurement method. Meanwhile, a graphical user interface was developed to monitor the pulses in real time, making the monitor convenient and timely. The heart rate, which is of clinical significance, was measured accurately. The data were being stored in the PC terminal simultaneously to obtain an overall heart health and fitness level. The superior sensing performance and economic fabrication process of the pulse monitoring system strengthen our confidence in the application of the smart fabrics in wearable sensing and home health care fields.

Hybrid electric vehicles (HEVs) provide a promising alternative to conventional engine-powered vehicles, with less emission and better fuel economy. This study proposes a hierarchical control for a power-split HEV over a driving cycle, featuring pre-trip optimisation and en-route speed adaption. Constraints including vehicle powertrain boundaries, road gradients, and speed limits, are taken into consideration. In the first stage, the HEV operating conditions, including the optimal vehicle SOC, speed profiles, and total driving time, are generated for the entire trip before departure. Based on the pre-trip results, the second stage adapts the vehicle speed for a short horizon when driving, while taking the safety spacing to the preceding vehicle into consideration, which acts as an indicator of actual traffic conditions and guarantees safe driving. Both optimisations are conducted under the distance domain for realising localisation in the optimal speed profile due to frequent changes in traffic conditions. An estimation of distribution algorithm is used to run the simulation so that the feasibility, robustness, and effectiveness of the proposed hierarchical control can be demonstrated.

The performance of routing strategies on complex networks can be characterized by two measurements, i.e., the traffic capacity and the average packets travel time. By efficiently synthesizing the degree and the dynamic queue length of nodes, we propose the global hybrid (GH) routing strategy. It can achieve higher traffic capacity, as well as shorter average packets, travel time compared with the state-of-the-art global dynamic (GD) routing strategy and efficient routing (ER) strategy. Moreover, such superiority can be maintained through the queue length information and the corresponding routing paths are updated periodically. The simulation results show that our GH routing strategy can provide the same traffic capacity as the GD routing strategy does, which is more than twice as high as the ER strategy. At the same time, the average packets travel time of the GH routing strategy is more than 20% smaller than that of the GD routing strategy. It is worth noted that longer updating delay makes our GH routing strategy have a greater decline in the average packets travel time. With the updating delay equals 100, the decline can be up to 40%. To illustrate the practicability of our GH routing strategy, we also applied it to a scale-free network-based data center network. The simulation results reveal that it is practical, effective, and can be used in real scenarios to improve network performance.

We present an ultrasensitive enhanced fabrication-tolerance refractometer utilizing the polarimetric interference of a tapered PANDA-air-hole fiber (PAHF). To obtain high birefringence and unique group birefringence, the PAHF is specially designed by introducing double air holes into the cladding. Ultrahigh sensitivity can be achieved by reducing the group birefringence difference to zero, defined as birefringent dispersion turning point (BDTP). By modifying the diameter of PAHF, the birefringent dispersion can be effectively manipulated to reduce the group birefringence difference. In this way, the workable diameter range for realizing the ultrahigh sensitivity is twice as large as that of conventional microfibers. Additionally, the ultrasensitive wavelength band is dramatically expanded by at least 600 nm, enabling a compact structure and a flexible fiber-length design. Due to the tunable dispersion optimization, the distinctive properties of ultrahigh sensitivity, enhanced fabrication tolerance, and broadband operation can be achieved. We experimentally verified the ultrahigh refractive index sensitivity of 47223 nm/RIU around the BDTP, and the experimental results matched well with the simulations.

We demonstrate an ultrasensitive broadband refractometer based on tapered single-solid-hole fiber operating near the dispersion turning point. Compared with SMF-based microfiber, the operation bandwidth of probing wavelength for ultrahigh sensitivities is broadened by 500 nm, © 2020 The Author(s)

We propose a twice cross correlation method to improve Brillouin frequency shift estimation performance in BOTDR. Compared with conventional fitting methods, the processing time is reduced by over 10 times while maintaining high accuracy. © 2020 The Author(s)

We propose and experimentally demonstrate a novel ultracompact silicon polarization rotator based on equivalent asymmetric waveguide cross section in only single-step etching procedure for densely integrated on-chip mode-division multiplexing system. In the conventional mode hybridization scheme, the asymmetric waveguide cross section is employed to excite the hybridized modes to realize high performance polarization rotator with compact footprint and high polarization extinction ratio. However, the fabrication complexity severely restricts the potential application of asymmetric waveguide cross section. We use inverse-designed photonic-crystal-like subwavelength structure to realize an equivalent asymmetric waveguide cross section, which can be fabricated in only single-step etching process. Besides, a theory-assisted inverse design method based on a manually-set initial pattern is employed to optimize the device to improve design efficiency and device perform. The fabricated device exhibited high performance with a compact footprint of only 1.2 × 7.2 µm2, high extinction ratio (> 19 dB) and low insertion loss (< 0.7 dB) from 1530 to 1590 nm.

Two channels Photoplethysmography are acquired by pulse oximeter. The scheme concludes multichannel signals, the conditional sum of cascaded adaptive filters and singular spectrum analysis, and peak selection proposed for monitoring heart rate in extensive exercise.

A comprehensive loop parameters designing method and an auxiliary pulling module for an optical phase-locked loop system were discussed. The experiment successfully demonstrated a pull-in frequency range as wide as 2.4GHz and a frequency accuracy of 2MHz.

A precise radial artery pulse monitor system integrated the graphene-coated fiber was designed and fabricated. The device is low-powered and wearable while ensuring the accuracy of the pulse signals by means of real-time signal processing.

This paper proposes a distributed control algorithm for determining the optimal power allocation of EV parking lots. A conventional central controller can be eliminated in order to reduce computational burden, remove single point of failures and improve interoperability. The proposed algorithm can iteratively share and update power mismatch with other parking lots to achieve optimal power allocation. Power mismatch as a shared information does not include any private information; thus, the privacy of the whole system will be improved. In addition, the proposed distributed method is evaluated through two case studies using software simulations.

As the number of smart meters/sensors increases to more than hundreds of thousands, it is rather intuitive that the state-of-the-art centralized information processing architecture will no longer be sustainable under such a big data explosion. Hence, an innovative data management system is urgently needed to facilitate the real-world deployment of a future residential distribution system. In this paper, we investigate a radically different approach through distributed software agents to translate the legacy centralized data storage and processing scheme to a completely distributed cyber-physical architecture. We further substantiate the proposed distributed data storage and processing framework on a proof-of-concept testbed using a cluster of low-cost and credit-card-sized single-board computers. Finally, we evaluate the proposed distributed framework and proof-of-concept testbed with a comprehensive set of performance measures.

In this chapter, we will explore the challenges and opportunities of grid integration of electric and hybrid electric vehicles in cyber-physical-social systems (CPSS). Transportation systems that move people, goods, and services in societies worldwide pose unprecedented environmental, economic, and social challenges, particularly with the growing urgency to conserve energy, cut back on carbon emissions and pollution, avoid crashes, and relieve congestion. Advances in intelligent transportation systems and Smart Grid offer great promise to address these challenges and have the potential to revolutionize future transportation systems. In the last decade, the government around the world has spurred efforts to boost the utilization of transportation electrification technologies because of their low-pollution emissions, energy independence, and high fuel economy. An ever-increasing number of electric and hybrid electric vehicles will radically change the traditional view of the power industry, transportation industry, social environment, and business world. Research on grid integration of electric and hybrid electric vehicles typically addresses topics at the vehicle-grid boundary, such as peak load impacts and agent-based charging control. While researchers around the world are making significant advances in these areas, there is very little work addressing the coupled cyber-physical-social effects of electric and hybrid electric vehicle charging with the mobility-focused, transportation ecosystem to meet the dynamic needs of a changing society. It is important to recognize that today's critical infrastructure is an interdependent network of networks. A single network consists of millions of subnetworks and individual agents. The “tie point” is critical to its reliability, cost-effectiveness, and resiliency. As such “tie points,” the emerging deployment of electric and hybrid electric vehicle charging facilities would complicate the understanding and design of interdependent critical infrastructure systems. For example, as a transportation tool and electricity carrier, electric and hybrid electric vehicle can be charged at any charging facility and at any time, which brings more spatial and temporal uncertainty to the power grid's load forecast. The retail electricity price and parking fee may also have an impact on customer behavior, eventually leading to a change in traffic flow. Besides engineering considerations, the placement of electric and hybrid electric vehicle charging stations are constrained by applicable local policy and regulation, financial incentives, and public interests. We will discuss the state of the art of grid integration of electric and hybrid electric vehicles in a CPSS environment. Moreover, we will present a future perspective to enable the dramatic increase of electrified vehicles, and ultimately lead to (1) reduced fossil fuel consumption; (2) reduced carbon emissions and pollution; (3) increased customer satisfaction; (4) increased reliability and efficiency for moving people and goods; (5) improved efficiency of intelligent transportation systems; (6) accelerated adoption of Smart Grid technologies; and (7) increased use of infrastructure capacity.

This paper addresses a two-stage framework for the economic operation of a microgrid-like electric vehicle (EV) parking deck with on-site renewable energy generation (roof-top photovoltaic panel). This microgrid-like EV parking deck is a localized grouping of distributed generation (solar), energy storage (EV batteries), and load (EV charging load). Although EV parking decks can enable greater adoption of renewable energy sources by scheduling charging loads to coincide with periods of strong sun, the inherent intermittency of renewable energy resources and variable EV parking behaviors complicates the economic operation. In this paper, the proposed first stage of this framework provides the parking deck operators with a stochastic approach for dealing with the uncertainty of solar energy so as to make an optimal price decision (marginal electricity sale price and parking fee rebate) at the day-ahead time scale. The second stage introduces a model predictive control-based operation strategy of EV charging dealing with the uncertainty of parking behaviors within the real-time operation. Case studies demonstrate the better performance of the proposed framework, offering an effective day-ahead marginal electricity price for tomorrow’s operation and increasing the microgrid-like EV parking deck’s revenue during the real-time operation.

This paper explores the game-theory-based market clearing mechanisms for an open and transactive distribution grid. Driven by the Smart Grid paradigm shift, hundreds of thousands of distributed electricity prosumers have the potential to participate into the residential electricity market by managing their own generators and loads. Therefore, there is an urgent need to establish an efficient and effective market framework for plug-and-play of a tremendous amount of highly distributed and scalable electricity prosumers. In this paper, the novel game-theory-based methodologies and algorithms are utilized to mimic the dynamic behaviors of these distributed electricity prosumers. The concept of Distribution Locational Marginal Price (DLMP) is also adopted to provide tempo-spatial price signals to all market participants. A suite of case studies are conducted to evaluate and validate the proposed electricity market clearing mechanisms.

This paper presents a software framework of smart management systems of plug-in electric vehicle (PEV) charging services. The proposed management system aims at the economic operation of a micro grid-like public charging facility with multiple on-site renewable energy generations (e.g., rooftop photovoltaic panels). The overall objective is to maximize the charging facility's net revenue by optimizing the day-ahead energy schedule and by controlling the real-time PEV charging process. The user-friendly graphical interface is developed to facilitate the information-directed operations for the charging facility operator. The proposed system can be extended and tailored to economic operations of other microgrid-like systems.

In this paper, we present a proof-of-concept demonstration for the electrification of transportation, aiming to create great opportunities for integrating research and education. Multiple unmanned ground electric vehicles are emulated using Lego Mindstorms EV3 education kits. We implement traffic logic (e.g., path tracking algorithm), voice and gesture control, and collision avoidance functionality into each vehicle. Every vehicle is able to communicate with each other and self-navigate on a 240-sqft platform using local measurement (e.g., touch sensor, color sensor, and infrared sensor). Multiple vehicles are used to simulate the real-world traffic scenarios. Two demo-scale plug-in and wireless charging stations are also built up to mimic the dynamic charging for electric vehicles. Meanwhile, each vehicle can wirelessly communicate with the centralized transportation coordinator to find the best charging spot depending on location, user preference, and real-time pricing. In addition, each vehicle is equipped with an on-board battery monitoring system to measure the real-time battery state (e.g., current, voltage, and temperature) and estimate the battery state-of-charge (SOC). The user-friendly graphical interface is developed in Labview for each component of proof-of-concept demonstration. Most importantly, this demonstration is fully compatible with a variety of general purpose engineering software (e.g., Matlab, and Labview), and creates great opportunities for integrating research and education in Smart Grid and intelligent transportation systems.