Image Understanding Algorithms on Fine-Grained Tree-Structured SIMD Machines

An Important goal for researchers In computer vision is the construction vision systems that Interpret Image data in real time. Such systems typically require a large amount of computation for processing raw Image data at the lowest level, and for sophisticated decision making at the highest level Recent advances In VLSI circuitry· have led to several proposals for parallel architectures for computer vision systems. In this theSIS. we demonstrate that fine-grained tree-structured SIMD machines, which have favorable characteristics for efficient VLSI Implementation, can be used for the rapid execution of a wide range of Image understanding tasks We also Identify the limitations of these architectures and propose methods to ameliorate these difficulties. The NON-VON supercomputer, currently being constructed at Columbia University, is an example of such an architecture. The major contribution of this thesis IS the development and analysis of several parallel Image understanding algorithms for the class of architectures under consideration The algorithms developed In this research have been selected to span different levels of computer vision tasks They Include Image correlation, hlstogrammlng, connected component labeling, the computation of geometric properties, set operations, the Hough transform method for detecting object boundaries, and the correspondence problem In moving light display applications. The algorithms Incorporate novel approaches to reduce the effects of communication bottleneck usually associated With tree architecture

The Connected Component Algorithm on The NON-VON Supercomputer

The NON-VON Supercomputer is a highly parallel tree-structured computer that is being Implemented at Columbia University. In this paper, we demonstrate that tree architectures with their favorable characteristics for VLSI Implementation, and fast global broadcast, lend themselves easily and naturally to the representation and manipulation of Images represented by hierarchical data structures A description of NON-VON architecture IS presented With an emphasis on the special architectural features that will be used m our Image understanding algorithms. We adopt a variation of the quad tree data structure, called the binary Image tree, to represent images in the NON-VON tree We show how Images are loaded in the NON-VON tree, and present the algorithm for budding the binary Image trees. An efficient Implementation of the connected component labeling algorithm on NON-VON is then presented Simulation results are discussed, and we show the fast execution time of the algorithm on NON-VON. Other algorithms are also developed, such as hlstogrammlng, Hough transform, Set operations and Image correlation, and we can conclude that NON-VON can be used to Implement efficiently several :important Image understanding task

A comprehensive study of distributed Denial-of-Service attack with the detection techniques

With the dramatic evolution in networks nowadays, an equivalent growth of challenges has been depicted toward implementing and deployment of such networks. One of the serious challenges is the security where wide range of attacks would threat these networks. Denial-of-Service (DoS) is one of the common attacks that targets several types of networks in which a huge amount of information is being flooded into a specific server for the purpose of turning of such server. Many research studies have examined the simulation of networks in order to observe the behavior of DoS. However, the variety of its types hinders the process of configuring the DoS attacks. In particular, the Distributed DoS (DDoS) is considered to be the most challenging threat to various networks. Hence, this paper aims to accommodate a comprehensive simulation in order to figure out and detect DDoS attacks. Using the well-known simulator technique of NS-2, the experiments showed that different types of DDoS have been characterized, examined and detected. This implies the efficacy of the comprehensive simulation proposed by this study

Brown macroalgae as bio-indicators for heavy metals pollution of Al-Jubail coastal area of Saudi Arabia

Wastes from both industrial and domestic sources, as well as habitat destruction have a substantial impact on the coastal environments. It causes serious problems in many countries and for several seas and oceans which leads to the extinction of several plant and animal species. Many water resources are no longer suitable for drinking or for agriculture as a result of pollution. The main aim of this study was to investigate the efficiency of four brown macroalage as bioindicators for toxic heavy metals (manganese (Mn), copper (Cu), zinc (Zn), arsenite (As), cadmium (Cd), and lead (Pb)) along Al-jubail industrial city coast at Persian Gulf (Saudi Arabia). Brown macroalage samples were collected from three different sites in three time points, January, March and May, 2010. The four collected brown macroalgae were identified as Sargassum angustifolium, Sargassum boveanum, Sargassum latifolium, and Padina gymnospora. The algal samples were cleaned using sea water and distilled water, dried, and the concentrations of various toxic metals were determined. The average concentrations of Mn, Co, Ni and Cd were within the expected limits of un-contaminated areas. However, the results indicate the high level of Zn ion accumulation in all tested brown algae, showing highest concentration in S. angustifolium > P. gymnospora > S. latifolium > S. boveanum with highest Zn concentration of 991 ± 49.1, 988 ± 47.5, 980 ± 44.2, and 911 ± 39.7 µg g-1 dry weights, respectively. In addition, Cu was detected at high concentration of 92.1 ± 3.7 ìg g-1 dry weight in S. boveanum. These results clearly indicate the high pollution levels of Al-jubail industrial city coast with Zn and Cu toxic heavy metals, which is mostly due to uncontrolled disposal of industrial waste into coastal area. Furthermore, the consistency of Zn concentrations in all tested brown algae indicated the efficiency of the tested algae, including P. gymnospora, S. angustifolium, S. latifolium, and S. boveanum, for bioaccumulation and bio-monitoring studies of Zn.Key words: Brown algae, heavy metals, bio-indicators, Sargassum sp., Padina sp

Design and implement WSN/IoT smart parking management system using microcontroller

With the dramatic expansion of new networks such as Wireless Sensor Network (WSN) and Internet-of-Things (IoT), tremendous opportunities have been emerged to incorporate such technologies for valuable tasks. One of these tasks is the smart car parking where there is an imperative demand to manage the parkings in various facilities which may help drivers to save their time. Several research studies have addressed this task using wide range of approaches. However, the energy consumption is still a serious concern. This paper proposes a smart car parking based on cloud-based approach along with variety of sensors. Passive Infrared Sensors (PIRs) have been used to sense the object motion. While Light Dependent Resistor (LDR) sensors have been utilized to sense the light of the parking alarm and display inmformation regarding the occupied and non-occupied parking lots. Finally, multi-micro controller of Arduino have been exploited in order to transmit the information collected to the server. Finally, a prototype Android application has been developed in order to recieve the infromation from the server. Results of simulation showed the efficacy of the proposed method

Prospective study for commercial and low-cost hyperspectral imaging systems to evaluate thermal tissue effect on bovine liver samples

Thermal ablation modalities, for example radiofrequency ablation (RFA) and microwave ablation, are intended to prompt controlled tumour removal by raising tissue temperature. However, monitoring the size of the resulting tissue damage during the thermal removal procedures is a challenging task. The objective of this study was to evaluate the observation of RFA on an ex vivo liver sample with both a commercial and a low-cost system to distinguish between the normal and the ablated regions as well as the thermally affected regions. RFA trials were conducted on five different ex vivo normal bovine samples and monitored initially by a custom hyperspectral (HS) camera to measure the diffuse reflectance (Rd) utilising a polychromatic light source (tungsten halogen lamp) within the spectral range 348–950 nm. Next, the light source was replaced with monochromatic LEDs (415, 565 and 660 nm) and a commercial charge-coupled device (CCD) camera was used instead of the HS camera. The system algorithm comprises image enhancement (normalisation and moving average filter) and image segmentation with K-means clustering, combining spectral and spatial information to assess the variable responses to polychromatic light and monochromatic LEDs to highlight the differences in the Rd properties of thermally affected/normal tissue regions. The measured spectral signatures of the various regions, besides the calculation of the standard deviations (δ) between the generated six groups, guided us to select three optimal wavelengths (420, 540 and 660 nm) to discriminate between these various regions. Next, we selected six spectral images to apply the image processing to (at 450, 500, 550, 600, 650 and 700 nm). We noticed that the optimum image is the superimposed spectral images at 550, 600, 650 and 700 nm, which are capable of discriminating between the various regions. Later, we measured Rd with the CCD camera and commercially available monochromatic LED light sources at 415, 565 and 660 nm. Compared to the HS camera results, this system was more capable of identifying the ablated and the thermally affected regions of surface RFA than the side-penetration RFA of the investigated ex vivo liver samples. However, we succeeded in developing a low-cost system that provides satisfactory information to highlight the ablated and thermally affected region to improve the outcome of surgical tumour ablation with much shorter time for image capture and processing compared to the HS system

Candida albicans and Napkin Dermatitis: Relationship and Lesion Severity Correlation

Introduction: Napkin Dermatitis (ND) is a common problem in infancy that affects almost every child during the early months and years of their lifetime. It is a skin disease that becomes a challenge for both parents and physicians because of its frequency and difficulty in eliminating all of the causative factors in diapered infants. Usually Napkin dermatitis is self-limiting but when associated with Candida albicans (C. albicans) seems to be moderate to severe.Aim: The aim of the present study was to determine the colonization of C. albicans in children with Napkin dermatitis and to correlate between intensity of C. albicans colonization and the severity of napkin rash.Patients and Methods: This case-controlled study was conducted at Qassim University pediatric outpatient clinics, during the period from August 2014 to July 2015. Sixty patients with diaper dermatitis and 33 healthy controls were enrolled to this study. Sociodemographic and clinical data were obtained from the parents of each participant using questionnaires Paired (stool and skin) samples were collected from all cases and healthy control children. The samples were cultured on differential and selective chromogenic medium for isolation and initial identification of candida species. Identification confirmation of the isolates was determined by the Vitek 2 compact automated system.Results: Diaper dermatitis shows significant outcome to washing diaper area (per day) (P=0.001), History of diarrhea last 7 Days (P˂0.001), skin lab results (+/-) for Candida albicans, (P˂0.001), skin colony count, (P˂0.001), However, there is no correlation to age (P=0.828), gender (P=0.368) and feeding style (P=0.401).Conclusion: The severity score of napkin dermatitis was significantly observed among cases with diaper dermatitis (p-value<0.001) and control children (p-value<0.001) respectively.Keywords: Candida albicans; Napkin dermatitis; Diaper dermatitis; Vitek 2 compact system; Qassim

False Beliefs About Diabetes Mellitus in the Kurdistan Region of Iraq: A Population-Based Study

Background. Diabetes mellitus (DM) is a chronic, non-transmissible health condition distinguished by high blood glucose levels caused by faulty insulin secretion and impaired insulin activity. People play an essential role in preventing and managing their illnesses. Thus, the misconceptions may negatively influence the prevention and management of DM. The aim of this study was to gauge the extent of knowledge among the general population concerning DM, to determine the prevalence of misconceptions about DM in the community, and to find the factors influencing them. Methods. A population-based study was conducted in Duhok Province, the Kurdistan Region of Iraq. A total of 2,305 adults were enrolled in the study. The study data were collected by face-to-face interview. The survey questionnaire comprised two sections: the first section included basic demographic characteristics of participants, while the second section consisted of ten questions to identify common misconceptions about DM among participants. Results. Among the participants, there were 1,406 (61.0%) females. Participants’ age ranged from 18 to 90 years (the mean age: 54 ± 13.69 years). The most common misconceptions positively responded to were “Will I become addicted to insulin if I start taking it?”, followed by“ Does DM occur because of increased sugar intake?”. Male gender was associated with higher level of misconceptions. In addition, the misconceptions were more prevalent among diabetics as they might seek treatment from non-professionals. There was a significant association between education status and the prevalence of misconceptions. Healthcare workers were found to have a better knowledge about DM compared to the general population. Surprisingly, certain myths were prevalent even among healthcare workers. Conclusions. Certain myths and misconceptions have been pervasive in our society. Actions must be taken to dispel these misconceptions as they lead to an avoidable burden of disease. Therefore, people’s knowledge of DM needs to be enhanced through educational programs, social media, television, newspapers and campaigns