1. Introduction

The role of clinical laboratory is to measure and test patient samples. These measurements are a central part of modern clinical management; they are used by clinicians to diagnose disease states, to guide treatment course and to determine prognosis. The modern clinical laboratory uses a plethora of instruments to quantify and measure different analytes and reports results that are used by clinicians. The most important metrics that a test must possess to be used in clinical laboratory are technical accuracy and precision [1].

A test is technically accurate if it produces valid information. A precise test will produce similar results when the test is repeated multiple times. Accuracy (or rather trueness) is a measure of the

© The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited.

proximity of the test results to the true value. Precision measures reliability and reproducibility. These metrics are complementary and a good clinical test needs to be both accurate and precise [2]. Some have suggested that trueness should be used to refer to the agreement of the measurement to the true value and accuracy to encompass both trueness and precision.

error. All tests need to be checked continuously for presence of error and identifying systematic error is part of the function of a clinical laboratory. The measurement error can be regarded as a noise that can obscure the signal or true test value. In the presence of noise, drawing conclusions from the signal that may change the true value in a clinically significant manner risks jeopardizing the patient's health. As a result, the lab should strive to identify noise, minimize it or reduce its impact on patient outcomes. In this regard, systematic error is especially dangerous since it will skew the test results in a manner that cannot be corrected by repeat measurements. Unfortunately, systematic error can be very difficult to identify and/ or quantify. In this chapter we focus on approaches for identification of systematic error using

Systematic Error Detection in Laboratory Medicine http://dx.doi.org/10.5772/intechopen.72311 51

2. Systematic error detection using quality control experiments

Simply stated, the aim of quality control experiments is to determine the performance of the laboratory tests with measuring of known samples or references, that is, samples in which the true value of the analyte being tested is known. These methods are mainly set up to detect random error and check instrument precision. However, the same results can be used to detect

The laboratories can use certified reference materials to measure and identify systematic error. If the reference sample is measured with each analytical run, you would expect the results of the reference sample measurements to show a random distribution around the true value, yet if the results are consistently lower or higher than the reference value then you would suspect

For systematic error measurement, a method comparison method is needed to identify systematic error. Any systematic error found needs to be corrected using a recovery experiment

The first step in identification of systematic error is to visually inspect the quality control process. Levey-Jennings plot shows the fluctuation of reference sample measurements around the mean against time. The chart's reference lines include control limits, 2 standard deviation

The mean, standard deviation and the control limits are calculated by a replication study where the certified reference material is repeatedly measured. The repeated measurements allow for calculation of mean and standard deviation of the control sample levels. The trial limits are mean 3 standard deviations. The next step is to eliminate the replication study results that are beyond the 3 standard deviations. Then the mean and standard deviation are recalculated and the trial limits are again set. Again, results beyond the trial limits are excluded. The process continues until all the remaining results are within the trial limits. These final trial limits, mean and standard deviation are set as the reference measures for that

lines, 1 standard deviation lines and the mean reference line.

within-laboratory comparisons [5, 6].

bias and systematic error [7].

that a bias exists [2, 8].

2.1. Levey-Jennings plots

and calibration.

reference sample.

Accuracy and precision are related to a concept called measurement error: every measurement is associated with a degree of error or uncertainty. The goal in laboratory medicine is to minimize the measurement error so that it does not adversely affect the clinical decision-making process. Measurement error can never be truly nullified, but it can be decreased to a scale that is acceptable by clinicians, laboratory directors and laboratory regulatory agencies [2, 3].

Measurement errors can be random, i.e. they can be unpredictable. All measurements have random error. Random errors are due to unpredictable variations in sample, instrument, measurement process or analysis and it can be said to follow a Gaussian distribution, i.e. random error follows randomness and chance and thus laws of probability apply to random error. As the instruments get more precise the Gaussian distribution of the random error gets narrower and the random error decreases. At the same time, if we repeat an experiment or test multiple times we can average out random error from our measurements. i.e. the mean of multiple repeated measurements gets closer to the true value as the number of repeats increases. This forms the basis of reporting confidence intervals for measurements [2, 4].

Bias or systematic error is a form of measurement error that skews the results to one side. Repeating the measurements cannot eliminate bias. In other words, bias is a non-zero error which will consistently affect the results and can show a problem with the measurement process often requiring corrective action. The corrective action can be in form of calibration by introducing a correction factor or by changing components of measurement. Systematic error can be short-term or long-term, with very short-term systematic error often manifesting as random error.

Systematic error and random error have a cumulative effect on the measurement results (Figure 1). Thus, measurement error is often considered as total error with both bias and random error contributing. Laboratories often have limits for total error, bias and random

Figure 1. This figure depicts the cumulative effect of systematic error and random error. The X-axis represents the value determined and the Y-axis plots the frequency of occurrence of each value.

error. All tests need to be checked continuously for presence of error and identifying systematic error is part of the function of a clinical laboratory. The measurement error can be regarded as a noise that can obscure the signal or true test value. In the presence of noise, drawing conclusions from the signal that may change the true value in a clinically significant manner risks jeopardizing the patient's health. As a result, the lab should strive to identify noise, minimize it or reduce its impact on patient outcomes. In this regard, systematic error is especially dangerous since it will skew the test results in a manner that cannot be corrected by repeat measurements. Unfortunately, systematic error can be very difficult to identify and/ or quantify. In this chapter we focus on approaches for identification of systematic error using within-laboratory comparisons [5, 6].


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## *Edited by Subrata Kumar Dey*

Down syndrome, the most cutting-edge book in the field congenital disorders. This book features up-to-date, well referenced research and review articles on Down syndrome. Research workers, scientists, medical graduates and pediatricians will find it to be an excellent source for references and review. It is hoped that such individuals will view this book as a resource that can be consulted during all stages of their research and clinical investigations. Key features of this book are: Common diseases in Down syndrome; Molecular Genetics; Neurological Disorders; Prenatal Diagnosis and Genetic Counselling. Whilst aimed primarily at research workers on Down syndrome, we hope that the appeal of this book will extend beyond the narrow confines of academic interest and be of interest to a wider audience, especially parents, relatives and health-care providers who work with infants and children with Down syndrome.

Down Syndrome

Down Syndrome

*Edited by Subrata Kumar Dey*

Photo by Cappan / iStock


**Abstract**

Increasing amount of organic dyes in the ecosystem particularly in wastewater has propelled the search for more efficient low-cost bio adsorbents. Different techniques have been used for the treatment of wastewater containing toxic dyes such as: biological degradation, oxidation, adsorption, reverse osmosis, and membrane filtration. Among all these processes mentioned, adsorption with low cost adsorbents has been recognized as one of the cost effective and efficient techniques for treatment of industrial wastewater from organic and inorganic pollutants. Clays as material adsorbents for the removal of various toxic dyes from aqueous solutions as potential alternatives to activated carbons has recently received widespread attention because of the environmental-friendly nature of clay materials. This chapter presents a comprehensive account of the techniques used for the removal of industrial cationic and anionic dyes from water during the last 10 years with special reference to the adsorption by using low cost materials in decontamination processes. Effects of different adsorption parameters on the performance of clays as adsorbents have been also discussed. Various challenges encountered in using clay materials are highlighted and a number of future prospects for the adsorbents are proposed.

**Keywords:** adsorption, toxic dyes, Clays, wastewater treatment

#### **1. Introduction**

The treatment of industrial wastewater particularly loaded with dangerous dyes is considered among the global environmental issues and the concerns of researchers [1]. According to recent reports, over one million dyes are available commercially with an annual output of over 7x105 tons [2]. The textile industry around the world consumes approximately 104 tons of dyes annually and discharges about 100 tons of dyes into wastewater every year [3]. These dyes are highly toxic, carcinogenic, and cause dire consequences for human health and the marine system. The removal of these toxic dyes from polluted water and wastewater is highly desirable in order to meet regulatory obligations for wastewater recycling or discharging into natural environments [4]. Currently, there is several physico-chemical and

biological technologies in the use for the treatment of these polluted effluents namely, ion exchange, membrane separation, biological treatment and adsorption [5–10]. Physico-chemical processes such as ion exchange, electro dialysis and reverse osmosis are expensive, difficult to operate and require significant technologies. While in recent years, adsorption has continued to attract the attention of the researchers worldwide [11, 12] and appears to be an alternative, which has some advantages such as simple design, and ease of operation. Whereas, the biological treatment which is based on the microbial digestive metabolism, has the major drawback of the risk of microbiological contamination and a significant production of sludge, which poses problems of storage and handling [13, 14]. In the present work a comparative study between the capacities of raw, synthetic and modified clays for the removal of toxic dyes from aqueous solution has been given, with particular review of the main factors influencing the adsorption of dyes by clays such as pH of the solution, temperature and initial dye concentration on the adsorption capacities of the these clays.


*3.4. Vitro Cell Viability Assay*

A standard MTT assay was used to assess the cytotoxicity of the UCNPs, R-TiO2, and UCNPs@R-TiO2. Human Embryonic Kidney 293 cells (HEK293) were selected for the assay. The temperature of the whole incubation process was controlled at 37 ◦C. The HEK293 cells present in a 96-well plate (with 10,000 cells per well) were first cultured for 12 h. After that, the different calculated concentrations of UCNPs, R-TiO2, and UCNPs@R-TiO2 nanomaterials were added to the above plate and the mixture was further incubated for 24 h. Afterwards, 100 μL of MTT solution was added to each well. After incubation for 2 h, the sediment was retained. Then, 100 μL of DMSO (dimethyl sulfoxide) was added into each sample mentioned above and the mixture was shaken for 20 min. The absorbance at 595 nm detected by a microplate reader was used to calculate the cell viability rate.


**Ra**ff**aele Albano**

School of Engineering, University of Basilicata, 85100 Potenza, Italy<EMAIL_ADDRESS>
Received: 18 September 2019; Accepted: 31 October 2019; Published: 2 November 2019

**Abstract:** Three-dimensional (3D) reconstruction techniques are increasingly used to obtain 3D representations of buildings due to the broad range of applications for 3D city models related to sustainability, efficiency and resilience (i.e., energy demand estimation, estimation of the propagation of noise in an urban environment, routing and accessibility, flood or seismic damage assessment). With advancements in airborne laser scanning (ALS), 3D modeling of urban topography has increased its potential to automatize extraction of the characteristics of individual buildings. In 3D building modeling from light detection and ranging (LIDAR) point clouds, one major challenging issue is how to efficiently and accurately segment building regions and extract rooftop features. This study aims to present an investigation and critical comparison of two different fully automatic roof segmentation approaches for 3D building reconstruction. In particular, the paper presents and compares a cluster-based roof segmentation approach that uses (a) a fuzzy c-means clustering method refined through a density clustering and connectivity analysis, and (b) a region growing segmentation approach combined with random sample consensus (RANSAC) method. In addition, a robust 2.5D dual contouring method is utilized to deliver watertight 3D building modeling from the results of each proposed segmentation approach. The benchmark LIDAR point clouds and related reference data (generated by stereo plotting) of 58 buildings over downtown Toronto (Canada), made available to the scientific community by the International Society for Photogrammetry and Remote Sensing (ISPRS), have been used to evaluate the quality of the two proposed segmentation approaches by analysing the geometrical accuracy of the roof polygons. Moreover, the results of both approaches have been evaluated under different operating conditions against the real measurements (based on archive documentation and celerimetric surveys realized by a total station system) of a complex building located in the historical center of Matera (UNESCO world heritage site in southern Italy) that has been manually reconstructed in 3D via traditional Building Information Modeling (BIM) technique. The results demonstrate that both methods reach good performance metrics in terms of geometry accuracy. However, approach (b), based on region growing segmentation, exhibited slightly better performance but required greater computational time than the clustering-based approach.

**Keywords:** LIDAR point clouds; 3D urban model; rooftop modeling; segmentation; reconstruction; 3D building


5.2 Experiment No. 2: Movement of an autonomous vehicle

Experiment No. 2 was carried out using the simulation of an autonomous vehicle in the form of a Yamaha Grizzly off-road four-wheeler (Figure 5) driven by one person and powered by a gasoline engine. The four-wheeler was selected due to similar terrain passability characteristics as a modern UGV. The need for direct

Model of the Optimal Maneuver Route DOI: http://dx.doi.org/10.5772/intechopen.85566

#### Figure 5.

The Yamaha Grizzly four-wheeler (source: own).

control and the influence of microrelief were eliminated thanks to the four-wheeler driver. The optimal movement route model implemented in the TDSS was used to plan and cover the UVG movement route. Entering the identified enemy positions in the TDSS and the minimum necessary preparation for the movement took 3 minutes. The UGV route was designed in the passability mode "for wheeled vehicles," and its course can be seen in Figure 6. As in the BRAVO reconnaissance group, the TDSS assessed the passability possibilities of the vehicles in the field and calculated the areas concealed from the observation and fire of the identified enemy. Covering the proposed fastest and safest route (3419 m long) of the fourwheeler took 7 minutes and 25 seconds. During the movement, the vehicle at 124 cm high was not observed by the enemy. However, its engine running was audible, which could be replaced by a silent electric motor. The time calculated by the TDSS to cover this route was 7 minutes and 4 seconds.


**3. Control agents related to HABs**

Studies have been carried out to find a better way of controlling HABs. Different biotic factors have been identified to mitigate the option of HABs. Where use of different bacteria was done to tone down HABs in coastal and freshwater community [21].

## **4. Toxins**

The most toxic algal strain is *M. aeruginosa,* which constantly produces microcystins, acts harmful to aquatic organisms as well as to humans. Were microcystins have been reported as tumor-promoting [22]. Some bacteria mainly acts

antagonistic towards Cyanobacteria and are predatory bacteria, some other acts as toxin-degrading. Were Predatory helps to make an environmentally pleasant solution to available HABs. Also number of prey–predator and the mechanism of Cyanobacterial lysis, were some effectual biological control approach [23]. Few of Cyanobacteria mitigated using the secretion of *Cyanobacteriolytic* substances by some *Bacillus* Sp. typically *Bacillus cereus* and [24], *S. neyagawaensis* [25], *Streptomyces* [26], *Pseudomonas fluorescens* species [27]. *Pedobacter* Sp*.* secretes some mucus-like secretion as self-defense against *M. aeruginosa*. *Raoultella* Sp. removes *M. aeruginosa* by dissolving microbial metabolites and humic acid [28]. *Agrobacterium vitis* use Quorum sensing to lyse *M. aerugenosa* [29]. *Sandaracinobactor sibiricus, Methylobacterium zatmanii* and *Rhizobium* Sp. use lytic mechanism to remove *M. aeruginosa* [30]. Some *Bacillus* Sp. use cell-to-cell contact mechanism and production of an extracellular product to remove *Aphanizomenon flos-aquae* [31] and *M. aeruginosa* [32, 33]. Several reports suggested that algal viruses often existed at stable numbers, even when their hosts were absent [34]. With claim of that summer and spring season showing the eminent decay of cultivated viruses succeeding to four-seasons of analysis [35]. The regular seasonal study also noticed that the stumpy decay of algal virus during the wintry weather that permitted for the survival of about 126 continues days under the ice-cover in the freezing freshwater pond [36]. These agents show high specificity and high efficiency, but it gain limited attention due to high cost, as also requires upscaled level experiment confirmation [37].


**4. Conclusion**

The number of individuals visiting hospitals has grown in recent decades as a result of population expansion and changing lifestyles throughout the world. As a result, the medicare healthcare system is overburdened. On the other hand, for the old, crippled, underprivileged, or those who live far away, visiting the hospital is quite tough. As a consequence, their health may worsen to the point where they die. The Internet has now become a necessary component of our daily life. Education, finance, business, industry, entertainment, social networking, retail, and e-commerce are just a few of the uses of the internet. The IoT is the web's next big thing (IoT). As a consequence, remote healthcare solutions were created to meet the medicare health system's above-mentioned difficult challenges. The patient's vital signs are monitored by electronic sensors and communicated to the hospital server through the Internet, allowing the doctor to examine, diagnose, and prescribe the required medicine to treat the patient without the patient needing to visit the hospital.


*2.4. Statistical Analysis*

Validity of the lMU-based algorithm was assessed using a combination of Intra-class Correlation Coefficients (ICC(A,1)) for assessing absolute agreement, root mean square error (RMSE), as well as percent error between the IMU and criterion value of the motion capture data. Interpretation of ICC values were based on Koo and Li [19], with the following cut-offs: poor <0.5; moderate = 0.5–0.75; good = 0.75–0.9; and excellent >0.9. Bland-Altman plots [20] were also examined to gain an understanding of any patterns relating to bias.


**Abstract**

High purity germanium remains the material of choice for the detection of photons in the range of MeV or higher, down to the hard X-ray range. Since the operation of HPGe-based detectors is possible only at or below the liquid nitrogen temperature, their advantage is mainly the resolution, which matches the Fano factor if appropriate cooled electronic readout is used. We focus here on present-day applications of HPGe detectors, which are now broader than ever despite the recent development of room-temperature photon detectors based on binary compounds. We present in particular dark matter detectors and γ-ray trackers as examples of the recent applications of HPGe as a detecting medium. More generally, we discuss the future of γ-ray detectors and the role that the semiconductor detectors will keep with respect to alternative detection materials. This chapter is an introduction to this general topic, and the reader is encouraged to refer to research and review articles on this subject published in the past or recently.

**Keywords:** high purity germanium, traps and defects, gamma-ray detection, dark matter, nuclear spectroscopy, nuclear material monitoring


Meet the editors

Umar Zakir Abdul Hamid, PhD, is one of the pioneering researchers in the autonomous vehicle (AV) field in Malaysia. Dr. Hamid was a member of the Intelligent Drive Team of the Vehicle System Engineering Research Group at the Universiti Teknologi Malaysia. The team researches Advanced Driver Assistance Systems (ADAS) and autonomous vehicles in Malaysia; previous collaborators include PROTON Holdings Berhad and the Smart Mobility Research

Center (Tokyo). Dr. Zakir served as an AV Scientist with Moovita (Singapore and Malaysia) from 2017 to 2018, before embarking on a new journey as an AV Senior Engineer with Sensible 4 in Espoo, Finland. He is a reviewer for several prestigious journals and conferences in the automotive and robotics field.

Dr. Volkan Sezer received a BSc in Electronics and Telecommunication Engineering from Yildiz Technical University, Istanbul, Turkey, in 2005, and an MSc in Mechatronics Engineering and a PhD in Control and Automation Engineering from Istanbul Technical University, Istanbul, Turkey, in 2008 and 2012, respectively. His research covers mostly robotics and automotive technology. During his undergraduate studies, he founded and managed a stu-

dent team in alternative energy vehicles. They developed a solar car and fuel cell car and were awarded in Turkey's national competitions. He also worked on a hybrid electric vehicle development project. He designed and applied a new energy management strategy to a real hybrid electric vehicle. During his graduate studies, he continued his research on hybrid and electric vehicles together with autonomous robots. He founded and managed the first autonomous vehicle project team and developed, along with three of his students, the first fully autonomous vehicle in Turkey. Dr. Sezer developed and applied several new methods in obstacle avoidance, speed planning, and low-level control of the vehicle. After obtaining his PhD, he worked as a researcher in Singapore for the Massachusetts Institute of Technology (MIT) in its Future Urban Mobility (FM) group. His fields of interest are autonomous/semi-autonomous ground vehicles, mobile robots, control of hybrid electric vehicles, trajectory planning, obstacle/collision avoidance, active safety in road vehicles, machine learning, control of hybrid electric vehicles, and energy efficiency.

Bin Li, PhD, is currently working with Aptiv PLC, USA, as Lead Algorithm Engineer, focusing on system architecture and algorithm development and verification of motion planning and control of autonomous vehicles. He received a PhD in Mechanical Engineering from Shanghai Jiao Tong University, Shanghai, China, in 2010. Dr. Li has more than 15 years' research experience in vehicle dynamics and control, electric vehicles, active safety, and

autonomous driving. He has published more than 50 papers and chapters. He was a researcher on active safety control for commercial vehicles at Concordia University, a research engineer on mobile robotic control at McGill University, Montreal, Quebec, and a research fellow on next-generation electric vehicle at the University of Waterloo, Ontario, Canada. Dr. Li has been an active organizer for SAE World Congress and ASME conferences since 2015.

Yanjun Huang is a postdoctoral fellow at the Department of Mechanical and Mechatronics Engineering at the University of Waterloo, Ontario, Canada, where he received his PhD in 2016. His research interest is mainly on vehicle holistic control in terms of safety, energy savings, and intelligence, including vehicle dynamics and control, HEV/EV optimization and control, motion planning and control of connected and autonomous vehicles, and

Contents

**Section 1**

**Section 2**

**Section 3**

Infrastructure Purpose

**Preface III**

Path Planning for Autonomous Vehicles **1**

**Chapter 1 3** Introductory Chapter: Roles of Path Planning in Providing Reliable Navigation

**Chapter 2 9**

Path Planning in Varied Environments **27**

**Chapter 3 29**

**Chapter 4 51** Vision-Based Path Finding Strategy of Unmanned Aerial Vehicles for Electrical

**Chapter 5 63**

Towards an Optimal Path Planning **77**

**Chapter 6 79**

Extending the Limits of the Random Exploration Graph for Efficient

and Control for Autonomous Vehicles and Robots

*by Jaroslav Kozůbek, Zdeněk Flasar and Ivo Dumišinec*

Path Planning for Autonomous Vehicle in Off-Road Scenario

Military Factors Influencing Path Planning

*by Boyuan Li, Haiping Du and Bangji Zhang*

*by Alexander Cerón, Flavio Prieto and Luis Mejias*

Model of the Optimal Maneuver Route *by Jan Nohel, Petr Stodola and Zdeněk Flasar*

Autonomous Exploration in Unknown Environments *by Alfredo Toriz Palacios and Abraham Sánchez López*

*and Muhammad Aizzat Zakaria*

*by Umar Zakir Abdul Hamid, Volkan Sezer, Bin Li, Yanjun Huang*

human-machine cooperative driving. Dr. Huang is also an associate editor for several international journals.

One of the pioneering researchers in the field of autonomous vehicles in Malaysia, Dr. Muhammad Aizzat Zakaria is a senior lecturer at Universiti Malaysia Pahang. His current research focuses on intelligent vehicle navigation. Dr. Zakaria has worked on the intelligent mobile robot systems of autonomous vehicles. He is also interested in working with robotic system modelling and mechatronic systems for robotic intelligent control applications.