Automatic Musical Genre Classification Using Divergence and Average Information Measures

Recently many research has been conducted to retrieve pertinent parameters and adequate models for automatic music genre classification. In this paper, two measures based upon information theory concepts are investigated for mapping the features space to decision space. A Gaussian Mixture Model (GMM) is used as a baseline and reference system. Various strategies are proposed for training and testing sessions with matched or mismatched conditions, long training and long testing, long training and short testing. For all experiments, the file sections used for testing are never been used during training. With matched conditions all examined measures yield the best and similar scores (almost 100%). With mismatched conditions, the proposed measures yield better scores than the GMM baseline system, especially for the short testing case. It is also observed that the average discrimination information measure is most appropriate for music category classifications and on the other hand the divergence measure is more suitable for music subcategory classifications.

Tritium Determination in Danube River Water in Serbia by Liquid Scintillation Counter

Tritium activity concentration in Danube river water in Serbia has been determinate using a liquid scintillation counter Quantulus 1220. During December 2010, water samples were taken along the entire course of Danube through Serbia, from Hungarian- Serbian to Romanian-Serbian border. This investigation is very important because of the nearness of nuclear reactor Paks in Hungary. Sample preparation was performed by standard test method using Optiphase HiSafe 3 scintillation cocktail. We used a rapid method for the preparation of environmental samples, without electrolytic enrichment.

Surface Charge Based Rapid Method for Detection of Microbial Contamination in Drinking Water and Food Products

Microbial contamination, most of which are fecal born in drinking water and food industry is a serious threat to humans. Escherichia coli is one of the most common and prevalent among them. We have developed a sensor for rapid and an early detection of contaminants, taking E.coli as a threat indicator organism. The sensor is based on co-polymerizations of aniline and formaldehyde in form of thin film over glass surface using the vacuum deposition technique. The particular doping combination of thin film with Fe-Al and Fe-Cu in different concentrations changes its non conducting properties to p- type semi conductor. This property is exploited to detect the different contaminants, believed to have the different surface charge. It was found through experiments that different microbes at same OD (0.600 at 600 nm) have different conductivity in solution. Also the doping concentration is found to be specific for attracting microbes on the basis of surface charge. This is a simple, cost effective and quick detection method which not only decreases the measurement time but also gives early warnings for highly contaminated samples.

Urban-Rural Balance, Regional Coordination and Land Transfer in China

It-s difficult for China-s current land transfer institutions limited to county-wide to solve the contradiction between urban-rural development and construction land shortage. On the basis of analyzing China-s construction land transfer system, and evaluation toward Transfer of development rights (TDR) practices in Anhui and Chongqing, the passage proposes: (1) we should establish a multi-level land indicators trade market under the guidance of regional spatial objectives, and allow land transfer paid across cities and counties within a specific area following the regulation of both government and market; (2) it would be better to combine organically the policy ntentions of land plan, regional plan, urban plan and economic plan, and link them with land indicators transfer to promote a wider range of urban-rural balance and regional coordination.

Quantum Dot Cellular Automata Based Effective Design of Combinational and Sequential Logical Structures

The use of Quantum dots is a promising emerging Technology for implementing digital system at the nano level. It is effecient for attractive features such as faster speed , smaller size and low power consumption than transistor technology. In this paper, various Combinational and sequential logical structures - HALF ADDER, SR Latch and Flip-Flop, D Flip-Flop preceding NAND, NOR, XOR,XNOR are discussed based on QCA design, with comparatively less number of cells and area. By applying these layouts, the hardware requirements for a QCA design can be reduced. These structures are designed and simulated using QCA Designer Tool. By taking full advantage of the unique features of this technology, we are able to create complete circuits on a single layer of QCA. Such Devices are expected to function with ultra low power Consumption and very high speeds.

A New Approach to Steganography using Sinc-Convolution Method

Both image steganography and image encryption have advantages and disadvantages. Steganograhy allows us to hide a desired image containing confidential information in a covered or host image while image encryption is decomposing the desired image to a non-readable, non-comprehended manner. The encryption methods are usually much more robust than the steganographic ones. However, they have a high visibility and would provoke the attackers easily since it usually is obvious from an encrypted image that something is hidden! The combination of steganography and encryption will cover both of their weaknesses and therefore, it increases the security. In this paper an image encryption method based on sinc-convolution along with using an encryption key of 128 bit length is introduced. Then, the encrypted image is covered by a host image using a modified version of JSteg steganography algorithm. This method could be applied to almost all image formats including TIF, BMP, GIF and JPEG. The experiment results show that our method is able to hide a desired image with high security and low visibility.

Fuzzy Based Environmental System Approach for Impact Assessment - Case Studies

Environmental studies have expanded dramatically all over the world in the past few years. Nowadays businesses interact with society and the environment in ways that put their mark on both sides. Efforts improving human standard living, through the control of nature and the development of new products, have also resulted in contamination of the environment. Consequently companies play an important role in environmental sustainability of a region or country. Therefore we can say that a company's sustainable development is strictly dependent on the environment. This article presents a fuzzy model to evaluate a company's environmental impact. Article illustrates an example of the automotive industry in order to prove the usefulness of using such a model.

A Generic e-Tutor for Graphical Problems

For a variety of safety and economic reasons, engineering undergraduates in Australia have experienced diminishing access to the real hardware that is typically the embodiment of their theoretical studies. This trend will delay the development of practical competence, decrease the ability to model and design, and suppress motivation. The author has attempted to address this concern by creating a software tool that contains both photographic images of real machinery, and sets of graphical modeling 'tools'. Academics from a range of disciplines can use the software to set tutorial tasks, and incorporate feedback comments for a range of student responses. An evaluation of the software demonstrated that students who had solved modeling problems with the aid of the electronic tutor performed significantly better in formal examinations with similar problems. The 2-D graphical diagnostic routines in the Tutor have the potential to be used in a wider range of problem-solving tasks.

Feature Selection with Kohonen Self Organizing Classification Algorithm

In this paper a one-dimension Self Organizing Map algorithm (SOM) to perform feature selection is presented. The algorithm is based on a first classification of the input dataset on a similarity space. From this classification for each class a set of positive and negative features is computed. This set of features is selected as result of the procedure. The procedure is evaluated on an in-house dataset from a Knowledge Discovery from Text (KDT) application and on a set of publicly available datasets used in international feature selection competitions. These datasets come from KDT applications, drug discovery as well as other applications. The knowledge of the correct classification available for the training and validation datasets is used to optimize the parameters for positive and negative feature extractions. The process becomes feasible for large and sparse datasets, as the ones obtained in KDT applications, by using both compression techniques to store the similarity matrix and speed up techniques of the Kohonen algorithm that take advantage of the sparsity of the input matrix. These improvements make it feasible, by using the grid, the application of the methodology to massive datasets.

Micro-aerobic, Anaerobic and Two-stage Condition for Ethanol Production by enterobacter aerogenes from Biodiesel-derived Crude Glycerol

The microbial production of ethanol from biodiesel¬derived crude glycerol by Enterobacter aerogenes TISTR1468, under micro-aerobic and anaerobic conditions, was investigated. The experimental results showed that micro-aerobic conditions were more favorable for cellular growth (4.0 g/L DCW), ethanol production (20.7 g/L) as well as the ethanol yield (0.47 g/g glycerol) than anaerobic conditions (1.2 g/L DCW, 6.3 g/L ethanol and 0.72 g/g glycerol, respectively). Crude glycerol (100 g/L) was consumed completely with the rate of 1.80 g/L/h. Two-stage fermentation (combination of micro-aerobic and anaerobic condition) exhibited higher ethanol production (24.5 g/L) than using one-stage fermentation (either micro-aerobic or anaerobic condition. The two- stage configuration, exhibited slightly higher crude glycerol consumption rate (1.81 g/L/h), as well as ethanol yield (0.56 g/g) than the one-stage configuration. Therefore, two-stage process was selected for ethanol production from E. aerogenes TISTR1468 in scale-up studies.

Quality of Service Evaluation using a Combination of Fuzzy C-Means and Regression Model

In this study, a network quality of service (QoS) evaluation system was proposed. The system used a combination of fuzzy C-means (FCM) and regression model to analyse and assess the QoS in a simulated network. Network QoS parameters of multimedia applications were intelligently analysed by FCM clustering algorithm. The QoS parameters for each FCM cluster centre were then inputted to a regression model in order to quantify the overall QoS. The proposed QoS evaluation system provided valuable information about the network-s QoS patterns and based on this information, the overall network-s QoS was effectively quantified.

A New Predictor of Coding Regions in Genomic Sequences using a Combination of Different Approaches

Identifying protein coding regions in DNA sequences is a basic step in the location of genes. Several approaches based on signal processing tools have been applied to solve this problem, trying to achieve more accurate predictions. This paper presents a new predictor that improves the efficacy of three techniques that use the Fourier Transform to predict coding regions, and that could be computed using an algorithm that reduces the computation load. Some ideas about the combination of the predictor with other methods are discussed. ROC curves are used to demonstrate the efficacy of the proposed predictor, based on the computation of 25 DNA sequences from three different organisms.

A Real-time Computer Vision System for VehicleTracking and Collision Detection

Recent developments in automotive technology are focused on economy, comfort and safety. Vehicle tracking and collision detection systems are attracting attention of many investigators focused on safety of driving in the field of automotive mechatronics. In this paper, a vision-based vehicle detection system is presented. Developed system is intended to be used in collision detection and driver alert. The system uses RGB images captured by a camera in a car driven in the highway. Images captured by the moving camera are used to detect the moving vehicles in the image. A vehicle ahead of the camera is detected in daylight conditions. The proposed method detects moving vehicles by subtracting successive images. Plate height of the vehicle is determined by using a plate recognition algorithm. Distance of the moving object is calculated by using the plate height. After determination of the distance of the moving vehicle relative speed of the vehicle and Time-to-Collision are calculated by using distances measured in successive images. Results obtained in road tests are discussed in order to validate the use of the proposed method.

Is the Expansion of High-Tech Leaders Possible Within the New EU Members? A Case Study of Ammono S.A. and the High-Tech Financing System in Poland

Innovations, especially technological, are considered key-drivers for sustainable economic growth and competitiveness in the globalised world. As such they should also play an important role in the process of economical convergence inside the EU. Unfortunately, the problem of insufficient innovation performance concerns around half of the EU countries. Poland shows that a lack of a consistent high-tech financing system constitutes a serious obstacle for the development of innovative firms. In this article we will evaluate these questions referring to the example of Ammono S.A., a Polish company established to develop and commercialise an original technology for the production of bulk GaN crystals. We will focus on its efforts to accumulate the financial resources necessary at different stages of its development. The purpose of this article is to suggest possible ways to improve the national innovative system, which would make it more competent in generating high-tech leaders.

Analysis of Driver Point of Regard Determinations with Eye-Gesture Templates Using Receiver Operating Characteristic

An Advance Driver Assistance System (ADAS) is a computer system on board a vehicle which is used to reduce the risk of vehicular accidents by monitoring factors relating to the driver, vehicle and environment and taking some action when a risk is identified. Much work has been done on assessing vehicle and environmental state but there is still comparatively little published work that tackles the problem of driver state. Visual attention is one such driver state. In fact, some researchers claim that lack of attention is the main cause of accidents as factors such as fatigue, alcohol or drug use, distraction and speeding all impair the driver-s capacity to pay attention to the vehicle and road conditions [1]. This seems to imply that the main cause of accidents is inappropriate driver behaviour in cases where the driver is not giving full attention while driving. The work presented in this paper proposes an ADAS system which uses an image based template matching algorithm to detect if a driver is failing to observe particular windscreen cells. This is achieved by dividing the windscreen into 24 uniform cells (4 rows of 6 columns) and matching video images of the driver-s left eye with eye-gesture templates drawn from images of the driver looking at the centre of each windscreen cell. The main contribution of this paper is to assess the accuracy of this approach using Receiver Operating Characteristic analysis. The results of our evaluation give a sensitivity value of 84.3% and a specificity value of 85.0% for the eye-gesture template approach indicating that it may be useful for driver point of regard determinations.

An Artificial Immune System for a Multi Agent Robotics System

This paper explores an application of an adaptive learning mechanism for robots based on the natural immune system. Most of the research carried out so far are based either on the innate or adaptive characteristics of the immune system, we present a combination of these to achieve behavior arbitration wherein a robot learns to detect vulnerable areas of a track and adapts to the required speed over such portions. The test bed comprises of two Lego robots deployed simultaneously on two predefined near concentric tracks with the outer robot capable of helping the inner one when it misaligns. The helper robot works in a damage-control mode by realigning itself to guide the other robot back onto its track. The panic-stricken robot records the conditions under which it was misaligned and learns to detect and adapt under similar conditions thereby making the overall system immune to such failures.

Arsenate Removal by Nano Zero-valent Iron in the Gas Bubbling System

This study focused on arsenate removal by nano zero-valent iron (NZVI) in the gas-bubbled aqueous solution. It appears that solution acidified by H2SO4 is far more favorable than by CO2-bubbled acidification. In addition, as dissolved oxygen was stripped out of solution by N2 gas bubbling, the arsenate removal dropped significantly. To take advantages of common practice of carbonation and oxic condition, pretreatment of CO2 and air bubbling in sequence are recommended for a better removal of arsenate.

Do C-Test and Cloze Procedure Measure what they Purport to be Measuring? A Case of Criterion-Related Validity

This article investigated the validity of C-test and Cloze test which purport to measure general English proficiency. To provide empirical evidence pertaining to the validity of the interpretations based on the results of these integrative language tests, their criterion-related validity was investigated. In doing so, the test of English as a foreign language (TOEFL) which is an established, standardized, and internationally administered test of general English proficiency was used as the criterion measure. Some 90 Iranian English majors participated in this study. They were seniors studying English at a university in Tehran, Iran. The results of analyses showed that there is a statistically significant correlation among participants- scores on Cloze test, C-test, and the TOEFL. Building on the findings of the study and considering criterion-related validity as the evidential basis of the validity argument, it was cautiously deducted that these tests measure the same underlying trait. However, considering the limitations of using criterion measures to validate tests, no absolute claims can be made as to the construct validity of these integrative tests.

Using Knowledge Management and Critical Thinking to Understand Thai Perceptions and Decisions towards Work-Life Balance in a Multinational Software Development Firm

Work-life balance has been acknowledged and promoted for the sake of employee retention. It is essential for a manager to realize the human resources situation within a company to help employees work happily and perform at their best. This paper suggests knowledge management and critical thinking are useful to motivate employees to think about their work-life balance. A qualitative case study is presented, which aimed to discover the meaning of work-life balance-s meaning from the perspective of Thai knowledge workers and how it affects their decision-making towards work resignation. Results found three types of work-life balance dimensions; a work- life balance including a workplace and a private life setting, an organizational working life balance only, and a worklife balance only in a private life setting. These aspects all influenced the decision-making of the employees. Factors within a theme of an organizational work-life balance were involved with systematic administration, fair treatment, employee recognition, challenging assignments to gain working experience, assignment engagement, teamwork, relationship with superiors, and working environment, while factors concerning private life settings were about personal demands such as an increasing their salary or starting their own business.

Using HMM-based Classifier Adapted to Background Noises with Improved Sounds Features for Audio Surveillance Application

Discrimination between different classes of environmental sounds is the goal of our work. The use of a sound recognition system can offer concrete potentialities for surveillance and security applications. The first paper contribution to this research field is represented by a thorough investigation of the applicability of state-of-the-art audio features in the domain of environmental sound recognition. Additionally, a set of novel features obtained by combining the basic parameters is introduced. The quality of the features investigated is evaluated by a HMM-based classifier to which a great interest was done. In fact, we propose to use a Multi-Style training system based on HMMs: one recognizer is trained on a database including different levels of background noises and is used as a universal recognizer for every environment. In order to enhance the system robustness by reducing the environmental variability, we explore different adaptation algorithms including Maximum Likelihood Linear Regression (MLLR), Maximum A Posteriori (MAP) and the MAP/MLLR algorithm that combines MAP and MLLR. Experimental evaluation shows that a rather good recognition rate can be reached, even under important noise degradation conditions when the system is fed by the convenient set of features.