Search results for: Image complexity

Authors: Nestor A. Garcia Fragoso, Tetyana Baydyk, Ernst Kussul

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This article contains information from our investigation in the field of voice recognition. For this purpose, we created a voice database that contains different phrases in two languages, English and Spanish, for men and women. As a classifier, the LIRA (Limited Receptive Area) grayscale neural classifier was selected. The LIRA grayscale neural classifier was developed for image recognition tasks and demonstrated good results. Therefore, we decided to develop a recognition system using this classifier for voice recognition. From a specific set of speakers, we can recognize the speaker’s voice. For this purpose, the system uses spectrograms of the voice signals as input to the system, extracts the characteristics and identifies the speaker. The results are described and analyzed in this article. The classifier can be used for speaker identification in security system or smart buildings for different types of intelligent devices.

Keywords: Extreme learning, LIRA neural classifier, speaker identification, voice recognition.

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Authors: G. Rezai, P.K.Teng, Z. Mohamed, M.N Shamsudin

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Changing in consumers lifestyles and food consumption patterns provide a great opportunity in developing the functional food sector in Malaysia. There is only a little knowledge about whether Malaysian consumers are aware of functional food and if so what image consumers have of this product. The objective of this research is to determine the extent to which selected socioeconomic characteristics and attitudes influence consumers- awareness of functional food. A survey was conducted in the Klang Valley, Malaysia where 439 respondents were interviewed using a structured questionnaire. The result shows that most respondents have a positive attitude towards functional food. For the binary logistic estimation, the results indicate that age, income and other factors such as concern about food safety, subscribing to cooking or health magazines, being a vegetarian and consumers who have been involved in a food production company significantly influence Malaysian consumers- awareness towards functional food.

Keywords: Binary logistic model, functional foods, knowledge and awareness, perception

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Authors: Dariush Semnani, Javad Yekrang, Hossein Ghayoor

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Concerning the measurement of friction properties of textiles and fabrics using Kawabata Evaluation System (KES), whose output is constrained to the surface friction factor of fabric, and no other data would be generated; this research has been conducted to gain information about surface roughness regarding its surface friction factor. To assess roughness properties of light nonwovens, a 3-dimensional model of a surface has been simulated with regular sinuous waves through it as an ideal surface. A new factor was defined, namely Surface Roughness Factor, through comparing roughness properties of simulated surface and real specimens. The relation between the proposed factor and friction factor of specimens has been analyzed by regression, and results showed a meaningful correlation between them. It can be inferred that the new presented factor can be used as an acceptable criterion for evaluating the roughness properties of light nonwoven fabrics.

Keywords: Surface roughness, Nonwoven, Machine vision, Image processing.

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Authors: M. F. Yaqub, I. Gondal, J. Kamruzzaman

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Wavelet transform has been extensively used in machine fault diagnosis and prognosis owing to its strength to deal with non-stationary signals. The existing Wavelet transform based schemes for fault diagnosis employ wavelet decomposition of the entire vibration frequency which not only involve huge computational overhead in extracting the features but also increases the dimensionality of the feature vector. This increase in the dimensionality has the tendency to 'over-fit' the training data and could mislead the fault diagnostic model. In this paper a novel technique, envelope wavelet packet transform (EWPT) is proposed in which features are extracted based on wavelet packet transform of the filtered envelope signal rather than the overall vibration signal. It not only reduces the computational overhead in terms of reduced number of wavelet decomposition levels and features but also improves the fault detection accuracy. Analytical expressions are provided for the optimal frequency resolution and decomposition level selection in EWPT. Experimental results with both actual and simulated machine fault data demonstrate significant gain in fault detection ability by EWPT at reduced complexity compared to existing techniques.

Keywords: Envelope Detection, Wavelet Transform, Bearing Faults, Machine Health Monitoring.

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Authors: Ashly Joseph, Jithu Paulose

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The increased usage of cloud computing has revolutionized the IT landscape, but it has also raised new security concerns. DevSecOps emerged as a way for tackling these difficulties by integrating security into the software development process. However, the rising complexity and sophistication of cyber threats need more advanced solutions. This paper looks into the usage of artificial intelligence (AI) techniques in the DevSecOps framework to increase cloud security. This study uses quantitative and qualitative techniques to assess the usefulness of AI approaches such as machine learning, natural language processing, and deep learning in reducing security issues. This paper thoroughly examines the symbiotic relationship between AI and DevSecOps, concentrating on how AI may be seamlessly integrated into the continuous integration and continuous delivery (CI/CD) pipeline, automated security testing, and real-time monitoring methods. The findings emphasize AI's huge potential to improve threat detection, risk assessment, and incident response skills. Furthermore, the paper examines the implications and challenges of using AI in DevSecOps workflows, considering factors like as scalability, interpretability, and adaptability. This paper adds to a better understanding of AI's revolutionary role in cloud security and provides valuable insights for practitioners and scholars in the field.

Keywords: Cloud Security, DevSecOps, Artificial Intelligence, AI, Machine Learning, Natural Language Processing, NLP, cybersecurity, AI-driven Security.

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Authors: Husam Kaid, Abdulrahman Al-Ahmari, Zhiwu Li

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This paper develops a robust deadlock control technique for shared and unreliable resources in automated manufacturing systems (AMSs) based on structural analysis and colored Petri nets, which consists of three steps. The first step involves using strict minimal siphon control to create a live (deadlock-free) system that does not consider resource failure. The second step uses an approach based on colored Petri net, in which all monitors designed in the first step are merged into a single monitor. The third step addresses the deadlock control problems caused by resource failures. For all resource failures in the Petri net model a common recovery subnet based on colored petri net is proposed. The common recovery subnet is added to the obtained system at the second step to make the system reliable. The proposed approach is evaluated using an AMS from the literature. The results show that the proposed approach can be applied to an unreliable complex Petri net model, has a simpler structure and less computational complexity, and can obtain one common recovery subnet to model all resource failures.

Keywords: Automated manufacturing system, colored Petri net, deadlock, siphon.

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Authors: Fabian Horn, Reinhard Guthke

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In molecular biology, microarray technology is widely and successfully utilized to efficiently measure gene activity. If working with less studied organisms, methods to design custom-made microarray probes are available. One design criterion is to select probes with minimal melting temperature variances thus ensuring similar hybridization properties. If the microarray application focuses on the investigation of metabolic pathways, it is not necessary to cover the whole genome. It is more efficient to cover each metabolic pathway with a limited number of genes. Firstly, an approach is presented which minimizes the overall melting temperature variance of selected probes for all genes of interest. Secondly, the approach is extended to include the additional constraints of covering all pathways with a limited number of genes while minimizing the overall variance. The new optimization problem is solved by a bottom-up programming approach which reduces the complexity to make it computationally feasible. The new method is exemplary applied for the selection of microarray probes in order to cover all fungal secondary metabolite gene clusters for Aspergillus terreus.

Keywords: bottom-up approach, gene clusters, melting temperature, metabolic pathway, microarray probe design, probe selection

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Authors: Sara A.Yones, Ahmed S. Moussa

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Quantitative measurements of tumor in general and tumor volume in particular, become more realistic with the use of Magnetic Resonance imaging, especially when the tumor morphological changes become irregular and difficult to assess by clinical examination. However, tumor volume estimation strongly depends on the image segmentation, which is fuzzy by nature. In this paper a fuzzy approach is presented for tumor volume segmentation based on the fuzzy connectedness algorithm. The fuzzy affinity matrix resulting from segmentation is then used to estimate a fuzzy volume based on a certainty parameter, an Alpha Cut, defined by the user. The proposed method was shown to highly affect treatment decisions. A statistical analysis was performed in this study to validate the results based on a manual method for volume estimation and the importance of using the Alpha Cut is further explained.

Keywords: Alpha Cut, Fuzzy Connectedness, Magnetic Resonance Imaging, Tumor volume estimation.

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Authors: Hadi Aghazadeh, Seyed Ebrahim Afjei, Alireza Siadatan

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In this paper, a proper approach is taken to assess a set of the most effective rotor design parameters for an external-rotor permanent magnet assisted synchronous reluctance motor (PMaSynRM) and therefore to tackle the design complexity of the rotor structure. There are different advantages for introducing permanent magnets into the rotor flux barriers, some of which are to saturate the rotor iron ribs, to increase the motor torque density and to improve the power factor. Moreover, the d-axis and q-axis inductances are of great importance to simultaneously achieve maximum developed torque and low torque ripple. Therefore, sensitivity analysis of the rotor geometry of an 8-pole external-rotor permanent magnet assisted synchronous reluctance motor is performed. Several magnetically accurate finite element analyses (FEA) are conducted to characterize the electromagnetic performance of the motor. The analyses validate torque and power factor equations for the proposed external-rotor motor. Based upon the obtained results and due to an additional term, permanent magnet torque, added to the reluctance torque, the electromagnetic torque of the PMaSynRM increases.

Keywords: Permanent magnet assisted synchronous reluctance motor, flux barrier, flux carrier, electromagnetic torque, and power factor.

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Authors: Ebrahim Taherzadeh, Helmi Z. M. Shafri, Kaveh Shahi

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One of the most important tasks in urban remote sensing is the detection of impervious surfaces (IS), such as roofs and roads. However, detection of IS in heterogeneous areas still remains one of the most challenging tasks. In this study, detection of concrete roof using an object-based approach was proposed. A new rule-based classification was developed to detect concrete roof tile. This proposed rule-based classification was applied to WorldView-2 image and results showed that the proposed rule has good potential to predict concrete roof material from WorldView-2 images, with 85% accuracy.

Keywords: Urban remote sensing, impervious surface, Object- Based, Roof Material, Concrete tile, WorldView-2.

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Authors: Kahraman Ayyildiz, Stefan Conrad

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In this paper we present a system for classifying videos by frequency spectra. Many videos contain activities with repeating movements. Sports videos, home improvement videos, or videos showing mechanical motion are some example areas. Motion of these areas usually repeats with a certain main frequency and several side frequencies. Transforming repeating motion to its frequency domain via FFT reveals these frequencies. Average amplitudes of frequency intervals can be seen as features of cyclic motion. Hence determining these features can help to classify videos with repeating movements. In this paper we explain how to compute frequency spectra for video clips and how to use them for classifying. Our approach utilizes series of image moments as a function. This function again is transformed into its frequency domain.

Keywords: action recognition, frequency feature, motion recognition, repeating movement, video classification

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Authors: Hamid Reza Boveiri

Abstract:

Classification of Persian printed numeral characters has been considered and a proposed system has been introduced. In representation stage, for the first time in Persian optical character recognition, extended moment invariants has been utilized as characters image descriptor. In classification stage, four different classifiers namely minimum mean distance, nearest neighbor rule, multi layer perceptron, and fuzzy min-max neural network has been used, which first and second are traditional nonparametric statistical classifier. Third is a well-known neural network and forth is a kind of fuzzy neural network that is based on utilizing hyperbox fuzzy sets. Set of different experiments has been done and variety of results has been presented. The results showed that extended moment invariants are qualified as features to classify Persian printed numeral characters.

Keywords: Extended moment invariants, optical characterrecognition, Persian numerals classification.

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Authors: S. Ganesh, J. Janani, G. Besliya Angel

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Photovoltaic (PV) energy is one of the most important renewable energy sources. Maximum Power Point Tracking (MPPT) techniques should be used in photovoltaic systems to maximize the PV panel output power by tracking continuously the maximum power point which depends on panel’s temperature and on irradiance conditions. Incremental conductance control method has been used as MPPT algorithm. The methodology is based on connecting a pulse width modulated dc/dc SEPIC converter, which is controlled by a microprocessor based unit. The SEPIC converter is one of the buck-boost converters which maintain the output voltage as constant irrespective of the solar isolation level. By adjusting the switching frequency of the converter the maximum power point has been achieved. The main difference between the method used in the proposed MPPT systems and other technique used in the past is that PV array output power is used to directly control the dc/dc converter thus reducing the complexity of the system. The resulting system has high efficiency, low cost and can be easily modified. The tracking capability has been verified experimentally with a 10 W solar panel under a controlled experimental setup. The SEPIC converter and their control strategies has been analyzed and simulated using Simulink/Matlab software.

Keywords: Maximum Power Point Tracking, Microprocessor, PV Module, SEPIC Converter.

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Authors: Najam Aziz, Nasru Minallah, Ahmad Junaid, Kashaf Gul

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Landcover classification using automated classification techniques, while employing remotely sensed multi-spectral imagery, is one of the promising areas of research. Different land conditions at different time are captured through satellite and monitored by applying different classification algorithms in specific environment. In this paper, a SPOT-5 image provided by SUPARCO has been studied and classified in Environment for Visual Interpretation (ENVI), a tool widely used in remote sensing. Then, Artificial Neural Network (ANN) classification technique is used to detect the land cover changes in Abbottabad district. Obtained results are compared with a pixel based Distance classifier. The results show that ANN gives the better overall accuracy of 99.20% and Kappa coefficient value of 0.98 over the Mahalanobis Distance Classifier.

Keywords: Landcover classification, artificial neural network, remote sensing, SPOT-5.

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Authors: Ahsan Bin Tufail, Ali Abidi, Adil Masood Siddiqui, Muhammad Shahzad Younis

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An early and accurate detection of Alzheimer's disease (AD) is an important stage in the treatment of individuals suffering from AD. We present an approach based on the use of structural magnetic resonance imaging (sMRI) phase images to distinguish between normal controls (NC), mild cognitive impairment (MCI) and AD patients with clinical dementia rating (CDR) of 1. Independent component analysis (ICA) technique is used for extracting useful features which form the inputs to the support vector machines (SVM), K nearest neighbour (kNN) and multilayer artificial neural network (ANN) classifiers to discriminate between the three classes. The obtained results are encouraging in terms of classification accuracy and effectively ascertain the usefulness of phase images for the classification of different stages of Alzheimer-s disease.

Keywords: Biomedical image processing, classification algorithms, feature extraction, statistical learning.

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Authors: C. Iraklis, G. Evmiridis, A. Iraklis

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Renewable energy sources and distributed power generation units already have an important role in electrical power generation. A mixture of different technologies penetrating the electrical grid, adds complexity in the management of distribution networks. High penetration of distributed power generation units creates node over-voltages, huge power losses, unreliable power management, reverse power flow and congestion. This paper presents an optimization algorithm capable of reducing congestion and power losses, both described as a function of weighted sum. Two factors that describe congestion are being proposed. An upgraded selective particle swarm optimization algorithm (SPSO) is used as a solution tool focusing on the technique of network reconfiguration. The upgraded SPSO algorithm is achieved with the addition of a heuristic algorithm specializing in reduction of power losses, with several scenarios being tested. Results show significant improvement in minimization of losses and congestion while achieving very small calculation times.

Keywords: Congestion, distribution networks, loss reduction, particle swarm optimization, smart grid.

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Authors: Linyu Wang, Furui Huo, Jianhong Xiang

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The Doppler shift generated by high-speed movement and multipath effects in the channel are the main reasons for the generation of a time-frequency doubly-selective (DS) channel. There is severe inter-carrier interference (ICI) in the DS channel. Channel estimation for an orthogonal frequency division multiplexing (OFDM) system over a DS channel is very difficult. The simultaneous orthogonal matching pursuit (SOMP) algorithm under distributed compressive sensing theory (DCS-SOMP) has been used in channel estimation for OFDM systems over DS channels. However, the reconstruction accuracy of the DCS-SOMP algorithm is not high enough in the low Signal-to-Noise Ratio (SNR) stage. To solve this problem, in this paper, we propose an improved DCS-SOMP algorithm based on the inner product difference comparison operation (DCS-DCSOMP). The reconstruction accuracy is improved by increasing the number of candidate indexes and designing the comparison conditions of inner product difference. We combine the DCS-DCSOMP algorithm with the basis expansion model (BEM) to reduce the complexity of channel estimation. Simulation results show the effectiveness of the proposed algorithm and its advantages over other algorithms.

Keywords: OFDM, doubly selective, channel estimation, compressed sensing

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Authors: Akhilesh G. Naik, Dipankar Pal

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In today’s scenario, the complexity of digital signal processing (DSP) applications and various microcontroller architectures have been increasing to such an extent that the traditional approaches to multiplier design in most processors are becoming outdated for being comparatively slow. Modern processing applications require suitable pipelined approaches, and therefore, algorithms that are friendlier with pipelined architectures. Traditional algorithms like Wallace Tree, Radix-4 Booth, Radix-8 Booth, Dadda architectures have been proven to be comparatively slow for pipelined architectures. These architectures, therefore, need to be optimized or combined with other architectures amongst them to enhance its performances and to be made suitable for pipelined hardware/architectures. Recently, Vedic algorithm mathematically has proven to be efficient by appearing to be less complex and with fewer steps for its output establishment and have assumed renewed importance. This paper describes and shows how the Vedic algorithm can be better suited for pipelined architectures and also can be combined with traditional architectures and algorithms for enhancing its ability even further. In this paper, we also established that for complex applications on DSP and other microcontroller architectures, using Vedic approach for multiplication proves to be the best available and efficient option.

Keywords: Wallace tree, Radix-4 Booth, Radix-8 Booth, Dadda, Vedic, Single-Stage Karatsuba, Looped Karatsuba.

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Authors: M. Ovinis, D. Kerr, K. Bouazza-Marouf, M. Vloeberghs

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The use of machine vision to inspect the outcome of surgical tasks is investigated, with the aim of incorporating this approach in robotic surgery systems. Machine vision is a non-contact form of inspection i.e. no part of the vision system is in direct contact with the patient, and is therefore well suited for surgery where sterility is an important consideration,. As a proof-of-concept, three primary surgical tasks for a common neurosurgical procedure were inspected using machine vision. Experiments were performed on cadaveric pig heads to simulate the two possible outcomes i.e. satisfactory or unsatisfactory, for tasks involved in making a burr hole, namely incision, retraction, and drilling. We identify low level image features to distinguish the two outcomes, as well as report on results that validate our proposed approach. The potential of using machine vision in a surgical environment, and the challenges that must be addressed, are identified and discussed.

Keywords: Visual inspection, machine vision, robotic surgery.

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Authors: L. Edirisinghe, P. Mukherjee, H. Edirisinghe

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Virtual Container Yard (VCY) is a modern concept that helps to reduce the empty container repositioning cost of carriers. The concept of VCY is based on container interchange between shipping lines. Although this mechanism has been theoretically accepted by the shipping community as a feasible solution, it has not yet achieved the necessary momentum among container shipping lines (CSL). This paper investigates whether there is any legal influence on this industry myopia about the VCY. It is believed that this is the first publication that focuses on the legal aspects of container exchange between carriers. Not much literature on this subject is available. This study establishes with statistical evidence that there is a phobia prevailing in the shipping industry that exchanging containers with other carriers may lead to various legal implications. The complexity of exchange is two faceted. CSLs assume that offering a container to another carrier (obviously, a competitor in terms of commercial context) or using a container offered by another carrier may lead to undue legal implications. This research reveals that this fear is reflected through four types of perceived components, namely: shipping associate; warehouse associate; network associate; and trading associate. These components carry eighteen subcomponents that comprehensively cover the entire process of a container shipment. The statistical explanation has been supported through regression analysis; INCO terms were used to illustrate the shipping process.

Keywords: Container, legal, shipping, virtual.

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Authors: Siti Norul Huda Sheikh Abdullah, Farshid Pirahan Siah, Nor Hanisah Haji Zainal Abidin, Shahnorbanun Sahran

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The objective of this paper is to propose an adaptive multi threshold for image segmentation precisely in object detection. Due to the different types of license plates being used, the requirement of an automatic LPR is rather different for each country. The proposed technique is applied on Malaysian LPR application. It is based on Multi Layer Perceptron trained by back propagation. The proposed adaptive threshold is introduced to find the optimum threshold values. The technique relies on the peak value from the graph of the number object versus specific range of threshold values. The proposed approach has improved the overall performance compared to current optimal threshold techniques. Further improvement on this method is in progress to accommodate real time system specification.

Keywords: Multi-threshold approach, license plate recognition system.

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Authors: J. Silvestre, L. Almeida, R. Marau, P. Pedreiras

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Currently, there are many local area industrial networks that can give guaranteed bandwidth to synchronous traffic, particularly providing CBR channels (Constant Bit Rate), which allow improved bandwidth management. Some of such networks operate over Ethernet, delivering channels with enough capacity, specially with compressors, to integrate multimedia traffic in industrial monitoring and image processing applications with many sources. In these industrial environments where a low latency is an essential requirement, JPEG is an adequate compressing technique but it generates VBR traffic (Variable Bit Rate). Transmitting VBR traffic in CBR channels is inefficient and current solutions to this problem significantly increase the latency or further degrade the quality. In this paper an R(q) model is used which allows on-line calculation of the JPEG quantification factor. We obtained increased quality, a lower requirement for the CBR channel with reduced number of discarded frames along with better use of the channel bandwidth.

Keywords: Industrial Networks, Multimedia.

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Authors: D. Sumathi, P. Poongodi

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Cloud Computing refers to applications delivered as services over the internet, and the datacenters that provide those services with hardware and systems software. These were earlier referred to as Software as a Service (SaaS). Scheduling is justified by job components (called tasks), lack of information. In fact, in a large fraction of jobs from machine learning, bio-computing, and image processing domains, it is possible to estimate the maximum time required for a task in the job. This study focuses on Trust based scheduling to improve cloud security by modifying Heterogeneous Earliest Finish Time (HEFT) algorithm. It also proposes TR-HEFT (Trust Reputation HEFT) which is then compared to Dynamic Load Scheduling.

Keywords: Software as a Service (SaaS), Trust, Heterogeneous Earliest Finish Time (HEFT) algorithm, Dynamic Load Scheduling.

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Authors: Nareenad Panbun

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The objective is to study the knowledge utilization from the participants of the announcer training program by National Broadcasting and Telecommunications Commission (NBTC). This study is a quantitative research based on surveys and self-answering questionnaires. The population of this study is 100 participants randomly chosen by non-probability sampling method. The results have shown that most of the participants were satisfied with the topics of general knowledge about the broadcasting and television business for 37 people representing 37%, followed by the topics of broadcasting techniques. The legal issues, consumer rights, television business ethics, and credibility of the media are, in addition to the media's role and responsibilities in society, the use of language for successful communication. Therefore, the communication language skills are the most important for all of the trainees and will also build up the image of the broadcasting center.

Keywords: Announcer training program, participant, requirements announced, theory of utilization.

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Authors: A. Mohammadi Torkashvand, N. Haghighat

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Some methodologies were compared in providing erosion maps of surface, rill and gully and erosion features, in research which took place in the Varamin sub-basin, north-east Tehran, Iran. A photomorphic unit map was produced from processed satellite images, and four other maps were prepared by the integration of different data layers, including slope, plant cover, geology, land use, rocks erodibility and land units. Comparison of ground truth maps of erosion types and working unit maps indicated that the integration of land use, land units and rocks erodibility layers with satellite image photomorphic units maps provide the best methods in producing erosion types maps.

Keywords: Erosion Features, Geographic Information System, Remote Sensing.

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Authors: Erik Lilienblum, Robert Niese, Bernd Michaelis

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This paper proposes a novel stereo vision technique for top view book scanners which provide us with dense 3d point clouds of page surfaces. This is a precondition to dewarp bound volumes independent of 2d information on the page. Our method is based on algorithms, which normally require the projection of pattern sequences with structured light. We use image sequences of the moving stripe lighting of the top view scanner instead of an additional light projection. Thus the stereo vision setup is simplified without losing measurement accuracy. Furthermore we improve a surface model dewarping method through introducing a difference vector based on real measurements. Although our proposed method is hardly expensive neither in calculation time nor in hardware requirements we present good dewarping results even for difficult examples.

Keywords: stereo vision, 3d surface reconstruction, dewarpingdocuments, book scanner

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Authors: Chung-Hung Tsai

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Global environmental changes lead to increased frequency and scale of natural disaster, Taiwan is under the influence of global warming and extreme weather. Therefore, the vulnerability was increased and variability and complexity of disasters is relatively enhanced. The purpose of this study is to consider the source and magnitude of hazard characteristics on the tourism industry. Using modern risk management concepts, integration of related domestic and international basic research, this goes beyond the Taiwan typhoon disaster risk assessment model and evaluation of loss. This loss evaluation index system considers the impact of extreme weather, in particular heavy rain on the tourism industry in Taiwan. Consider the extreme climate of the compound impact of disaster for the tourism industry; we try to make multi-hazard risk assessment model, strategies and suggestions. Related risk analysis results are expected to provide government department, the tourism industry asset owners, insurance companies and banking include tourist disaster risk necessary information to help its tourism industry for effective natural disaster risk management.

Keywords: Tourism industry, extreme weather, multi-hazard, vulnerability analysis, loss exceeding probability, risk management.

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Authors: Thuy Thi Nguyen, Nguyen Dang Binh, Horst Bischof

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In this work, we present a novel active learning approach for learning a visual object detection system. Our system is composed of an active learning mechanism as wrapper around a sub-algorithm which implement an online boosting-based learning object detector. In the core is a combination of a bootstrap procedure and a semi automatic learning process based on the online boosting procedure. The idea is to exploit the availability of classifier during learning to automatically label training samples and increasingly improves the classifier. This addresses the issue of reducing labeling effort meanwhile obtain better performance. In addition, we propose a verification process for further improvement of the classifier. The idea is to allow re-update on seen data during learning for stabilizing the detector. The main contribution of this empirical study is a demonstration that active learning based on an online boosting approach trained in this manner can achieve results comparable or even outperform a framework trained in conventional manner using much more labeling effort. Empirical experiments on challenging data set for specific object deteciton problems show the effectiveness of our approach.

Keywords: Computer vision, object detection, online boosting, active learning, labeling complexity.

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Authors: Arpan Paul, Joy Sen

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Livability is a holistic concept whose factors include many complex characteristics and levels of interrelationships among them. It has been considered as people’s need for public amenities and is recognized as a major element to create social welfare. The concept and principles of livability are essential for recognizing the significance of community well-being. The attributes and dimensions of livability are also important aspects to measure the overall quality of environment. Livability potential is mainly considered as the capacity to develop into the overall well-being of an urban area in future. The intent of the present study is to identify the prime factors to evaluate livability potential within a metropolis. For ground level case study, the paper has selected Kolkata Metropolitan Area (KMA) as it has wide physical, social, and economic variations within it. The initial part of the study deals with detailed literature review on livability and its significance of evaluating its potential within a metropolis. The next segment is dedicated for identifying the primary factors which would evaluate livability potential within a metropolis. In pursuit of identifying primary factors, which have a direct impact on urban livability, this study delineates the metropolitan area into various clusters, having their distinct livability potential. As a final outcome of the study, variations of livability potential of those selected clusters are highlighted to explain the complexity of the metropolitan development.

Keywords: Livability potential, metropolis, Kolkata Metropolitan Area (KMA), well-being.

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Authors: Manpreet Singh, V. P. Agrawal, Gurmanjot Singh Bhatti

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From the last decades, there is a significant technological advancement in the field of robotics, and a number of modular self-reconfigurable robots were introduced that can help in space exploration, bucket to stuff, search, and rescue operation during earthquake, etc. As there are numbers of self-reconfigurable robots, choosing the optimum one is always a concern for robot user since there is an increase in available features, facilities, complexity, etc. The objective of this research work is to present a multiple attribute decision making based methodology for coding, evaluation, comparison ranking and selection of modular self-reconfigurable robots using a technique for order preferences by similarity to ideal solution approach. However, 86 attributes that affect the structure and performance are identified. A database for modular self-reconfigurable robot on the basis of different pertinent attribute is generated. This database is very useful for the user, for selecting a robot that suits their operational needs. Two visual methods namely linear graph and spider chart are proposed for ranking of modular self-reconfigurable robots. Using five robots (Atron, Smores, Polybot, M-Tran 3, Superbot), an example is illustrated, and raking of the robots is successfully done, which shows that Smores is the best robot for the operational need illustrated, and this methodology is found to be very effective and simple to use.

Keywords: Self-reconfigurable robots, MADM, TOPSIS, morphogenesis, scalability.

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