Implicit Association Test (IAT) assessing the association between the Mental Illness and Physical Illness target categories to the Psychological and Biologic attribute categories, representative of the causal explanation domains, was developed. The IAT presented 22 stimuli (words and pictures) to be categorized into the four categories. After 360 university students completed the IAT, a Many-Facet Rasch Measurement (MFRM) modelling approach was applied. The model specified a person latency parameter and a stimulus latency parameter. Two additional parameters were introduced to denote the order of presentation of the task associative conditions and the general response accuracy. Beyond the overall definition of the latent measurement dimension, the MFRM was also applied to disentangle the effect of the task block order and the general response accuracy on the stimuli response latency. Further, the MFRM allowed detecting any differential functioning of each stimulus in relation to both block ordering and accuracy. The results evidenced: a) the existence of a latency measurement dimension underlying the Mental Illness versus Physical Illness - Implicit Association Test; b) significant effects of block order and accuracy on the overall latency; c) a differential functioning of specific stimuli. The results of the present study can contribute to a better understanding of the functioning of an implicit measure of semantic associations with mental illness and give a first blueprint for the examination of relevant issues in the development of an IAT.The present study aimed at the definition of a latent measurement dimension underlying an implicit measure of automatic associations between the concept of mental illness and the psychosocial and biogenetic causal explanatory attributes. To this end, an  Research on the aetiology of psychiatric disorders has vastly expanded our knowledge on the genetic and neurobiological underpinning of mental illness origin of mental illness, i.e., the causes to which the condition is causally attributed, has been hypothesized to be one of the main components underlying stigmatizing processes towards mental illness The The heterogeneity of findings pointed out the different theoretical accounts of mental illness stigma, which go beyond the scope of the present study, and the myriad of stigma measures, which is the starting point of the present study by calling for a standardization of measurement and for an exploration of instruments targeting more subtle processes in mental illness stigmatization.A substantial line of research has indeed developed several instruments for the assessment of stereotypes, causal beliefs, cognitive representations, and affective reactions  related to mental illness So far, nearly all research on mental illness stigma has assessed individuals’ explicit attitudes, beliefs, and/or stereotypes by considering their voluntary and controllable features, as measured by self-report instruments Implicit Association Test (IAT) A relevant advance in research on stigma covers the investigation of the role of implicit processes in the expression of bias and the related development of measures assessing implicit cognitions and attitudes expressed outside the individual’s conscious control. Speeded reaction-time tasks such as the A recent line of research has examined the value of including indirect assessments of implicit stigmatizing attitudes and stereotypes and of examining the role of stigma dual processes in both healthy and clinical samples, providing promising results about the differential functioning of negative attitudes and mental illness-related cognitive representations when measured at both the explicit and implicit level When considering the indirect assessment of mental illness-related causal beliefs, only one study addressed it specifically about depression compared to physical illness among psychology undergrads The literature sketch provided so far encourages the experimentation of indirect measures for the assessment of more covertly expressed features of stigma, which can open an additional window on stigma by focusing on more automatic aspects of stigma towards mental illness. More precisely, the present work focuses on one of the elements hypothesized to be an antecedent or at least a component of stigma: the attribution of mental disorders to either biogenetic or psychosocial causal factors Mental Illness and Physical Illness with the attribute dimension .The main purpose was to analyse the semantic automatic association between the concept of Mental Illness, relative to its natural and most obvious contrast category, i.e., Physical Illness The IAT was chosen for the following reasons:The relative nature of the concept of mental illness in comparison to the physical illness, in terms of salience and negative valence, prompted the use of a relative measure of implicit associations towards the two concepts e.g., ;Within the corollary of indirect measures of implicit cognition, the IAT has been the most widely used and tested and is one of the most reliable Mental Illness and Psychological categories on one side and Physical Illness and Biologic categories on the other side, are hypothesized to be categorized faster when sharing the same response key  than when sharing different response keys , pointing to a stronger association of target and attributive domain. It is here hypothesized that the individual associative network surrounding the concept of mental illness may influence the process of associating mental illness to the psychological or biological domain, following the automatic associations network account theorized by dual-process models of cognitions According to the traditional IAT structure . An additional variable was considered in the analysis: the standard procedure of counterbalancing the IAT critical blocks. The reason for considering the order of presentation of the task blocks deals with the so-called ‘compatibility effect’, which refers to the facilitation effect of presenting the congruent block first on the task performance and which is one of the main criticisms raised towards the IAT Many-Facet Rasch Measurement model (MFRM) The objective of going in depth into the functioning of the IAT measure here devised is to be considered as an attempt for a detailed analysis and decomposition of the ‘working mechanisms’ of an IAT. The methodology used for the analysis is inscribed in the family of Rasch models. The additive conjoint measurementThe Rasch modelling approach has a long tradition in the development and psychometric analysis of psychological, educational, and medical assessment tools e.g.,  and it hGiven the inner assessment features of implicit measures, including the IAT, the adoption of a Rasch modelling perspective seemed to be a possible answer to the question of whether it is possible to reach a deeper comprehension of the IAT measure and to run a first attempt to establish its measurement validity. The main idea underlying the application of a Rasch modelling perspective lies on the consideration of the stimuli categorization task required in the IAT as a variant of the item responding performance required when responding to traditional self-report measures. According to this conceptualization, IAT stimuli can thus be considered just like questionnaire items are traditionally conceived in a Rasch analysis, since in both cases respondents should reply according to the hypothesised underlying psychological process(es) and/or construct(s) There are several advantages for using the MFRM, and more in general Rasch models, in the investigation of implicit associations and implicit measures:All Rasch models conform to the properties of stochastic independence, specific objectivity, linearity, and measurement unit  or of a task (stimuli categorization), such as, in the case of the present study, the order of presentation of the task associative conditions and the general response accuracy The MFRM allows modelling, besides the traditional subject and item parameters, other variables, or All model parameters, or facets, are located on the same latent continuous trait, allowing comparisons between their elements All facets lie on a common latent dimension of categorization latency; the speed of categorization of the stimuli is expressed by interval measures characterized by a common measurement unit, which, if the data fit the model, maintains the same size over the entire continuum;As a consequence of the specific objectivity, the measures obtained by the model are sample-, stimulus-, condition-, and all other facet-free and can be compared with any other;Specific goodness-of-fit statistics assess the fit of the data to the model and are highly informative about the results interpretation of each single stimulus, participants, response accuracy, order of task blocks, or any other relevant variable in the model;The MFRM allows interaction analyses among different facet parameter estimates, to detect any differential functioning of any facet parameter estimate in relation to the other variables entered in the model. This feature is of great importance in so far as it provides a powerful tool to examine systematic patterns of deviations from the model expectations in the data, and to identify possible factors causing this patterns .A detailed description of the MFRM model applied in the present study appears further in the Material and Methods section.In keeping with the local Institution Ethics Review Board regulation, the study was presented during regular lectures in the university course of Research Methodology in Psychology as an example of how to conduct scientific research in Psychology. Participants who refused to take part in the research did not incur any disadvantage in fulfilling the course requirements.Written informed consent was obtained from all participants after the procedure had been fully explained and data anonymity and for research use only was guaranteed. Participants were told that they were totally free to withdraw from the study at any time. Responses were anonymised by assigning to participants an identification number before starting the study. Collected data were then aggregated and analysed at the group level.SD = 3.146).The study involved 360 undergraduate students of the University of Padua  versus biogenetic (Biologic) causal explanations of mental illness. Mental Illness was contrasted to Physical Illness for two main reasons: first, mental illness is a negative concept given that it reflects illness, so physical illness seemed the most obvious comparison term for its salience and since it is akin to entail negative evaluations as mental illness  or a right (I) key on the keyboard.The IAT comprises seven blocks of trials see : three sMental Illness/Psychological versus Physical Illness/Biologic) matches respondents’ automatic associations between the two concepts, versus the other block, where the target and attribute category pairing is mismatched . Therefore, an individual who presents a stronger automatic association of psychosocial features and elements with mental illness is expected to respond more quickly when Mental Illness and Psychological categories are paired and contrasted to Physical Illness and Biologic pairing (compatible block), when compared to the reversed pairing (incompatible block).The logic behind the IAT is that stimuli are classified more quickly during one critical block, when the target and attribute category pairing , the two single classification tasks are combined in such a way that participants have to respond to Mental Illness words and Psychological pictures with one key (E), and to Physical Illness words and Biologic pictures with another key (I). In the other double categorization task (blocks 6 and 7), the target and attribute categories pairing is reversed.The participants were successively presented with mental and physical disease words and pictures depicting psychosocial situations and objects pertaining to the biology and natural sciences realms. The stimuli had to be classified into the The IAT was administered on a 15-inch personal computer in a controlled laboratory setting. Each block was preceded by a short instruction page reminding the exact key assignment and to be ready to correct the answer in case of mistake (a red cross appeared at the centre of the screen).Compatible refers to the block order condition in which participants completed the congruent task first, whereas the term Incompatible refers to the reversed block order.The presentation order of the combined tasks was counterbalanced across participants A set of stimuli for the four IAT categories was developed by selecting the most representative exemplars for each concept. Within each IAT block the stimuli were presented equally often in a random order across participants on a blank black background.Mental Illness category the five selected words were the following: depression, schizophrenia, psychopathy, paranoia, and hysteria. For the Physical Illness category the five stimuli were tumour, heart attack , pneumonia, flu, and diabetes.For each target category five words were chosen from a set of 20 exemplars for both concepts. The selection of ten mental diseases and ten physical diseases followed the diagnostic categories of DSM-IV-TR and ICD10 and a Google search typing Italian words for different mental and physical diseases and picking up the most cited. The 20 words were then evaluated by a team of experts in mental health care and medicine on the following criteria: ease of categorization into the appropriate category, frequency and usage of the word in the daily language, and representativeness of the target concept. For the Psychological and Biologic, six pictures were chosen from a set of 15 cartoon images depicting common exemplars of the two concepts. The use of pictures instead of words was due to the difficulty in conveying aspects of biological and psychological semantic areas by means of words without ambiguity or misinterpretation. A Google search of pictures was carried out by using key words such as ‘relationship’, ‘psychological’, ‘social’, ‘psychology’, ‘biology’, ‘genetics’, and ‘natural sciences’ and looking for coloured and stylised cartoons. The final 12 pictures had to meet the following criteria: representativeness of the domain and pictorial features . For the Psychological category, the six pictures depicted several interpersonal relationships: a mother/child relation, a grandparents/grandchildren relation, a work meeting, peers fighting, a romantic couple relation, and a family relation. For the Biologic category, the six pictures depicted several objects pertaining to the area of natural sciences, biology, and chemistry: the image of a cell under the microscope, a filament of DNA, a coloured image of an atom structure, test tubes for clinical analysis, a microscope, and two chromosomes.For each attribute, Psychological referred to the individual experience and the relations between individuals and the surrounding environment, whereas the category Biologic referred to anything related to the organic and biological aspects of life.To improve the visual understanding of the pictures an instruction page at the beginning of the task informed the participants that the category Prior to the MFRM analysis, the IAT data were reduced and checked out for abnormal response patterns according to the standard IAT data cleaning procedure i the median value of its response latencies distribution in the pooled critical blocks was computed. The median statistic was chosen as a measure of the latency central tendency of participants’ responses to the stimuli due to the lower sensitivity to the distribution tails than the mean statistic. The distribution of the stimuli median latencies was successively discretised into a three-category rating scale according to two percentile values (33rd and 66th) computed on the N participants×22 stimuli full data matrix to identify fast, medium, and slow response latency For each stimulus Participants’ average response accuracy and Compatible and Incompatible order conditions were further added in the matrix. Similarly to the response latency data discretization procedure, the percentage distribution of correct responses in the pooled critical blocks was discretised into two categories according to its median value. Participants were then indexed by two levels of average response accuracy, Low  or High .. A binary variable sorting out participants’ assignment to the two block order conditions was then added in the data matrix.Given the acknowledged effect of the counterbalanced order of task blocks presentation on the IAT effect and task performance Data collected and used for the analyses in the present study are available upon request.Simple Logistic Model (SLM) x to a test, which can be correct (1) of incorrect (0), as an additive function of the ability of respondent v and difficulty of the item i, as expressed on the logit scale The MFRM facets), in addition to respondents’ ability and item (or stimulus) difficulty, accounting for the likelihood of a response.However, in the measurement contexts complex situations are more the rule than the exception, and other aspects may interfere with the person and item attributes, such as specific experimental, social, and personality attributes. Within the context of Rasch modelling mono-dimensionality and mathematical properties  and the stimuli parameters (facet 2), the parameters describing the facilitating effect of the block order (facet 3) and the general accuracy (facet 4) on response times were added in the model equation. An additional parameter accounting for the response latency rating scale v would respond to stimulus i in the task order setting b with a general accuracy level c, with a response speed k rather than v ability (categorization ability/speed) parameter, i difficulty (ease/speed of categorization) parameter, b of presentation of the critical blocks on the categorization speed, c, and k rather than The MFRM Equation specifies the probability that a respondent logit scale, i.e., the logarithmic transformation of the probability of giving a particular response given certain conditions . In the MFRM Equation, the logit of a certain response k can be seen as the dependent variable, whereas the various factors act as independent variables that influence (or control) the response.The Rasch model parameters are additive, thus satisfying one of the requisites for interval measures, and are based on the transformation of scores into a All parameter estimates were positively scaled in the analyses, so that high positive values indicate fast responses, whereas negative measures indicate slow responses.mean square Outfit and mean square Infit statistics. These statistics are calculated for each participant, each stimulus, and any other facet parameter and express the relationship between observed and model-derived expected responses, ranging from zero to infinity  χ – is also provided for each facet and tests the hypothesis that the elements of a facet have the same logit in relation to the measurement error (SE). In other words, the Chi-square statistic helps to reject the null hypothesis that there is no group-level difference in the different elements composing a facet. For instance, a Fixed  χ2 with an associated p-value lower than. 05 for the stimuli facet points to the presence of group-level differences among the stimuli, which are sorted out with different speeds. A conceptually similar index is the separation reliability (R), which indicates how well the elements of a facet are separated to reliably represent the facet and ranges from 0 to 1. It reflects an estimation of the relationfship between true scores and true variance: observedSD is the standard deviation of the estimates (not corrected for measurement error).A Chi-square statistic – the t distribution, df) equal to the sum of the respective dfs.Once estimated each facet measure, it is possible to compare different parameter estimates by standardizing their paired difference, which approximates to the Student’s t statistic. This feature allows identifying possible factors causing any systematic deviation from the model expectations in the data, such as the ordering condition the stimuli are presented in . In particular, the differential stimulus functioning (DSF) analyses the interaction between elements of the facet stimuli and elements of other facets. For instance, the bias index involves introducing an interaction parameter between the facets into the model . The logit of a stimulus i in the order condition b is computed by adding a bias smeasure to the overall speed of categorization of the same stimulus i if the response to the latter is faster in that order condition than overall and by subtracting it if the response to the stimulus is slower. The two biased stimulus measures are then subtracted and tested via the above-mentioned paired t-test.The MFRM gives the possibility to carry out the bias/interaction analysis, i.e., the analysis of the interactions between elements of different facets The application of the MFRM analysis to the IAT data was operationalized as follows:Verification of a common latent trait wherein the IAT stimuli parameter estimates and participants’ parameter estimates express their location on the latent dimension on a common measurement unit, which describe their speed of categorization;Estimation of the other facets parameters , which allows a decomposition of the overall response latency on the measurement dimension;Analysis of the effect of task block order on the stimuli functioning across the critical blocks ;Analysis of the effect of the two-way interaction of task block order and general accuracy on the stimuli functioning;The analyses were performed using FACETS 3.60.0 software SD = 1.51), with satisfactory Infit and Outfit statistics in the range. 70–1.30. The Fixed Chi-Square evidenced that participants presented significantly different categorization speed estimates on the latent dimension (χ2(357) = 3044, p<.001, R = .91).The mean value of the person parameter estimates (2(21) = 1721.8, p<.001, R = .99). In Each stimulus parameter estimate . The stimuli from 1 to 10  showed positive parameter estimates in the range [.43–1.42]  can provide further information about the interpretation of the stimuli parameter estimates. By considering the word stimuli, Physical Illness stimuli . This means that the stimuli referring to the psychological evaluative category were categorized slightly faster.The cross-tabulation of the types of stimuli (word vs. pictures) by the four IAT categories  = 81.2, p<.001, R = .99), with Infit and Outfit statistics in the range. 70–1.30. The two parameter values suggested that in the Compatible order of presentation the sorting task was performed quicker than in the Incompatible condition. In other words, at the group level people who completed the congruent critical blocks first replied faster than people who completed the critical blocks in the reversed order.The Compatible (C) and Incompatible (I) order of critical blocks resulted to be located on the latent continuum and were categorized with different speeds  = 107.2, p<.001, R = .99). At the group level, low general response accuracy was associated to quicker responses.The High (H) and Low (L) general accuracy levels were associated to different speeds of categorization on the latent dimension  and 14 , compared to the Incompatible block order. Conversely, the response latency measure was smaller in the Incompatible condition for stimuli 7  and 21 . These results showed that four out of the 22 stimuli were differently categorized in the two block order conditions: two pictures pertaining to the Psychological category and two stimuli pertaining to the Physical Illness and the Biologic category, respectively. No statistically significant evidence was found for the remaining stimuli, showing that although participants who completed the congruent critical blocks first replied on average faster than people who completed the critical blocks in the reversed order, the comparison between Compatible and Incompatible order conditions on each stimulus response latency showed only a few affected stimuli.The bias/interaction analysis for stimuli and order of presentation of the critical blocks showed several DSFs. The response latency parameter estimate was significantly smaller in the Compatible order condition for stimuli 12  were given in the Low accuracy condition, whereas the slowest responses (.452) were found for the interaction High accuracy/Compatible. In the top left part of Psychological stimuli are summarized, the accuracy marginal mean values showed that the fastest responses (−.478) were given in the Low accuracy performance, whereas the slowest responses (−.923) occurred in the combination of Incompatible block order and High accuracy. Also in the bottom left part of Physical Illness stimuli, faster responses (1.161) were associated to Low accuracy and, similarly to Mental Illness stimuli (−.573), the slowest responses (.722) were found in the High accuracy/Compatible cross-tabulation. In the bottom right part of Biologic attribute stimuli were similar to the Psychological ones, i.e., the accuracy marginal mean values showed that the fastest responses (−.573) were related to the Low accuracy, whereas the slowest responses (−1.013) were associated to the interaction High accuracy/Incompatible. The block order marginal mean values did not differ significantly across the four sub-tables, except for the Psychological stimuli, where responses were clearly faster (−.535) in the Compatible order.In the top left part of L) was devised, which combines the effect of the two variables. The parameter estimates for response accuracy and block order were cross-tabulated for each stimulus. Twenty-two 2×2 tables (see l) and High (h), and the block orders, Compatible (c) and Incompatible (i), was computed. The L measure was then obtained by combining the four interaction parameter estimates To analyse the interaction effect of general accuracy and block order on each stimulus latency estimate, a single, comprehensive index  and Incompatible (i) block order at a High (h) level of accuracy;l) level of accuracy.S(L) index was then computed by subtracting the two standard values, The standardized S(L) for the 22 stimuli are presented in Mental Illness and Psychological categories. The remaining 55.6% of stimuli showed a positive S(L) index value, indicating that the blocks order effect was higher even when the accuracy level was also high. This means that despite of the presence of a general high level of accuracy, the order of presentation of the critical blocks affected the categorization of these stimuli. In this case most of the stimuli belonged to the Biologic category.The standardized latency indices S(L) index, grandparents/grandchildren relation (S(L) = −2.46, p<.01), family relation (S(L) = −2.24, p<.01), test tubes (S(L) = 2.82, p<.01), and chromosomes (S(L) = 2.39, p<.01). For these stimuli the effect of the ordering conditions on the latency measures was clearly different when distinguishing the two levels of response accuracy.When testing the statistical significance of the Within a measure validation perspective, this result demonstrated that 18 out of the 22 stimuli did not present any differential stimulus functioning, i.e., they did not evidence any significant bias related to the individual response accuracy and the IAT block order conditions.The present study aimed at the development of a latent measurement dimension representing an implicit measure of the semantic associations of mental illness with the psychological and biological domains, to evaluate the extent to which the concept of mental illness, relative to physical illness, is automatically associated to the two causal explanatory domains. To this aim, an IAT was designed. The adoption of a latent trait modelling approach in the form of the MFRM model pursued the psychometric investigation of the hypothesised latent dimension. The model identified the targeted measurement dimension wherein the IAT stimuli are located according to their latency parameter estimates, which describe the ease of categorization in terms of speed into the IAT categories they belonged to. Furthermore, the response latency was decomposed into the additive combination of several facets, i.e., the person’s general categorization speed, the stimuli easiness of categorization, the facilitating effect of the IAT method-variable of block ordering, and the general response accuracy in the critical blocks.Mental Illness versus Physical Illness-Implicit Association Test (MIPI-IAT). The ten word-stimuli were categorized faster than the 12 picture-stimuli, meaning that it was easier for the respondents to categorize both Mental Illness and Physical Illness target categories than Psychological and Biologic attribute categories. This might be due to several reasons. The actual type of stimuli used, words versus pictures, may have a different effect on the task performance and on the response latency – since words emerged to be processed quicker that pictures – as already pointed out in the discussion on implicit measurement The 22 stimuli showed a satisfactory fit to the model requirements and identified the latent dimension underlying the performance of the Physical Illness stimuli were recognized faster than Mental Illness stimuli. This result may deal with the higher familiarity in daily life with physical health problems, but also with the fact that mental illness exemplars might differ in valence  when compared to physical diseases. However, these hypotheses need to be further tested, since no familiarity and/or valence measure was provided to the participants.However, although targets were categorized quicker than attributes, when considering the target stimuli only, it was also evidenced that Psychological category were categorized more easily  than stimuli depicting the Biologic category. Also this result might be related to the higher familiarity with daily, social relationship-related contents, such as the mother-child or the grandparents-grandchildren relations, or to the difference in valence, since psychosocial aspects might as well entails a positive valence and therefore being categorised quicker than Biologic stimuli. The above-mentioned limitation applies in this case as well, since participants have not been asked to evaluate familiarity and valence of the pictures presented in the task.On the other hand, when considering the picture-stimuli only, it emerged that pictures depicting the Psychological semantic category has been used by the participants as the salient dimension to perform the categorization task in the critical blocks, since when it first appeared in combination with the Mental Illness target category the task was easier and performed quicker than when it was first presented in combination with the Physical Illness target category. This hypothesis can encompass two possible accounts: a first one pointing to the hypothesised automatic association of mental illness to the psychological domain and physical illness to the biologic one. In this case, being presented first with a mismatched pairing could slow down responses and make the task more difficult, as evidenced by the Rasch measure of the Incompatible ordering. A second potential account for the difference in the categorization speed of the attributes and target categories could be related to a task-recoding strategy applied by the participants during the performance of the task Nonetheless, it could be also likely that the Recoding-Free IAT (RFIAT) Single Category IAT (SCIAT) Single Attribute IAT (SAIAT) Mental Illness/Psychological versus Mental Illness/Biologic).Beyond the addition of a control measure of salience and/or valence, to probe these possible accounts of the difference in the categories classification a feasible strategy could be the use of a Considering the response accuracy, the results showed the effect on the speed of categorization of generally performing the task more or less accurately, with a low accuracy associated to faster reaction times.Mental Illness/Psychological versus Physical Illness/Biologic) was presented first, the sorting task resulted to be more efficient and faster than when the incongruent pairing was the first critical block . This result is consistent with the literature on the ‘compatibility effect’, which evidenced how the discrimination task tends to be easier and quicker, with a consequent larger IAT effect, in the Compatible block order grandparent/grandchildren relation and peer fighting, whereas speed was higher in the Incompatible condition for stimuli heart attack and microscope. These four stimuli then presented a differential functioning related to the counterbalanced order of the critical blocks, most probably triggering the associate ‘compatibility effect’.When the hypothesised congruent pairing , since their categorization performance may result quite different for different reasons, as previously evidenced in the univariate analysis of the stimuli parameter estimates.L) index was proposed, which easily describes the order effect and participants’ general level of accuracy at the same time and directly elucidates in one shot the stimulus differential functioning resulting from the combination of the two facets. The standardized S(L) index evidenced that the combination of the block order and accuracy levels significantly affected only four out of the 22 stimuli. This means that 18 of the 22 stimuli were invariant across conditions and individual accuracy, suggesting that in large part the stimuli functioning was equal independently of task performance and IAT method variable.To further summarize the findings about the interaction effect of accuracy by block order on the stimuli latency, a new latency ((L) index, further analyses with different IAT versions and datasets are recommended.To better explore and deepen the properties and possible applications of the latency Mental Illness category, such as schizophrenia, depression, or hysteria, required more time to be categorized than Physical Illness stimuli, such as pneumonia, flu, or diabetes. On the other hand, Psychological stimuli depicting psychosocial situations were recognized faster than stimuli depicting Biologic cues. A possible implication of this result focuses on the assessment utility of the MIPI-IAT, i.e., when people were asked to discriminate mental diseases exemplars, the implicit measure is able, at least to some extent, to catch the occurrence of uncontrolled and automatic underlying mechanisms that may interfere with participants’ response speed, including the higher familiarity with psychosocial representations when compared to objects pertaining to the natural and biological sciences realms, but also of possible task-recoding strategies in the performance of the task and, last but not least, of the enquired associative links between the targeted concepts and the attribute categories.In summary, the MIPI-IAT devised in this study is represented by a latent measurement dimension composed of 22 stimuli. One of the main results claimed that word-stimuli for the The subsequent step in the investigation of the implicit associations between mental illness and the explanatory realms of psychosocial and biogenetic causal attribution might be to separately test the potential contribution of each above-mentioned mechanism by, for instance, comparing on the same latent measurement dimension identified by the MFRM the performance of an absolute implicit measure  and a structurally modified version of the IAT (e.g. RFIAT) using the same stimuli.From a methodological perspective, the present study evidenced the usefulness of considering the specific contributions of stimuli and categories in the performance of an IAT. The MFRM model proved to be a suitable and flexible tool to devise a measurement latent dimension underlying the MIPI-IAT. The model represents a rigorous frame of reference in which estimating and comparing the speed of categorization of the stimuli. By allowing the analysis of differential stimulus functioning, the model unravelled the contribution of each stimulus to the overall IAT measure. Moreover, it allowed the decomposition of the stimuli response latency into the different facets that can interact one to each other in producing a response, such as the block order and the general accuracy.Mental Illness and Psychological is activated first. Conversely, in the Incompatible order participants are slower and more careful, given the presentation of the incongruent block first , which is more difficult and in contrast with hypothesized implicit association between Mental Illness and the Psychological domain.Indeed, the Compatible order evidenced a pattern of fast and less accurate responses, suggesting a trade-off between speed and accuracy when the automatic association between In order to provide further validity to the measurement properties of the MIPI-IAT, additional studies are recommended to better clarify the relations between target and attribute categories by also considering the type and variety of stimuli to be categorized. Further, the IAT was administered to a group of Psychology undergraduate students at the end of their university career, which could have contributed to the complex pattern of results. The respondents’ considerable mental health literacy and strong psychological background are arguable to have influenced the associative network in which the concept of mental illness resides. In order to refine the predictive properties and sensitivity of the measure, such analyses should be replicated with a laymen group and the MIPI-IAT should be compared with other related implicit and explicit measures to better define its theoretical, predictive, and practical aspects.In conclusion, the MFRM analysis evidenced the MIPI-IAT to be a promising tool for the exploration of the stigmatizing processes towards mental illness. The use of the implicit measure here devised can be useful and potentially insightful, firstly to verify whether semantic associations to mental illness are related to more explicitly expressed prejudicial attitudes, and secondly to shed light on the theoretical status of the (implicit and explicit) components of stigma.


In this paper, we demonstrate a comprehensive method for segmenting the retinal vasculature in camera images of the fundus. This is of interest in the area of diagnostics for eye diseases that affect the blood vessels in the eye. In a departure from other state-of-the-art methods, vessels are first pre-grouped together with graph partitioning, using a spectral clustering technique based on morphological features. Local curvature is estimated over the whole image using eigenvalues of Hessian matrix in order to enhance the vessels, which appear as ridges in images of the retina. The result is combined with a binarized image, obtained using a threshold that maximizes entropy, to extract the retinal vessels from the background. Speckle type noise is reduced by applying a connectivity constraint on the extracted curvature based enhanced image. This constraint is varied over the image according to each region's predominant blood vessel size. The resultant image exhibits the central light reflex of retinal arteries and veins, which prevents the segmentation of whole vessels. To address this, the earlier entropy-based binarization technique is repeated on the original image, but crucially, with a different threshold to incorporate the central reflex vessels. The final segmentation is achieved by combining the segmented vessels with and without central light reflex. We carry out our approach on DRIVE and REVIEW, two publicly available collections of retinal images for research purposes. The obtained results are compared with state-of-the-art methods in the literature using metrics such as sensitivity (true positive rate), selectivity  and accuracy rates for the DRIVE images and measured vessel widths for the REVIEW images. Our approach out-performs the methods in the literature. Retinal vascular disorders refer to a range of eye diseases that affect the blood vessels in the eye. Assessment of vascular characteristics plays an important role in various medical diagnoses, such as diabetes Fundus photography involves taking digital images of the back of the eye, such as the retina, optic disc, and macula et al.et al.Classifier based approaches are perhaps the simplest. Two distinct categories of pattern classification techniques for vessel segmentation are supervised (which requires training) et al.Apart from classifier-based approaches, other main categories of techniques in the literature include: matched filtering, morphological processing, vessel tracing, multi-scale processing, and model-based approaches Studies in the literature provide inspiration for our proposed framework. In particular, our approach uses Hessian matrix analysis for curvature evaluation, which delineates the texture features of retinal vessels accurately, in combination with binarization via thresholded entropy to achieve a basic segmentation of retinal vessels. Fine-tuning the segmentation is performed by a further application of morphological operations to prune and identify the vessel segments and remove noise pixels. A novel aspect of this work is the separation of post processing for vessels of different thicknesses, partitioned into two broad classes. This is accomplished using texture mapping with a spectral clustering approach This paper makes three contributions. First, image enhancement is a significant pre-processing step in this paper's algorithm for smoothing retinal images and enhancing the contrast between vessels and the background. It aims to remove noisy regions from the overall retinal image to enable accurate segmentation of retinal vessels. One way to increase the image contrast is to enhance the image ridges associated with the retinal vessels. However, in order to better enhance vessels of different widths, traditional approaches require construction of multi-scale matched filters at multiple orientations Second, accurate segmentation of retinal vessels has the potential to improve the diagnosis of retinal disorders. To achieve accurate segmentation, our approach pre-groups vessels using a morphology based spectral clustering technique. This produces a simple colour map of the fundus images according to textural vasculature features instead of complex threshold processing for the evaluation of multi-scale images. Thereafter, the connectivity constraint is applied to the extracted vessels, with the constraints varied according to different texture regions: for regions where fine-grained noise in relation to small vessels are dominant, a smaller connectivity constraint is selected, and vice versa for regions that mainly consist of coarse-grained noise in relation to large vessels. The texture mapping operation also yields a partition of the pathological vessels from heathy ones, and allows to accurate removal of noise via morphological processing, aiming for accurate isolation of noise from vessels. The results in this paper show that the proposed algorithm outperforms other supervised and unsupervised segmentation methods in achieving high accuracy.Third, one of the key goals of this paper is to achieve mostly automatic width measurement of blood vessels in retinal images from the segmented vessels. An important step in measuring retinal vessels is to extract centrelines and localise vessel edges from the segment image, by making use of the thinning morphology operation and calculating the number of pixels with overlap between the line perpendicular to each of the local vessel centrelines and the pixels from vessel segments. In this method, we introduce a 3×3 window to deal with each of the vessel branches, which is especially effective for vessel branches that cross. Using images from the REVIEW database The method presented in this paper is based on unsupervised classification by finding inherent patterns of blood vessels in retinal images that can then be used to determine whether a particular pixel belongs to a vessel or not. The method uses region-based properties of retinal blood vessels for segmentation via using colour coded mapping to partition eigenvalue related enhancement of retinal images. A flow chart for image segmentation process is shown in The standard paradigm for validating medical image processing algorithms is to compare their outputs with a ground truth, or gold standard, generated by one or more human experts. To enable comparative assessment, we use image and associated manual segmentations from two public data sets available on the web, DRIVE The DRIVE database contains 40 colour images of the retina, watershed algorithmPhotography of the eye fundus typically gives rise to complications such as inadequate contrast, lighting variations, influence of noise and anatomic variability affecting both the retinal background texture and the blood vessel structures In the context of this paper, spectral clustering groups together those regions in the RSG that have come from the same perceptual region, as illustrated in el al.The vessel enhancement technique used in this paper is an eigenvalue analysis of the image Hessian matrix at a single scale, and is adapted from the multiscale version of Frangi When a grayscale image is binarised, a threshold value must be specified. In our approach, the optimum threshold value is determined as the pixel intensity from the histogram of the image that exhibits the maximum entropy over the entire image. To represent spatial structural information of an image, a co-occurrence matrix is generated from the pre-processed image. It is a mapping of the pixel to pixel greyscale transitions  shown in This subsection addresses accurate segmentation techniques when combining the applications of several classical image processing algorithms mentioned above. Segmentations using a curvature based method  show obvious signs of central light reflex. According to Spencer qualitative segmentation results.To achieve clear segmentation of blood vessels in the images of the retina, we conduct simple morphology post-processing, i.e. morphological connectivity constraint operations on the extracted curvature based enhanced images. The connectivity constraint is varied according to different background texture of noise that dominates the image. The fine-grained noise texture  determines small connectivity constraint, and vice versa for coarse-grained noise texture (relatively large contiguous bright region). The morphological spectral clustering is applied for the identification of textural regions. This is to consider the fact that texture appearance is changing with image recording parameters, for instance, illumination variation and direction of view, a problem common to any real surface. The extracted segmentation of texture works like windowing an image, which determines window size, position and shape with different texture appearance, different intensity distribution associated with different texture of background noise discussion section.In order to achieve accurate segmentation of the retinal images, there are nine parameters produced in the segmentation procedure that need be adjusted manually. Actually, according to the resultant segmentation, it is found that such adjustments are simple and slightly changed among each retinal image. For reproducibility, these parameters are illustrated in the We propose a vessel width measurement method to identify a pair of edge points representing the width of a vessel at a specific center point. The first step is to apply a morphological thinning algorithm All edge points are detected using We linearly extrapolate the pixels which form the centreline and make rotation afterwards, as a result, each of the resultant profile contains widest segmented pixels and pixels from additional background region. The principle is to approximate the tangent of windowed centreline mentioned above at any point of it via the connected neighbor pixels in the local region. The resultant profiles perpendicularly cross through tangents and go through the pixel with central coordinate. For windowed centreline consisting of pixels with the same The algorithm has been implemented in MATLAB version R2013a on a personal computer running Windows 7 with an Intel(R) Core(TM) i5-3470 CPU (3.20 GHz) and 8 GB of memory. On this platform, it takes about 24 seconds to process a DRIVE retina image to complete the segmentation. Considering that these results are obtained with MATLAB on a standard PC, the processing times are reasonable, and there is more headroom for improvement with further optimisation or customised hardware et al.et al.et al.et al.In the remainder of this section we first report qualitative results aimed at giving a visual appreciation for the quality of the vessel segmentation and vessel width measurements generated by our method. We then report the quantitative results for our method. The resultant vessel segmentation is calculated for images on the DRIVE database and compared with those reported by Jiang To evaluate the performance of segmentation, we apply our approach to all 20 images of the test set of the DRIVE database. Considering that the masks depicting the FOV included with the DRIVE images are not enough to clean the noise edge of the FOV produced by applying algorithms shown in , for ourTo begin, we apply our method to process the image named 02_test from DRIVE database for illustrative purposes, as the retinal image is clear and without complicating pathology requiring further processing. Following our proposed method, top-hat based morphology preprocessing is first applied on the selected channel shown in By applying different connectivity constraints to the two sub-segmentations, we extract the curvature based segmentation, illustrated in More results of the proposed method that are related to the difference size of noise corresponding to the coloured mapped partitions, as applied to images 04_test and 14_test in the DRIVE database, are illustrated in Three measures are used to quantitatively assess our algorithm's performance: true positive rate (TPR), false positive rate (FPR) and accuracy (ACC). Note that TP and TN are the number of blood vessel pixels and background pixels which are correctly detected, respectively; FP is the number of pixels not belonging to a vessel, but is recognised as one, and FN is the number of pixels belonging to a vessel, but is recognised as background pixels, mistakenly. Based on these definitions, TPR, FPR and ACC are defined as follows:The three measures are calculated for the final segmentations from our algorithm, as well as several other retina vessel segmentation algorithms in the literature. We divide the 20 test images from DRIVE database into three classes: the images with approximately uniform illumination throughout the background and well localised central light reflex (Class 1); the images with approximately uniform illumination but with broken vessels due to central light reflex effect (Class 2); the images with non-uniform illumination and pathological tissues (Class 3). Characteristics of these three class members are easily observed. According to different classes of the images, we adopt different steps of our algorithm. For example, well localised central light reflex is viewed as the large vessels with thin central light reflex effect compared to the space between the vessels and their neighbours. The uniform illumination is viewed such that after a global threshold, the size of background pixels is distributed uniformly, and are not obviously large in one region and small in another. The pathological tissues in retinal images show complex morphology, appearing as a bright protuberance on or around a vessel branch. For the first class of the images with enough spacing between two vessels, we apply multiplication between curvature segmentation and binarisation, with morphological closing operation to eliminate the central light reflex. We adopt disk-shaped structuring element with the radius of 2 pixels to conduct morphological closing operation. For the second class of the images, where the spacing is small and the large vessels show low contrast, we apply entropy filtering to eliminate the central light reflex, after multiplication operation for segmentation. For the third class of the images, we apply the entire set of operations of our algorithm. In The average quantitative results of the three classes of images are listed in In order to evaluate the reliability of automatic vessel edge detection including width measurements, we make use of the images included in the REVIEW database. This comprises of three Image Sets (IS) containing full fundus images: high-resolution (HRIS), central light reflex (CLRIS) and vascular disease (VDIS) with each set containing representative images that are particularly large, show visible pathologies and have vessels exhibiting prominent central light reflexes, respectively. A fourth set, the kick-point image set (KPIS), contains downsampled high-resolution images of several large diameter non-tortuous vessels. The database also offers manual width measurements made by three independent observers using a custom software tool for marking vessel edge points, so that the ground truth edge points are considered to be the average of the measurement made by the three observers at the same location in a vessel segment. A total of around 2000 locations are available for vessel width analysis.Considering the similarity between the vessel widths in ground truth manually delineated edge points and the width measured entirely by our algorithm, width measurement accuracy cannot readily be quantified. The edge image produced by our method will differ from the manual edge detection, which will cause measurement locations and angles to not match up. One way, however, results in good agreement with the manually delineated vessels by overlaying the vessel edge points calculated from our algorithm located on top of the manually segmented images. In order to achieve the manually segmented images, we conduct a morphological close operation on the ground truth points with structuring elements of size The errors mainly arise from the following: (i) The vessel width calculated using our algorithm is 1–2 pixels wider than the ground truth, i.e. In order to qualify the comparison of the images mentioned above, we select successful measurement percentages  or 8 ; intensity threshold value to produce mask using binarisation with threshold chosen for entropy maximisation: from 0.3 to 0.8; intensity threshold value to produce entropy filtering regarding large vessels with central light reflex: from 0.8 to 3; Alternatively, we also suggest to use gray level of colour image, instead of green channel image, to achieve entropy filtered vessel segmentation in the relevant large vessel regions; the threshold used for detection vessel intensity (larger than): from 0.04 to 0.2; the connectivity constraint (larger than): from 5 to 16.The results presented in As distinct from multiscale detection algorithms, which are designed for specific range of vessel sizes, our proposed combined approach for retinal image segmentation adaptively explores local intensity characteristics and local vessel width information via conducting colour coded texture mapping. Two types of feature textures are investigated to identify noise regions with different size. A major feature of the method is its adaptability to particular image intensity properties with different noise contents. In addition, the algorithm described here automates the analysis of retinal vessel widths. It allows the fast calculation of vessel widths all along the length of each vessel rather than at specific points of interest. The quantitative performance results of both segmentation and width measurement show that our method effectively detects the blood vessels with average accuracy of above


The large volume of hyperspectral images (HSI) generated creates huge challenges for transmission and storage, making data compression more and more important. Compressive Sensing (CS) is an effective data compression technology that shows that when a signal is sparse in some basis, only a small number of measurements are needed for exact signal recovery. Distributed CS (DCS) takes advantage of both intra- and inter- signal correlations to reduce the number of measurements needed for multichannel-signal recovery. HSI can be observed by the DCS framework to reduce the volume of data significantly. The traditional method for estimating endmembers  first recovers the images from the compressive HSI and then estimates endmembers via the recovered images. The recovery step takes considerable time and introduces errors into the estimation step. In this paper, we propose a novel method, by designing a type of coherent measurement matrix, to estimate endmembers directly from the compressively observed HSI data via convex geometry (CG) approaches without recovering the images. Numerical simulations show that the proposed method outperforms the traditional method with better estimation speed and better (or comparable) accuracy in both noisy and noiseless cases. Hyperspectral images (HSI) are collections of hundreds of images that have been acquired simultaneously in narrow and adjacent spectral bands, typically by airborne sensors ,2. ThrouIn conventional HSI sensing systems, the full data are acquired and are then compressed before transmission. This paradigm has several disadvantages: first, all the data should be stored; second, the computationally costly implementation of the compression is required to reside on board, housed within the sensing modality. Typically, the sensor platform is a severely resource-constrained environment such as a plane or satellite. As an alternative to the conventional sensing systems, compressive sensing (CS) ,7 is an Blind hyperspectral unmixing (HU) is one of the most prominent research topics in signal processing (SP) for hyperspectral remote sensing ,12. BlinThe traditional method for endmember estimation under the CS/DCS framework consists of 2 steps: (1) recovering the HSI data by CS/DCS methods and (2) estimating the endmembers from the recovered data by HU methods. The recovery step takes considerable time and also introduces errors into the estimation step, which will degrade the speed and accuracy of the endmember estimation.In this paper, by designing a type of coherent measurement matrix, we propose a novel method that estimates the endmembers directly from the compressive HSI with convex geometry (CG) approaches, which outperforms the traditional method with better estimation speed and better (or comparable) accuracy.The paper is structured as follows. The necessary theoretical background and notations are provided in HU refers to any process that separates the pixel spectral from a hyperspectral image into a collection of constituent spectral or spectral signatures, called endmembers and a set of fractional abundances, one set per pixel . Mixing Owing to physical constraints, the proportions are non-negative and satisfy the full additivity constraint:LetObviously, rowThere are several key approaches for HU, including convex geometry (CG) approaches, statistical approaches, sparse regression approaches and nonnegative matrix factorization ,13. CG aWe introduce some mathematical notations in convex analysis: spanned space, affine hull and convex hull.The space spanned by a set of vectorThe affine hull of a set of vectorsThe convex hull of a set of vectori.e.,P − 1)-simplex inAssuming thatThere is a strong connection between the convex hull and the LMM of HSI. From Equations (1)–(3), we can see that each measured hyperspectral pixel vectorCS theory indicates that if a signal is sparse or compressive in some basis, it can be exactly recovered by a small number of measurements, much less than the number required by the Nyquist sampling theory. LetThe design ofroperty) . For eace to one . The corThe length ofll-posed , for theTheractable .Fortunately, it has been proven that thenditions ,21. TheThe(M2N3/2) . The recGreedy algorithms, such as Orthogonal Matching Pursuit (OMP)  and SubsDCS ,9,10 is JSM-2 is the most concise model, as shown in A key prior that will be essential for compressive HSI is that each source image/mixture image is piecewise smooth in the spatial domain, implying a sparse representation in the DCT (discrete cosine transform) domain or the wavelet domain.According to the assumption above, the image in each spectral band has a sparse representation, whose observations measured by the CS method can be written asFrom our assumption; each source image is also sparse in some domain; and thus; the sparse representation of each mixture  has the same sparse location with different coefficients due to the different mixing parameters of each source.The SOMP  method is proposed in  to reconHence, the compressive observation of HSI can be denoted asTo summarize, the task of endmember estimation from compressive HSI can be described as follows: given the compressive observation matrixThe framework of the traditional method for solving the problem mentioned above is shown in The aim of endmember estimation is to estimate the endmember matrixIn the next section, we will propose a new method that can estimateAs discussed in The compressive observation of HSI is denoted as follows:The valueEquation (14) can be considered as LMM, as shown in Equation (3). Thus, we wish to estimate the endmember matrixUnfortunately, the properties of the measurement matrixbability . Hence,To the knowledge of the authors and the referenced materials, we have not found a measurement matrix that not only satisfies the incoherence (or RIP), but also makesWe propose a novel method, as shown in First, we design a coherent measurement matrix that makesThe coherent matrix that does not hold the RIP cannot be used for exact and robust signal recovery, as mentioned above. In this paper, the aim is to estimate endmembers directly from the compressive HSI data, not to recover the HSI data. Therefore, we do not have to use an incoherent matrix . We give an example of such a coherent matrix. LetOther measurement matricesThis type of measurement matrix achieves undersampling of HSI. It will lose some proportion information. Therefore, the undersampled data cannot be used to recover the proportion information. If this information is required, one can recover it by the data captured by an incoherent measurement matrix, as shown in In this paper, we use the VCA algorithm to estimate the endmembers directly in the proposed method. We assume the presence of pure pixels in the undersampled data as required by VCA, and we also assume the presence of all of the materials in the data. These assumptions are easy to realize for the increasing spatial resolution of hyperspectral sensors.In the proposed method, we first use a coherent measurement matrix to compressively sense HSI and then use the VCA method to estimate the endmember directly from the HSI observations without recovering the images, which is a necessary step in the traditional method. As shown in the dashed parts in portions ,20.Under the linear observation model, HSI data are in a subspace of dimension 3 to 20 . In the [ O(P2M) .The traditional methods used in this paper are SOMP-VCA (SOMP for HSI data recovery and VCA for endmember estimation) and OMP-MVCA. The computational complexity of the SOMP isIn the proposed method, the memory required isIn this section, we first evaluate the practicability of the proposed method. Then, we compare the performance of the proposed method with the performance of the SOMP-VCA method.The data used in this paper are (1) the semi-synthetic HSI of a rural suburb of Geneva and (2) real-work urban HSI, both of which are also used in   error distance of vectors of anglesi.e.,i.e.,We uset grows, the rmsSAE does not change a lot, especially when the SNR is large, compared with the non-compression case t = 1 . However, the runtime decreases greatly as t increases compared with the non-compression case. We can see that the proposed method can estimate the endmember with comparable accuracy to the Nyquist-based method (t = 1) with much faster estimation speed as t increases, as long as the pure pixel and all material presence assumptions hold. We note that the value of t is user-defined as long as the presence of each material and pure pixel assumption holds.From To assess the performance of the proposed method for a large number of endmembers, we vary the number from library . Each enFrom Next, we compare the performance of the proposed method with the traditional SOMP-VCA and OMP-MVCA methods.a priori condition. We setIn SOMP-VCA and OMP-MVCA methods, the compression ration matrix . Each ofFrom Standard deviations of rmsSAE under different conditions are listed in From Different CS recovery algorithms affect the performance and the runtime for estimating the endmembers. To eliminate the influence of the choice of the CS method, we suppose that the sparsity of each signal is known and that the CS method can recover the HSI data accurately, and the recovered data are as accurate as the Nyquist-based data at the same noise level. From In this paper, we proposed a new method to directly estimate the endmembers from the compressive observations of the HSI data, while traditional methods first have to recover the HSI data from the compressive observations and then estimate the endmembers. Simulation results demonstrated that the proposed method outperforms the traditional method with better estimation speed and better (or comparable) accuracy.


This paper takes advantage of the complementary characteristics of Global Positioning System (GPS) and Light Detection and Ranging (LiDAR) to provide periodic corrections to Inertial Navigation System (INS) alternatively in different environmental conditions. In open sky, where GPS signals are available and LiDAR measurements are sparse, GPS is integrated with INS. Meanwhile, in confined outdoor environments and indoors, where GPS is unreliable or unavailable and LiDAR measurements are rich, LiDAR replaces GPS to integrate with INS. This paper also proposes an innovative hybrid scan matching algorithm that combines the feature-based scan matching method and Iterative Closest Point (ICP) based scan matching method. The algorithm can work and transit between two modes depending on the number of matched line features over two scans, thus achieving efficiency and robustness concurrently. Two integration schemes of INS and LiDAR with hybrid scan matching algorithm are implemented and compared. Real experiments are performed on an Unmanned Ground Vehicle (UGV) for both outdoor and indoor environments. Experimental results show that the multi-sensor integrated system can remain sub-meter navigation accuracy during the whole trajectory. The techniques of positioning can be roughly categorized into relative positioning and absolute positioning. Relative positioning is the process of calculating the current position by using a known initial position, and the estimated or known speed over elapsed time. Inertial Navigation System (INS), which is a self-contained system and is immune to interference, belongs to this category. Due to the advancements in microelectronics and micromachining technologies, inertial sensors have evolved from bulky and expensive mechanical design to compact and affordable grade. However, whether it be the traditional mechanical or the Micro-Electro-Mechanical System (MEMS)-based inertial sensors, they are subject to different level of systematic and stochastic errors. These errors will accumulate in the integration process and grow without bound in the long run. Absolute positioning is to make measurements with respect to given reference systems, including Global Positioning System (GPS), active beacons , etc.), passive beacons  30], we rGPS and LiDAR are used as aiding systems to alternatively provide periodic corrections to INS in different environments. A quaternion-based error model is used to fuse multi-sensor information.An innovative hybrid scan matching algorithm that combines feature-based scan matching method with ICP-based scan matching method is proposed due to their complementary characteristics.Based on the proposed hybrid scan matching algorithm, both loosely coupled and tightly coupled INS and LiDAR integration are implemented and compared using real experimental data.Different from existing works, a multi-sensor integrated navigation system for both outdoor and indoor environments with an innovative scan matching algorithm is proposed and implemented on an Unmanned Ground Vehicle (UGV). The main contributions of this paper are summarized as below:The INS mechanization involves different frames and their transformation. The navigation solutions are represented in navigation frame while inertial sensors measure angular velocity and acceleration of the vehicle directly in the body frame. The definitions of the frames used in the paper are listed in There are many formulations to represent the orientation of the vehicle such as Direct Cosine Matrix (DCM), Eular angles, and quaternion. In this work, the quaternion formulation is used due to its computational efficiency and lack of singularities. The quaternion vector is denoted as:The quaternion-based rotation matrix from body frame to navigation frame The position vector The measurements of the three-axis accelerometer are the vehicle specific force in the body frame. It should be transformed to navigation frame first, and then integrates to derive vehicle velocity in the navigation frame. To transform the specific force from body frame to navigation frame, the quaternion-based rotation matrix is used. The transformation is given by Equation (7):(1) Earth rotation rate (2) Angular velocity caused by the change of orientation of the navigation frame with respect to the earth (3) The earth’s gravity field These factors need to be compensated for in The differential equation of the quaternion is :(13)Q˙=However, since the earth rotation rate and the transportation rate are negligible, Equation (14) can be simplified as:By implementing matrix multiplication in Equation (13), it can be rewritten as:After updating the quaternion using Equation (16), the quaternion-based rotation matrix is also updated. The attitude of the UGV can be computed based on the relationship between attitude angles and the quaternion-based rotation matrix. The formulations of pitch, roll and azimuth are given as:x-axis of the body frame and the norm of the extracted line In this paper, the line feature is used due to its richness in indoor environments. The line feature is characterized by two parameters: the perpendicular distance from the origin of the vehicle body frame to the extracted line The relation between vehicle azimuth change and line feature angle change is straightforward, which can be represented as:(1)Inertial sensors provide initial rotation and translation;(2)Transform the current scan using the current rotation and translation;(3)For each point in the transformed current scan, find its two closest corresponding points in the reference scan;(4)Minimize the sum of the square distance from point in the transformed current scan to the line segment containing the two closest corresponding points.(5)Check whether the convergence is reached. If so, the algorithm will continue to process the next new scan. Otherwise, it will return to step two to search new correspondences again and repeat the procedures.Given the current scan and the reference scan, the ICP-based scan matching method is described as follows:Although the point-to-line distance metric has quadratic convergence with a good initial rotation and translation guess, the correspondences search and the iteration are still time-consuming, which limits its ability for real-time application.From the description above, the featured-based scan matching method works accurately and efficiently in feature rich environments. However, it heavily relies on the features in the environments and has limited ability to cope with unstructured or cluttered environments. The ICP-based scan matching method can perform accurately and robustly in more general environments. However, it requires time-consuming correspondences search and iterations to optimally align the scans. It is easy to find out that the advantages and disadvantages of the two scan matching methods are complementary. Therefore, the hybrid scan matching algorithm is proposed to combine the two scan matching methods to benefit from the advantages and avoid the disadvantages. Specifically, the hybrid scan matching method will work in the default feature-based scan matching mode. However, when less than two non-collinear line features (for loosely coupled system) or no lines (for tightly coupled system) are matched over two scans, the ICP-based scan matching will be activated, and the hybrid scan matching algorithm will transit to ICP-based scan matching mode. In this way, the hybrid scan matching algorithm can achieve efficiency and robustness concurrently.Information from multi-sensor are fused through an Extended Kalman Filter (EKF). The system model and measurement model are described in the following subsections. The block diagram of the multi-sensor integrated navigation system is shown in In the proposed system, the error state vector for the EKF is defined as:itten as :(22)F=[In the derivation of In outdoor environments where GPS signals are available, position and velocity solutions from GPS are integrated with INS at the rate of 1 Hz. The measurement model for INS/GPS loosely coupled scheme is:The relative position and azimuth changes from LiDAR and INS respectively are integrated at the rate of 5 Hz. If the estimated vehicle relative position and azimuth changes If change of the perpendicular distance from LiDAR to the line feature over two scans The velocity from odometer and height information from barometer are fused with INS velocity and altitude estimation at the rate of 10 Hz. The measurement model is written as below:The UGV used in the experiment is called Husky A200 from Clearpath Robotics Inc.  as shown in The experiment starts outdoors on a square without building or foliage blockage. Then the UGV travels from outdoor into the building through four ramps. For the rest of the experiment, the UGV moves along the corridor clockwise for one loop and stops in front of the elevator where it starts the motion indoors. While moving in the corridor, the UGV enters a copy room, a classroom and a lab room respectively. The length of the trajectory that the UGV travels is around 440 m and the travelling time is around 16 min. GPS is integrated with INS/odometer/barometer in the beginning of the experiment outdoor for 234 s before the GPS solutions become unreliable. Then, LiDAR replaces GPS to integrate with INS/odometer/barometer in the ramps part and indoor environments.Two integration scheme of INS and LiDAR with hybrid scan matching algorithm are implemented and compared. The generated trajectories are very close and only the trajectory from tightly coupled navigation system is presented in For better presentation, the trajectory after GPS ends has been rotated counter clockwise by an angle of 17 degrees. For the indoor part, the trajectory is plotted on the map of the building. While traveling indoor, the UGV stops at seven points for at least 10 s to generate reference locations, which are defined as waypoints. The LiDAR-aided part trajectory, the rooms that UGV enters, and seven waypoints are illustrated in During the LiDAR-aided navigation period, the filter implements 3527 times LiDAR measurement updates. For loosely coupled INS/LiDAR system with hybrid scan matching algorithm, the hybrid scan matching algorithm works in line feature-based scan matching mode for most of the time, and ICP-based scan matching mode is activated for 135 times. Among them, 105 times happen outside on the ramps and 30 times occur mostly in the copy room and classroom. This is reasonable since the railings of the ramps are made of vertical metal bars, and the laser beams can go through the space between the bars and does not reflect back. This will cause discontinuous short line segments. The line segments shorter than a threshold will be discarded in the line extraction process. Therefore, in unstructured environments like ramps and rooms with cluttered objects, it is difficult to extract and match at least two non-collinear line features.However, for tightly coupled INS/LiDAR system with hybrid scan matching algorithm, ICP-based scan matching mode is activated for only 6 times and all of them happen on the ramps. This means for 99.83% of the trajectory, at least one line feature can be matched over two scans. The comparison of the two integration schemes of INS and LiDAR with hybrid scan matching algorithm is shown in Position errors are calculated at the waypoints to assess the accuracy of the integrated solutions after GPS ends for INS, INS/LiDAR loosely coupled and tightly coupled systems. The results are demonstrated in If we compare the loosely coupled system and tightly coupled system, it can be found that the position accuracy of the tightly coupled system is slightly higher than the loosely coupled system with shorter running time and less limitation on the environment structure. This makes the tightly coupled system preferable over the loosely coupled system.The attitude angles are shown in In this paper, a multi-sensor integrated navigation system for both urban and indoor environments has been proposed. GPS and LiDAR are used as aiding systems to alternatively provide periodic corrections to INS in different environments since they work in complementary environments. Besides, due to the complementary properties of feature-based scan matching method and ICP-based scan matching method, the hybrid scan matching algorithm is proposed to combine the two scan matching methods to benefit from the advantages of each method. Two integration schemes of INS and LiDAR are implemented and compared. The tightly coupled system is preferable over the loosely coupled system when considering accuracy, running time and limitations on environments. Experimental results show that sub-meter navigation accuracy and accurate attitude angles estimation can be accomplished during the whole trajectory including outdoor and indoor environments.


C-means performs better than K-means algorithm.Understanding of the compressive strength of concrete is important for activities like construction arrangement, prestressing operations, and proportioning new mixtures and for the quality assurance. Regression techniques are most widely used for prediction tasks where relationship between the independent variables and dependent (prediction) variable is identified. The accuracy of the regression techniques for prediction can be improved if clustering can be used along with regression. Clustering along with regression will ensure the more accurate curve fitting between the dependent and independent variables. In this work cluster regression technique is applied for estimating the compressive strength of the concrete and a novel state of the art is proposed for predicting the concrete compressive strength. The objective of this work is to demonstrate that clustering along with regression ensures less prediction errors for estimating the concrete compressive strength. The proposed technique consists of two major stages: in the first stage, clustering is used to group the similar characteristics concrete data and then in the second stage regression techniques are applied over these clusters (groups) to predict the compressive strength from individual clusters. It is found from experiments that clustering along with regression techniques gives minimum errors for predicting compressive strength of concrete; also fuzzy clustering algorithm  Concrete is the most commonly used structural material and is composed of individual base materials . MateriaK-means and C-means are used for estimating the compressive strength of concrete in this work.A number of methods and techniques have been developed and proposed by many researchers to predict the properties of concrete. Determination of the compressive strength of concrete requires preparation, curing, and testing of special specimens . The earA number of works have been reported for estimation of compressive strength of the concrete and lots of recent computing techniques are used for better prediction of concrete compressive strengths. Artificial neural networks are used massively for estimating the concrete compressive strength apart from the other computing techniques. Some of the recent works related to estimating compressive strength of the concrete are discussed in this section.There are numerous applications of Artificial Neural Networks (ANN) in structural engineering. ANN is used for modeling the concrete strength in a number of works –11. ANNsFuzzy set theory has been applied in a wide range of scientific and construction research areas . A numbeAn extensive study of concrete curing process is performed by Abdel-Jawad (2006) , where iANNs and most of the soft computing based techniques are supervised learning techniques which require the efficient learning datasets for preparing the prediction models and always give low accuracies for prediction and other related tasks. To address this problem cluster regression based technique is proposed in this work to predict the compressive strength of concrete. The purpose of the proposed work is to demonstrate that regression technique along with clustering gives better prediction of compressive strength of the concrete. Rather than applying regression directly over the concrete dataset, it is applied over the groups (clusters) of concrete records in the proposed work. These clusters are formed on the basis of similarities between concrete records. Applying regression techniques over the clusters identifies more suitable estimations for each cluster  which ensures the minimum forecasting errors for estimating the compressive strength of concrete.The methodology of proposed technique of estimating (predicting) the concrete compressive strength is summarized in Clustering. In the first steps groups of similar concrete records from the concrete datasets are created. The number of groups can be decided by the user. In this study different numbers of groups are considered for the experiments. The groups are created using the clustering techniques K-means and fuzzy clustering technique C-means.Regression Analysis. After creating the group of similar concrete records, regression techniques are applied to identify the relationship between concrete compressive strength with other components of concrete. In this work three regression techniques SLR, LR, and LMS are used for estimating compressive strength of concrete.Performance Evaluation. Performance of prediction tasks performed in the previous steps is carried out with the help of predicting two popular error parameters MAE (Mean Absolute Error) and RMSE (Root Mean Square Error). These errors are calculated for each individual cluster and overall weighted average is also calculated for measuring the prediction errors.K-means and C-means algorithms for creating the groups of similar characteristics concrete data. Overviews for both of these algorithms are given in this section. Distance functions play an important role in clustering, which is also discussed here in brief.Clustering is performed using K number of disjoint partitions to create K number of clusters. These partitions are created using certain statistical measures like mean, median, and so forth. K-means [Clustering is a process of creating groups of similar objects. Clustering algorithms are categorized into five major categories, namely, partitioning techniques, hierarchical techniques, density based techniques, grid based techniques, and model based techniques. Partitioning techniques are the simplest techniques which create  K-means  is a claK-means algorithm [K, that is, the number of clusters to be formed, which partitions a set of n objects to generate the k clusters. The algorithm works in three steps. In the first step, K number of the objects are selected randomly, each of which represents the initial mean or center of the cluster. In the second step, the remaining objects are assigned to the cluster with minimum distance from cluster center or mean. In the third step, the new mean for each cluster is computed and the process iterates until the criterion function converges. The performance of K-means is measured using the square-error function defined in the following:E is the sum of the square error, p is the point in space representing a given object, and mi is the mean of cluster Ci. This criterion tries to make the resulting K clusters as compact and as separate as possible. The algorithm is consisting of five major steps which are summarizes as given in The lgorithm  is a parC-means (FCM) algorithm [c partitioning which is summarized as follows.Choose a number of clusters.Assign randomly to each point coefficients for being in the clusters.ε the given sensitivity threshold).Repeat until the algorithm has converged  of concrete records and Euclidian distance function is used to measure the distances between the pair of concrete records. Euclidian distance formula to measure the distance between two concrete records Ci and Cj with N features is presented in the following:Clustering algorithms creates the groups of similar records. In order to find the similar records distance functions are used. Distance functions are used to measure the distance between two objects (or records). If the distance between a pair of records is less (minimum) then the records are said to be similar. In this work two clustering algorithms N = 8) attributes (features) in this dataset, namely, Cement (C), Blast Furnace Slag (BFS), Fly Ash (F), Water (W), Superplasticizer (S), Coarse Aggregate (CA), Fine Aggregate (FA), and Age (A). The description of the dataset is presented in the In this work UCI machine learning repository  of concrRegression techniques are used to discover the relationship between a set of variables. These techniques are used for identifying the patterns (relations) of independent and dependent variables, the independent variables are technically termed as response variables, and dependent variable is termed as predictors. Regression techniques typically try to relate some statistical measures like mean or average between the set of variables to identify the relationship between them. In this paper three regression techniques, namely, Simple Linear Regression (SLR), Logistic Regression (LR), and Least Median of Squares Regression technique (LMS) are used to estimate the compressive strength of concrete.Y = c1X1 + c2X2 + ⋯+cNXN + b) is used to relate the predictor variables  and response to the predictor (Y). Simple linear regression is a statistical technique that fits a straight line to a set of  data pairs. The slope and intercept of the fitted line are chosen so as to minimize the sum of squared differences between observed response values and fitted response values. That is, a method of ordinary least squares is used to fit a straight line model to the data.Simple linear regression is the simplest regression analysis technique, where a line equation , and an odds ratio can be estimated. Logistic Regression is mostly used for prediction tasks involving multiple dependent variables and is also used for exploring the strong (dominating) dependent variables in prediction tasks. Logistic Regression is used for prediction of the probability of occurrence of an event by fitting data to a logistic curve. Logistic regression techniques are designed using logistic model which can be expressed as given in the following:p is the probability of a classification match, and x1, x2,…, xi are the explanatory, independent variables.Logistic Regression technique is the other regression technique, which is used to perform regression on multiple independent variables presented simultaneously to predict membership of one or other of the two dependent variable categories. If the dependent variable, Rousseeuw (1984)  introducK subsets each with a simpler distribution matching its guiding function. Each function is regressed on its own subset of data with a much smaller residue error. Both the regressions and the clustering optimize a common objective function.In regression clustering (RC) , regressThe performance of regression based prediction techniques is carried in terms of errors in regression. Two such common errors in regression based prediction are Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).The Mean Absolute Error (MAE) is the average of the absolute value of the residuals (error). The MAE is very similar to the RMSE but is less sensitive to large errors. The MAE is calculated using the following:The Root Mean Squared Error (RMSE) is the square root of the average squared distance of a data point from the fitted line. The RMSE is calculated using the following:K, which is the number of clusters, and D, a dataset containing n objects of Concrete samples. The algorithm is composed of five steps. In the first step the UCI machine repository concrete dataset is selected and given as input to the algorithm, and then in the second step, K numbers of clusters are created using K-means or C-means clustering techniques. In third step regression techniques are applied on each cluster created in step 2 to identify the relationship between the compressive strength and other concrete variables. In the fourth step, estimation of compressive strength of concrete is performed for individual clusters created in step 3 and prediction error MAE and RMSE are calculated for each cluster. Finally in the last step, weighted average (on the basis of number of concrete records belonging to various clusters) prediction errors MAE and RMSE are calculated for measuring overall errors in prediction of the compressive strength of concrete.Based on the methodology presented in previous section, the algorithm for predicting the compressive strength of concrete is now presented in this section . The algIn order to perform the experiments of proposed technique popular dataset of compressive concrete strength from UCI machine learning repository  is takenK-means clustering technique and regression techniques is taken from the WEKA. The implementation is performed in three major steps  with independent variables (other variables) for a cluster. In the third step, errors are calculated for the estimations. The model is thus prepared for predicting the compressive strength of concrete for the new concrete records (for which compressive strength is unknown). In case of a new concrete record, mapping of the record to a suitable cluster is carried out by measuring the belongingness to a cluster then using the equation of that cluster estimation of the compressive strength of concrete is performed.All the experiments are carried using Java programming language and Java based WEKA  tool . WEKA isK-means and C-means are applied to create hard and soft (fuzzy) clusters. The distribution of number of concrete instances for different number of clusters using K-means algorithm is shown in C-means algorithm is shown in C-means algorithm is fuzzy technique, one instance can belong to more than one cluster hence more number of instances is belonging to the clusters than the K-means algorithm.In order to estimate the compressive strength of concrete clustering is applied  and thus are more important in estimating compressive strength of concrete, components S and FA also affects slightly in SLR regression technique. Similarly, for LR regression technique for forecasting the compressive strength of concrete the components C, BFS, W, and FA have more weights than the other components. For the LMS regression technique also the weights of components C, BFS, W, and S are more in regression equations which indicate that these components are more important than the other components for estimating the compressive strength of concrete.K-means clustering and C-means fuzzy clustering techniques along with the three regression techniques, namely, simple linear regression (SLR), logistic regression (LR), and least median of squares (LMS), are used to predict (estimate) the compressive strength of the concrete. The purpose of the work is to demonstrate that if clustering can be combined along with the regression technique then prediction errors for estimating compressive strength can be minimized. It is demonstrated from the experiments that if the optimum number of clusters can be created on concrete data before applying the regression then prediction errors can be minimized in efficient manners. Clustering techniques K-means and C-means are generic in nature and are capable of creating the groups objects of any data type and any number of features (attributes); hence, the same proposed model can be used for estimating compressive strength of data for different concrete datasets. In this study it is found that for UCI machine learning repository concrete data, four to seven numbers of clusters along with the regression technique give minimum prediction errors for prediction the compressive strength of concrete. It is also found that fuzzy clustering algorithm C-means is more efficient than the K-means clustering algorithm for creating the clusters and gives minimum errors in predicting the compressive strength of the concrete. Partitioning based clustering algorithms K-means and C-means are used in this work; density based clustering algorithms can be explored for creating the groups of concrete data as a future scope of the work. The proposed model can also be validated by selecting other standard datasets and comparisons can be made between partitioning based and density based clustering techniques for predicting the compressive strength of concrete.In the presented work