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With the development of communication technologies, the use of wireless systems in biomedical implanted devices has become very useful. Bio-implantable devices are electronic devices which are used for treatment and monitoring brain implants, pacemakers, cochlear implants, retinal implants and so on. The inductive coupling link is used to transmit power and data between the primary and secondary sides of the biomedical implanted system, in which efficient power amplifier is very much needed to ensure the best data transmission rates and low power losses. However, the efficiency of the implanted devices depends on the circuit design, controller, load variation, changes of radio frequency coil's mutual displacement and coupling coefficients. This paper provides a comprehensive survey on various power amplifier classes and their characteristics, efficiency and controller techniques that have been used in bio-implants. The automatic frequency controller used in biomedical implants such as gate drive switching control, closed loop power control, voltage controlled oscillator, capacitor control and microcontroller frequency control have been explained. Most of these techniques keep the resonance frequency stable in transcutaneous power transfer between the external coil and the coil implanted inside the body. Detailed information including carrier frequency, power efficiency, coils displacement, power consumption, supplied voltage and CMOS chip for the controllers techniques are investigated and summarized in the provided tables. From the rigorous review, it is observed that the existing automatic frequency controller technologies are more or less can capable of performing well in the implant devices; however, the systems are still not up to the mark. Accordingly, current challenges and problems of the typical automatic frequency controller techniques for power amplifiers are illustrated, with a brief suggestions and discussion section concerning the progress of implanted device research in the future. This review will hopefully lead to increasing efforts towards the development of low powered, highly efficient, high data rate and reliable automatic frequency controllers for implanted devices.

Modeling sediment load is a significant factor in water resources engineering as it affects directly the design and management of water resources. In this study, artificial neural networks (ANNs) are employed to estimate the daily sediment load. Two different ANN algorithms, the feed forward neural network (FFNN) and radial basis function (RBF) have been used for this purpose. The neural networks are trained and tested using daily sediment and flow data from Rantau Panjang station on Johor River. The results show that combining flow data with sediment load data gives an accurate model to predict sediment load. The comparison of the results indicate that the FFNN model has superior performance than the RB model in estimating daily sediment load.

The increasing requirement for Solid Waste Management (SWM) has become a significant challenge for municipal authorities. A number of integrated systems and methods have introduced to overcome this challenge. Many researchers have aimed to develop an ideal SWM system, including approaches involving software-based routing, Geographic Information Systems (GIS), Radio-frequency Identification (RFID), or sensor intelligent bins. Image processing solutions for the Solid Waste (SW) collection have also been developed; however, during capturing the bin image, it is challenging to position the camera for getting a bin area centralized image. As yet, there is no ideal system which can correctly estimate the amount of SW. This paper briefly discusses an efficient image processing solution to overcome these problems. Dynamic Time Warping (DTW) was used for detecting and cropping the bin area and Gabor wavelet (GW) was introduced for feature extraction of the waste bin image. Image features were used to train the classifier. A Multi-Layer Perceptron (MLP) classifier was used to classify the waste bin level and estimate the amount of waste inside the bin. The area under the Receiver Operating Characteristic (ROC) curves was used to statistically evaluate classifier performance. The results of this developed system are comparable to previous image processing based system. The system demonstration using DTW with GW for feature extraction and an MLP classifier led to promising results with respect to the accuracy of waste level estimation (98.50%). The application can be used to optimize the routing of waste collection based on the estimated bin level.

Reservoir planning and management are critical to the development of the hydrological field and necessary to Integrated Water Resources Management. The growth of forecasting models has resulted in an excellent model known as the Support Vector Machine (SVM). This model uses linearly separable patterns based on an optimal hyperplane, which are extended to non-linearly separable patterns by transforming the raw data to map into a new space. SVM can find a global optimal solution equipped with Kernel functions. These Kernel functions have high flexibility in the forecasting computation, enabling data to be mapped at a higher and infinite-dimensional space in an implicit manner. This paper presents a new solution to the expert system, using SVM to forecast the daily dam water level of the Klang gate. Four categories are identified to determine the best model: the input scenario, the type of SVM regression, the number of V-fold cross-validation and the time lag. The best input scenario employs both the rainfall R(t-i) and the dam water level L(t-i). Type 2 SVM regression is selected as the best regression type, and 5-fold cross-validation produces the most accurate results. The results are compared with those obtained using ANFIS: all the RMSE, MAE and MAPE values prove that SVM is a superior model to ANFIS. Finally, all the results are combined to determine the best time lag, resulting in R(t-2) L(t-2) for the best model with only 1.64 % error.

Efficient waste paper recycling has a significant role in the sustainable environment. Recyclable waste paper as a fundamental ingredient of municipal solid wastes (MSWs) is indeed an “urban ore”. Waste papers are considered as the solid recovered fuel which is recovered from MSW. Recyclable waste papers are segregated into various grades to produce high-quality products. Moreover, sorted paper streams save energy, chemicals, and water, as well as reduce sludge and rejects. Information technology is widely integrated with the waste management industry into its operations such as recycling, reuse, segregating based on categories and so on. This review article focuses on the life cycle of waste paper and existing waste paper sorting techniques. In the paper industry, many types of sensors are used in different mechanical and optical waste paper sorting systems. Such sensors include lignin, gloss, stiffness, mid-infrared, infrared, and color sensors. In this review, also described the effectiveness of different waste paper sorting systems, and finally, recommended appropriate waste paper sorting techniques based on effectiveness and low-cost implementation.

This paper deals with the design of small-sized bio- implanted spiral circular coils (pancake) with an operating frequency of 13.56MHz. The external and internal coils' geometric dimensions are dout = 56mm, din = 10mm and dout = 11:6mm, din = 5mm, respectively, in which the electrical performance is verifled through the commercial fleld solver High Frequency Structural Simulator (HFSS 13.0), which employs the flnite-element method (FEM) technique. Mathematical models for the proposed coils are developed. The simulation is performed-based on the developmental model in the air and at depths 6mm in a human biological tissue of dry and wet skin. The results demonstrate that the external and internal coils have maximum near-fleld gains of 54.15dB and 53.30dB in air. The maximum gains of the external coil contacted the wet and dry skin are 49.80dB and 48.95dB, respectively. The maximum gains of the internal coil at depths of 6mm in the wet and dry tissue are 41.80dB and 41.40dB, respectively. However, the external coil radiation e-ciencies on wet- and dry-skin are 92% and 90%, respectively, compared with that on air. The internal coil radiation e-ciencies on wet- and dry-skin are 78.4% and 77.6%, respectively, compared with that on air. In this study, the speciflc absorption rate (SAR) and radiated power results of the internal coil are investigated using SEMCAD 16.4 software. The SAR and power loss studies show that the designed implanted coil has a negligible efiect on the wet and dry skin and can be ignored.

Background and bjective Pterygium is an ocular disease caused by fibrovascular tissue encroachment onto the corneal region. The tissue may cause vision blurring if it grows into the pupil region. In this study, we propose an automatic detection method to differentiate pterygium from non-pterygium (normal) cases on the basis of frontal eye photographed images, also known as anterior segment photographed images. Methods The pterygium screening system was tested on two normal eye databases (UBIRIS and MILES) and two pterygium databases (Australia Pterygium and Brazil Pterygium). This system comprises four modules: (i) a preprocessing module to enhance the pterygium tissue using HSV-Sigmoid; (ii) a segmentation module to differentiate the corneal region and the pterygium tissue; (iii) a feature extraction module to extract corneal features using circularity ratio, Haralick's circularity, eccentricity, and solidity; and (iv) a classification module to identify the presence or absence of pterygium. System performance was evaluated using support vector machine (SVM) and artificial neural network. Results The three-step frame differencing technique was introduced in the corneal segmentation module. The output image successfully covered the region of interest with an average accuracy of 0.9127. The performance of the proposed system using SVM provided the most promising results of 88.7%, 88.3%, and 95.6% for sensitivity, specificity, and area under the curve, respectively. Conclusion A basic platform for computer-aided pterygium screening was successfully developed using the proposed modules. The proposed system can classify pterygium and non-pterygium cases reasonably well. In our future work, a standard grading system will be developed to identify the severity of pterygium cases. This system is expected to increase the awareness of communities in rural areas on pterygium.

Interest about security and asset safety escalates due to the increasing crimes in this century. However, almost all existing surveillance systems have limited self-learning ability that only allow real time monitoring and are unable to identify the actual events that take place in the monitored ambient. As such, this research aims to implement a smart surveillance system with embedded Complex Event Processing (CEP) technology to assist the intrusion detection by correlating raw data extracted from different sources. Four classifiers are used in the CEP engine to predict the event occurrences from the raw data sequence pattern acquired from the door sensors and surveillance camera via intelligent rule template matching algorithm. Confusion matrix in terms of sensitivity, specificity and average detection accuracy as well as ROC plot are employed in classifier performance evaluation to quantify the efficiency of the surveillance system developed.

This paper proposes a fully-automated retinal vascular tortuosity identification based on an early hypothesis from previous researches that better Diabetic Retinopathy (DR) detection accuracy can be obtained using retinal vascular tortuosity measurement than the current DR detection algorithms. In this work, a combination of local and global tortuosity measurements using non-linear curvature-based method is computed before a predicted tortuosity condition is determined by comparing the obtained average tortuosity value with a computed threshold value, T. The retinal vascular network is classified as normal if the average tortuosity value is less than the T and vice versa. This algorithm has been tested using twenty ground truth images from the online databases; DRIVE and STARE digital fundus datasets. The results show a vague correlation of retinal vascular tortuosity with diabetic retinopathy (DR) disease. Therefore, it can be concluded that the association between retinal vascular tortuosity and DR is still in its infancy and an objective and reliable tortuosity index is clearly needed to support the earlier hypothesis. Hence, in future work, we will develop an algorithm to compute the tortuosity of retinal arteries and veins and determine their correlation with DR.

Image segmentation is an important task in medical image processing to assist physicians or radiologists in making faster and effective diagnosis and treatment. However, there is still lack of effective segmentation methods to extract human spine in Magnetic resonance imaging (MRI) images. In this study, we propose a segmentation method using 12-anatomical point representation (12-APR) method for human spine vertebra. The proposed method is a semi-automatic segmentation in which 12-points will be manually annotated on the region of interest (ROI) before the ROI can be extracted automatically. The performance of this segmentation is evaluated using six performance metrics and the results show that the proposed method gives the highest accuracy (99.87%), specificity (99.89%), Dice similarity coefficient (94.04%), Jaccard similarity coefficient (88.81%) and Cosine similarity coefficient (94.14%).

This paper presents a performance evaluation of a radio frequency identification (RFID) signal-transmission framework in software-defined radio (SDR) using phase shift keying and quadrature amplitude modulation schemes. The RFID signals were compared within the SDR framework with respect to SDR performance. The SDR system performance was evaluated by analyzing signal transmission, estimating bit-error rate and comparing signal-to-noise ratios (SNR), with satisfactory results. The eye diagrams were also computed and compared with the SNR to evaluate the SDR signal-transmission performance.

All institutions offering engineering programs are faced with significant challenges, especially in preparing students so that they can receive information, learn the technology, the principles and practice of engineering as well as adapting to the rapidly changing needs to compete globally. Criteria and targets set by the Engineering Accreditation Council (EAC) and the Malaysian Qualifications Agency (MQA) stipulate that these students must be able to process the information actively and critically, evaluate them in order to achieve the high level of professional skills. Apart from the use of teaching and learning strategies, assessment methods, such as open-ended questions from the higher level of Bloom's taxonomy can be used to develop the necessary professional skills. This study aims to examine and provide examples, the form and level of open-ended questions in the field of electrical engineering that can test the mind, and encourage students to think analytically, critically and outside the box. This paper discusses the analysis performed on four questions from the final exam in the second semester 2010/2011 session to measure qualitatively the open-ended questions posed by the lecturer and whether they complied with the features as described. Out of those four questions, one completely complied with the set feature, two out of four parts in another question complied while one question has not complied at all. Some suggestions are given to improve the level of a given question.

In this paper, a variable threshold voice activity detector (VAD) is developed to control the operation of a two-sensor adaptive noise canceller (ANC). The VAD prohibits the reference input of the ANC from containing some strength of actual speech signal during adaptation periods. The novelty of this approach resides in using the residual output from the noise canceller to control the decisions made by the VAD. Thresholds of full-band energy and zero-crossing features are adjusted according to the residual output of the adaptive filter. Performance evaluation of the proposed approach is quoted in terms of signal to noise ratio improvements as well mean square error (MSE) convergence of the ANC. The new approach showed an improved noise cancellation performance when tested under several types of environmental noise. Furthermore, the computational power of the adaptive process is reduced since the output of the adaptive filter is efficiently calculated only during non-speech periods.

Environmental sustainability through end-of-life recovery has become the main items of contest in the automotive industries. Component reuse as one of the product recovery strategy is now gaining importance in view of its impact on the environment. Disassembly as one of the determinant factors for reuse is a very important and difficult process in life cycle engineering. To enable reuse, a certain level of disassembly of each component is necessary so that parts of the products that have arrived at their end-of life can be easily taken apart. Improvements to the disassembly process of products can be achieved at two levels: in the design phase, making choices that favours the ease of disassembly of the constructional system (design for disassembly) and planning at best and optimising the disassembly sequence (disassembly sequence planning). Hence, finding an optimal disassembly sequence is important to increase the reusability of the product. This paper presents the development work on an optimisation model for disassembly sequence using the genetic algorithms (GA) approach. GA is chosen to solve this optimisation model due to its capability in solving many large and complex optimisation problems compared with other heuristic methods. The fitness function of the GA in this study is dependent on the increment in disassembly time. Comparison of results using different combinatorial operators and tests with different probability factors are shown. This paper will present and discuss the disassembly sequence of an engine block, as a case example which achieves the minimum disassembly time.

Rainfall is considered as one of the major components of the hydrological process; it takes significant part in evaluating drought and flooding events. Therefore, it is important to have an accurate model for rainfall forecasting. Recently, several data-driven modeling approaches have been investigated to perform such forecasting tasks as multi-layer perceptron neural networks (MLP-NN). In fact, the rainfall time series modeling involves an important temporal dimension. On the other hand, the classical MLP-NN is a static and has a memoryless network architecture that is effective for complex nonlinear static mapping. This research focuses on investigating the potential of introducing a neural network that could address the temporal relationships of the rainfall series. Two different static neural networks and one dynamic neural network, namely the multi-layer perceptron neural network (MLP-NN), radial basis function neural network (RBFNN) and input delay neural network (IDNN), respectively, have been examined in this study. Those models had been developed for the two time horizons for monthly and weekly rainfall forecasting at Klang River, Malaysia. Data collected over 12 yr (1997–2008) on a weekly basis and 22 yr (1987–2008) on a monthly basis were used to develop and examine the performance of the proposed models. Comprehensive comparison analyses were carried out to evaluate the performance of the proposed static and dynamic neural networks. Results showed that the MLP-NN neural network model is able to follow trends of the actual rainfall, however, not very accurately. RBFNN model achieved better accuracy than the MLP-NN model. Moreover, the forecasting accuracy of the IDNN model was better than that of static network during both training and testing stages, which proves a consistent level of accuracy with seen and unseen data.

This paper reports the early results of the evaluation of driver's pre-driving skill on a driving simulator using the Intelligent Dynamic Event Analyzer approach. The IDEA Evaluator module, developed as an out-of-process Component Object Model ActiveX-Exe, is integrated into the ASISTM driving simulator to monitor drivers action during pre-driving activities. Fundamental actions detected from ASISTM simulator are fed to the Intelligent Dynamic Event Analyzer evaluator module and the result (Correct / Incorrect) is returned back to ASISTM. The integrated system was observed to be able to successfully evaluate the driver activities. The whole process is executed online utilizing ASISTM driving simulator module.

Implanted medical devices are very important electronic devices because of their usefulness in monitoring and diagnosis, safety and comfort for patients. Since 1950s, remarkable efforts have been undertaken for the development of bio-medical implanted and wireless telemetry bio-devices. Issues such as design of suitable modulation methods, use of power and monitoring devices, transfer energy from external to internal parts with high efficiency and high data rates and low power consumption all play an important role in the development of implantable devices. This paper provides a comprehensive survey on various modulation and demodulation techniques such as amplitude shift keying (ASK), frequency shift keying (FSK) and phase shift keying (PSK) of the existing wireless implanted devices. The details of specifications, including carrier frequency, CMOS size, data rate, power consumption and supply, chip area and application of the various modulation schemes of the implanted devices are investigated and summarized in the tables along with the corresponding key references. Current challenges and problems of the typical modulation applications of these technologies are illustrated with a brief suggestions and discussion for the progress of implanted device research in the future. It is observed that the prime requisites for the good quality of the implanted devices and their reliability are the energy transformation, data rate, CMOS size, power consumption and operation frequency. This review will hopefully lead to increasing efforts towards the development of low powered, high efficient, high data rate and reliable implanted devices.

Problem statement: Optical flow inter-frame enhancement is one of the techniques to improve the quality of ultrasound images by reducing the speckle noise. The performance depends on accuracy of the optical flow inter-frame reconstruction which is a part of the technique. The speckle noise of an ultrasound image forms certain pattern, of which size of the speckle varies depends on the distance from the transducer. Approach: This study proposed that the use of warping technique prior to as well as after the optical flow to improve the accuracy of the optical flow calculation. The warping function was derived to transform the image from circular grid to rectangular grid. Results: The technique was applied to a series of synthetic moving frames generated from actual ultrasound image taken from a patient heart. The technique improved the accuracy of motion vectors generated from the optical flow. The MSE of the reconstructed frame was also smaller using warped technique compared to that of the non-warped technique. Conclusion: The proposed warping operation was shown to be successful in increasing the accuracy of the frame reconstruction for the optical flow inter-frame enhancement technique.

In the last decade, structural health monitoring (SHM) systems became essential to accurately monitor structural response due to real-time loading conditions, detect damage in the structure, and report the location and nature of this damage. Spectral analysis using Fourier transform has been widely used in SHM. In this research, a novel approach for the characterization of in structure damage in civil structure is introduced. The target is to develop vibration-based damage detection algorithms that can relate structural dynamics changes to damage occurrence in a structure. This article presents a new method utilizing high resolution spectral analysis based on Fast Orthogonal Search (FOS) techniques. FOS is a signal processing tool developed to provide high-resolution spectral estimation. In addition, it is a general-purpose non-linear modeling technique that finds functional expansions using an arbitrary set of non-orthogonal candidate functions. In order to examine the proposed method, the IASC-ASCE structural health monitoring benchmark structure is used in this study to illustrate the merits and limitation of the proposed approach. We also discuss the merits and the limitations of FOS as applied to SHM.

Problem statement: Myocardial motion is important information for physicians in diagnosing cardiac abnormalities. The motion vector of myocardial can be computed using optical flow technique, which then can be further analyzed based on its mag nitude and angle. In practice, physicians are not concern about the angle of vector itself, but are m ore interested on whether a segment is moving to th e center or not. Approach: Therefore, in this study we propose a relative moti on direction with respect to the center of the cardiac cavity, called radial directi on, which is more useful for diagnosis. The radial direction is computed as the difference between the angle of optical flow at a point of interest and the angle b etween the point and the cavity center. Because of the dif ficulty in performing analysis based solely on indi vidual vectors, it is helpful to visualize and extract the overall trend by representing motion vectors by th eir angular distribution. Results: This method has been tested on clinical echocardiog raphy sequences and has been shown to be successful in providing a radial d irection profile of every segment for each echocardiographic frame. A comparison between the normal angular distribution and the proposed radial direction profile was also presented. Conclusion: The proposed profile was shown to be successful in providing the pattern of segmental motion which is easier for physician to analyze the myocardial moti on compared with the normal angular distribution as we ll as more invariant to segment locations.

Keratoconus is a corneal ectatic disorder with complex aetiology and may induce mild to severe visual impairment and consequently decrease the quality of life. This paper presents a new keratoconus detection method using corneal curvature features to differentiate normal and keratoconus cases. In this study, the eye images known as anterior segmented photographed images (ASPIs) are captured from side view using a smartphone’s camera. For the side-view images, the corneal curvature is segmented using spline function to measure the corneal curvature. A template disc method is implemented to quantitatively measure the steepening of the corneal curvature of the captured ASPIs. Parameters obtained from three different template disc methods, namely, nonlinear, 𝑐𝑛𝑙, crossover point, 𝑐𝑐𝑝, and trigonometric, 𝑐𝑡𝑟, are investigated to represent the most suitable curvature feature. SVM is then employed to classify normal and keratoconus eyes. Results reveal that a standalone nonlinear method gives a reliable parameter with 90% accuracy in classifying the data. However, the classification performance has increased to 99.5% accuracy with the use of all combined features known as a feature vector, 𝑓𝑐 =< 𝑐𝑛𝑙, 𝑐𝑐𝑝, 𝑐𝑡𝑟 >. Additionally, classification with the proposed 𝑓𝑐 has successfully distinguished normal and keratoconus cases with sensitivity and specificity rates of 99% and 100%, respectively. The results portray the bright potential of this method in assisting experts during ocular screening specifically to detect keratoconus disease.

Segmentation of the carpal bones (CBs) especially for children above seven years old is a challenging task in computer vision mainly because of poor definitions of the bone contours and the occurrence of the partial overlapping of the bones. Although active contour methods are widely employed in image bone segmentation, they are sensitive to initialization and have limitation in segmenting overlapping objects. Thus, there is a need for a robust segmentation method for bone segmentation. This paper presents an automatic active boundary-based segmentation method, gradient inverse coefficient of variation, based on dynamic programming (DP-GICOV) method to segment carpal bones on radiographic images of children age 5 to 8 years old. A mapping procedure is designed based on a priori knowledge about the natural growth and the arrangement of carpal bones in human body. The accuracy of the DP-GICOV is compared qualitatively and quantitatively with the de-regularized level set (DRLS) and multi-scale gradient vector flow (MGVF) on a dataset of 20 images of carpal bones from University of Southern California. The presented method is capable to detect the bone boundaries fast and accurate. Results show that the DP-GICOV is highly accurate especially for overlapping bones, which is more than 85% in many cases, and it requires minimal user’s intervention. This method has produced a promised result in overcoming both issues faced by active contours method; initialization and overlapping objects.

Internet of Things (IoT) has been implemented in most advance technologies as part of the emerging 4th industrial revolution, and recently, blockchain technology is being welcomed. These rapid growing technologies giving a big challenge to the students. As such, learning these new technologies should be made easy and simple. However, current education does not specifically offer a curriculum to empower skills and knowledge in IoT and blockchain technology. The aim of this research is to develop learning kit that can provide suitable training to understand concept of IoT and blockchain technology. The kit consists of three parts namely “brain”, “muscle” and “cloud”. Raspberry Pi is used as “brain” of operation that will be interact with the Caiser Cloud platform. A testbox operates as “muscle” to provide simple data input. The input from testbox will be sent, stored and displayed on a platform created using Xojo software. The results show that the learning kit is successfully interact with Caiser Cloud platform and can be used as a training tools for education purpose.

The current practice in determining oil palm fresh fruit bunches (FFB) ripeness is by its colour which could be inaccurate. This study investigates the classification of oil palm FFB ripeness using Raman spectroscopy. A feature extraction model is developed based on the different organic compositions that contribute to the ripeness classification. Samples are collected according to the Malaysian Palm Oil Board (MPOB) standards which are unripe, underripe, ripe, overripe, and rotten. Different characteristics of the Raman shift were detected which represent the material composition for each sample. The Raman intensity of the oil palm fruit increases from unripe to ripe before decreasing to rotten due to the carotenoid content in the fruit. In conclusion, Raman spectroscopy is a suitable technique to observe the changes in the composition of oil palm fruit classified by its ripeness.

The use of attributes in person re-identification and video surveillance applications has grabbed attentions of many researchers in recent times. Attributes are suitable tools for mid-level representation of a part or a region in an image as it is more similar to human perception as compared to the quantitative nature of the normal visual features description of those parts. Hence, in this paper, the preliminary experimental results to evaluate the robustness of attribute detectors against pose and light variations in contrast to the use of local appearance features is discussed. Results attained proven that the attribute-based detectors are capable to overcome the negative impact of pose and light variation towards person re-identification activities. In addition, the degree of importance of different attributes in re-identification is evaluated and compared with other previous works in this field.

Nutrient management in oil palm plantation is considered as one of the prominent issues especially for smallholder farmer. The nutrient contained in the tress has always been neglected and untreated and these may cause the trees to suffer from nutrient deficiencies. Therefore, in leveraging the oil yield at the maximum, a telemonitoring system is developed to assess and monitor the lack of nutrients for respective trees. This is done using image processing technique and artificial intelligence in detecting the nutritional deficiencies by analyzing the leaf. The categorization focused by classifying into four major types either as magnesium deficiencies, potassium deficiencies, nitrogen deficiencies or healthy that is based on the oil palm’s leaf surface. This is achieved by extracting the features namely number of red pixels, entropy and correlations. Further, two classifiers specifically support vector machine and artificial neural network is used for classification purpose along with performance measure using accuracy(ACC), Mean Square Error (MSE), Mean Absolute Error (MAE), Sensitivity (SN), Specificity (SP), Positive Predictive Value (PPV), Negative Predictive Value (NPV) based on ten-fold cross-validation. Results attained showed that the best classifier is SVM using RBF kernel (SVM-RBF) that is capable to accurately recognize the nutrient deficiencies with 100% accuracy.

Most 3D scanners are heavy, bulky and costly. These are the major factors that make them irrelevant to be attached to a drone for autonomous navigation. With modern technologies, it is possible to design a simple 3D scanner for autonomous navigation. The objective of this study is to design a cost effective 3D indoor mapping system using a 2D light detection and ranging (LiDAR) sensor for a drone. This simple 3D scanner is realised using a LiDAR sensor together with two servo motors to create the azimuth and elevation axes. An Arduino Uno is used as the interface between the scanner and computer for the real-time communication via serial port. In addition, an open source Point-Cloud Tool software is used to test and view the 3D scanner data. To study the accuracy and efficiency of the system, the LiDAR sensor data from the scanner is obtained in real-time in point-cloud form. The experimental results proved that the proposed system can perform the 2D and 3D scans with tolerable performance.

The objective of this paper is to optimise the proposed control strategy for an active anti-roll bar system using non-dominated sorting genetic algorithm (NSGA-II) tuning method. By using an active anti-roll control strategy, the controller can adapt to current road conditions and manoeuvres unlike a passive anti-roll bar. The optimisation solution offers a rather noticeable improvement results compared to the manually-tuned method. From the application point of view, both tuning process can be used. However, using optimisation method gives a multiple choice of solutions and provides the optimal parameters compared to manual tuning method.

This work presents a qualitative measurement of anterior segment photographed images (ASPIs) to identify between normal eyes and eyes with pterygium and pinguecula through Otsu multi-thresholding approach without contrast enhancement. In addition, we also propose a mobile screening framework of ASPIs through smartphones. ASPIs were directly sent to the cloud storage once an ASPI was captured using a smartphone camera, and then each image was processed through a digital image processing approach in a processing platform. Three important steps, namely, pre-processing, image segmentation and qualitative assessment, are involved in the processing platform of the mobile screening framework. The ASPIs are pre-processed to minimise or eliminate any unwanted areas within the image. Then, these ASPIs are segmented through multi-thresholding Otsu approach with clustering number n = 3. Segmentation result shows that the accuracy of the proposed method is 87.5%, which is comparable with the previously established work that has applied three-step differencing (3SD) method. However, the proposed approach has better computational time which is six times faster than the 3SD method. These results demonstrate a remarkable effort to produce a simple but straightforward digital image processing approach to be implemented in cloud computing for future studies.

This paper presents a proposed supervised classification technique namely partial histogram Bayes (PHBayes) learning algorithm. Conventional classifier based on Gaussian function has limitation when dealing with different probability distribution functions and requires large memory for large number of instance. Alternatively, histogram based classifiers are flexible for different probability density function. The aims of PHBayes are to handle large number of instances in datasets with lesser memory requirement, and fast in training and testing phases. The PHBayes depends on portion of the observed histogram that is similar to the probability density function. PHBayes was analyzed using synthetic and real data. Several factors affecting classification accuracy were considered. The PHBayes was compared with other established classifiers and demonstrated higher accurate classification, lesser memory even when dealing with large number of instance, and faster in training and testing phases.

Crop management is one of the key aspects in precision agriculture. On-site crop management includes a scheduled prediction and prescription of fertilization and pesticide application on specific areas of the field. A prediction of the tree health is much needed in order to decide a suitable prescription for the plant. An autonomous vehicle, equipped with at least one LiDAR (light detection and ranging) sensor, could be used not only for detecting and mapping its surrounding, but to also help evaluate tree health for early distress detection. Currently, farmers have to rely on eyesight to identify trees or plants in distress. Big-scale plantations depend on costly scheduled airborne monitoring routines, which also relies on human vision by scouring through hours of aerial video footage. Both techniques have similar weaknesses in terms of the time it takes to detect a tree in distress and the accuracy of the detection using human vision. The objective of this research is to propose a technique in evaluating tree health using a simple LiDAR sensor that is commonly used in autonomous vehicles. In order to achieve this objective, an evaluation of the different intensity characteristics of tree leaves versus fruits was carried out, both in the lab and in the field. This study has chosen oil palm trees as its subject, as the problem of health monitoring in oil palm plantations is evident. A LiDAR with a 905 nm near-infrared (NIR) laser is used to scan both individual healthy leaves in the lab and different oil palm trees in the field. Since a LiDAR sensor is normally used for ranging, a systematic process was proposed to capture the reflected intensity value of the laser beam that was transmitted to the object. This whole system can be realized using a LiDAR sensor, servo motors, and an Arduino board. Processing software was used to test and store the captured information from the sensor. Later, MATLAB was used to plot the intensity map of the leaves and oil palm tree, classify the range intensity into histograms, and calculate the leaf area index (LAI) for the oil palm trees. From the experimental results, it is found that the reflectance intensity of the leaves shows consistent range values between 155-160 magnitude both in the lab and in the field. From there, three different trees with different number of leaves were scanned and evaluated based on their LAI values. The health of the tree is then predicted, where a healthy tree is estimated to have a higher LAI value. The resulting LAI value is found to correlate with the evaluation using eyesight. This proves that although using a single-wavelength NIR laser beam provided by the LiDAR sensor, as compared to multiple wavelengths of a spectrometer, the difference between the oil palm leaves, fruits and the background noise could be determined. In the future, where multi-wavelength laser LiDAR sensors could be possible, more materials could be characterized. In conclusion, detection, mapping, and materials characterization could be done by an autonomous vehicle utilizing a LiDAR sensor, where tree health could be predicted for crop care management.

The uncertainty of system operating conditions is a part of consequences which may cause to the volatility of a transmission system. This will hinder the performance of transmission system to effectively transfer the power between areas. Therefore, accurate estimation of transmission reliability margin (TRM) is required to ensure effective power transfer between areas during the occurrence of uncertainties. The power transfer is also called as the available transfer capability (ATC) in which it is the information required by the utilities and marketers to instigate selling and buying the electric energy. The TRM is estimated by taking into account the uncertainties of line outages and system parameters generated by the bootstrap technique. A case study of Malaysia Power System is used to verify the robustness of bootstrap technique in the TRM determination. The results show that the combined impact of several uncertainties which significantly affect the value of TRM. Copyright © 2010 John Wiley & Sons, Ltd.

This paper presents a fast and accurate method to determine the power transfer so-called the available transfer capability (ATC). The methodology utilizes the cubic-spline interpolation technique that is incorporated into the Ralston's method in order to provide fast and accurate assessment of ATC. The Ralston's method is used to predict the two trajectory points of voltage magnitude (U), power flow (S), and maximum generator rotor angle difference (Δδ). Then, the cubic-spline interpolation technique is used to accurately trace the P-U, P-S, or P-Δδ curves between two points of trajectory. The P-U, P-S, and P-Δδ curves represent as the variations of voltage magnitude, power flow and maximum generator rotor angle difference due to the increase of power transfer or ATC. The actual value of ATC is then determined when either the voltage magnitude limit, power flow limit or generator rotor angle difference limit intersects the curve. The effectiveness of the proposed method is verified by referring to the results of ATC for a case study of 2737-Polish system and 39-New England bus system. The proposed method gives a satisfactorily accurate and fast computation of ATC as compared to recursive AC power flow method. Copyright © 2010 John Wiley & Sons, Ltd.

In this paper, a subband noise canceller that is based on using low complexity infinite impulse response IIR filter banks at the analysis stage is proposed. Aliasing distortion is greatly reduced this way, while amplitude distortion is minimised by optimising a finite impulse response FIR filter bank at the synthesis stage. The motivation behind this work is the fact that, the least mean square LMS adaptive noise cancellers suffer from slow convergence due to coloured interfering signals, as well as high computational burden, due to long signal paths in acoustic environments. Efficiency of the proposed canceller was evaluated experimentally for white and coloured interferences. The contribution of this work is brought about by offering a design that is capable of improving convergence behaviour of the adaptive noise canceller with low implementation cost. Compared to similar literature solutions, the proposed scheme avoids the use of notch filtering or phase equalisers. The new scheme offers an improved p...

This paper presents a fast and accurate method to determine the available transfer capability. Ralston's method is used to predict the two trajectory points of voltage magnitude, power flow, and maximum generator rotor angle difference. Then, the cubic spline interpolation technique is used to accurately trace theP-V, P-S,orP-curves between two points of trajectory. TheP-V, P-SandP-curves represent as the variations of voltage magnitude, power, flow and maximum generator rotor angle difference due to the increase of power transfer. The actual available transfer capability value is determined at the intersection point between the curve and the constraints limit. The effectiveness of the proposed method is verified by referring to the results of ATC for a case study of 2737-Polish system and 39-New England bus system. The proposed method gives satisfactorily accurate and fast computation of ATC as compared to recursive AC power flow method.

Problem statement: Segmentation is the first and fundamental step in the process of computer vision and object classification. However, complicate or similar colour pattern add complexity to the segmentation of touching objects. The objective of this study was to develop a robust technique for the automatic segmentation and classification of touching plastic bottles, whose features were previously stored in a database. Approach: Our technique was based on the possibility to separate the two objects by means of a segment of straight line, whose position was determined by a genetic approach. The initial population of the genetic algorithm was heuristically determined among a large set of cutting lines, while further generations were selected based on the likelihood of the two objects with the images stored in the database. Results: Extensive testing, which was performed on random couples out of a population of 50 bottles, showed that the correct segmentation could be achieved in success rates above 90% with only a limited number of both chromosomes and iterations, thus reducing the computing time. Conclusion: These findings proved the effectiveness of our method as far as touching plastic bottles are concerned. This technique, being absolutely general, can be extended to any situation in which the properties of single objects were previously stored in a database.

Problem statement: Electroencepharogram (EEG) is an extremely complex signal with very low signal to noise ratio and these attributed to difficulty in analyzing the signal. Hence for detecting abnormal segment, a distinctive method is required to train the technologist to distinguish the anomalous in EEG data. The objective of this study was to create a framework to analyze EEG signals recorded from epileptic patients by evaluating the potential of UMACE filter to detect changes in single-channel EEG data during routine epilepsy monitoring. Approach: Normally, the peak to side lobe ratio (PSR) of a UMACE filter was employed as an indicator if a test data is similar to an authentic class or vice versa, however in this study, the consistent changes of the correlation output known as Region Of Interest (ROI) was plotted and monitored. Based on this approach, a novel method to analyze and distinguish variances in scalp EEG as well as comparing both normal and abnormal regions of the patient’s EEG was assessed. The performance of the novelty detection was examined based on the onset and end time of each seizure in the ROI plot. Results: Results showed that using ROI plot of variances one can distinguish irregularities in the EEG data. The advantage of the proposed technique was that it did not require large amount of data for training. Conclusion: As such, it was feasible to perform seizure analysis as well as localizing seizure onsets. In short, the technique can be used as a guideline for faster diagnosis in a lengthy EEG recording.

An experimental machine vision apparatus was used to identify and extract recyclable plastic bottles out of a conveyor belt. Color images were taken with a commercially available Webcam, and the recognition was performed by our homemade software, based on the shape and dimensions of object images. The software was able to manage multiple bottles in a single image and was additionally extended to cases involving touching bottles. The identification was fulfilled by comparing the set of measured features with an existing database and meanwhile integrating various recognition techniques such as minimum distance in the feature space, self-organized maps, and neural networks. The recognition system was tested on a set of 50 different bottles and provided so far an accuracy of about 97% on bottle identification. The extraction of the bottles was performed by means of a pneumatic arm, which was activated according to the plastic type; polyethylene-terephthalate (PET) bottles were left on the conveyor belt, while non-PET bottles were extracted. The software was designed to provide the best compromise between reliability and speed for real-time applications in view of the commercialization of the system at existing recycling plants.

Modeling of synchronous static compensator (STATCOM) of a power system based on the dynamic phasor model to investigate the performance of STATCOM for power quality analysis is described. It is compared with electromagnetic transient program (EMTP) like simulation. The dynamic phasor model and electromagnetic transient (EMT) model of the STATCOM including the power system are implemented in Matlab/Simulink toolbox and PSCAD/EMTDC, respectively. STATCOM dynamic phasor model including switching functions and their control system are presented. A satisfactory solution for power quality problems on typical distribution network is analyzed using the dynamic phasor model and EMTP like PSCAD/EMTDC simulation techniques. The simulation results revealed that the dynamic phasor model of STATCOM is in excellent agreement with the detailed time-domain EMT model of PSCAD/EMTDC simulation. The dynamic behavior of STATCOM using phasor model can be applied for analyzing power quality issues. It is found faster in speed and higher accuracy can be obtained and correlates well with PSCAD/EMTDC simulation results.

This paper presents an image analysis technique that utilizes a combination of a Gabor wavelet (GW) and gradient field distribution (GFD) techniques to extract a new set of feature vectors based on their directional texture properties for the classification of weed types. The feature extraction process involves the use of GW to enhance the directional feature of the images, followed by GFD implementation to produce histogram gradient orientation angles and additional steps to generate the histogram envelope. Next, curve fitting technique is used to estimate the envelope function to determine its quadratic polynomial equation, y=ax2+bx+c and by taking the second derivative, the curvature value, a, is determined and used as a single input feature vector. The proposed technique was compared with another technique that also uses a single input feature obtained via GW algorithm implementation only. The overall classification accuracy utilizing the proposed technique is 94%, whereas using a GW only feature obtained 84% accuracy. The results obtained showed that this proposed technique is effective in performing weed classification.

Problem statement: An accurate knowledge of geographic positions of sonobuoys is critical for the conduct of antisubmarine warfare operations and detected target localization. Deployed from an airborne platform or a surface vessel, arrays of sonobuoys could be used to efficiently track and localize submarines. Lastly, some sonobuoys were being equipped with GPS for improving system accuracy and potentially allowing networked Sonobuoy positioning. However, the computation of the range using the propagation loss profile and the data of one sonobuoy usually leads to inaccurate target localization due to several effects and uncertainties. It was, alternatively, reported that if the target is within the detection rage of two or more sonobuoys, greatly improved target localization can be achieved. Approach: Aim of this research was to investigate the feasibility of fusing data from a distributed field of GPS sonobuoys to create an Artificial Intelligence (AI) based model for the error of the range computation in case of the target being detected by only one sonobuoy. Proposed module was designed utilizing Adaptive Neuron-Fuzzy Inference Systems (ANFIS) to estimate the range error associated with the computation using the propagation loss profile when the target is within the detection range of only one sonobuoy. The architecture of the proposed ANFIS system had two unique features. First was the real-time cross-validation applied during the update (training) procedure of the ANFIS-based module while the target was detected by two sonobuoys and the range was computed. Second feature was the use of non-overlapping and moving window for the real-time implementation of the ANFIS-based data fusion module. Results: Performance of the proposed system was examined with simulation data considering different scenarios for both the array of GPS sonobuoys and the target. Results showed that the corrected positioning by one sonobuoy is completely following the positioning by two sonobuoys over the entire experiment with the error in between evaluated to have RMSE value of 0.004 Nm and 0.008 for both scenarios. Conclusion: These results revealed that with aided from the proposed ANFIS model; significant enhancements to the underwater target tracking accuracy in cases of single sonobuoy detection could be achieved and thus maintaining consistent levels of accuracy over the whole tracking mission.

The study proposes an algorithm for noise cancellation by using recursive least square (RLS) and pattern recognition by using fusion method of Dynamic Time Warping (DTW) and Hidden Markov Model (HMM). Speech signals are often corrupted with background noise and the changes in signal characteristics could be fast. These issues are especially important for robust speech recognition. Robustness is a key issue in speech recognition. The algorithm is tested on speech samples that are a part of a Malay corpus. It is shown that the fusion technique can be used to fuse the pattern recognition outputs of DTW and HMM. Furthermore refinement normalization was introduced by using weight mean vector to obtain better performance. Accuracy of 94% on pattern recognition was obtainable using fusion HMM and DTW compared to 80.5% using DTW and 90.7% using HMM separately. The accuracy of the proposed algorithm is increased further to 98% by utilization the RLS adaptive noise cancellation.