Search results for: Feature generation

Authors: M. Hadi Mashinchi, M. Reza Mashinchi, Siti Mariyam H. J. Shamsuddin

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In this paper a genetic algorithms approach for solving the linear and quadratic fuzzy equations Ãx̃=B̃ and Ãx̃² + B̃x̃=C̃ , where Ã, B̃, C̃ and x̃ are fuzzy numbers is proposed by genetic algorithms. Our genetic based method initially starts with a set of random fuzzy solutions. Then in each generation of genetic algorithms, the solution candidates converge more to better fuzzy solution x̃_b . In this proposed method the final reached x̃_b is not only restricted to fuzzy triangular and it can be fuzzy number.

Keywords: Fuzzy coefficient, fuzzy equation, genetic algorithms.

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Authors: Dávid Csercsik, Péter Kádár

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In the case of the proposed method, the problem is parallelized by considering multiple possible mode of operation profiles, which determine the range in which the generators operate in each period. For each of these profiles, the optimization is carried out independently, and the best resulting dispatch is chosen. For each such profile, the resulting problem is a quadratic programming (QP) problem with a potentially negative definite Q quadratic term, and constraints depending on the actual operation profile. In this paper we analyze the performance of available MATLAB optimization methods and solvers for the corresponding QP.

Keywords: Economic dispatch, optimization, quadratic programming, MATLAB.

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Authors: Qiu Chen, Koji Kotani, Feifei Lee, Tadahiro Ohmi

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In this paper, we propose an improved face recognition algorithm using histogram-based features in spatial and frequency domains. For adding spatial information of the face to improve recognition performance, a region-division (RD) method is utilized. The facial area is firstly divided into several regions, then feature vectors of each facial part are generated by Binary Vector Quantization (BVQ) histogram using DCT coefficients in low frequency domains, as well as Local Binary Pattern (LBP) histogram in spatial domain. Recognition results with different regions are first obtained separately and then fused by weighted averaging. Publicly available ORL database is used for the evaluation of our proposed algorithm, which is consisted of 40 subjects with 10 images per subject containing variations in lighting, posing, and expressions. It is demonstrated that face recognition using RD method can achieve much higher recognition rate.

Keywords: Face recognition, Binary vector quantization (BVQ), Local Binary Patterns (LBP), DCT coefficients.

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Authors: Ventura Assuncao

Abstract:

In automotive systems almost all steps concerning the calibration of several control systems, e.g., low idle governor or boost pressure governor, are made with the vehicle because the timeto- production and cost requirements on the projects do not allow for the vehicle analysis necessary to build reliable models. Here is presented a procedure using parametric and NN (neural network) models that enables the generation of vehicle system models based on normal ECU engine control unit) vehicle measurements. These models are locally valid and permit pre and follow-up calibrations so that, only the final calibrations have to be done with the vehicle.

Keywords: Automotive systems, neuro-hybrid models, demodulator, nonlinear systems, identification, and neural networks.

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Authors: Pavel Zikmund, Robert Popela

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Extreme formation is a theoretical concept of selfsustain flight when a big airliner is followed by a small UAV glider flying in the airliner wake vortex. The paper presents results of a climb analysis with the goal to lift the gliding UAV to airliners cruise altitude. Wake vortex models, the UAV drag polar and basic parameters and airliner’s climb profile are introduced at first. Afterwards, flight performance of the UAV in a wake vortex is evaluated by analytical methods. Time history of optimal distance between an airliner and the UAV during a climb is determined. The results are encouraging. Therefore available UAV drag margin for electricity generation is figured out for different vortex models.

Keywords: Flight in formation, self-sustained flight, UAV, wake vortex.

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Authors: Z. Veselý, M. Honner, J. Mach

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The aim of the performed work is to establish the 2D and 3D model of direct unsteady task of sample heat treatment by moving source employing computer model on the basis of finite element method. Complex boundary condition on heat loaded sample surface is the essential feature of the task. Computer model describes heat treatment of the sample during heat source movement over the sample surface. It is started from 2D task of sample cross section as a basic model. Possibilities of extension from 2D to 3D task are discussed. The effect of the addition of third model dimension on temperature distribution in the sample is showed. Comparison of various model parameters on the sample temperatures is observed. Influence of heat source motion on the depth of material heat treatment is shown for several velocities of the movement. Presented computer model is prepared for the utilization in laser treatment of machine parts.

Keywords: Computer simulation, unsteady model, heat treatment, complex boundary condition, moving heat source.

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Authors: Qi Xiu, Hiromu Hota, Yohsuke Ishii, Takuya Oda

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Recently, collectable manufacturing data are rapidly increasing. On the other hand, mega recall is getting serious as a social problem. Under such circumstances, there are increasing needs for preventing mega recalls by defect analysis such as root cause analysis and abnormal detection utilizing manufacturing data. However, the time to classify strings in manufacturing data by traditional method is too long to meet requirement of quick defect analysis. Therefore, we present String Length Distribution Classification method (SLDC) to correctly classify strings in a short time. This method learns character features, especially string length distribution from Product ID, Machine ID in BOM and asset list. By applying the proposal to strings in actual manufacturing data, we verified that the classification time of strings can be reduced by 80%. As a result, it can be estimated that the requirement of quick defect analysis can be fulfilled.

Keywords: Data quality, feature selection, probability distribution, string classification, string length.

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Authors: Manasjyoti Bhuyan, Kandarpa Kumar Sarma, Hirendra Das

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Signature represents an individual characteristic of a person which can be used for his / her validation. For such application proper modeling is essential. Here we propose an offline signature recognition and verification scheme which is based on extraction of several features including one hybrid set from the input signature and compare them with the already trained forms. Feature points are classified using statistical parameters like mean and variance. The scanned signature is normalized in slant using a very simple algorithm with an intention to make the system robust which is found to be very helpful. The slant correction is further aided by the use of an Artificial Neural Network (ANN). The suggested scheme discriminates between originals and forged signatures from simple and random forgeries. The primary objective is to reduce the two crucial parameters-False Acceptance Rate (FAR) and False Rejection Rate (FRR) with lesser training time with an intension to make the system dynamic using a cluster of ANNs forming a multiple classifier system.

Keywords: offline, algorithm, FAR, FRR, ANN.

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Authors: Heeseung Choi, Raechoong Kang, Kyungtaek Choi, Jaihie Kim

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Fake finger submission attack is a major problem in fingerprint recognition systems. In this paper, we introduce an aliveness detection method based on multiple static features, which derived from a single fingerprint image. The static features are comprised of individual pore spacing, residual noise and several first order statistics. Specifically, correlation filter is adopted to address individual pore spacing. The multiple static features are useful to reflect the physiological and statistical characteristics of live and fake fingerprint. The classification can be made by calculating the liveness scores from each feature and fusing the scores through a classifier. In our dataset, we compare nine classifiers and the best classification rate at 85% is attained by using a Reduced Multivariate Polynomial classifier. Our approach is faster and more convenient for aliveness check for field applications.

Keywords: Aliveness detection, Fingerprint recognition, individual pore spacing, multiple static features, residual noise.

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Authors: Bruno Škrinjarić

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Rapid technological developments and increase in organizations’ interdependence on international scale are changing the traditional workplace paradigm. A key feature of knowledge based economy is that employers are looking for individuals that possess both specific academic skills and knowledge, and also capability to be proactive and respond to problems creatively and autonomously. The focus of this paper is workers with Economics and Business background and its goals are threefold: (1) to explore wide range of competences and identify which are the most important to employers; (2) to investigate the existence and magnitude of gap between required and possessed level of a certain competency; and (3) to inquire how this gap is connected with performance of a company. A study was conducted on a representative sample of Croatian enterprises during the spring of 2016. Results show that generic, rather than specific, competences are more important to employers and the gap between the relative importance of certain competence and its current representation in existing workforce is greater for generic competences than for specific. Finally, results do not support the hypothesis that this gap is correlated with firms’ performance.

Keywords: Competency gap, competency matching, key competences, firm performance.

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Authors: Yunyoung Nam, Junghun Ryu, Yoo-Joo Choi, We-Duke Cho

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This paper presents a novel approach for representing the spatio-temporal topology of the camera network with overlapping and non-overlapping fields of view (FOVs). The topology is determined by tracking moving objects and establishing object correspondence across multiple cameras. To track people successfully in multiple camera views, we used the Merge-Split (MS) approach for object occlusion in a single camera and the grid-based approach for extracting the accurate object feature. In addition, we considered the appearance of people and the transition time between entry and exit zones for tracking objects across blind regions of multiple cameras with non-overlapping FOVs. The main contribution of this paper is to estimate transition times between various entry and exit zones, and to graphically represent the camera topology as an undirected weighted graph using the transition probabilities.

Keywords: Surveillance, multiple camera, people tracking, topology.

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Authors: Yang-Keun Ahn, Kwang-Soon Choi, Young-Choong Park, Kwang-Mo Jung

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This paper proposes a method to recognize the tip of a finger and space touch hand gesture using an infrared camera in a smart device environment. The proposed method estimates the tip of a finger with a curvature-based ellipse fitting algorithm, and verifies that the estimated object is indeed a finger with an ellipse fitting rectangular area. The feature extracted from the verified finger tip is used to implement the movement of a mouse and clicking gesture. The proposed algorithm was implemented with an actual smart device to test the proposed method. Empirical parameters were obtained from the keypad software and an image analysis tool for the performance optimization, and a comparative analysis with conventional research showed improved performance with the proposed method.

Keywords: Infrared camera, Hand gesture, Smart device, Space touch.

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Authors: Naeem Muhammad, Yves Vandewoude, Yolande Berbers, Robert Feldt

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The Requirements Abstraction Model (RAM) helps in managing abstraction in requirements by organizing them at four levels (product, feature, function and component). The RAM is adaptable and can be tailored to meet the needs of the various organizations. Because software requirements are an important source of information for developing high-level tests, organizations willing to adopt the RAM model need to know the suitability of the RAM requirements for developing high-level tests. To investigate this suitability, test cases from twenty randomly selected requirements were developed, analyzed and graded. Requirements were selected from the requirements document of a Course Management System, a web based software system that supports teachers and students in performing course related tasks. This paper describes the results of the requirements document analysis. The results show that requirements at lower levels in the RAM are suitable for developing executable tests whereas it is hard to develop from requirements at higher levels.

Keywords: Market-driven requirements engineering, requirements abstraction model, requirements abstraction, system testing.

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Authors: J. Skrovan, A. Alfantazi, T. Troczynski

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For relatively small particles of aluminum (<60 μm), a measurable percentage of the aluminum (>5%) is observed to corrode before passivation occurs at moderate temperatures (>50oC) in de-ionized water within one hour. Physical contact with alumina powder results in a significant increase in both the rate of corrosion and the extent of corrosion before passivation. Whereas the resulting release of hydrogen gas could be of commercial interest for portable hydrogen supply systems, the fundamental aspects of Al corrosion acceleration in presence of dispersed alumina particles are equally important. This paper investigates the effects of various amounts of alumina on the corrosion rate of aluminum powders in water and the effect of multiple additions of aluminum into a single reactor.

Keywords: Alumina, Aluminum, Corrosion, Hydrogen

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Authors: Pichet Sriyanyong

Abstract:

This paper presents the application of an enhanced Particle Swarm Optimization (EPSO) combined with Gaussian Mutation (GM) for solving the Dynamic Economic Dispatch (DED) problem considering the operating constraints of generators. The EPSO consists of the standard PSO and a modified heuristic search approaches. Namely, the ability of the traditional PSO is enhanced by applying the modified heuristic search approach to prevent the solutions from violating the constraints. In addition, Gaussian Mutation is aimed at increasing the diversity of global search, whilst it also prevents being trapped in suboptimal points during search. To illustrate its efficiency and effectiveness, the developed EPSO-GM approach is tested on the 3-unit and 10-unit 24-hour systems considering valve-point effect. From the experimental results, it can be concluded that the proposed EPSO-GM provides, the accurate solution, the efficiency, and the feature of robust computation compared with other algorithms under consideration.

Keywords: Particle Swarm Optimization (PSO), GaussianMutation (GM), Dynamic Economic Dispatch (DED).

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Authors: Susmitha Vekkot, Pancham Shukla

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In this paper, we focus on the fusion of images from different sources using multiresolution wavelet transforms. Based on reviews of popular image fusion techniques used in data analysis, different pixel and energy based methods are experimented. A novel architecture with a hybrid algorithm is proposed which applies pixel based maximum selection rule to low frequency approximations and filter mask based fusion to high frequency details of wavelet decomposition. The key feature of hybrid architecture is the combination of advantages of pixel and region based fusion in a single image which can help the development of sophisticated algorithms enhancing the edges and structural details. A Graphical User Interface is developed for image fusion to make the research outcomes available to the end user. To utilize GUI capabilities for medical, industrial and commercial activities without MATLAB installation, a standalone executable application is also developed using Matlab Compiler Runtime.

Keywords: Filter mask, GUI, hybrid architecture, image fusion, Matlab Compiler Runtime, wavelet transform.

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Authors: Abdel Ilah Alshabtat, Liang Dong

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In this paper, we proposed a new routing protocol for Unmanned Aerial Vehicles (UAVs) that equipped with directional antenna. We named this protocol Directional Optimized Link State Routing Protocol (DOLSR). This protocol is based on the well known protocol that is called Optimized Link State Routing Protocol (OLSR). We focused in our protocol on the multipoint relay (MPR) concept which is the most important feature of this protocol. We developed a heuristic that allows DOLSR protocol to minimize the number of the multipoint relays. With this new protocol the number of overhead packets will be reduced and the End-to-End delay of the network will also be minimized. We showed through simulation that our protocol outperformed Optimized Link State Routing Protocol, Dynamic Source Routing (DSR) protocol and Ad- Hoc On demand Distance Vector (AODV) routing protocol in reducing the End-to-End delay and enhancing the overall throughput. Our evaluation of the previous protocols was based on the OPNET network simulation tool.

Keywords: Mobile Ad-Hoc Networks, Ad-Hoc RoutingProtocols, Optimized link State Routing Protocol, Unmanned AerialVehicles, Directional Antenna.

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Authors: M. Anidha, K. Premalatha

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Feature Selection is significant in order to perform constructive classification in the area of cancer diagnosis. However, a large number of features compared to the number of samples makes the task of classification computationally very hard and prone to errors in microarray gene expression datasets. In this paper, we present an innovative method for selecting highly informative gene subsets of gene expression data that effectively classifies the cancer data into tumorous and non-tumorous. The hybrid gene selection technique comprises of combined Mutual Information and Fisher score to select informative genes. The gene selection is validated by classification using Support Vector Machine (SVM) which is a supervised learning algorithm capable of solving complex classification problems. The results obtained from improved Mutual Information and F-Score with SVM as a classifier has produced efficient results.

Keywords: Gene selection, mutual information, Fisher score, classification, SVM.

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Authors: Lei Zhang, Tingting Xu, Jun He, Zhenyu Yan, Roger Brooks

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Email marketing is one of the most important segments of online marketing. Email content is vital to customers. Different customers may have different familiarity with a product, so a successful marketing strategy must personalize email content based on individual customers’ product affinity. In this study, we build our personalized email marketing strategy with three types of emails: nurture, promotion, and conversion. Each type of emails has a different influence on customers. We investigate this difference by analyzing customers’ open rates, click rates and opt-out rates. Feature importance from response models is also analyzed. The goal of the marketing strategy is to improve the click rate on conversion-type emails. To build the personalized strategy, we formulate the problem as a reinforcement learning problem and adopt a Q-learning algorithm with variations. The simulation results show that our model-based strategy outperforms the current marketer’s strategy.

Keywords: Email marketing, email content, reinforcement learning, machine learning, Q-learning.

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Authors: Adilson M. Enes, Juliana A. Fracarolli, Inácio M. Dal Fabbro, Silvestre Rodrigues

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This research work proposed a study of fruit bruise detection by means of a biospeckle method, selecting the papaya fruit (Carica papaya) as testing body. Papaya is recognized as a fruit of outstanding nutritional qualities, showing high vitamin A content, calcium, carbohydrates, exhibiting high popularity all over the world, considering consumption and acceptability. The commercialization of papaya faces special problems which are associated to bruise generation during harvesting, packing and transportation. Papaya is classified as climacteric fruit, permitting to be harvested before the maturation is completed. However, by one side bruise generation is partially controlled once the fruit flesh exhibits high mechanical firmness. By the other side, mechanical loads can set a future bruise at that maturation stage, when it can not be detected yet by conventional methods. Mechanical damages of fruit skin leave an entrance door to microorganisms and pathogens, which will cause severe losses of quality attributes. Traditional techniques of fruit quality inspection include total soluble solids determination, mechanical firmness tests, visual inspections, which would hardly meet required conditions for a fully automated process. However, the pertinent literature reveals a new method named biospeckle which is based on the laser reflectance and interference phenomenon. The laser biospeckle or dynamic speckle is quantified by means of the Moment of Inertia, named after its mechanical counterpart due to similarity between the defining formulae. Biospeckle techniques are able to quantify biological activities of living tissues, which has been applied to seed viability analysis, vegetable senescence and similar topics. Since the biospeckle techniques can monitor tissue physiology, it could also detect changes in the fruit caused by mechanical damages. The proposed technique holds non invasive character, being able to generate numerical results consistent with an adequate automation. The experimental tests associated to this research work included the selection of papaya fruit at different maturation stages which were submitted to artificial mechanical bruising tests. Damages were visually compared with the frequency maps yielded by the biospeckle technique. Results were considered in close agreement.

Keywords: Biospeckle, papaya, mechanical damages, vegetable bruising.

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Authors: Se-Jin Ko, Deok-Min Kim, Seok-Yoon Kim

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The trend of growing density on chips has increases not only the temperature in chips but also the gradient of the temperature depending on locations. In this paper, we propose the balanced skew tree generation technique for minimizing the clock skew that is affected by the temperature gradients on chips. We calculate the interconnect delay using Elmore delay equation, and find out the optimal balanced clock tree by modifying the clock trees generated through the Deferred Merge Embedding(DME) algorithm. The experimental results show that the distance variance of clock insertion points with and without considering the temperature gradient can be lowered below 54% and we confirm that the skew is remarkably decreased after applying the proposed technique.

Keywords: clock, clock-skew, temperature, thermal.

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Authors: R. AliMohammadzadeh, M. Rahgozar, A. Zarnani

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The inherent flexibilities of XML in both structure and semantics makes mining from XML data a complex task with more challenges compared to traditional association rule mining in relational databases. In this paper, we propose a new model for the effective extraction of generalized association rules form a XML document collection. We directly use frequent subtree mining techniques in the discovery process and do not ignore the tree structure of data in the final rules. The frequent subtrees based on the user provided support are split to complement subtrees to form the rules. We explain our model within multi-steps from data preparation to rule generation.

Keywords: XML, Data Mining, Association Rule Mining.

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Authors: Yang Zhang, Jian He

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Heart disease is one of the leading causes of death in the world, and coronary heart disease (CHD) is one of the major heart diseases. Electrocardiogram (ECG) is widely used in the detection of heart diseases, but the traditional manual method for CHD prediction by analyzing ECG requires lots of professional knowledge for doctors. This paper presents sliding window and continuous wavelet transform (CWT) to transform ECG signals into images, and then ResNet and Bi-LSTM are introduced to build the ECG feature extraction network (namely ECGNet). At last, an auxiliary system for CHD prediction was developed based on modified ResNet18 and Bi-LSTM, and the public ECG dataset of CHD from MIMIC-3 was used to train and test the system. The experimental results show that the accuracy of the method is 83%, and the F1-score is 83%. Compared with the available methods for CHD prediction based on ECG, such as kNN, decision tree, VGGNet, etc., this method not only improves the prediction accuracy but also could avoid the degradation phenomenon of the deep learning network.

Keywords: Bi-LSTM, CHD, coronary heart disease, ECG, electrocardiogram, ResNet, sliding window.

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Authors: Jito Vanualailai, Bibhya Sharma

Abstract:

Swarm principles are increasingly being used to design controllers for the coordination of multi-robot systems or, in general, multi-agent systems. This paper proposes a two-dimensional Lagrangian swarm model that enables the planar agents, modeled as point masses, to swarm whilst effectively avoiding each other and obstacles in the environment. A novel method, based on an extended Lyapunov approach, is used to construct the model. Importantly, the Lyapunov method ensures a form of practical stability that guarantees an emergent behavior, namely, a cohesive and wellspaced swarm with a constant arrangement of individuals about the swarm centroid. Computer simulations illustrate this basic feature of collective behavior. As an application, we show how multiple planar mobile unicycle-like robots swarm to eventually form patterns in which their velocities and orientations stabilize.

Keywords: Attractive-repulsive swarm model, individual-based swarm model, Lagrangian swarm model, Lyapunov stability, Lyapunov-like function, practical stability, unicycle.

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Authors: Seongykyoon Jeong, Sungki Lee, Jaeyun Kim, Seunghun Oh, Kiho Kwak

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The purpose of study is to demonstrate how the characteristics of technology and the process required for development of technology affect technology transfer from public organisations to industry on the technology level. In addition, using the advantage of the analytic level and the novel means of measuring technology convergence, we examine the characteristics of converging technologies as compared to non-converging technologies in technology transfer process. In sum, our study finds that a technology from the public sector is likely to be transferred when its readiness level is closer to generation of profit, when its stage of life cycle is early and when its economic values is high. Our findings also show that converging technologies are less likely to be transferred.

Keywords: Interdisciplinary, Technology transfer, Technology convergence.

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Authors: H. B. Kekre, Tanuja K. Sarode, Bhakti Raul

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In this paper we propose segmentation approach based on Vector Quantization technique. Here we have used Kekre-s fast codebook generation algorithm for segmenting low-altitude aerial image. This is used as a preprocessing step to form segmented homogeneous regions. Further to merge adjacent regions color similarity and volume difference criteria is used. Experiments performed with real aerial images of varied nature demonstrate that this approach does not result in over segmentation or under segmentation. The vector quantization seems to give far better results as compared to conventional on-the-fly watershed algorithm.

Keywords: Image Segmentation, , Codebook, Codevector, data compression, Encoding

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Authors: A. Lauvray, F. Poulhaon, P. Michaud, P. Joyot, E. Duc

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Additive Friction Stir Manufacturing, or AFSM, is a new industrial process that follows the emergence of friction-based processes. The AFSM process is a solid-state additive process using the energy produced by the friction at the interface between a rotating non-consumable tool and a substrate. Friction depends on various parameters like axial force, rotation speed or friction coefficient. The feeder material is a metallic rod that flows through a hole in the tool. There is still a lack in understanding of the physical phenomena taking place during the process. This research aims at a better AFSM process understanding and implementation, thanks to numerical simulation and experimental validation performed on a prototype effector. Such an approach is considered a promising way for studying the influence of the process parameters and to finally identify a process window that seems relevant. The deposition of material through the AFSM process takes place in several phases. In chronological order these phases are the docking phase, the dwell time phase, the deposition phase, and the removal phase. The present work focuses on the dwell time phase that enables the temperature rise of the system due to pure friction. An analytic modeling of heat generation based on friction considers as main parameters the rotational speed and the contact pressure. Another parameter considered influential is the friction coefficient assumed to be variable, due to the self-lubrication of the system with the rise in temperature or the materials in contact roughness smoothing over time. This study proposes through a numerical modeling followed by an experimental validation to question the influence of the various input parameters on the dwell time phase. Rotation speed, temperature, spindle torque and axial force are the main monitored parameters during experimentations and serve as reference data for the calibration of the numerical model. This research shows that the geometry of the tool as well as fluctuations of the input parameters like axial force and rotational speed are very influential on the temperature reached and/or the time required to reach the targeted temperature. The main outcome is the prediction of a process window which is a key result for a more efficient process implementation.

Keywords: numerical model, additive manufacturing, frictional heat generation, process

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Authors: Yuwaraj M. Ghugal, Ajay G. Dahake

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A trigonometric shear deformation theory for flexure of thick beams, taking into account transverse shear deformation effects, is developed. The number of variables in the present theory is same as that in the first order shear deformation theory. The sinusoidal function is used in displacement field in terms of thickness coordinate to represent the shear deformation effects. The noteworthy feature of this theory is that the transverse shear stresses can be obtained directly from the use of constitutive relations with excellent accuracy, satisfying the shear stress free conditions on the top and bottom surfaces of the beam. Hence, the theory obviates the need of shear correction factor. Governing differential equations and boundary conditions are obtained by using the principle of virtual work. The thick cantilever isotropic beams are considered for the numerical studies to demonstrate the efficiency of the. Results obtained are discussed critically with those of other theories.

Keywords: Trigonometric shear deformation, thick beam, flexure, principle of virtual work, equilibrium equations, stress.

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Authors: Antoni Ivanov, Nikolay Dandanov, Nicole Christoff, Vladimir Poulkov

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Spectrum underutilization has made cognitive radio a promising technology both for current and future telecommunications. This is due to the ability to exploit the unused spectrum in the bands dedicated to other wireless communication systems, and thus, increase their occupancy. The essential function, which allows the cognitive radio device to perceive the occupancy of the spectrum, is spectrum sensing. In this paper, the performance of modern adaptations of the four most widely used spectrum sensing techniques namely, energy detection (ED), cyclostationary feature detection (CSFD), matched filter (MF) and eigenvalues-based detection (EBD) is compared. The implementation has been accomplished through the PlutoSDR hardware platform and the GNU Radio software package in very low Signal-to-Noise Ratio (SNR) conditions. The optimal detection performance of the examined methods in a realistic implementation-oriented model is found for the common relevant parameters (number of observed samples, sensing time and required probability of false alarm).

Keywords: Cognitive radio, dynamic spectrum access, GNU Radio, spectrum sensing.

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Authors: Amjad Nusayr

Abstract:

Software security is a general term used to any type of software architecture or model in which security aspects are incorporated in this architecture. These aspects are not part of the main logic of the underlying program. Software security can be achieved using a combination of approaches including but not limited to secure software designs, third part component validation, and secure coding practices. Memory safety is one feature in software security where we ensure that any object in memory is have a valid pointer or a reference with a valid type. Aspect Oriented Programming (AOP) is a paradigm that is concerned with capturing the cross-cutting concerns in code development. AOP is generally used for common cross-cutting concerns like logging and Database transaction managing. In this paper we introduce the concepts that enable AOP to be used for the purpose of memory and type safety. We also present ideas for extending AOP in software security practices.

Keywords: Aspect oriented programming, programming languages, software security, memory and type safety.

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