Search results for: peak/valley segmentation

Authors: Natália Botica, Luís Luís, Paulo Bernardes

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The Côa Valley, listed World Heritage since 1998, presents more than 1300 open-air engraved rock panels. The Archaeological Park of the Côa Valley recorded the rock art motifs, testing various techniques based on direct tracing processes on the rock, using natural and artificial lighting. In this work, integrated in the "Open Access Rock Art Repository" (RARAA) project, we present the methodology adopted for the vectorial drawing of the rock art motifs based on orthophotos taken from the photogrammetric survey and 3D models of the rocks. We also present the information system designed to integrate the vector drawing and the characterization data of the motifs, as well as the open access sharing, in order to promote their reuse in multiple areas. The 3D models themselves constitute a very detailed record, ensuring the digital preservation of the rock and iconography. Thus, even if a rock or motif disappears, it can continue to be studied and even recreated.

Keywords: rock art, archaeology, iron age, 3D models

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Authors: K. M. Salim Reza, M. Hafiz Mia, M. A. Aziz, M. A. Motin, M. M. Rahman, M. A. Hasem

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The redox behavior of Fe (III) in presence of Catechol (Cc) has been carried out in buffer solution of different pH, scan rate, variation of Fe (III) concentration and Cc concentration. Uncoordinated Fe(III) or Cc has been found to undergo reversible electrode reaction whereas coordinated Fe-Cc is irreversible. The peak positions of the voltammogram of Fe- Cc shifted with respect to that of free Fe (III) or Cc and also developed a new peak at 0.12 V. The peak current of Fe-Cc decreases significantly compared with that of free Fe(III) or Cc in the same experimental conditions. These behaviors ascribed the formation of complex of Fe with Cc. The complex was formed either by the addition of Cc into Fe(III) or by the addition of Fe(III) into Cc. The effect of pH of Fe-Cc complex was studied by varying pH from 2 to 8.5. The electro chemical oxidation of Fe-Cc is facilitated in lower pH media. The slope of the plots of anodic peak current, Ep against pH of Fe-Cc complexe is 30 mV, indicates that the oxidation of Fe-Cc complexes proceeded via the 2e−/2H+ processes. The proportionality of the anodic and cathodic peak currents with square root of scan rate of suggests that the peak current of the different complexes at each redox reaction is controlled by diffusion process.

Keywords: cyclic voltammetry, Fe-Cc Complex, pH effect, redox interaction

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Authors: Haresh Sharma, Jaimini Luharia

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The ecology in the Western Himalayan region transforms with the change in altitude. This change is observed in terms of topography, species of flora and fauna and the quality of the soil. The current study focuses on women of indigenous communities of Pangi Valley, which is located in the state of Himachal Pradesh, India. The valley is bifurcated into three different areas –Saichu, Hudan Bhatori, and Sural Bhatori valleys. It is one of the most remote, rugged and difficult to access tribal regions of Chamba district. The altitude of the valley ranges from 2,000 m to 6,000 m above sea level. The Pangi valley is inhabited by ‘Pangwals’ and ‘Bhots’ tribes of the Himalayas who speak their local tribal language called’ Pangwali’. The valley is cut-off from the mainland due to heavy snow and lack of proper roads during peak winters. Due to difficult geographical location, the daily lives of the people are constantly challenged, and they are most of the times deprived of benefits targeted through government programs. However, the indigenous communities earn their livelihood through livestock and forest-based produce while some of them migrate to nearby places for better work. The current study involves snowball sampling methodology for data collection along with in-depth interviews of women members of Self-Help Groups and women farmers. The findings reveal that the lives of these indigenous communities largely depend on forest-based products. So, it creates all the more significance of enhancing, maintaining, and consuming natural resources sustainably. Under such circumstances, the women of the community play a significant role of guardians in conservation and protection of the forests. They are the custodians of traditional knowledge of environment conservation practices that have been followed for many years in the region. The present study also sought to establish a relationship between some of the development initiatives undertaken by the women in the valley that stimulate sustainable mountain economy and conservation practices. These initiatives include cultivation of products like hazelnut, ‘Gucchi’ rare quality mushroom, medicinal plants exclusively found in the region, thereby promoting long term sustainable conservation of agro-biodiversity of the Western Himalayan region. The measures taken by the community women are commendable as they ensure access and distribution of natural resources as well as manage them for future generations. Apart from this, the tribal women have actively formed Self-Help Groups promoting financial inclusion through various activities that augment ownership and accountability towards the overall development of the communities. But, the results also suggest that there’s not enough recognition given to women’s role in forests conservation practices due to several local socio-political reasons. There are not enough research studies done on communities of Pangi Valley due to inaccessibility created out of lack of proper roads and other resources. Also, there emerged a need to concretize indigenous and traditional knowledge of conservation practices followed by women in the community.

Keywords: forest conservation, indigenous community women, sustainable livelihoods, sustainable development, poverty alleviation, Western Himalayas

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Authors: Mohamed El Kory Yacoub, Ahmed Bezeid El Mamy Beyatt, Djibril Barry, Yanogo Pauline, Nicolas Meda

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Introduction: Rift Valley Fever is a zoonotic arbovirosis that severely affects ruminants, as well as humans. It causes abortions in pregnant females and deaths in young animals. The disease occurs during heavy rains followed by large numbers of mosquito vectors. The objective of this work is to evaluate the surveillance system for Rift Valley Fever. Methods: We conducted an evaluation of the Rift Valley Fiver surveillance system. Data were collected from the analysis of the national database of the Mauritanian Network of Animal Disease Epidemiological Surveillance at the Ministry of Rural Development, of RVF cases notified from the whole national territory, of questionnaires and interviews with all persons involved in RVF surveillance at the central level. The quality of the system was assessed by analyzing the quantitative attributes defined by the Centers for Disease Control and Prevention. Results: In 2019, 443 cases of RVF were notified by the surveillance system, of which 36 were positive. Among the notified cases of Rift Valley Fever, the 0- to the 3-year-old age group of small ruminants was the most represented with 49.21% of cases, followed by 33.33%, which was recorded in large ruminants in the 0 to 7-year-old age group, 11.11% of cases were older than seven years. The completeness of the data varied between 14.2% (age) and 100% (species). Most positive cases were recorded between October and November 2019 in seven different regions. Attribute analysis showed that 87% of the respondents were able to use the case definition well, and 78.8% said they were familiar with the reporting and feedback loop of the Rift Valley Fever data. 90.3% of the respondents found it easy, while 95% of them responded that it was easy for them to transmit their data to the next level. Conclusions: The epidemiological surveillance system for Rift Valley Fever in Mauritania is simple and representative. However, data quality, stability, and responsiveness are average, as the diagnosis of the disease requires laboratory confirmation and the average delay for this confirmation is long (13 days). Consequently, the lack of completeness of the recorded data and of description of cases in terms of time-place-animal, associated with the delay between the stages of the surveillance system can make prevention, early detection of epidemics, and the initiation of measures for an adequate response difficult.

Keywords: evaluation, epidemiological surveillance system, rift valley fever, mauritania, ruminants

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Authors: Lan Du, Yan Wang, Hui Dai

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Synthetic aperture radar (SAR) image change detection has recently become a challenging problem owing to the existence of speckle noises. In this paper, an unsupervised distribution-free change detection for SAR image based on scale-invariant feature transform (SIFT) keypoints and segmentation is proposed. Firstly, the noise-robust SIFT keypoints which reveal the blob-like structures in an image are extracted in the log-ratio image to reduce the detection range. Then, different from the traditional change detection which directly obtains the change-detection map from the difference image, segmentation is made around the extracted keypoints in the two original multitemporal SAR images to obtain accurate changed region. At last, the change-detection map is generated by comparing the two segmentations. Experimental results on the real SAR image dataset demonstrate the effectiveness of the proposed method.

Keywords: change detection, Synthetic Aperture Radar (SAR), Scale-Invariant Feature Transformation (SIFT), segmentation

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Authors: Sasidhar B., Bhaskar Rao N., Ramesh Babu D. R., Ravi Shankar M.

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Segmentation of lung pleura is a preprocessing step in Computer-Aided Diagnosis (CAD) which helps in reducing false positives in detection of lung cancer. The existing methods fail in extraction of lung regions with the nodules at the pleura of the lungs. In this paper, a new method is proposed which segments lung regions with nodules at the pleura of the lungs based on curvature analysis and morphological operators. The proposed algorithm is tested on 06 patient’s dataset which consists of 60 images of Lung Image Database Consortium (LIDC) and the results are found to be satisfactory with 98.3% average overlap measure (AΩ).

Keywords: curvature analysis, image segmentation, morphological operators, thresholding

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Authors: Ananya Ananya, Karthik Rao

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Accurate segmentation of knee cartilage in 3-D magnetic resonance (MR) images for quantitative assessment of volume is crucial for studying and diagnosing osteoarthritis (OA) of the knee, one of the major causes of disability in elderly people. Radiologists generally perform this task in slice-by-slice manner taking 15-20 minutes per 3D image, and lead to high inter and intra observer variability. Hence automatic methods for knee cartilage segmentation are desirable and are an active field of research. This paper presents design and experimental evaluation of 2D convolutional neural networks based fully automated methods for knee cartilage segmentation in 3D MRI. The architectures are validated based on 40 test images and 60 training images from SKI10 dataset. The proposed methods segment 2D slices one by one, which are then combined to give segmentation for whole 3D images. Proposed methods are modified versions of U-net and dilated convolutions, consisting of a single step that segments the given image to 5 labels: background, femoral cartilage, tibia cartilage, femoral bone and tibia bone; cartilages being the primary components of interest. U-net consists of a contracting path and an expanding path, to capture context and localization respectively. Dilated convolutions lead to an exponential expansion of receptive field with only a linear increase in a number of parameters. A combination of modified U-net and dilated convolutions has also been explored. These architectures segment one 3D image in 8 – 10 seconds giving average volumetric Dice Score Coefficients (DSC) of 0.950 - 0.962 for femoral cartilage and 0.951 - 0.966 for tibia cartilage, reference being the manual segmentation.

Keywords: convolutional neural networks, dilated convolutions, 3 dimensional, fully automated, knee cartilage, MRI, segmentation, U-net

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Authors: Gaokai Liu

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The distribution of material grain sizes reflects the strength, fracture, corrosion and other properties, and the grain size can be acquired via the grain boundary. In recent years, the automatic grain boundary detection is widely required instead of complex experimental operations. In this paper, an effective solution is applied to acquire the grain boundary of material images. First, the initial superpixel segmentation result is obtained via a superpixel approach. Then, a region merging method is employed to merge adjacent regions based on certain similarity criterions, the experimental results show that the merging strategy improves the superpixel segmentation result on material datasets.

Keywords: grain boundary detection, image segmentation, material images, region merging

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Authors: Tao Peng, Jing Zhao, Yanqing Xu, Jing Cai

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Due to missing/ambiguous boundaries between the prostate and neighboring structures, the presence of shadow artifacts, as well as the large variability in prostate shapes, ultrasound prostate segmentation is challenging. To handle these issues, this paper develops a hybrid method for ultrasound prostate segmentation by combining an optimized closed principal curve-based method and the evolutionary neural network; the former can fit curves with great curvature and generate a contour composed of line segments connected by sorted vertices, and the latter is used to express an appropriate map function (represented by parameters of evolutionary neural network) for generating the smooth prostate contour to match the ground truth contour. Both qualitative and quantitative experimental results showed that our proposed method obtains accurate and robust performances.

Keywords: ultrasound prostate segmentation, optimized closed polygonal segment method, evolutionary neural network, smooth mathematical model, principal curve

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Authors: Suparman

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This paper addresses the problem of the signal segmentation within a Bayesian framework by using reversible jump MCMC algorithm. The signal is modelled by piecewise constant Moving-Average (MA) model where the numbers of segments, the position of change-point, the order and the coefficient of the MA model for each segment are unknown. The reversible jump MCMC algorithm is then used to generate samples distributed according to the joint posterior distribution of the unknown parameters. These samples allow calculating some interesting features of the posterior distribution. The performance of the methodology is illustrated via several simulation results.

Keywords: piecewise, moving-average model, reversible jump MCMC, signal segmentation

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Authors: Donghwan Lee, Gichun Cha, Jooyoung Park, Junkyeong Kim, Seunghee Park

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3D shape models of the existing structure are required for many purposes such as safety and operation management. The traditional 3D modeling methods are based on manual or semi-automatic reconstruction from close-range images. It occasions great expense and time consuming. The Terrestrial Laser Scanner (TLS) is a common survey technique to measure quickly and accurately a 3D shape model. This TLS is used to a construction site and cultural heritage management. However there are many limits to process a TLS point cloud, because the raw point cloud is massive volume data. So the capability of carrying out useful analyses is also limited with unstructured 3-D point. Thus, segmentation becomes an essential step whenever grouping of points with common attributes is required. In this paper, members segmentation algorithm was presented to separate a raw point cloud which includes only 3D coordinates. This paper presents a clustering approach based on a fuzzy method for this objective. The Fuzzy C-Means (FCM) is reviewed and used in combination with a similarity-driven cluster merging method. It is applied to the point cloud acquired with Lecia Scan Station C10/C5 at the test bed. The test-bed was a bridge which connects between 1st and 2nd engineering building in Sungkyunkwan University in Korea. It is about 32m long and 2m wide. This bridge was used as pedestrian between two buildings. The 3D point cloud of the test-bed was constructed by a measurement of the TLS. This data was divided by segmentation algorithm for each member. Experimental analyses of the results from the proposed unsupervised segmentation process are shown to be promising. It can be processed to manage configuration each member, because of the segmentation process of point cloud.

Keywords: fuzzy c-means (FCM), point cloud, segmentation, terrestrial laser scanner (TLS)

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Authors: Shannon Steinmetz, Ellen Gethner

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In this paper, a theoretical foundation is developed for patterned segmentation of audio using the geometry of music and statistical manifold. We demonstrate image content clustering using conic space sonification. The algorithm takes a geodesic curve as a model estimator of the three-parameter Gamma distribution. The random variable is parameterized by musical centricity and centric velocity. Model parameters predict audio segmentation in the form of duration and frame count based on the likelihood of musical geometry transition. We provide an example using a database of randomly selected images, resulting in statistically significant clusters of similar image content.

Keywords: sonification, musical information geometry, image, content extraction, automated quantification, audio segmentation, pattern recognition

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Authors: Isha Rathore, Peeyush Jain, Elangovan Rajasekar

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The thermal capacity of the envelope impacts the cooling and heating demand of a building and modulates the peak electricity demand. This paper presents the thermal capacity tuning of a building envelope to minimize peak electricity demand for space cooling. We consider a 40 m² residential testbed located in Hyderabad, India (Composite Climate). An EnergyPlus model is validated using real-time data. A Parametric simulation framework for thermal capacity tuning is created using the Honeybee plugin. Diffusivity, Thickness, layer position, orientation and fenestration size of the exterior envelope are parametrized considering a five-layered wall system. A total of 1824 parametric runs are performed and the optimum wall configuration leading to minimum peak cooling demand is presented.

Keywords: thermal capacity, tuning, peak demand reduction, parametric analysis

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Authors: Aicha Majda, Abdelhamid El Hassani

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Lung CT image segmentation is a prerequisite in lung CT image analysis. Most of the conventional methods need a post-processing to deal with the abnormal lung CT scans such as lung nodules or other lesions. The simplest similarity measure in the standard Graph Cuts Algorithm consists of directly comparing the pixel values of the two neighboring regions, which is not accurate because this kind of metrics is extremely sensitive to minor transformations such as noise or other artifacts problems. In this work, we propose an improved version of the standard graph cuts algorithm based on the Patch-Based similarity metric. The boundary penalty term in the graph cut algorithm is defined Based on Patch-Based similarity measurement instead of the simple intensity measurement in the standard method. The weights between each pixel and its neighboring pixels are Based on the obtained new term. The graph is then created using theses weights between its nodes. Finally, the segmentation is completed with the minimum cut/Max-Flow algorithm. Experimental results show that the proposed method is very accurate and efficient, and can directly provide explicit lung regions without any post-processing operations compared to the standard method.

Keywords: graph cuts, lung CT scan, lung parenchyma segmentation, patch-based similarity metric

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Authors: Nuseiba M. Altarawneh, Suhuai Luo, Brian Regan, Guijin Tang

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Liver segmentation from medical images poses more challenges than analogous segmentations of other organs. This contribution introduces a liver segmentation method from a series of computer tomography images. Overall, we present a novel method for segmenting liver by coupling density matching with shape priors. Density matching signifies a tracking method which operates via maximizing the Bhattacharyya similarity measure between the photometric distribution from an estimated image region and a model photometric distribution. Density matching controls the direction of the evolution process and slows down the evolving contour in regions with weak edges. The shape prior improves the robustness of density matching and discourages the evolving contour from exceeding liver’s boundaries at regions with weak boundaries. The model is implemented using a modified distance regularized level set (DRLS) model. The experimental results show that the method achieves a satisfactory result. By comparing with the original DRLS model, it is evident that the proposed model herein is more effective in addressing the over segmentation problem. Finally, we gauge our performance of our model against matrices comprising of accuracy, sensitivity and specificity.

Keywords: Bhattacharyya distance, distance regularized level set (DRLS) model, liver segmentation, level set method

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Authors: Asma Bettahar, Imed Eddine Nezli, Sameh Habes

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Groundwater resources in the Oued Righ valley are represented like the parts of the eastern basin of the Algerian Sahara, superposed by two major aquifers: the Intercalary Continental (IC) and the Terminal Complex (TC). From a qualitative point of view, various studies have highlighted that the waters of this region showed excessive mineralization, including the waters of the terminal complex (EC Avg equal 5854.61 S/cm) .The present article is a statistical approach by two multi methods various complementary (ACP, CAH), applied to the analytical data of multilayered aquifer waters Terminal Complex of the Oued Righ valley. The approach is to establish a correlation between the chemical composition of water and the lithological nature of different aquifer levels formations, and predict possible connection between groundwater’s layers. The results show that the mineralization of water is from geological origin. They concern the composition of the layers that make up the complex terminal.

Keywords: complex terminal, mineralization, oued righ, statistical approach

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Authors: Fathi Kallel, Saida Khachira, Mohamed Ben Slima, Ahmed Ben Hamida

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Speckle noise is a main characteristic of cardiac ultrasound images, it corresponding to grainy appearance that degrades the image quality. For this reason, the ultrasound images are difficult to use automatically in clinical use, then treatments are required for this type of images. Then a filtering procedure of these images is necessary to eliminate the speckle noise and to improve the quality of ultrasound images which will be then segmented to extract the necessary forms that exist. In this paper, we present the importance of the pre-treatment step for segmentation. This work is applied to cardiac ultrasound images. In a first step, a comparative study of speckle filtering method will be presented and then we use a segmentation algorithm to locate and extract cardiac structures.

Keywords: medical image processing, ultrasound images, Speckle noise, image enhancement, speckle filtering, segmentation, snakes

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Authors: Sweeti, Anoop Kant Godiyal, Neha Singh, Sneh Anand, B. K. Panigrahi, Jayasree Santhosh

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This work is about developing a tool for visualization and segmentation of Electroencephalograph (EEG) signals based on frequency domain features. Change in the frequency domain characteristics are correlated with change in mental state of the subject under study. Proposed algorithm provides a way to represent the change in the mental states using the different frequency band powers in form of segmented EEG signal. Many segmentation algorithms have been suggested in literature having application in brain computer interface, epilepsy and cognition studies that have been used for data classification. But the proposed method focusses mainly on the better presentation of signal and that’s why it could be a good utilization tool for clinician. Algorithm performs the basic filtering using band pass and notch filters in the range of 0.1-45 Hz. Advanced filtering is then performed by principal component analysis and wavelet transform based de-noising method. Frequency domain features are used for segmentation; considering the fact that the spectrum power of different frequency bands describes the mental state of the subject. Two sliding windows are further used for segmentation; one provides the time scale and other assigns the segmentation rule. The segmented data is displayed second by second successively with different color codes. Segment’s length can be selected as per need of the objective. Proposed algorithm has been tested on the EEG data set obtained from University of California in San Diego’s online data repository. Proposed tool gives a better visualization of the signal in form of segmented epochs of desired length representing the power spectrum variation in data. The algorithm is designed in such a way that it takes the data points with respect to the sampling frequency for each time frame and so it can be improved to use in real time visualization with desired epoch length.

Keywords: de-noising, multi-channel data, PCA, power spectra, segmentation

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Authors: Asma Bettahar, Imed Eldine Nezli, Sameh Habes

Abstract:

Groundwater resources in the Oued Righ valley are represented like the parts of the eastern basin of the Algerian Sahara, superposed by two major aquifers: the Intercalary Continental (IC) and the Terminal Complex (TC). From a qualitative point of view, various studies have highlighted that the waters of this region showed excessive mineralization, including the waters of the terminal complex (EC Avg equal 5854.61 S/cm). The present article is a statistical approach by two multi methods various complementary (ACP CAH), applied to the analytical data of multilayered aquifer waters Terminal Complex of the Oued Righ valley. The approach is to establish a correlation between the chemical composition of water and the lithological nature of different aquifer levels formations, and predict possible connection between groundwater’s layers. The results show that the mineralization of water is from geological origin. They concern the composition of the layers that make up the complex terminal.

Keywords: oued righ, complex terminal, infill continental, mineralization

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Authors: Kyi Pyar Zaw, Zin Mar Kyu

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Character recognition is the process of converting a text image file into editable and searchable text file. Feature Extraction is the heart of any character recognition system. The character recognition rate may be low or high depending on the extracted features. In the proposed paper, 25 features for one character are used in character recognition. Basically, there are three steps of character recognition such as character segmentation, feature extraction and classification. In segmentation step, horizontal cropping method is used for line segmentation and vertical cropping method is used for character segmentation. In the Feature extraction step, features are extracted in two ways. The first way is that the 8 features are extracted from the entire input character using eight direction chain code frequency extraction. The second way is that the input character is divided into 16 blocks. For each block, although 8 feature values are obtained through eight-direction chain code frequency extraction method, we define the sum of these 8 feature values as a feature for one block. Therefore, 16 features are extracted from that 16 blocks in the second way. We use the number of holes feature to cluster the similar characters. We can recognize the almost Myanmar common characters with various font sizes by using these features. All these 25 features are used in both training part and testing part. In the classification step, the characters are classified by matching the all features of input character with already trained features of characters.

Keywords: chain code frequency, character recognition, feature extraction, features matching, segmentation

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Authors: Tudor Barbu

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An automatic moment-based texture segmentation approach is proposed in this paper. First, we describe the related work in this computer vision domain. Our texture feature extraction, the first part of the texture recognition process, produces a set of moment-based feature vectors. For each image pixel, a texture feature vector is computed as a sequence of area moments. Second, an automatic pixel classification approach is proposed. The feature vectors are clustered using some unsupervised classification algorithm, the optimal number of clusters being determined using a measure based on validation indexes. From the resulted pixel classes one determines easily the desired texture regions of the image.

Keywords: image segmentation, moment-based, texture analysis, automatic classification, validation indexes

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Authors: Mawloud Mosbah, Bachir Boucheham

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Color Histogram is considered as the oldest method used by CBIR systems for indexing images. In turn, the global histograms do not include the spatial information; this is why the other techniques coming later have attempted to encounter this limitation by involving the segmentation task as a preprocessing step. The weak segmentation is employed by the local histograms while other methods as CCV (Color Coherent Vector) are based on strong segmentation. The indexation based on local histograms consists of splitting the image into N overlapping blocks or sub-regions, and then the histogram of each block is computed. The dissimilarity between two images is reduced, as consequence, to compute the distance between the N local histograms of the both images resulting then in N*N values; generally, the lowest value is taken into account to rank images, that means that the lowest value is that which helps to designate which sub-region utilized to index images of the collection being asked. In this paper, we make under light the local histogram indexation method in the hope to compare the results obtained against those given by the global histogram. We address also another noteworthy issue when Relying on local histograms namely which value, among N*N values, to trust on when comparing images, in other words, which sub-region among the N*N sub-regions on which we base to index images. Based on the results achieved here, it seems that relying on the local histograms, which needs to pose an extra overhead on the system by involving another preprocessing step naming segmentation, does not necessary mean that it produces better results. In addition to that, we have proposed here some ideas to select the local histogram on which we rely on to encode the image rather than relying on the local histogram having lowest distance with the query histograms.

Keywords: CBIR, color global histogram, color local histogram, weak segmentation, Euclidean distance

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Authors: P. Hajyalikhani, B. Hudson, D. Boll, L. Boren, Z. Sparks, M. Ward

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Wood Light frame construction is one of the most common types of construction methods for residential and light commercial building in North America and parts of Europe. The typical roof framing for wood framed building is sloped and consists of several structural members such as rafters, hips, and valleys which are connected to the ridge and ceiling joists. The common slopes for roofs are 3/12, 8/12, and 12/12. Wood framed residential roof failure is most commonly caused by wind damage in such buildings. In the recent study, one of the weaknesses of wood framed roofs is long unsupported structural member lengths, such as hips and valleys. The purpose of this research is to find the critical support location for long hips and valleys with different slopes. ForteWeb software is used to find the critical location. The analysis results demonstrating the maximum unbraced hip and valley length are from 8.5 to 10.25 ft. dependent on the slope and roof type.

Keywords: wood frame, stick framing, hip, valley

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Authors: Wen Feiming

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As the ecological barrier of the Yangtze River, the ecological security of the Parallel Range-Valley area is very important. However, the unique geomorphic features aggravate the contradiction between man and land, resulting in the encroachment of ecological space. In recent years , relevant researches has focused on the single field of land science, ecology and landscape ecology, and it is difficult to systematically reflect the regularities of distribution and evolution trends of ecological patterns in the process of urban development. Therefore, from the perspective of "Production-Living-Ecological space", using spatial analysis methods such as Remote Sensing (RS) and Geographic Information Systems (GIS), this paper analyzes the evolution characteristics and driving factors of the ecological pattern of mountain towns in the parallel range-valley region from the aspects of land use structure, change rate, transformation relationship, and spatial correlation. It is concluded that the ecological pattern of mountain towns presents a trend from expansion and diffusion to agglomeration, and the dynamic spatial transfer is a trend from artificial transformation to the natural origin, while the driving effect analysis shows the significant characteristics of terrain attraction and construction barrier. Finally, combined with the evolution characteristics and driving mechanism, the evolution modes of "mountain area - concentrated growth", "trough area - diffusion attenuation" and "flat area - concentrated attenuation" are summarized, and the differentiated zoning and stratification ecological planning strategies are proposed here, in order to provide the theoretical basis for the sustainable development of mountain towns in parallel range-valley areas.

Keywords: parallel range-valley, ecological pattern, evolution characteristics, driving factors

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Authors: David Jurado, Carlos Ávila

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Detection of early signs of breast cancer development is crucial to quickly diagnose the disease and to define adequate treatment to increase the survival probability of the patient. Computer Aided Detection systems (CADs), along with modern data techniques such as Machine Learning (ML) and Neural Networks (NN), have shown an overall improvement in digital mammography cancer diagnosis, reducing the false positive and false negative rates becoming important tools for the diagnostic evaluations performed by specialized radiologists. However, ML and NN-based algorithms rely on datasets that might bring issues to the segmentation tasks. In the present work, an automatic segmentation and detection algorithm is described. This algorithm uses image processing techniques along with the Hough transform to automatically identify microcalcifications that are highly correlated with breast cancer development in the early stages. Along with image processing, automatic segmentation of high-contrast objects is done using edge extraction and circle Hough transform. This provides the geometrical features needed for an automatic mask design which extracts statistical features of the regions of interest. The results shown in this study prove the potential of this tool for further diagnostics and classification of mammographic images due to the low sensitivity to noisy images and low contrast mammographies.

Keywords: breast cancer, segmentation, X-ray imaging, hough transform, image analysis

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Authors: Noureddine Henka, Sami Tazi, Mohamad Assaad

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The electrical grid is a highly intricate system designed to transfer electricity from production areas to consumption areas. The Transmission System Operator (TSO) is responsible for ensuring the efficient distribution of electricity and maintaining the grid's safety and quality. However, due to the increasing integration of intermittent renewable energy sources, there is a growing level of uncertainty, which requires a faster responsive approach. A potential solution involves the use of electrical segmentation, which involves creating coherence zones where electrical disturbances mainly remain within the zone. Indeed, by means of coherent electrical zones, it becomes possible to focus solely on the sub-zone, reducing the range of possibilities and aiding in managing uncertainty. It allows faster execution of operational processes and easier learning for supervised machine learning algorithms. Electrical segmentation can be applied to various applications, such as electrical control, minimizing electrical loss, and ensuring voltage stability. Since the electrical grid can be modeled as a graph, where the vertices represent electrical buses and the edges represent electrical lines, identifying coherent electrical zones can be seen as a clustering task on graphs, generally called community detection. Nevertheless, a critical criterion for the zones is their ability to remain resilient to the electrical evolution of the grid over time. This evolution is due to the constant changes in electricity generation and consumption, which are reflected in graph structure variations as well as line flow changes. One approach to creating a resilient segmentation is to design robust zones under various circumstances. This issue can be represented through a multiplex graph, where each layer represents a specific situation that may arise on the grid. Consequently, resilient segmentation can be achieved by conducting community detection on this multiplex graph. The multiplex graph is composed of multiple graphs, and all the layers share the same set of vertices. Our proposal involves a model that utilizes a unified representation to compute a flattening of all layers. This unified situation can be penalized to obtain (K) connected components representing the robust electrical segmentation clusters. We compare our robust segmentation to the segmentation based on a single reference situation. The robust segmentation proves its relevance by producing clusters with high intra-electrical perturbation and low variance of electrical perturbation. We saw through the experiences when robust electrical segmentation has a benefit and in which context.

Keywords: community detection, electrical segmentation, multiplex graph, power grid

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Authors: Kenan Morani, Esra Kaya Ayana

Abstract:

This study aimed to develop a novel pipeline for COVID-19 detection using a large and rigorously annotated database of computed tomography (CT) images. The pipeline consists of UNet-based segmentation, lung extraction, and a classification part, with the addition of optional slice removal techniques following the segmentation part. In this work, a batch normalization was added to the original UNet model to produce lighter and better localization, which is then utilized to build a full pipeline for COVID-19 diagnosis. To evaluate the effectiveness of the proposed pipeline, various segmentation methods were compared in terms of their performance and complexity. The proposed segmentation method with batch normalization outperformed traditional methods and other alternatives, resulting in a higher dice score on a publicly available dataset. Moreover, at the slice level, the proposed pipeline demonstrated high validation accuracy, indicating the efficiency of predicting 2D slices. At the patient level, the full approach exhibited higher validation accuracy and macro F1 score compared to other alternatives, surpassing the baseline. The classification component of the proposed pipeline utilizes a convolutional neural network (CNN) to make final diagnosis decisions. The COV19-CT-DB dataset, which contains a large number of CT scans with various types of slices and rigorously annotated for COVID-19 detection, was utilized for classification. The proposed pipeline outperformed many other alternatives on the dataset.

Keywords: classification, computed tomography, lung extraction, macro F1 score, UNet segmentation

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Authors: M. A. Hernández-Ceballos, E. G. San Miguel, C. Galán, J. P. Bolívar

Abstract:

The evolution of 7Be and 210Pb activity concentrations in surface air along the Guadalquivir valley (southern Iberian Peninsula) is presented in this study. Samples collected for 48 h, every fifteen days, from September 2012 to November 2013 at two sampling sites (Huelva city in the mouth and Cordoba city in the middle (located 250 km far away)), are used to 1) analysing the spatial variability and 2) understanding the influence of wind conditions on 7Be and 210Pb. Similar average concentrations were registered along the valley. The mean 7Be activity concentration was 4.46 ± 0.21 mBq/m3 at Huelva and 4.33 ± 0.20 mBq/m3 at Cordoba, although registering higher maximum and minimum values at Cordoba (9.44 mBq/m3 and 1.80 mBq/m3) than at Huelva (7.95 mBq/m3 and 1.04 mBq/m3). No significant differences were observed in the 210Pb mean activity concentrations between Cordoba (0.40 ± 0.04 mBq/m3) and Huelva (0.35 ± 0.04 mBq/m3), although the maximum (1.10 mBq/m3 and 0.87 mBq/m3) and minimum (0.02 mBq/m3 and 0.04 mBq/m3) values were recorded in Cordoba. Although similar average concentrations were obtained in both sites, the temporal evolution of both natural radionuclides presents differences between them. The meteorological analysis of two sampling periods, in which large differences on 7Be and 210Pb concentrations are observed, indicates the different impact of surface and upper wind dynamics. The analysis reveals the different impact of the two sea-land breeze patterns usually observed along the valley (pure and non-pure) and the corresponding air masses at higher layers associated with each one. The pure, with short development (around 30 km inland) and increasing accumulation process, favours high concentrations of both radionuclides in Huelva (coastal site), while the non-pure, with winds sweeping the valley until arrive to Cordoba (250 km far away), causes high activity values at this site. These results reveal the impact of mesoscale conditions on these two natural radionuclides, and the importance of these circulations on its spatial and temporal variability.

Keywords: 7Be, 210Pb, air masses, mesoscale process

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Authors: Rik van Leeuwen, Ger Koole

Abstract:

Within hospitality, marketing departments use segmentation to create tailored strategies to ensure personalized marketing. This study provides a data-driven approach by segmenting guest profiles via hierarchical clustering based on an extensive set of features. The industry requires understandable outcomes that contribute to adaptability for marketing departments to make data-driven decisions and ultimately driving profit. A marketing department specified a business question that guides the unsupervised machine learning algorithm. Features of guests change over time; therefore, there is a probability that guests transition from one segment to another. The purpose of the study is to provide steps in the process from raw data to actionable insights, which serve as a guideline for how hospitality companies can adopt an algorithmic approach.

Keywords: hierarchical cluster analysis, hospitality, market segmentation

Procedia PDF Downloads 76

Authors: Abbas Al-Refaie, Nour Bata

Abstract:

A distributor of Apple products' experiences numerous difficulties in developing marketing strategies for new and existing mobile product entries that maximize customer satisfaction and the firm's profitability. This research, therefore, integrates market segmentation in platform-based product family design and conjoint analysis to identify iSystem combinations that increase customer satisfaction and business profits. First, the enhanced market segmentation grid is created. Then, the estimated demand model is formulated. Finally, the profit models are constructed then used to determine the ideal product family design that maximizes profit. Conjoint analysis is used to explore customer preferences with their satisfaction levels. A total of 200 surveys are collected about customer preferences. Then, simulation is used to determine the importance values for each attribute. Finally, sensitivity analysis is conducted to determine the product family design that maximizes both objectives. In conclusion, the results of this research shall provide great support to Apple distributors in determining the best marketing strategies that enhance their market share.

Keywords: market segmentation, conjoint analysis, market strategies, optimization

Procedia PDF Downloads 328