Rapid population migration has been viewed as a critical factor impacting urban network construction and regional sustainable development. The supervision and analysis of population migration are necessary for guiding the optimal allocation of urban resources and for attaining the high efficiency development of region. Currently, the explorations of population migration are often restricted by the limitation of data. In the information era, search engines widely collect public attention, implying potential individual actions, and freely provide open, timelier, and large-scope search query data for helping explore regional phenomena and problems. In this paper, we endeavor to explore the possibility of adopting such data to depict population migration. Based on the search query from Baidu search engine, three migration attention indexes (MAIs) are constructed to capture public migration attention in cyber space. Taking three major urban agglomerations in China as case study, we conduct the correlation analysis among the cyber MAIs and population migration in geographical space. Results have shown that external-MAI and local-MAI can positively reflect the population migration inner regions and across regions from a holistic lens and that intercity-MAI can be a helpful supplement for the delineation of specific population flow. Along with the accumulation of cyber search query data, its potential in exploring population migration can be further reinforced. Along with the rise of a city network, which is constructed under the push of different kinds of urban elements flows, the interactions among different cities have been emphasized in the planning of urban areas, including the interaction of population, material, information, technique, etc. Hereinto, population interaction or population migration is one of the most important aspects. The floating of population is not only the flowing of individual human but also the transfer of demand, information, and technique carried by individuals ,2. They Related explorations on population migration have been concerned as hotspots since the 1990s. A larger body of researches have been conducted, such as the labor market performance, social and physical status of migration ,6,7, theThose researches have contributed largely to promoting the understanding of the progress of population migration and their impact. However, the deficiencies in migration data still exist. Studies based on national censuses data can explore the migrants in a large range but with a relatively large time interval of ten years, which hinders the short time-series analysis of population migration, and little can be inferred for specific years between censuses and for recent trends . The resWith the growing application of search engine in cyber space, search query data has been brought out to reflect the preference of public attention, which is generated from the personal behavior of Internet search. This kind of data with opening and timelier characteristic has provided effective support for analyzing regional phenomena and problems ,31,32,33Based on these questions, this paper endeavors to answer them and to propose a new angle to analyze population migration. A hypothesis can be made that the search queries generated from individual migration-related search can positively reflect population migration. Based on the search query data from Baidu search engine, we construct a series of migration attention indexes (MAIs) to explore public attention on migration. Taking three main urban agglomeration areas of China as study area, the correlation analysis has been utilized to explore the relationship between MAIs and population migration to test the hypothesis. This paper is organized as follows. To verify the relationship between search query data in cyber space and population migration in geographical space, we select three urban agglomerations in China as case study: Beijing-Tianjin-Hebei metropolitan region (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD). There are 38 cities located in these regions, 10 cities from BTH, 16 from YRD, and 9 from PRD, as shown in https://heat.qq.com/qianxi.php) through web crawler technology. Due to the specific Hukou policy in China , the floating population has been defined as the population living in the objective city more than six months without local registered Hukou [http://index.baidu.com/). The average daily queries of each migration keyword versus the name of the city  from 1 January 2015 to 31 December 2015 were collected based on Baidu Index. (3) Relevant socioeconomic data were acquired from regional statistical bureau, including the Tertiary Industrial Output-Value, Participant Rate of Urban Basic Medical Care System, the number of schools, etc. The migration reasons were collected from the dynamic monitoring survey of China’s migration population in 2015, which was conducted by the National Health and Family Planning Commission of China.The data used in this paper include the population migration data, search query data, and socioeconomic data. (1) There are three kinds of population migration data used in this study: the net inflow population, intercity population flow, and the floating population. The net inflow population delineates the total population migrated into the city during a specific period. Intercity population flow is the population migrate from the original city to the terminal city. Based on the prevalent use of series Tencent’s applications , more precise expression on the migration of population in China can be provided by Tencent migration map under the support of enormous user base. Considering the merit of Tencent migration map and avoiding the self-certification of Baidu, we obtained the net inflow population and intercity population flow from Tencent migration map  are proposed to express public attention on migration comprehensively. Based on the different original location of migration search, we construct three MAIs as local-MAI, external-MAI, and intercity-MAI to delineate the public attention generated from local city, attention from external areas, and attention flow among urban areas; then, the correlation analysis is conducted between MAIs in cyber space and urban migrants in geographical space to further verify the aforementioned hypothesis. The framework of this paper can be illustrated in Specially, the net inflow population, intercity population flow, and the floating population have been collectively adopted to depict the movement of population in this paper. The definition of migration for them can be separately clarified as follows: The net inflow population of a city is defined as population that the city has received from the external areas, which is the result of movement of people with different origins and the same destination; the intercity population flow is also defined as the movement of people, which happens among different cities; and the floating population of a city is defined under the Hukou policy of China , of which the migration can be explained as the change in the place of personal residence.To verify the hypothesis that the migration-related search queries from individual users can positively reflect the population migration, three issues should be concerned: (1) what are the main driving factors cause population migration; (2) how to express those factors in cyber space through search query data; and (3) how to synthesize those search query data to comprehensively express public attention on migration in cyber space. For the first issue, based on the dynamic monitoring survey of China’s migration population in 2015, we have conducted the statistic of population percentage on different migration reasons to confirm the main factors which cause population migration. For the second issue, a series of search keywords expressing different migration reasons has been selected. The Baidu Index of keywords versus the name of city has been collected to reflect the public attention on migration in cyber space. For the third issue, migration attention indexes (MAIs) have been constructed to integrate public attentions generated based on different migration reasons.To pointedly select search keywords that load public attention on migration. First, we confirm the main reason for population migration based on the dynamic monitoring survey of China’s migration population in 2015. The percentage statistics of migrant population based on diverse migration reasons in the three different urban agglomerations are deployed. The results have been shown in work and trade, study and training, and relocation. Due to the transferring of family members always accompanying family relocation , we havewww.google.com/trends/) and Baidu Index (http://index.baidu.com/). A series of researches have been conducted to analyze data from Google Trend and Baidu Index; the robustness and effectiveness of them have been assessed [To better exhibit and exploit search query data, relevant search exploit services based on search query data are produced, typically as Google Trend  are designed to comprehensively express public attention on migration in cyber space comprehensively. First, we combine the candidate search keywords with the name of objective cities to obtain the cityward migration keywords, such as “school + Beijing”, “house price + Shanghai”, “recruitment + Shenzhen”, etc.; second, the average daily search volume of these cityward keywords are acquired based on Baidu Index from 1 January 2015 to 31 December 2015; third, the population percentages of different migration reasons are viewed as index weight to synthesize the corresponding Baidu Index into MAIs; fourth, according to the origin location of Baidu Index, the r is the correlation coefficient of the two indexes and n is the number of cities.To investigate the relationship between public migration attentions in cyber space and population migration in geographical space, we conduct the correlation analysis between MAIs and urban migrants. In the cyber space, local-MAI, external-MAI, and intercity-MAI were selected to represent public migration attentions with different originations to objective cities; in geographical space, floating population, inflow population, and intercity population flow were collected. Regarding the diverse kinds of migration and different definition of MAIs, the correlation analysis have been conducted from three aspects: (1) the correlation between local-MAI and floating population, which reflects the relationship between migration attention generated from the local city and actual floating population inside the city; (2) the correlation analysis between external-MAI and inflow population, which explores the relationship between migration attentions received from the external areas and actual inflow population of the objective city; and (3) the correlation analysis between intercity-MAI and intercity population flow, which investigates the relationship between cyber migration attention flows and the actual population flows in the geographic space. Pearson correlation coefficient is employed to test such correlations, the formula can be shown as follows:Furthermore, we inquired about the relationship between urban external-MAI in cyber space and urban comprehensive attractiveness for migrants (UAM) in geographical space to further test the validity of the proposed indicators. Based on the push-pull theory which has been widely used in analyzing migration action and willing ,49,50,51kUAM is urban comprehensive attractiveness for migrants of city k; m is the number of major components which make the cumulative ratio of the total variance greater than 85%; iA contributes the major components i to UAM of the city; n is the number of indexes; ijC is the contribution of index j to the major components i; and *kjX is the standardized value of index j in city k.Last, we adopted the principal component analysis (PCA) to integrate the index system and to obtain the indicator which reflects urban comprehensive attractiveness for migrants. The components with eigenvalues greater than 1 and the cumulative ratio of total variance greater than 85% are extracted and rotated with the varimax method in SPSS 19.0 , so that each factor has the minimum number of high load variables, which can be expressed as follows:According to the definition of MAI, the migration tendency of the person from the outside areas can be conveyed through external-MAI. Under the assistance of relevant migration data from the Tencent map, we engaged in exploring the relationship between external-MAI and urban migration population. Pearson correlation coefficient was adopted to reveal the relationship between them; the results have been shown in Applying the principal component analysis, we obtained UAM of target cities based on the statistical data; the correlation study was deployed between the comprehensive UAM and external-MAI. As shown in Furthermore, the Pearson correlation coefficients between the selected indexes and external-MAI have been calculated, as shown in The results of correlation analysis between local-MAI and local floating population have been shown in p is 0.01. It is shown that the city with higher local-MAI also has a higher external-MAITo further excavate information from MAI, the relationship between local-MAI and external-MAI has been explored; the results are shown in To explore the relationship between intercity-MAI and intercity population flow, the results have been shown in Under the correlation analysis of these two indexes, a moderately positive correlation can be observed in the study area See . For theThe massive population migration is the specific phenomenon and the inevitable driving force promoting the urbanization of population in China and many developing countries. The collection of urban MAIs can obtain the public intention of migration based on individual search actions and can offer exploration of population migration. Depending on the MAIs, we analyzed the correlation relationship between local-MAI and external-MAI; a high correlation has been discovered. It implied that the city with relatively higher local-MAI has a higher external-MAI. Migration may be active in the high-high cities, such as Shanghai, Beijing, and Shenzhen. According to the dynamic monitoring survey of China’s migration population in 2015, the proportions of floating population inside these three cities separately reached 40.26%, 38.02%, and 67.51%, which are much higher than the average value of China at 18.00%. Besides, they have separately occupied 12.22%, 11.99%, and 8.64% (ranked top 3) of the whole inflow population of the three urban agglomerations, which has the most dynamic migration in China. The predominant roles of them in attracting population outside the city are declared. Active migration movement can be detected to support the hypothesis derived from the correlation relationship between local-MAI and external-MAI.work and trade as the predominant migration reason), which can represent the ability of MAI indexes in capturing the impact of migration reasons. With respect to the indexes described urban living conditions, there are no significant correlations between the population migration attention and unemployment rate or per capita living area, this results may correspond to the great exception of potential migrants for their future urban condition, which can be explained by the Todaro migration model from the perspective of development economics. Todaro migration model argues that the migration of population is based on the “expected profit” of migrants. The hardships in urban life have not obtained enough attention from potential migrants, particularly for the rural-urban migrants who lack the necessary information as they enter a new different world [Analyzing the correlation of external-MAI with UAM, the reasonability of external-MAI can be verified through the high correlation with conventional statistics analysis. Based on the push-pull theory of migration, in the cities with higher urban pulling force, more influx of population can be observed. Through the calculation of UAM, which depicted urban pulling force, the city with higher external-MAI can observe higher UAM. The feature of external-MAI coincides with the setting of push-pull theory. Further, exploring the relationship between external-MAI and the indexes which reflect urban migration attractiveness, there are significant correlations between tertiary industrial output-value and urban residents’ per capita disposable income with the external-MAI in the whole study area. Most of the cities with higher job opportunities and income levels receive more migration attentions from the outside area. This finding coincides with the dynamics monitoring survey of migration population suggesting a migration reason in nt world . FurtherBesides, we further adopted the neoclassical theory in population migration to explore the reasonability of MAIs. The per capita disposable income of urban residents, which has been viewed as the direct index depicting the possibility for migrants to improve economic benefit, has been adopted to conduct the correlation analysis with external-MAI; the results have shown that the external-MAI has significant positive correlation with the per capita disposable income of urban residents . The reasonability of MAIs can be further identified.With the correlation analysis of external-MAI and urban migration population, we could observe a significantly positive correlation. The resource endowment gap between different urban areas  triggers personal develop exception and forges migrant needs in flowing among diverse regions ,43. CollThe high correlation between local-MAI and the floating population inside cities was a signal to prove their close relationship. Nowadays, the floating population inner city has become an influential part in enacting urban planning and policy. Generally speaking, the floating population lived with relatively weaker urban amenities than the local population ,55. The p-value 0.00) in the whole area. The results show that the representativeness of intercity-MAI for population flow between different cities was effective, but the correlation relationship was relatively limited. It might be caused by two main reasons: (1) The selection of search keywords cannot cover every reason for migration flows. A unique keyword system may exhibit some deviation in reflecting the driving force of every population flow interaction; (2) migration movement has a lagging feature. It may happen a few months, years, or a much longer time after the search action. It also may be canceled or indefinitely delayed after information acquisition through searching, which makes the relatively lower correlation between intercity-MAI and intercity population inflow. Generally speaking, the correlation between intercity-MAI and population flow is still on an acceptable level. It can be a supplement for the population flow research of insufficient data. In future work, the construction of a targeted search keywords system for objective population flow can be adopted to remedy such drawbacks.Intercity migration has already become one of the significant migration models in current China. We analyzed the correlation between intercity-MAI and intercity population flow in 2015; a similar positive correlation can be observed as 0.57  and coefficient of variation (CV) of relevant correlation coefficients of three MAI indexes, as shown in Furthermore, we have tested the significance of slope of the three trend lines, which were separately fitted based on external-MAI and inflow population, local-MAI and floating population, and intercity-MAI and population flow to identify whether MAI indexes could steadily reflect the migration situation in different urban agglomerations. All the significances of slopes have been rejected by significant testing at a significant level of 0.05 . The null hypothesis could be accepted as there is no significant deviance between the slopes. Although the representations of MAI are diverse in different regions, the deviances are nonsignificant.Migration population has immense potential to push urbanization process in current China and other developing countries. Exploration of population migration based on multisource data can bring more information about the noticeable driving force of urban development. In the information and network era, the MAI indexes can reveal how the public put their attention on migration-related items. Based on the cyber-based indexes, we explore the relationship between public migration attention in cyber space and urban migration population in geographical space inner region, across region, and between regions. The results can answer the questions mentioned in the introduction that search query data based MAI indexes can positively reflect the situation of migration population inner region and across region and, for the population flow, that it is an alternative supplement and support when relevant data is deficient.Population migration is a complex process driven by diverse forces; this paper conducted a series of analyses from the perspective of search query data in cyber space. However, some limitations exist: First, the selection of continuous search keywords is limited by the short period of data acquisition from the search query engine. Following the incremental collection of search query data, more suitable search keywords should be selected to cover different aspects of public migration attention to thus better delineate the difference and characteristic of urban migrant population; second, this paper focus on the panel data analysis; future work will emphasize on the time-series analysis and excavate more information from a dynamic perspective.


In December 2019, an outbreak of novel coronavirus pneumonia occurred, and subsequently attracted worldwide attention when it bloomed into the COVID-19 pandemic. To limit the spread and transmission of the novel coronavirus, governments, regulatory bodies, and health authorities across the globe strongly enforced shut down of educational institutions including medical and dental schools. The adverse effects of COVID-19 on dental education have been tremendous, including difficulties in the delivery of practical courses such as restorative dentistry. As a solution to help dental schools adapt to the pandemic, we have developed a compact and portable teaching-learning platform called DenTeach. This platform is intended for remote teaching and learning pertaining to dental schools at these unprecedented times. This device can facilitate fully remote and physical-distancing-aware teaching and learning in dentistry. DenTeach platform consists of an instructor workstation (DT-Performer), a student workstation (DT-Student), advanced wireless networking technology, and cloud-based data storage and retrieval. The platform procedurally synchronizes the instructor and the student with real-time video, audio, feel, and posture (VAFP). To provide quantitative feedback to instructors and students, the DT-Student workstation quantifies key performance indices (KPIs) related to a given task to assess and improve various aspects of the dental skills of the students. DenTeach has been developed for use in teaching, shadowing, and practice modes. In the teaching mode, the device provides each student with tactile feedback by processing the data measured and/or obtained from the instructor's workstation, which helps the student enhance their dental skills while inherently learning from the instructor. In the shadowing mode, the student can download the augmented videos and start watching, feeling, and repeating the tasks before entering the practice mode. In the practice mode, students use the system to perform dental tasks and have their dental performance skills automatically evaluated in terms of KPIs such that both the student and the instructor are able to monitor student’s work. Most importantly, as DenTeach is packaged in a small portable suitcase, it can be used anywhere by connecting to the cloud-based data storage network to retrieve procedures and performance metrics. This paper also discusses the feasibility of the DenTeach device in the form of a case study. It is demonstrated that a combination of the KPIs, video views, and graphical reports in both teaching and shadowing modes effectively help the student understand which aspects of their work needs further improvement. Moreover, the results of the practice mode over 10 trials have shown significant improvement in terms of tool handling, smoothness of motion, and steadiness of the operation. The coronavirus disease 2019 (COVID-19) has been declared as a global pandemic by the World Health Organization (WHO). Globally, as of September 7, 2020, there have been 27,208,206 confirmed cases of COVID-19, including 889,989 deaths . COVID-1Social and physical distancing measures aim to slow the spread of COVID-19 by stopping chains of transmission of the SARS-CoV-2 virus . PhysicaCOVID-19 social distancing policies led to a widespread lockdown of schools and universities, including dental education institutions . To a laIn this paper, we develop a novel portable teaching-learning platform for remote teaching and learning in dentistry . This neIn this paper, we describe the available training and learning models, present the developed DenTeach platform, and demonstrate the feasibility of the DenTeach platform through a case study.In health sciences, the use of classroom and hands-on instructions by experts has been a training mechanism of choice for most educational programs. This training mechanism is also called the traditional novice-expert apprenticeship model . In thisHowever, the traditional novice-expert model cannot be continued due to the continued lockdown of the dental school in the age of the COVID-19 pandemic, as students always require the presence of their mentor to practice and learn the key operation skills in a classroom setting. Additionally, in the field of dentistry, this traditional model is time-consuming, and the training process is slow and lacks quantitative measures to assess aspects of technical skills. As a result, trial and error often constitute a major part of learning psychomotor skills for a student. To provide students with continued learning and training education in times of unprecedented crisis like COVID-19, decreased training hours, and increased training efficiency, there is an increasing demand to develop a portable intelligent teaching-learning platform capable of providing remote teaching and learning delivery and quantitative evaluation of dental performance.The current teaching-learning method involves an instructor to provide visual instructions at a central point in the classroom, while students watch, listen, ask questions, and then imitate tasks. As all dental pieces of equipment are placed in dental schools, students do not have access to the equipment once they leave the classroom. However, if practice units and tools at both the instructor’s and students’ work areas are portable, the teaching and learning can be performed remotely .In order to objectively assess technical dental skills , it is iUnderstanding the tactile skills could be made possible through the incorporation of sensory and actuation systems onto a conventional tool such as a dental rotary handpiece in restorative dentistry.A device for teaching and training dental treatment techniques has been developed that exerts a force on a tooth, preferably using tools, in order to examine or treat this tooth . The manIn the work of Additionally, the Iowa dental surgical simulator unit focuses on tactile skill development . The sysIn addition to physical devices for dentistry training, some studies  also exi® haptic 3D-VR dental trainer  flexible learning with self-teaching not limited to formal training hours, thus increasing students’ training time and reducing the overall future costs; 2) providing students with the opportunity to gain instant feedback and to practice assessment tasks using similar criteria used by examiners; 3) presenting tooth data as a 3D multi-resolution surface model, reconstructed from a patient’s volumetric data to improve real-time performance; 4) collision detection and collision response algorithms used to handle a non-spherical tool such as a cylindrical one; 5) simulation of tooth surface exploration and cutting with a cylindrical burr by utilizing a surface displacement technique .Augmented reality (AR) haptic systems have also been used for dental surgical skills training. In the work of Although a variety of dental surgical simulators for teaching and learning has been developed, the lack of quantitative key performance indices (KPIs) to assess aspects of dental skills is still a significant issue to be addressed. With decreasing operating hours and training resources, there is an increasing demand to improve training efficiency and to provide a quantitative evaluation of dental performance using KPIs.Dental Surgical Simulators) and quantify KPIs. Currently, in dental schools, dental laboratories, and clinics, this knowledge is often conveyed from the instructor to the apprentices through qualitative instructions, such as “be gentle,” “go deeper” or “push harder”. Quantitative vibrotactile data measured during the performance of dental tasks on human teeth remain largely unavailable. Therefore, in addition to developing advanced intelligent dental simulators to reform the traditional novice-expert apprenticeship model and improve teaching and learning performance, there is a strong demand for systematic quantitative evaluation of dental performance using KPIs. To this end, In order to objectively assess technical dental skills, it is implicit that one must be able to measure and study essential aspects of dental performance , a DT-Student software (Student’s software), advanced wireless networking technology, and cloud-based data storage and retrieval has been developed for use in teaching, shadowing, and practice modes. The data storage system stores VAFP data of the DT-Performer and the DT-Students in both modes, as well as KPIs, defined for evaluating students’ performance. DenTeach complements the traditional instructor and student working area by integrating into the existing working setup . For the instructor work area , the DenTo display and record the instructor’s hand operation during teaching procedures, four mini cameras show the top view, two side views, and inside view. All videos are transmitted simultaneously onto the students’ workstations. Additionally, DT-Performer software allows the instructor to select, record, and play over 30 psychomotor performance metrics to objectively measure effort, speed, accuracy, and learning curve.For the student work area , the DT-DenTeach allows learning activities in three modes namely teaching, shadowing, and practice.In the teaching mode, similar to a general traditional teaching and learning mode, an instructor conducts dental tasks in the instructor’s workstation and students mimic the tasks in the students’ workstations. The main difference is that the DenTeach device uses a data transmission system to provide each student with tactile feedback by processing the data measured and/or obtained from the dental tool of the instructor’s workstation. This helps students understand and perceive how their instructor is conducting the dental operation and tasks without their presence at the instructor’s workplace. Moreover, the data storage system saves information such as data of sensory systems from the instructor and students’ workstations as well as audiovisual recordings taken from the instructor’s workstation. This information can later be retrieved and used for various purposes, for example by students in the practice mode or by instructors for evaluation of student’s performance in both teaching mode and practice mode.A dental task is conducted by an instructor using a dental tool on a DT-Rightway Articulator. The instructor processing unit running DT-Performer software includes the main processor responsible for: 1) receiving and analyzing sensory systems data recorded during performance of a dental task by the instructor; 2) recording video and audio that are taken from the audiovisual recording system; 3) communicating with the students’ workstations and the data storage system via the data transmission system; and 4) providing the instructor with user-friendly software designed for teaching different dental tasks that are screened on the display see .The DT-Performer software enables the instructor to choose different options including the teaching session along with the time and date as well as the type of the dental task. Each set of students’ KPIs is displayed graphically on the screen located at the instructor's workstation, which helps the instructor monitor student performance during a teaching session . AdditioIn In teaching mode, the student processing unit is responsible for: 1) receiving and analyzing data of the sensory system located inside the custom-designed training tool; 2) communicating with both instructor’s workstation and the data storage system via the data transmission system; 3) generating control inputs for the vibrotactile actuation system and the vibrator that are located inside the custom-designed training tool, based on data received from the instructor’s workstation through the data transmission system; 4) displaying video and audio recordings, which includes the instructor’s hand, tool and tooth physical model received from the instructor’s workstation through the data transmission system in real-time; 5) superimposing 3D model of the custom-designed training tool onto the video in an AR environment screened on the display  Tool handling ability is determined by the acceptable deviation set by the instructor. At the beginning of the experiment, the instructor sets the acceptable amount of the student’s deviation to be less than 15° for the roll ((2)The smoothness of the motion or student’s ability to move the tool at the same speed as the instructor, which is defined as:There are two factors that students can continuously monitor during the teaching sessions; these factors are plotted in a real-time fashion using 6-bar charts in DT-Performer, as seen in In shadowing mode, a student can download augmented videos  and start watching, feeling, and repeating the task before entering the practice mode. In the shadowing mode, a student uses the DT-RealFeel handpiece to shadow dental tasks taught by the instructor. In this operating mode, the video of the dental task - that has already been performed by the instructor–is displayed on the DT-Student monitor while superimposing a 3D model of the training tool  onto the video, in an AR environment, when rehearsing the dental task.In practice mode, the setup components for a student are the same as the ones described in While a student is performing a dental task in practice mode, the DenTeach software displays KPIs of a student graphically. The software also generates statistical and graphical performance reports for dental tasks performed by a student in practice mode. These performance reports are uploaded to the data storage system via the data transmission system, and are made available to the instructor for evaluation purposes.In addition to the qualitative assessment of dental skills conducted by an instructor, the performance of each student is assessed quantitatively individually and comparatively. For quantitative evaluation, two sets of KPIs are used.i.e., pressure applied to the tooth; and 4) assessment of the steadiness factor of the student’s tool handling skill. DenTeach was used to measure the KPIs and the ability of the system to help an instructor and students teach and learn more effectively compared to existing traditional techniques. Plastic teeth were mounted onto the typodonts inside the DT-Rightway Dental Articulator. Three common dental tasks were completed by an experienced dentist as the instructor (MK), while a student (AM) mimicked the performance of dental tasks at the student workstation. The instructor used the sensor modified dental handpiece to perform a Class I, II, or V composite preparation, which involves different lesion sizes and caries, over an interval of active practicing on a plastic tooth that is characterized by rheostat engagement and drill operation. More detailed information on the procedures is given in Tam’s work . A screea range expected by the instructor (15 degrees of deviation). The amount of the deviation could change once the students become more experienced or if the instructor changes the deviation range. For this typical interval, the amount of angle deviation for roll angle was within the acceptable interval set by the instructor for most parts of the performance of the task, as depicted in During the experiments, the outputs of the DT-Performer and DT-Student software were exported in this section. The software recorded, analyzed, and plotted real-time data from the instructor’s dental handpiece. In shadowing mode, the student used the RealFeel handpiece to review the tasks taught by the instructor. In addition to acquiring more quantitative feedback on the tasks, this mode helps the student become confident and prepare for the practice mode to get hands-on practice with the actual dental handpiece. One advantage of the shadowing mode is to save material and time, with minimal supervision. Therefore, the student is not restricted to academic labs for extended hours, as the portable and compact unit can be used anywhere to practice dental operations over the Internet.In this case study, the student performed five trials of task 1 in shadowing mode. This was assessed in terms of the three In practice mode, the student used the actual dental handpiece to practice the three tasks. For this mode, a wireless sensory system that is identical to the sensors used by the instructor is used to measure the signals used for calculating the KPIs. The sensory system and camera then recorded and communicated the audiovisual vibrotactile information to the database to be compared with those of the instructor. Therefore, the student is able to submit the results of each trial to the instructor along with the audiovisual signals at the end of each trial. The KPIs of the first (#1) and last (#10) practice trials are listed in The COVID-19 pandemic response has resulted in remote and physical distancing restrictions to limit the spread and transmission of the novel coronavirus. This has caused significant adverse effects on dental education . To help dental institutions continue delivering education remotely, a compact and portable teaching-learning platform, DenTeach, has been developed for remote teaching and learning. The platform includes an instructor workstation (DT-Performer), a student workstation (DT-Student), advanced wireless networking technology, and cloud-based data storage and retrieval. By providing real-time video, audio, feel, and posture (VAFP) information, the platform synchronizes the operations of the instructor and the student. Besides, the platform can provide quantitative KPIs of the student to both the instructor and the student to evaluate the student’s skill level.DenTeach follows and expands on the traditional novice-expert apprenticeship model of instruction to enhance dental training programs. It has been developed for use in teaching, shadowing, and practice modes. In teaching mode, the student can perceive how the instructor is conducting the dental operation through tactile feedback obtained from the dental tool of the instructor's workstation. In shadowing mode, the student can watch, feel, and repeat the tasks alone by downloading the augmented videos. In practice mode, students can use the system to perform dental tasks and have their dental performance skills automatically evaluated in terms of KPIs. A case study was performed to demonstrate the feasibility of the device, and the results show that a combination of KPIs, video views, and graphical reports in both teaching and shadowing modes can effectively help the student understand which aspects of their work need further improvement.i.e., home) settings due to its compact and portable size. This device facilitates both fully remote and physical-distancing aware teaching and learning in dentistry. Additionally, the DenTeach platform can be useful during the pandemic recovery phase, when dental schools are allowed to return to normal operations. Once dental schools are reopened, there will be a surge in teaching, practicing, and exams. DenTeach can be used to increase the efficiency of the training process, thus allowing dental schools to clear the backlog of activities faster. Before the second wave of COVID-19 hits, decision-makers at dental colleges may want to ensure they have adequate resources to continue teaching and testing from a remote location and minimize the backlog of deferred activities. DenTeach can be used as an effective remote training tool. Moreover, the application of DenTeach could be further extended to other fields of health sciences such as general surgery and neurosurgery where a drill is used to conduct a task.DenTeach is a useful invention for pedagogical and professional purposes, which can be used for training and educating students in both clinical/laboratory and remote  of Canada under grant EGP 542825-19. The National Research Council Canada - Industrial Research Assistance Program (NRC-IRAP) under industrial grant 921303.AM and YM work for the Department of Research and Development Tactile Robotics Company, the IP holder of the technology.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


This paper presents a proof of concept for simultaneous load and structural health monitoring of a hybrid carbon fiber rudder stock sample consisting of carbon fiber composite and metallic parts in order to demonstrate smart sensors in the context of maritime systems. Therefore, a strain gauge is used to assess bending loads during quasi-static laboratory testing. In addition, six piezoelectric transducers are placed around the circumference of the tubular structure for damage detection based on the electro-mechanical impedance (EMI) method. A damage indicator has been defined that exploits the real and imaginary parts of the admittance for the detection of pin failure in the rudder stock. In particular, higher frequencies in the EMI spectrum contain valuable information about damage. Finally, the information about damage and load are merged in a cluster analysis enabling damage detection under load. The electro-mechanical impedance (EMI) method is widely used in structural health monitoring (SHM) applications to detect damage in isotropic and composite materials ,2. The uIn recent years, the EMI method has been applied successfully on composite materials. An example is the work presented by Wandowski et al.  who usedThis work aims at smart sensor systems for damage detection in maritime structures that require an extremely high reliability so that methods are crucially needed to continuously monitor structural conditions. In the literature, several SHM systems are presented for damage detection in ship structures: a first example is the work proposed by Osaka et al. , who intRelated works also come from the field of load monitoring systems of maritime components. In most cases, resistive or optical (fiber Bragg grating) strain gauges are used for this task . In addiHere, we focus on a rudder stock as part of a rudder system, as depicted in The remainder of the paper is organized in the following way: The rudder stock is made of a material combination of CFRP and steel. The main reason for this design is that the new rudder stock should be exchangeable with the state of the art full steel rudder stock. In these rudder stocks, the rudder blade and the steering gear, on the other hand, are mounted to the rudder stock with conical press fits. So couplings of this kind should not be touched, and thereby the other parts do not need any changes in construction. But a conical press fit due to the locally applied high surface pressures cannot be realized in CFRP. Due to the relatively low stiffness and strength in radial direction and creeping effects of the polymer resin, a CFRP cone could not withstand the surface loads permanently. As a solution, the new rudder stock is made of three components: keep steel as the material for both ends of the rudder stock and couple these two steel parts with a middle section made of CFRP see . Due to This work uses static alternating bending loads applied to the rudder stock to model reference conditions in the laboratory as realistically as possible. The varying operational conditions can be managed in practical SHM systems by means of a classification technique using multiple reference conditions that are learned during a training phase . The ideSimilarly to related works, compare, for example ,8,21,22,f denotes frequency and n the number of measurement points.In these equations, Similarly, the imaginary part of the DI shows an offset between the unloaded and the loaded rudder stock. The corresponding DI curve confirms the previous findings in the sense that the DI remains at an increased level after the first loading cycle. In addition, we see that After finalizing the measurements, the rudder stock was carefully disassembled to evaluate the condition of the metal pins. In a next step, we performed a cluster analysis exemplarily for the transducers form see . It is iThis work successfully demonstrated the application of the electromechanical impedance (EMI) method for damage detection in a steel-composite rudder stock sample. The proof of concept used six piezoelectric transducers aligned on the circumference of the rudder stock for EMI-based testing. In addition, strain gauges were used to assess static bending loads. The EMI method in the frequency range up to 500 kHz enabled the detection of pin failure as a highly loaded part of the rudder stock design. In a next step, the promising results obtained in this study need to be further investigated with a higher number of rudder stock samples and dynamic loading.


In recent times, security in cloud computing has become a significant part in healthcare services specifically in medical data storage and disease prediction. A large volume of data are produced in the healthcare environment day by day due to the development in the medical devices. Thus, cloud computing technology is utilised for storing, processing, and handling these large volumes of data in a highly secured manner from various attacks. This paper focuses on disease classification by utilising image processing with secured cloud computing environment using an extended zigzag image encryption scheme possessing a greater tolerance to different data attacks. Secondly, a fuzzy convolutional neural network (FCNN) algorithm is proposed for effective classification of images. The decrypted images are used for classification of cancer levels with different layers of training. After classification, the results are transferred to the concern doctors and patients for further treatment process. Here, the experimental process is carried out by utilising the standard dataset. The results from the experiment concluded that the proposed algorithm shows better performance than the other existing algorithms and can be effectively utilised for the medical image diagnosis. The development in the medical field and the advancement of medical devices such as magnetic resonance imaging (MRI) and computed topography (CT) produces huge data on daily basis, and the data collected from these devices are high dimensional and rich in variables . Thus, tMoreover, Internet of Things (IoT) is also extended in cloud computing for developing new facilities and applications in the healthcare process . IoT is While storing medical data in the cloud platform, it is necessary to safeguard the information so that the cloud cannot learn anything about the data. Thus, securing the medical images in cloud platform is necessary. Generally, medical images are highly sensitive to modifications, and thus, any alterations in their contents can cause errors in medical diagnosis . Hence, For medical image analysis and disease diagnosis, machine learning technique is extensively used in the medical field . It has The storage, handling, and processing are challenging for large sized medical images, and thus, high performance processors are required for medical image processing . Hence, Convolutional neural networks enable accurate and robust big data feature learning. They utilise many samples for extracting meaningful information from data. Yet, the data may be ambiguous because of inadequate information or complexity of the data source which is known as data ambiguity. Instead, the fuzzy logic is the effective tool for modelling human thinking and perception. It offers the mathematical model for processing ambiguous data by executing numerical computations using linguistic labels and fuzzy set degrees of membership, but the fuzzy systems lack learning ability and the fuzzy rules are determined by the human experts. Thus, the fuzzy system is combined with neural networks so that the fuzzy rules can be derived from a large source of training data by automatically learning the membership functions. Hence, fuzzy convolutional neural network is proposed by integrating fuzzy logic and convolutional neural networks. It utilises the advantages of both fuzzy logic and CNN for accurate and robust classification of data.This paper is formulated as follows: the literature survey related to the article is explained in Due to the momentary advancement in the healthcare services, there is a rapid growth in medical data. The healthcare services in medical field involve diagnosing the diseases, treatment, injury, disease prevention, and treating other mental or physical injuries in patients. The quality of these healthcare sectors lies in the problem detection efficiency and allocation of medical resources which require application and collection and management of medical data . PreviouThe developments in the medical technology made the storage of medical data in cloud become more convenient. The medical images contain much significant information about patients, and thus, the secure transmission of data and secure storage is important. Hence, for the security of the data, the encryption and the decryption algorithm has been utilised. The chaotic system is extensively applied in the encryption process as it provides better results for randomness and sensitivity of parameter and initial values . Many inThe progressions in the data storage capabilities, computational resources, hardware design, and safety procedures have increased the necessity of medical data. The medical data undergo processing for effective analysis of diseases using data processing techniques. The medical data processing includes classification, segmentation, and abnormality prediction by utilising the images produced from the medical devices. For large volume of medical data, an efficient data processing technique is required for collecting the valuable insights from datasets. Deep learning , 32 is aAmong the deep learning methods, the convolutional neural network provides better classification accuracy with most powerful architecture . CNN is The conceptual structure of the proposed system comprises the following phases. At the initial phase, the necessary datasets such as query images and medical images are collected for image processing. The collected medical image datasets are then transferred to cloud subsystem using IoT and are stored into cloud database. Before transmission, these medical images undergo the encryption process to eliminate the security threats. This encryption process is carried out using BP neural network and chaotic system. After encryption, the medical will be transferred to the cloud platform where disease diagnosis takes place. The diagnosing process in the medical images is carried out using the proposed algorithm called fuzzy convolutional neural network algorithm which classifies the processed images into normal and disease affected. The classification results from the cloud are transferred to the doctors or experts in context of person's health by IoT. The architecture of the proposed system is displayed in The medical images are generally distributed to various organisations for accurate analysis of diseases. Thus, medical data sharing is necessary for improving the quality of healthcare services. The connectivity made through the Internet by IoT devices and the development in the cloud computing technologies made the users to utilise accessible and distributed cloud computing platforms. Here, the medical images are stored in the cloud where the image classification for disease diagnosis takes place. It classifies the images into normal and abnormal images, and the results are then transferred to the doctors and patients using IoT devices. IoT-based medical image transmission is given in In cloud computing, the medical datasets are stored in the third party service provider which concerns the privacy of the user. Due to this privacy-related problems, the application of cloud sharing platforms is limited in the medical field. For secured data storage on cloud database, BP neural network- and chaotic system-based encryption algorithm is proposed in this system.The reason for implementing an encryption-decryption system is privacy. As information travels over the Internet, it is necessary to examine the access from unauthorized organisations or individuals. Hence, the data are encrypted to reduce data loss and theft. Encryption is the process in which the information is converted into secret code for concealing the actual information. The encryption and decryption processes are collectively called as cryptography. The algorithm utilised for encoding and decoding data is known as ciphers or encryption algorithms, while the unencrypted data are called plaintext and the encrypted data are known as ciphertext. The encryption process converts the plaintext into alternate form called ciphertext. The variable known as a key is required for decrypting the encrypted images. An authorized recipient can easily decrypt the message with the key provided by the originator to recipients but not to unauthorized users. Encryption is commonly used to protect data in transit and data at rest. Encryption has been a longstanding way for sensitive information to be protected. In modern times, encryption is used to protect data stored on computers and storage devices, as well as data in transit over networks. The time and difficulty in guessing the information is what makes encryption such a valuable security tool.(1) Preprocessing. For preprocessing, the input image of size P × Q is used. The image size P × Q is disintegrated into H subimage blocks, and each block is given as m × m. For accelerating the convergence of network training, it is necessary to normalise the generated subimage blocks. Here, mean distribution pretreatment is used for normalising the grayscale image range which is given as , and the transformation domain is given as [Ymax − Ymin]. The pixel value for processing is assumed as Xvalue and Yvalue which is obtained as given in the following equation: By equation , the pix(2) Image Compression. After preprocessing, image compression takes place in the medical images by employing the BP neural network. During compression, the image data samples are given as the input. The number of hidden nodes in the network is denoted as ut, and the number of nodes in input layer is denoted as ui. The compression rate of BP neural network is denoted as t, and the association among them is given as follows: During neural network training, the network coupling weight remains unaltered during the compression process. Thus, the newff function is employed for generating a trainable feed-forward network. The compression image is obtained by utilising newff function for training. The transfer functions are calculated as follows:(3) Image Scrambling Using Extended Zigzag Confusion. The extended zigzag confusion algorithm is utilised for image encryption which scrambles the BP neural network trained image data. It is an extended form of zigzag confusion which is designed for eliminating the drawbacks in zigzag confusion. The scrambling process in zigzag confusion begins with the upper left corner of the matrix. During zigzag confusion, some values in particular positions are not altered even after scrambling for numerous times, and thus, it can be cracked easily. Also, the zigzag transformation is only suitable for square matrix and not suitable for nonsquare matrix. Hence, extended zigzag confusion is used which overcomes the limitations in regular zigzag confusion. In extended zigzag confusion, the scrambling process can begin from any of the four corners which are chosen by the random number created in the chaotic system. The extended zigzag confusion is also suitable for nonsquare matrix. (m + P × Q) times. For improving the sensitivity of initial values and parameters in chaotic system, the initial m values are discarded and three chaotic series are obtained. By combining these three series, two pseudorandom series R1 and R2 are formed. These series R1 and R2 are utilised for diffusing pixel values. Equations   ,,(6)Ei=EiGenerally, the data are encrypted to make it difficult for someone to steal the information. The conversion of encrypted data into its original form is called decryption. It is generally a reverse process of encryption which recovers the original image from the encrypted image. It transforms the data that has been rendered unreadable through encryption back to its unencrypted form. Thus, it decodes the encrypted information so that an authorized user can only decrypt the data because decryption requires a secret key or password. If a decryption passcode or key is not available, special software may be needed to decrypt the data using algorithms to crack the decryption and make the data readable. In decryption, the system extracts and converts the corrupted data and transforms it to texts and images that are easily understandable not only by the reader but also by the system. Decryption may be accomplished manually or automatically. It may also be performed with a set of keys or passwords. Decryption is taking encoded or encrypted text or other data and converting it back into text user or the computer can read and understand.1 and S2 are obtained, and the inverse diffusion algorithms are expressed in equations ..1 and S2Positive inverse algorithm:Reverse inverse algorithm:Then, using the extended inverse zigzag algorithm, the scrambled pixels are recovered. The pixel values quantization and the pixel blocks are retrieved, and the vectors also retrieved into subimage blocks. Finally, the decrypted image is formed by combining every subimage. Medical image processing plays a major role in the diagnosis of diseases and makes the treatment process more efficient. The medical image processing in this system is carried out using the fuzzy convolutional neural network. The medical images are generally collected as high dimensional type. Due to this high dimensional data, the functioning of the learning process in the fuzzy CNN can be weakened. Hence, before the implementation of fuzzy CNN, there is a requirement for dimensionality reduction and segmentation for improving classification performance.Dimensionality reduction is very efficient for processing high dimensional images. The principal component analysis (PCA) is widely utilised for decreasing the dimensions of the original data. It utilises the linear transformation to create a simplified dataset while preserving the important features of original dataset. During principal component analysis, the output variables of eigenvectors are exposed through a variance and it has a huge impact in the analysis. This drawback in the analysis is improved using improved principal component analysis (IPCA). The IPCA requires lesser training time, and it experiences a smooth dimension reduction process.(1) Covariance Matrix Formation. In covariance matrix formation, Xn is assumed as training vector and it is given as Xn=T. The method for improved principal component analysis is illustrated as follows: n, dimension number is denoted as  m, and the normalization value of xsv is expressed as xsv′. In this process, there is no loss in information and the correlation matrix does not have any difference. The covariance Co matrix is formed as follows:(2) Calculation of Eigenvalue. For decomposing the covariance matrix Co, the eigenvalue is calculated. It is expressed as μy and the rank of matrix is considered as y. The subeigen values of the vector in the orthogonal basis of  is given as Ey. The diagonal of matrix  is given as Δ, and we have μ1 > μ2 > ,…, μy.(3) Principal Components Calculation. After setting up the covariance matrix Co and normalization, the original vector Mn is shifted to the uncorrelated vector zn, which is illustrated as αn consists of information proportion of the initial n principal components. Also, the threshold value of αn is taken as 85% for collecting necessary information. The contribution rate αs and cumulated contribution rate αn are determined from the following equations:Cumulative contribution rate n, the principal component of Ps sample is determined as follows:u=1,2,…, q).After estimating the accurate value of Image segmentation is utilised efficiently in many medical imaging practices such as recognising the tumour in brain and determining the size of the tumour and its response to the treatment. Having good segmentation process will help the clinicians and patients as they present necessary information for 3D visualization, surgical planning, and disease detection. K-means clustering is the widely utilised method for image segmentation. It is an unsupervised learning algorithm which has relatively low computational complexity. Also, it is the simplest clustering method which divides a set of data into precise number of clusters. Generally, K-means algorithm identifies the number of clusters (K) in the particular regions of images, and thus, it is suitable for medical image segmentation .n observations are iteratively assigned to exactly one of the k clusters defined by centroids, where k is selected before the initiation of the algorithm. Here,  C={c1, c2,…, ck} is given as the set of m cluster centres and Y={y1, y2, y3,…, ym} is given as the set of  n data points. These n data points will be clustered into m cluster centres. The objective function of K-means clustering algorithm is given asyi is the ith data point and cj is the jth cluster centre. By minimising the objective function, the optimal centres can be attained. The initial cluster centre greatly influences the K-means clustering results. Thus, the cluster centres must be selected delicately. K-means clustering is the data partitioning algorithm, where The most relevant way to measure the complexity of an algorithm is in terms of how much time it takes to run . For a complexity measure to reflect the intrinsic nature of the algorithm, it should summarize performance on all problem instances, and it should not reflect inconsequential implementation details.n denotes the number of clusters, Maxt denotes the maximum number of iterations, yi,j denotes the data point allotted to ith cluster centre, Ni,j denotes the total no. of data points allotted to ith cluster centre, and Ci is the ith cluster centre.  (i) Input: O(Maxt × n).  (ii) Ensure: best cluster centre: O(1).  (iii) Initialization: k, C=c1, c2,…, ck: O(1).  (iv) Randomly select cluster centres   (v) Calculate the distance to each centre.  (vi) Assign the data point to the cluster centre, whose distance from the data point is least of all cluster centres.Ci=∑j=1Ni,jyi,j: O(n).  (vii) Objective function evaluation, O(Maxt × n).  (viii) Update the location of cluster centre: D is minimised.  (ix) Repeat steps 3 to 6 until the objective function O(Maxt).  (x) Iteration counterincrement: Computational complexity is the method of measuring the complexity of the algorithm especially the running time of the algorithm. The space (memory) occupied by the algorithm can also be used for calculating the computational complexity, but time is generally more efficient than space. For reflecting the intrinsic nature of the algorithm, the complexity measure must summarize the performance on all problematic cases, and it must never reflect the insignificant implementation factors. The computational complexity of K-means clustering algorithm according to The image processing is complex in medical images because it has high resolution images. Thus, an effective classification algorithm like fuzzy convolutional neural network algorithm is utilised for medical image classification. The FCNN algorithm utilises both neural and fuzzy systems for the classification process. The information collected through CNN and fuzzy is merged together for creating the entire system for classification. In FCNN, fuzzy minimises the uncertainties and the neural network reduces the noises in the original data. The structure of FCNN is presented in The structure of the proposed FCNN consists of convolutional layer and fully connected layer. The convolutional layer undergoes three stages such as convolution stage, nonlinearity stage, and pooling stage. At the initial stage, the input data undergo processing in the fuzzification layer for creating the fuzzy logic representation. Accordingly, the fuzzy representation is convoluted in the fuzzy convolutional stage which contains fuzzified convolution kernels. Fuzzy convolution is the min-max composition of fuzzified kernels which obtain higher level fuzzy features from its input spatial features. During the defuzzification layer, the crisp values are produced from the features obtained by pooling. In the final stage, a fully connected layer functions as an output classifier of FCNN.xi, i=1,2,…, n and outputs yj=1,2,…, m are assigned and the kth fuzzy rule Rk is generated as follows:Fik is denoted as the fuzzy set with ith input and kth fuzzy rule. Based on membership functions, multiple linguistic labels are assigned for each element in input matrix. The input node membership to a certain fuzzy set is described as grade which is calculated by the fuzzy membership function. The fuzzy set . The fully connected phase of fuzzy convolutional neural network works as a classifier with input features. By the process of defuzzification with centre of gravity, the crisp value yi is calculated, which is illustrated asMy denotes the centre of the defuzzification membership function. The output of the FCNN is represented as In the convolution phase, the activation function is denoted as Wf. For evaluating the output error, cross entropy is utilised. Equation , the learning rate is denoted as δMy:Equation  gives thMz and learning rate as δMz:Equation  gives thMx and learning rate as δMx:Equation  gives thThe image classification is an important task in the proposed system which classifies the image dataset into normal dataset and disease affected dataset. Here, the experiments are performed on medical datasets for evaluating the proposed algorithm using parameters like specificity, sensitivity, and accuracy. Besides, the other existing algorithms like decision tree (DT), Naïve Bayes (NB), K-nearest neighbour (KNN), and artificial neural network (ANN) are also compared with the proposed algorithm for analysing the efficiency of the proposed algorithm. For evaluating the effect of preprocessing techniques on classification of medical images, two different types of datasets acquired through noninvasive modalities like MRI are utilised. The datasets comprise BRATS images  and BraiThe parameter settings of proposed algorithm and the comparative algorithms for classification are presented in The parameters like specificity, accuracy, sensitivity, and F-measure are considered in the evaluation process. The evaluation parameters are determined using the number of true positive (TP), number of false positive (FP), number of true negative (TN), and number of false negative (FP). The specificity, accuracy, and sensitivity are illustrated as follows:In true positive (TP), the right values will be predicted as the correct value. In true negative (TN), the right values will be predicted as the wrong value. In false positive (FP), the false values will be predicted as the right value, and in false negative (FN), the false value will be predicted as the wrong value.F-measure is utilised for measuring the effectiveness of the classification process. If the F-measure value is high, the predicting potential of classification process will be high. The F-measure is evaluated from the mean values of recall and precision. The recall is measured by the ratio of true positive values to the sum of true positive and false negative values. Instead, the precision is measured by the ratio of true positive values to the sum of true positive and false positive values. The F-measure based on precision and recall is expressed asThe comparison results are given for the classification methods mentioned above. From the results, it is known that the proposed approach has superior performance over other classification methods. Particularly, the proposed approach has been able to attain better accuracy compared to other techniques like KNN, NB, DT, and ANN, respectively. In case of specificity, the proposed approach has competently acquired the higher value as compared to KNN, NB, DT, and ANN. Similarly, the proposed approach yielded better value for sensitivity and F-measure, which is comparatively higher than other classification methods. Hence, it is concluded that, for medical image classification, the proposed model is highly efficient.The statistical analysis was performed for analysing the improvement in the performance of the proposed algorithms than the other existing algorithms. It analyses classification problems such as the error rates and classification accuracy. Many tests were conducted for this statistical analysis which includes post hoc test, Dunnett test, Tukey test, Friedman test, and ANOVA test. For concluding whether there is any statistical difference among the proposed algorithm and other compared algorithms, a one-way analysis of variance (ANOVA) test has been conducted which analyses the statistical correctness of the proposed algorithms over other existing algorithms. It comprises mean and variance for determining the test statistic. The test statistic is then utilised for determining either the group of data is same or different. The box plots for ANOVA test are illustrated in E as the source of information and the information entropy of E is computed as follows:Ei is denoted as n and the probability of occurrence of Ei is denoted as p(Ei). Based on the equation, information entropy for the proposed algorithm is analysed in the encrypted image.Entropy is known as the significant aspect of randomness, and it can be utilised for defining the degree of uncertainty of the image. Information entropy computes the uniform distribution of gray pixel throughout the image. If the information entropy value is greater, the image confusion level of the encrypted image will be high. Assume The comparative analysis is carried out in the proposed system for analysing the influence of proposed encryption algorithm in the security performance of medical images. Different types of encryption algorithms are utilised for comparative analysis of proposed algorithm. The most commonly used approaches for the encryption process are feed-forward, feed-forward backpropagation, and fitting neural network algorithms. All these algorithms have been proved as highly effective in many security-related approaches. The information entropy results for the proposed algorithm, and other existing algorithms are displayed in M  is represented as the overall pixels of the image and x and y are denoted as the neighbouring pixel values. The mean values are represented as B(x) and B(y), and the covariance and variance are represented as cov and A(x). Based on the correlation equations, 2000 sets of adjacent pixels are selected from the diagonal, vertical, and horizontal directions. During the proposed encryption process, the pixels in the image are entirely randomized so that there will be no leaking of statistical information from encrypted image. Hence, the proposed algorithm has the potential to withstand statistical attack.Correlation analysis is the statistical testing process which has the ability to crack the encryption algorithm. Also, the correlation coefficients provide evaluation standard for testing the encryption algorithm. For plain images, the correlation between adjacent pixels will be generally high. In order to avoid the statistical attacks due to correlation among adjacent pixels, correlated coefficients must be reduced for encrypted image. Here, the correlation coefficient of different pixels is utilised for determining the ability of encryption algorithm and to minimise the correlation between adjacent pixels. Correlation analysis of encrypted image considers all the possible adjacent situations such as diagonal, vertical, and horizontal directions. The following equations are utilised for assessing the correlation between the adjacent pixels in encrypted and plain images:E1 and E2 are represented as an encrypted image before and after changing one pixel of the plain image. If E1 ≠ E2, then H=1, if not H=0. The estimated average UACI value is 33.46% and NPCR value is 99.56%. If the proposed algorithm attains this value, then the algorithm will have better performance.Generally, a small variation in the original image can lead to noticeable variation in the encrypted image. To measure these changes in the original image, unified average changing intensity (UACI) and number of pixels change rate (NPCR) are utilised. The objective of NPCR is to calculate the exact number of altered pixel values in the differential attack. If the NPCR value is high, the result will be better. Instead, UACI focuses on the average variance between two paired encrypted images. If the value of UACI is low, the result will be better. NPCR and UACI quantitatively and qualitatively analyse the processed image and measure the sensitivity of original image. They can also determine whether the images will be able to resist differential attack. The UACI and NPCR are computed by the following equations:The experimental results for NPCR and UACI are compared with other existing encryption algorithms such as feed-forward, feed-forward backpropagation, and fitting neural network. The time consumed by the algorithm for encryption and decryption processes is utilised for analysing the efficiency of proposed algorithm. However, the time consumed by the same algorithm will be varied for different hardware platforms. Cancer has grown as the deadly disease for human beings from children to older generations. The earlier prediction of this disease can considerably reduce the mortality rate. By utilising IoT and cloud computing, the time required for predicting the disease can be minimised. This is because the data generated from the medical devices can be directly transferred to the cloud platform through Internet. Thus, the time required for the manual transmission of medical data will be reduced. Similarly, the image processing is carried out in the cloud, and thus, the processing time is also minimised. In medical images, the accuracy of the result should be precise for the effective treatment. Thus, an effective image processing technique is necessary for the effective prediction of diseases. Classification is one of the major tasks during image processing, and for obtaining more accurate classification results, the fuzzy logic system is integrated with convolutional neural network to form fuzzy convolutional neural network. It classifies the images into disease infected and not infected, and the results will be immediately transferred to the doctors and healthcare works. Thus, it helps the doctors to analyse the results and treat the patients at the initial stage. Furthermore, in order to increase the security and privacy of the data, encryption process based on BP neural network is employed in the proposed study. The perspective of this study is to create an efficient IoT-based cloud computing environment for earlier and accurate prediction of diseases. The proposed system can be effectively utilised in cancer centres for earlier prediction of disease and also analysing the progress in the treatment process. The proposed system can be also applied in large hospitals and healthcare centres with numerous patients' data. Implementing the IoT-based cloud computing system in the hospitals can greatly reduce the storage, and also it will ease managing patient's data.In this study, IoT  and cloud computing-based medical image analysis using fuzzy convolutional neural network has been proposed. IoT and cloud computing are the emerging technology and has been applied successfully in many fields. Many research works are conducted for utilising IoT and cloud computing in medical field. They can be effectively utilised for storing, processing, and sharing. Even though IoT and cloud computing has many advantages, it is not widely employed in the medical field due to the security aspects. The CNN has provided better performance for many applications in clinical domain, but it still has some limitations. More computational power and huge amount of training data are required for the architecture of CNN. If there is any deficiency in computational power, more time is required for training the data which depends on the size of the training data. These limitations can be minimised by better architecture of CNNs, increasing the number of digitally stored medical images, improved computational power, and enhanced data storage facilities. IoT-based cloud computing can be utilised for enhancing the data storage facilities. Even though the Internet of Things and cloud computing can be of huge benefit to healthcare, there are still major challenges to tackle before the complete implementation of connected devices in healthcare. Security and privacy are the major concerns which prevent the users from utilising IoT technology for medical purposes, as healthcare monitoring systems have the possibility to get hacked or breached. The disclosure of sensitive information about the patient's health and location and interfering with sensor data may cause great consequences, which would counteract the advantages of IoT. For privacy protection, encryption and decryption processes are utilised in the proposed approach. However, the encrypted image can be potentially decrypted without acquiring the key if there is an availability of considerable computational resources and skills. Thus, the skilled hackers may be able to decrypt the encrypted data. Also, failure or bugs in the hardware or even power failure can impact the performance of sensors and connected equipment placing healthcare operations at risk. In addition, skipping a scheduled software update may be even more hazardous than skipping a doctor checkup. Moreover, regarding IoT protocols and standards, there is no consent, so devices made by different manufacturers may not work well together. The lack of uniformity prevents full-scale integration of IoT, therefore limiting its potential effectiveness. Although IoT promises to reduce the cost of healthcare in the long-term, the cost of its implementation in hospitals and staff training is quite high.IoT and cloud computing are considered as the significant techniques in big data processing since they are closely related to each other. The experimental process mainly focuses on utilising both IoT and cloud computing in healthcare procedure especially in brain tumour prediction. The dataset required for disease diagnosis is collected and securely transformed to the cloud. Then, the fuzzy convolutional neural network algorithm is proposed for medical image diagnosis. The proposed algorithm classifies the images into normal and abnormal images, and the results from the analysis are transferred to the doctors and healthcare providers with the help of IoT for further treatment process. From the results, it is concluded that the proposed algorithm outperforms other existing algorithms and can be effectively utilised for image diagnosis. In future, other deep learning neural network algorithms can be applied for improving the accuracy of disease prediction and for efficiently analysing large volume of medical data by utilising the medical resources.


We present a compact optical head design for wide-range and low noise displacement sensing using deep frequency modulation interferometry (DFMI). The on-axis beam topology is realised in a quasi-monolithic component and relies on cube beamsplitters and beam transmission through perpendicular surfaces to keep angular alignment constant when operating in air or in a vacuum, which leads to the generation of ghost beams that can limit the phase readout linearity. We investigated the coupling of these beams into the non-linear phase readout scheme of DFMI and implemented adjustments of the phase estimation algorithm to reduce this effect. This was done through a combination of balanced detection and the inherent orthogonality of beat signals with different relative time-delays in deep frequency modulation interferometry, which is a unique feature not available for heterodyne, quadrature or homodyne interferometry. Laser interferometric displacement sensing is a central tool in precision metrology, inertial sensing, quantum technologies and prominently, gravitational wave detection experiments . Differeiometers , and foriometers ,5,6,7 aniometers ,9. Deep iometers ,12,13.DFMI can be used to implement multiple displacement (and tilt) sensors with minimal complexity of the ultra stable optics, the so called optical heads. Its major feature is the multi-fringe dynamic range phase readout with single-beam, unequal-arm-length interferometer topologies, which are enabled by creating a deep phase-modulation-like interferogram from which the desired phase information can be extracted in real time . An impoA schematic of a DFMI multi-sensor displacement and tilt readout system is shown in The modulation index As first demonstrated by Heinzel et al. , one cano 100 Hz ,17. Non-o 100 Hz , (3) ampo 100 Hz . Active Related techniques using a similar laser modulation scheme with different readout algorithms have also been developed, one prominently with an additional, range-resolved multiplexing capability that is enabled by using stronger modulation indices and windowed signal extraction . MultiplAvoidance of ghost beams generated at points of transmission between inner and outer medium.Avoidance of back-reflections into the fibre and resulting parasitic beams.Minimal assembly (bonding) efforts.Avoidance of polarisation optics and/or major optical power losses.DFMI is capable of performing displacement sensing with sub-picometer-level displacement noise using small, single-component interferometers as optical heads , and conLimited longitudinal operating range due to an inherent lateral beam-shift.Angular alignment change between air and vacuum operation, leading to increased alignment efforts with required pointing prediction.Angular coupling of refractive index fluctuations when operated in air.The presence of ghost beams is well known to cause non-linear, cyclic phase noise in heterodyne and homodyne interferometers, and this especially includes parasitic reflections from fibres, which often have significantly higher phase dynamics than ghost beams generated within the often ultra-stable optical heads . ReducinThe limited longitudinal range of operation is not a major factor for applications with very limited macroscopic length changes, such as test mass readouts in satellite gradiometers , but it To avoid some of the off-axis design disadvantages, we study here an on-axis design of a compact, quasi-monolithic, single-component optical head. In the following section we present the conceptual design of the optical head; discuss beam propagation, manufacturability and thermal effects; and show the simulated dependence of the interferometer signals on different test mass alignments. In A top view schematic of the on-axis optical head design is shown in The usage of polarisation elements has been studied for the Laser Interferometer Space Antenna (LISA) optical bench and test mass readout , and comL to the test mass is 5 cm. The paths of the two nominal beams are largely in common within the cube, with the propagation of the long arm towards the test mass being the major difference between them. Temperature-induced thermal expansion and changes of refractive index will cancel out to a large degree for the component internal beam path, leaving only the long-arm length change due to thermal expansion of the component as first order coupling. This effect is of course dependent on the mounting strategy of the interferometer and on the expansion of the spacer that defines the distance to the test mass, two effects which can partly cancel each other if materials with suitable coefficients of thermal expansion are used. For a characteristic length of 5 mm and a coefficient of thermal expansion of We assume that the minimal distance The dimensions of the optical head are compatible with a beam-diameter of about 1 mm, taking into account chamfers and that coatings typically do not extend into the corners on each surface. Compared to previous off-axis designs this leads to further reductions of component volume and size, giving even more prominence to the fibre collimator dimensions in the design of the optical head.All parts that are used for constructing the QMC are standard optical components that can be manufactured, and are available, with low angular tolerances ,26, provWe studied the influence of test mass tilts on the interferometer signals using the IfoCAD software library . We assuInterferometric contrast within the analysed range of tilt stayed above r design . The posIn an idealised set-up, with perfect alignment and anti-reflective coatings, all light sent into the optical head is used for the readout, no light is reflected back into the fibre, only the nominal polarisation is present and no parasitic ghost beams are generated. Since anti-reflective coatings and bonding interfaces have some residual reflection in any real device and polarisation leakage also occurs, we have to assume that non-negligible ghost beams are generated on various surfaces in our design. Theses parasitic beams will lead to additional interference signals that can limit the phase readout noise and linearity. Estimating the coupling of these parasitic beams to the phase readout is non-trivial for DFMI, because the effective modulation depth of each interference beat signal depends on the relative propagation delays between any two beams. This effect not only complicates the analysis; it also provides opportunities to adapt the phase estimation algorithm to reduce the influence of parasitic beams. For large differences in effective modulation depth, the resulting signals become orthogonal, a fact that is used in other interferometer techniques to implement multiplexing capabilities. In the following analysis we use this fact to reduce the influence of parasitic ghost beams that are caused by parasitic surface reflections.Another technique we apply is prominently used in heterodyne interferometers to reduce the influence of ghost beams, the so-called balanced detection. It uses the two complementary output port signals of the interferometer, from which amplitude and phase are extracted and then combined in the complex plane to null the additional optical interference terms . ApplicaBefore we can begin the analysis, we must introduce some simplifications to make the equations more readable. In the following, we write interference signals without considering the constant DC terms  of Please note that this result is not physical; it defies energy conservation because of the unitary transmission for the nominal beams. However, this description is sufficient to demonstrate our ability to effectively cancel all of the here listed parasitic contributions.To probe into the effect of this signal on the phase estimation, we expand the three beat notes using the Bessel functions of the first kind and then we add their contributions to the injected harmonic amplitudes of the modulation frequency for each output port in a numerical simulation. Then we use a non-linear Levenberg–Marquardt fit operating on the harmonics We also make the following assumptions: The pathlength difference corresponding to Using Since we assume that n, and we extend the fit function with two parameters, a ghost beam phase Thus far we have shown the use of balanced detection as a dedicated post-processing step, but we recognise that the separate fitting of two complementary output ports could be integrated into a single fit routine that also takes the presence of a parasitic ghost beam beat-note at the same modulation depth into account. Since the fit is inherently non-linear, we also hoped to increase robustness when making the fit function more physical for later experiments. We adapted our fit to operate on two sets of harmonics, with the indirect output port harmonics having a negative sign in their index, The fit with included balanced detection achieved the same linearity results in our simulation; it increased computation times by up to a factor of two and reduced the parameter error standard deviations by a factor of In the next step of our analysis, we include the second parasitic reflection from surface 8, as shown in inset (b) of m and for the worst-case value of The second beat has already successfully been suppressed by excluding the first harmonic, Since the third beat signal has twice the effective modulation depth, we can use the orthogonality to expand our fit algorithm such, that it searches for two additional parameters, the power of the second tone m and for the worst-case value of The results using this fit are shown in Thus far we have shown that we can adapt the DFMI readout algorithm to account for parasitic reflections occurring at surface 8 of our design in such a way that non-linearity can be reduced to levels below As shown in the two lower sketches of c being the speed of light, and the amplitude is To complete the analysis for the design based on perpendicular external surfaces, we analyse the effect of ghost beam 3 . To simplify the description, we assume from here on that the differential delay is negligible d, which is With the previous analysis we covered all signals of the upper left quadrant of this matrix. The lower right quadrant produces beat signals at the same modulation depth; as covered by our fit algorithm, their phases should by largely coherent and the corresponding beat amplitudes are in the order of As expected from our previous analysis, the beat signals further away in modulation depth contributed less, and the tones that were suppressible via balanced detection also had a slightly weaker influence when compared to other signals at the same modulation depth. d and L can be adjusted, one could study other combinations that might be more or less susceptible to these couplings. Applying even more complex fit algorithms is of course also possible, but the number of extracted parameters (seven for Since the ratio of Estimating the coupling of ghost beams that are caused by polarisation leakage requires a different analysis not covered here and will also depend strongly on the quality of the optics, coatings and alignment. The most dominant polarisation ghost beams will create beats at the nominal modulation depth, making them non-suppressible by extending the fit. Since such beats will follow the nominal phase, to first order their coupling is expected to be, at most, a second order effect. An experimental study of a similar combination of a polarising beamsplitter and a quarter waveplate for LISA found no measurable non-linearity within their sensitivity . CompariFor actual implementations, we recommend operating the optical head first in a calibration mode in which the long arm beam is dumped. In this mode, only beat signals between R. For R therefore reduce the phase measurement spurious-free dynamic range but might be acceptable if the test mass motion is sufficiently small during times in which the full phase readout sensitivity is required. Deviations of the beam splitter cube reflectivity from The noise sources neglected in the above analysis will always limit the displacement readout noise floor to similar levels as in previous implementations, if the same opto-electronics and power levels are used, to about 230fm/Hz . Withouttopology . Larger ocessing .Realising on-axis optical heads for DFMI is an interesting approach to enable picometer-level displacement and nano-radian tilt sensing and micrometer-accuracy absolute ranging that can be realised using compact QMCs. Major advantages are the extended longitudinal measurement range and fixed beam-angles, which will simplify alignment strategies, especially if several sensors are to be used to probe a single test mass. One expected disadvantage of our design, non-linearity due to zero-angle generation of ghost beams, can be suppressed by extending the readout algorithm of DFMI. Balanced detection and fitting of higher modulation depth signals were shown to be effective extensions that reduce ghost-beam-induced non-linearity in simulations to levels below The complexity and orthogonality of DFMI beat signals also provides opportunities for other multiplexing and ghost beam suppression schemes, and reducing non-linearities due to the laser modulation . The red