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  • 1.
    Fleyeh, Hasan
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Davami, Erfan
    Jomaa, Diala
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Segmentation of fingerprint images based on bi-level processing using fuzzy rules2012Ingår i: Fuzzy Information Processing Society (NAFIPS), 2012 Annual Meeting of the North American, 2012, s. 1-6Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper presents a new approach to segment low quality fingerprint images which are collected by low quality fingerprint readers. Images collected using such readers are easy to collect but difficult to segment. The proposed approach is based on combining global and local processing to achieve segmentation of fingerprint images. On the global level, the fingerprint is located and extracted from the rest of the image by using a global thresholding followed by dilation and edge detection of the largest object in the image. On the local level, fingerprint's foreground and its border image are treated using different fuzzy rules which the two images are segmented. These rules are based on the mean and variance of the block under consideration. The approach is implemented in three stages; preprocessing, segmentation, and post-processing. Segmentation of 100 images was performed and compared with manual examinations by human experts. The experiments showed that 96% of images under test are correctly segmented. The results from the quality of segmentation test revealed that the average error in block segmentation was 2.84% and the false positive and false negatives were approximately 1.4%. This indicates the high robustness of the proposed approach.

  • 2.
    Fleyeh, Hasan
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Jomaa, Diala
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Dougherty, Mark
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Segmentation of low quality fingerprint images2010Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper presents a new algorithm to segment fingerprint images. The algorithm uses four features, the global mean, the local mean, variance and coherence of the image to achieve the fingerprint segmentation. Based on these features, a rule based system is built to segment the image. The proposed algorithm is implemented in three stages; pre-processing, segmentation, and post-processing. Gaussian filter and histogram equalization are applied in the pre-processing stage. Segmentation is applied using the local features. Finally, fill the gaps algorithm and a modified version of Otsu thresholding are invoked in the post-processing stage. In order to evaluate the performance of this method, experiments are performed on FVC2000 DB1. Segmentation of 100 images is performed and compared with manual examinations of human experts. It shows that the proposed algorithm achieves a correct segmentation of 82% of images under test.

  • 3.
    Fleyeh, Hasan
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Jomaa, Diala
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Dougherty, Mark
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Davami, Erfan
    Segmentation of fingerprint images based on bi-level combination of global and local processing2012Ingår i: Journal of Intelligent Systems, ISSN 2191-026X, Vol. 21, nr 2, s. 97-120Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This paper presents a new approach to segment low quality fingerprint imageswhich are collected by low quality fingerprint readers. Images collected using such readersare easy to collect but difficult to segment. The proposed approach is based on combiningglobal and local processing to achieve segmentation of fingerprint images. On the globallevel, the fingerprint is located and extracted from the rest of the image by using a globalthresholding followed by dilation and edge detection of the largest object in the image.On the local level, fingerprint’s foreground and its border image are treated using differentfuzzy rules. These rules are based on the mean and variance of the block under consideration.The approach is implemented in three stages: pre-processing, segmentation, andpost-processing.Segmentation of 100 images was performed and compared with manual examinationsby human experts. The experiments showed that 96% of images under test are correctlysegmented. The results from the quality of segmentation test revealed that the averageerror in block segmentation was 2.84% and the false positive and false negatives wereapproximately 1.4%. This indicates the high robustness of the proposed approach.

  • 4.
    Jomaa, Diala
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    A data driven approach for automating vehicle activated signs2016Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    Vehicle activated signs (VAS) display a warning message when drivers exceed a particular threshold. VAS are often installed on local roads to display a warning message depending on the speed of the approaching vehicles. VAS are usually powered by electricity; however, battery and solar powered VAS are also commonplace. This thesis investigated devel-opment of an automatic trigger speed of vehicle activated signs in order to influence driver behaviour, the effect of which has been measured in terms of reduced mean speed and low standard deviation. A comprehen-sive understanding of the effectiveness of the trigger speed of the VAS on driver behaviour was established by systematically collecting data. Specif-ically, data on time of day, speed, length and direction of the vehicle have been collected for the purpose, using Doppler radar installed at the road. A data driven calibration method for the radar used in the experiment has also been developed and evaluated.

    Results indicate that trigger speed of the VAS had variable effect on driv-ers’ speed at different sites and at different times of the day. It is evident that the optimal trigger speed should be set near the 85th percentile speed, to be able to lower the standard deviation. In the case of battery and solar powered VAS, trigger speeds between the 50th and 85th per-centile offered the best compromise between safety and power consump-tion. Results also indicate that different classes of vehicles report differ-ences in mean speed and standard deviation; on a highway, the mean speed of cars differs slightly from the mean speed of trucks, whereas a significant difference was observed between the classes of vehicles on lo-cal roads. A differential trigger speed was therefore investigated for the sake of completion. A data driven approach using Random forest was found to be appropriate in predicting trigger speeds respective to types of vehicles and traffic conditions. The fact that the predicted trigger speed was found to be consistently around the 85th percentile speed justifies the choice of the automatic model.

  • 5.
    Jomaa, Diala
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    The Optimal trigger speed of vehicle activated signs2014Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    The thesis aims to elaborate on the optimum trigger speed for Vehicle Activated Signs (VAS) and to study the effectiveness of VAS trigger speed on drivers’ behaviour. Vehicle activated signs (VAS) are speed warning signs that are activated by individual vehicle when the driver exceeds a speed threshold. The threshold, which triggers the VAS, is commonly based on a driver speed, and accordingly, is called a trigger speed. At present, the trigger speed activating the VAS is usually set to a constant value and does not consider the fact that an optimal trigger speed might exist. The optimal trigger speed significantly impacts driver behaviour.

    In order to be able to fulfil the aims of this thesis, systematic vehicle speed data were collected from field experiments that utilized Doppler radar. Further calibration methods for the radar used in the experiment have been developed and evaluated to provide accurate data for the experiment. The calibration method was bidirectional; consisting of data cleaning and data reconstruction. The data cleaning calibration had a superior performance than the calibration based on the reconstructed data.

    To study the effectiveness of trigger speed on driver behaviour, the collected data were analysed by both descriptive and inferential statistics. Both descriptive and inferential statistics showed that the change in trigger speed had an effect on vehicle mean speed and on vehicle standard deviation of the mean speed. When the trigger speed was set near the speed limit, the standard deviation was high. Therefore, the choice of trigger speed cannot be based solely on the speed limit at the proposed VAS location.

    The optimal trigger speeds for VAS were not considered in previous studies. As well, the relationship between the trigger value and its consequences under different conditions were not clearly stated. The finding from this thesis is that the optimal trigger speed should be primarily based on lowering the standard deviation rather than lowering the mean speed of vehicles. Furthermore, the optimal trigger speed should be set near the 85th percentile speed, with the goal of lowering the standard deviation.

  • 6.
    Jomaa, Diala
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Dougherty, Mark
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Yella, Siril
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Triggering Radar Speed Warning signs using Association Rules and Clustering Techniques2012Konferensbidrag (Refereegranskat)
    Abstract [en]

    Radar speed warning signs (RSWS) have been used in recent years across Sweden and elsewhere in the world. Such signs measure vehicle speed using radar and are designed to display a message when the driver exceeds a pre-set threshold speed, which is often relative to the speed limit on a particular road segment. RSWS are typically placed on locations which are perceived to be problematic by relevant authorities. Excessive speeding or road accidents are examples of such perceived problems.  Deploying RSWS in many relevant locations is often impractical due to the lack of necessary power supply needed for operation. Battery driven RSWS are an alternative but are less attractive because of limited running time and frequent maintenance (changing batteries etc). Therefore, solar powered RSWS are more desirable. However, these signs are also dependent on batteries that need to be charged. The duration of operation of solar powered RSWS largely depend on how often the sign is triggered. Constant activation of the sign drains the battery. It is desirable to trigger the sign only when necessary. Hence, the main goal of this research is to design a model that optimises the performance of RSWS depending on prevailing conditions i.e traffic flows during different times of the day and so on.  Vehicle speed data had been collected at a test site in Sweden all hours of the day. This paper attempts to use a hybrid system based on Apriori and K-means clustering algorithm. Apriori algorithm is simple and efficient to determine associations’ rules among attributes in particular to discover the most common combination that can occur within the data set.  K-means clustering is basically used to quantize the input variables into smaller clusters that can easily derive the trigger threshold value. The proposed hybrid system indicated that the system was able to trigger solar RWWS efficiently.

  • 7.
    Jomaa, Diala
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Dougherty, Mark
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Yella, Siril
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Edvardsson, Karin
    Högskolan Dalarna, Akademin Industri och samhälle, Byggteknik.
    Effectiveness of trigger speed of vehicle-activated signs on mean and standard deviation of speed2016Ingår i: Journal of Transportation Safety and Security, ISSN 1943-9962, Vol. 8, nr 4, s. 293-309Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Excessive or inappropriate speeds are a key factor in traffic fatalities and crashes. Vehicle-activated signs (VASs) are therefore being extensively used to reduce speeding to increase traffic safety. A VAS is triggered by an individual vehicle when the driver exceeds a speed threshold, otherwise known as trigger speed (TS). The TS is usually set to a constant, normally proportional to the speed limit on the particular segment of road. Decisions concerning the TS largely depend on the local traffic authorities. The primary objective of this article is to help authorities determine the TS that gives an optimal effect on the Mean and Standard Deviation of speed. The data were systematically collected using radar technology whilst varying the TS. The results show that when the applied TS was set near the speed limit, the standard deviation was high. However, the Standard Deviation decreased substantially when the threshold was set to the 85th percentile. This decrease occurred without a significant increase in the mean speed. It is concluded that the optimal threshold speed should approximate the 85th percentile, though VASs should ideally be individually calibrated to the traffic conditions at each site.

  • 8.
    Jomaa, Diala
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Dougherty, Mark
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Yella, Siril
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Edvardsson, Karin
    Högskolan Dalarna, Akademin Industri och samhälle, Byggteknik.
    Effectiveness of vehicle activated signs on mean speed and standard deviation of vehicle speed2014Rapport (Övrigt vetenskapligt)
  • 9.
    Jomaa, Diala
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Yella, Siril
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Automatic trigger speed for vehicle activated signs using Adaptive Neuro fuzzy system and Random ForestIngår i: International Journal on Advances in Intelligent Systems, ISSN 1942-2679, E-ISSN 1942-2679Artikel i tidskrift (Refereegranskat)
  • 10.
    Jomaa, Diala
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Yella, Siril
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Dynamic trigger speed for vehicle activated signs2016Konferensbidrag (Refereegranskat)
    Abstract [en]

    Optimal trigger speeds for vehicle activated signs were not considered in previous studies. The main aim of this paper is to summarise the findings of optimum trigger speed for vehicle activated signs. A secondary aim is to be able to build and report a dynamic trigger speed based on an accurate predictive model to be able to trigger operation of vehicle activated signs. A data based calibration method for the radar used in the experiment has been developed and evaluated. Results from the study indicate that the optimal trigger speed should be primarily aimed at lowering the standard deviation. Results also indicate that the optimal trigger speed should be set near the 85th percentile speed, to be able to lower the standard deviation. A comparative study investigating the use of several predictive models showed that random forest is an appropriate model to dynamically predict trigger speeds.

  • 11.
    Jomaa, Diala
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Yella, Siril
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Predicting automatic trigger speed for vehicle-activated signs2018Ingår i: Journal of Intelligent Systems, ISSN 0334-1860, E-ISSN 2191-026XArtikel i tidskrift (Refereegranskat)
    Abstract [en]

    Vehicle-activated signs (VAS) are speed-warning signs activated by radar when the driver speed exceeds a pre-set threshold, i.e. the trigger speed. The trigger speed is often set relative to the speed limit and is displayed for all types of vehicles. It is our opinion that having a static setting for the trigger speed may be inappropriate, given that traffic and road conditions are dynamic in nature. Further, different vehicle classes (mainly cars and trucks) behave differently, so a uniform trigger speed of such signs may be inappropriate to warn different types of vehicles. The current study aims to investigate an automatic VAS, i.e. one that could warn vehicle users with an appropriate trigger speed by taking into account vehicle types and road conditions. We therefore investigated different vehicle classes, their speeds, and the time of day to be able to conclude whether different trigger speeds of VAS are essential or not. The current study is entirely data driven; data are initially presented to a self-organising map (SOM) to be able to partition the data into different clusters, i.e. vehicle classes. Speed, time of day, and length of vehicle were supplied as inputs to the SOM. Further, the 85th percentile speed for the next hour is predicted using appropriate prediction models. Adaptive neuro-fuzzy inference systems and random forest (RF) were chosen for speed prediction; the mean speed, traffic flow, and standard deviation of vehicle speeds were supplied as inputs for the prediction models. The results achieved in this work show that RF is a reliable model in terms of accuracy and efficiency, and can be used in finding appropriate trigger speeds for an automatic VAS. 

  • 12.
    Jomaa, Diala
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Yella, Siril
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Dougherty, Mark
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    A comparative study between vehicle activated signs and speed indicator devices2017Ingår i: Transportation Research Procedia, ISSN 2324-9935, E-ISSN 2352-1465, Vol. 22, s. 115-123Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Vehicle activated signs and Speed indicator devices are safety signs used to warn and remind drivers that they are exceeding the speed limit on a particular road segment. This article has analysed and compared such signs with the aim of reporting the most suitable sign for relevant situations. Vehicle speeds were recorded at different test sites and the effects of the signs were studied by analyzing the mean and standard deviation. Preliminary results from the work indicate that both types of signs have variable effects on the mean and standard deviation of speed on a given road segment. Speed indicator devices were relatively more effective than vehicle activated signs on local roads; in contrast their effectivity was only comparable when tested on highways.

  • 13.
    Jomaa, Diala
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Yella, Siril
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Dougherty, Mark
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Review of the effectiveness of vehicle activated signs2013Ingår i: Journal of Transportation Technologies, ISSN 2160-0481, Vol. 3, nr 2, s. 123-130Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This paper reviews the effectiveness of vehicle activated signs. Vehicle activated signs are being reportedly used in recent years to display dynamic information to road users on an individual basis in order to give a warning or inform about a specific event. Vehicle activated signs are triggered individually by vehicles when a certain criteria is met. An example of such criteria is to trigger a speed limit sign when the driver exceeds a pre-set threshold speed. The preset threshold is usually set to a constant value which is often equal, or relative, to the speed limit on a particular road segment.

    This review examines in detail the basis for the configuration of the existing sign types in previous studies and explores the relation between the configuration of the sign and their impact on driver behavior and sign efficiency. Most of previous studies showed that these signs have significant impact on driver behavior, traffic safety and traffic efficiency. In most cases the signs deployed have yielded reductions in mean speeds, in speed variation and in longer headways. However most experiments reported within the area were performed with the signs set to a certain static configuration within applicable conditions. Since some of the aforementioned factors are dynamic in nature, it is felt that the configurations of these signs were thus not carefully considered by previous researchers and there is no clear statement in the previous studies describing the relationship between the trigger value and its consequences under different conditions. Bearing in mind that different designs of vehicle activated signs can give a different impact under certain conditions of road, traffic and weather conditions the current work suggests that variable speed thresholds should be considered instead.

  • 14.
    Jomaa, Diala
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Yella, Siril
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Dougherty, Mark
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Speed prediction for triggering vehicle activated signs2016Rapport (Övrigt vetenskapligt)
    Abstract [en]

    Accurate speed prediction is a crucial step in the development of a dynamic vehcile activated sign (VAS). A previous study showed that the optimal trigger speed of such signs will need to be pre-determined according to the nature of the site and to the traffic conditions. The objective of this paper is to find an accurate predictive model based on historical traffic speed data to derive the optimal trigger speed for such signs. Adaptive neuro fuzzy (ANFIS), classification and regression tree (CART) and random forest (RF) were developed to predict one step ahead speed during all times of the day. The developed models were evaluated and compared to the results obtained from artificial neural network (ANN), multiple linear regression (MLR) and naïve prediction using traffic speed data collected at four sites located in Sweden. The data were aggregated into two periods, a short term period (5-min) and a long term period (1-hour). The results of this study showed that using RF is a promising method for predicting mean speed in the two proposed periods.. It is concluded that in terms of performance and computational complexity, a simplistic input features to the predicitive model gave a marked increase in the response time of the model whilse still delivering a low prediction error.

  • 15.
    Jomaa, Diala
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Yella, Siril
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Dougherty, Mark
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Triggering Solar-Powered Vehicle Activated Signs using Self Organising Maps with K-means2014Konferensbidrag (Refereegranskat)
    Abstract [en]

    Solar-powered vehicle activated signs (VAS) are speed warning signs powered by batteries that are recharged by solar panels. These signs are more desirable than other active warning signs due to the low cost of installation and the minimal maintenance requirements. However, one problem that can affect a solar-powered VAS is the limited power capacity available to keep the sign operational. In order to be able to operate the sign more efficiently, it is proposed that the sign be appropriately triggered by taking into account the prevalent conditions. Triggering the sign depends on many factors such as the prevailing speed limit, road geometry, traffic behaviour, the weather and the number of hours of daylight. The main goal of this paper is therefore to develop an intelligent algorithm that would help optimize the trigger point to achieve the best compromise between speed reduction and power consumption. Data have been systematically collected whereby vehicle speed data were gathered whilst varying the value of the trigger speed threshold. A two stage algorithm is then utilized to extract the trigger speed value. Initially the algorithm employs a Self-Organising Map (SOM), to effectively visualize and explore the properties of the data that is then clustered in the second stage using K-means clustering method. Preliminary results achieved in the study indicate that using a SOM in conjunction with K-means method is found to perform well as opposed to direct clustering of the data by K-means alone. Using a SOM in the current case helped the algorithm determine the number of clusters in the data set, which is a frequent problem in data clustering.

  • 16.
    Jomaa, Diala
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Yella, Siril
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Dougherty, Mark
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Edvardsson, Karin
    Högskolan Dalarna, Akademin Industri och samhälle, Byggteknik.
    Data based Calibration System for Radar used by Vehicle Activated Signs2014Ingår i: Journal of Data Analysis and Information Processing, ISSN 2327-7203, nr 2, s. 11s. 106-116Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The accurate measurement of a vehicle’s velocity is an essential feature in adaptive vehicle activated sign systems. Since the velocities of the vehicles are acquired from a continuous wave Doppler radar, the data collection becomes challenging. Data accuracy is sensitive to the calibration of the radar on the road. However, clear methodologies for in-field calibration have not been carefully established. The signs are often installed by subjective judgment which results in measurement errors. This paper develops a calibration method based on mining the data collected and matching individual vehicles travelling between two radars. The data was cleaned and prepared in two ways: cleaning and reconstructing. The results showed that the proposed correction factor derived from the cleaned data corresponded well with the experimental factor done on site. In addition, this proposed factor showed superior performance to the one derived from the reconstructed data.

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