ALBERT

Description: Localisation in wireless networks faces challenges such as high levels of signal attenuation and unknown path-loss exponents, especially in urban environments. In response to these challenges, this paper proposes solutions to localisation problems in noisy environments. A new observation model for localisation of static nodes is developed based on hybrid measurements, namely angle of arrival and received signal strength data. An approach for localisation of sensor nodes is proposed as a weighted linear least squares algorithm. The unknown path-loss exponent associated with the received signal strength is estimated jointly with the coordinates of the sensor nodes via the generalised pattern search method. The algorithm’s performance validation is conducted both theoretically and by simulation. A theoretical mean square error expression is derived, followed by the derivation of the linear Cramer-Rao bound which serves as a benchmark for the proposed location estimators. Accurate results are demonstrated with 25%–30% improvement in estimation accuracy with a weighted linear least squares algorithm as compared to linear least squares solution.

Description: A physics-based explicit mathematical model for the external voltage-dependent forward dark current in bulk heterojunction (BHJ) organic solar cells is developed by considering Shockley-Read-Hall (SRH) recombination and solving the continuity equations for both electrons and holes. An analytical model for the external voltage-dependent photocurrent in BHJ organic solar cells is also proposed by incorporating exponential photon absorption, dissociation efficiency of bound electron-hole pairs (EHPs), carrier trapping, and carrier drift and diffusion in the photon absorption layer. Modified Braun’s model is used to compute the electric field-dependent dissociation efficiency of the bound EHPs. The overall net current is calculated considering the actual solar spectrum. The mathematical models are verified by comparing the model calculations with various published experimental results. We analyze the effects of the contact properties, blend compositions, charge carrier transport properties (carrier mobility and lifetime), and cell design on the current-voltage characteristics. The power conversion efficiency of BHJ organic solar cells mostly depends on electron transport properties of the acceptor layer. The results of this paper indicate that improvement of charge carrier transport (both mobility and lifetime) and dissociation of bound EHPs in organic blend are critically important to increase the power conversion efficiency of the BHJ solar cells.

Description: The synergy of computational and physical network components leading to the Internet of Things, Data and Services has been made feasible by the use of Cyber Physical Systems (CPSs). CPS engineering promises to impact system condition monitoring for a diverse range of fields from healthcare, manufacturing, and transportation to aerospace and warfare. CPS for environment monitoring applications completely transforms human-to-human, human-to-machine and machine-to-machine interactions with the use of Internet Cloud. A recent trend is to gain assistance from mergers between virtual networking and physical actuation to reliably perform all conventional and complex sensing and communication tasks. Oil and gas pipeline monitoring provides a novel example of the benefits of CPS, providing a reliable remote monitoring platform to leverage environment, strategic and economic benefits. In this paper, we evaluate the applications and technical requirements for seamlessly integrating CPS with sensor network plane from a reliability perspective and review the strategies for communicating information between remote monitoring sites and the widely deployed sensor nodes. Related challenges and issues in network architecture design and relevant protocols are also provided with classification. This is supported by a case study on implementing reliable monitoring of oil and gas pipeline installations. Network parameters like node-discovery, node-mobility, data security, link connectivity, data aggregation, information knowledge discovery and quality of service provisioning have been reviewed.

Description: Vehicular traffic congestion is a significant problem that arises in many cities. This is due to the increasing number of vehicles that are driving on city roads of limited capacity. The vehicular congestion significantly impacts travel distance, travel time, fuel consumption and air pollution. Avoidance of traffic congestion and providing drivers with optimal paths are not trivial tasks. The key contribution of this work consists of the developed approach for dynamic calculation of optimal traffic routes. Two attributes (the average travel speed of the traffic and the roads’ length) are utilized by the proposed method to find the optimal paths. The average travel speed values can be obtained from the sensors deployed in smart cities and communicated to vehicles via the Internet of Vehicles and roadside communication units. The performance of the proposed algorithm is compared to three other algorithms: the simulated annealing weighted sum, the simulated annealing technique for order preference by similarity to the ideal solution and the Dijkstra algorithm. The weighted sum and technique for order preference by similarity to the ideal solution methods are used to formulate different attributes in the simulated annealing cost function. According to the Sheffield scenario, simulation results show that the improved simulated annealing technique for order preference by similarity to the ideal solution method improves the traffic performance in the presence of congestion by an overall average of 19.22% in terms of travel time, fuel consumption and CO2 emissions as compared to other algorithms; also, similar performance patterns were achieved for the Birmingham test scenario.

Description: Environmental perception and information processing are two key steps of active safety for vehicle reversing. Single-sensor environmental perception cannot meet the need for vehicle reversing safety due to its low reliability. In this paper, we present a novel multi-sensor environmental perception method using low-rank representation and a particle filter for vehicle reversing safety. The proposed system consists of four main steps, namely multi-sensor environmental perception, information fusion, target recognition and tracking using low-rank representation and a particle filter, and vehicle reversing speed control modules. First of all, the multi-sensor environmental perception module, based on a binocular-camera system and ultrasonic range finders, obtains the distance data for obstacles behind the vehicle when the vehicle is reversing. Secondly, the information fusion algorithm using an adaptive Kalman filter is used to process the data obtained with the multi-sensor environmental perception module, which greatly improves the robustness of the sensors. Then the framework of a particle filter and low-rank representation is used to track the main obstacles. The low-rank representation is used to optimize an objective particle template that has the smallest L-1 norm. Finally, the electronic throttle opening and automatic braking is under control of the proposed vehicle reversing control strategy prior to any potential collisions, making the reversing control safer and more reliable. The final system simulation and practical testing results demonstrate the validity of the proposed multi-sensor environmental perception method using low-rank representation and a particle filter for vehicle reversing safety.

Description: The Urban Heat Island (UHI) is the phenomenon of altered increased temperatures in urban areas compared to their rural surroundings. UHIs grow and intensify under extreme hot periods, such as during heat waves, which can affect human health and also increase the demand for energy for cooling. This study applies remote sensing and land use/land cover (LULC) data to assess the cooling effect of varying urban vegetation cover, especially during extreme warm periods, in the city of Munich, Germany. To compute the relationship between Land Surface Temperature (LST) and Land Use Land Cover (LULC), MODIS eight-day interval LST data for the months of June, July and August from 2002 to 2012 and the Corine Land Cover (CLC) database were used. Due to similarities in the behavior of surface temperature of different CLCs, some classes were reclassified and combined to form two major, rather simplified, homogenized classes: one of built-up area and one of urban vegetation. The homogenized map was merged with the MODIS eight-day interval LST data to compute the relationship between them. The results revealed that (i) the cooling effect accrued from urban vegetation tended to be non-linear; and (ii) a remarkable and stronger cooling effect in terms of LST was identified in regions where the proportion of vegetation cover was between seventy and almost eighty percent per square kilometer. The results also demonstrated that LST within urban vegetation was affected by the temperature of the surrounding built-up and that during the well-known European 2003 heat wave, suburb areas were cooler from the core of the urbanized region. This study concluded that the optimum green space for obtaining the lowest temperature is a non-linear trend. This could support urban planning strategies to facilitate appropriate applications to mitigate heat-stress in urban area.

Description: Detection of disease at an early stage is one of the biggest challenges in medicine. Different disciplines of science are working together in this regard. The goal of nanodiagnostics is to provide more accurate tools for earlier diagnosis, to reduce cost and to simplify healthcare delivery of effective and personalized medicine, especially with regard to chronic diseases (e.g., diabetes and cardiovascular diseases) that have high healthcare costs. Up-to-date results suggest that DNA-based nanobiosensors could be used effectively to provide simple, fast, cost-effective, sensitive and specific detection of some genetic, cancer, and infectious diseases. In addition, they could potentially be used as a platform to detect immunodeficiency, and neurological and other diseases. This review examines different types of DNA-based nanobiosensors, the basic principles upon which they are based and their advantages and potential in diagnosis of acute and chronic diseases. We discuss recent trends and applications of new strategies for DNA-based nanobiosensors, and emphasize the challenges in translating basic research to the clinical laboratory.

Description: This paper describes an electrostatic excited microcantilever sensor operating in static mode that is more sensitive than traditional microcantilevers. The proposed sensor comprises a simple microcantilever with electrostatic excitation ability and an optical or piezoresistive detector. Initially the microcantilever is excited by electrostatic force to near pull-in voltage. The nonlinear behavior of the microcantilever in near pull-in voltage i.e., the inverse-square relation between displacement and electrostatic force provides a novel method for force amplification. In this situation, any external load applied to the sensor will be amplified by electrostatic force leading to more displacement. We prove that the proposed microcantilever sensor can be 2 to 100 orders more sensitive compared with traditional microcantilevers sensors of the same dimensions. The results for surface stress and the free-end point force load are discussed.

Description: The assessment of oxygen saturation in arterial blood by pulse oximetry (SpO2) is based on the different light absorption spectra for oxygenated and deoxygenated hemoglobin and the analysis of photoplethysmographic (PPG) signals acquired at two wavelengths. Commercial pulse oximeters use two wavelengths in the red and infrared regions which have different pathlengths and the relationship between the PPG-derived parameters and oxygen saturation in arterial blood is determined by means of an empirical calibration. This calibration results in an inherent error, and pulse oximetry thus has an error of about 4%, which is too high for some clinical problems. We present calibration-free pulse oximetry for measurement of SpO2, based on PPG pulses of two nearby wavelengths in the infrared. By neglecting the difference between the path-lengths of the two nearby wavelengths, SpO2 can be derived from the PPG parameters with no need for calibration. In the current study we used three laser diodes of wavelengths 780, 785 and 808 nm, with narrow spectral line-width. SaO2 was calculated by using each pair of PPG signals selected from the three wavelengths. In measurements on healthy subjects, SpO2 values, obtained by the 780–808 nm wavelength pair were found to be in the normal range. The measurement of SpO2 by two nearby wavelengths in the infrared with narrow line-width enables the assessment of SpO2 without calibration.

Description: Sustainability, Vol. 10, Pages 3333: Fujian’s Industrial Eco-Efficiency: Evaluation Based on SBM and the Empirical Analysis of lnfluencing Factors Sustainability doi: 10.3390/su10093333 Authors: Xiaoqing Wang Qiuming Wu Salman Majeed Donghao Sun The coordinated development of industrialization and its ecological environment are vital antecedents to sustainable development in China. However, along with the accelerating development of industrialization in China, the contradiction between industrial development and environment preservation has turned out to be increasingly evident and inevitable. Eco-efficiency can be seen either as an indicator of environmental performance, or as a business strategy for sustainable development. Hence, industrial eco-efficiency promotion is the key factor for green industrial development. This study selects indicators relevant to resources, economy, and the environment of industrial development, and the indicators can well reflect the characteristics of industrial eco-efficiency. The SBM (Slacks-Based Measure) model overcomes the limitations of a radial model and directly accounts for input and output slacks in the efficiency measurements, with the advantage of capturing the entire aspect of inefficiency. This study evaluates the industrial eco-efficiency of nine cities in Fujian province during the period of 2006–2016, based on undesired output SBM (Slacks-Based Measure) model and also uses a Tobit regression model to analyze the influencing factors. The results show that there is a positive correlation among the economic development level, opening level, research and development (R&D) innovation, and industrial eco-efficiency in Fujian Province. However, a negative correlation was found between the industrial structure and industrial eco-efficiency in Fujian Province. Moreover, environmental regulation in Fujian Province was not found to significantly influence the industrial eco-efficiency. Hence, through the systematic analysis of industrial eco-efficiency and its influencing factors in Fujian, the study gives further insight on how policy-making can help achieve sustainable development, balancing between economic benefits and ecological improvements.