Now showing 1 - 10 of 32
  • Publication
    An Analysis of Interpolation Implementation for LNS Addition and Subtraction Function in Positive Region
    Interpolation is among of the most popular approach used in approximating the values in logarithmic number system (LNS) arithmetic design. This method that often combines with lookup tables (LUTs) manages to produce efficient LNS design in area, latency and accuracy. Current research proves that the combination of interpolators with co-transformation in LNS subtraction had shown extreme improvements in terms of speed and area, which is comparable to floating point (FLP) performance. Taking the advantage, this research had been conducted to analyze the implementation of these three interpolators, which are Taylor, Lagrange and modified Lagrange, in a 32-bit environment of the LNS addition and subtraction procedures with the first-order co-transformation in positive region. The designs were analyzed in two categories, which are the accuracy towards FLP and the total memory consumption. The best interpolator was selected based on the most optimum area consumption design with manageable accuracy in FLP limit. The outcome of this analysis could benefit further improvements in LNS design.
  • Publication
    Improved classification of orthosiphon stamineus by data fusion of electronic nose and tongue sensors
    An improved classification of Orthosiphon stamineus using a data fusion technique is presented. Five different commercial sources along with freshly prepared samples were discriminated using an electronic nose (e-nose) and an electronic tongue (e-tongue). Samples from the different commercial brands were evaluated by the e-tongue and then followed by the e-nose. Applying Principal Component Analysis (PCA) separately on the respective e-tongue and e-nose data, only five distinct groups were projected. However, by employing a low level data fusion technique, six distinct groupings were achieved. Hence, this technique can enhance the ability of PCA to analyze the complex samples of Orthosiphon stamineus. Linear Discriminant Analysis (LDA) was then used to further validate and classify the samples. It was found that the LDA performance was also improved when the responses from the e-nose and e-tongue were fused together.
  • Publication
    A biomimetic sensor for the classification of honeys of different floral origin and the detection of adulteration
    ( 2011-08) ; ; ;
    Norazian Subari
    ;
    Nazifah Ahmad Fikri
    ;
    ;
    Mohd Noor Ahmad
    ;
    Mahmad Nor Jaafar
    ;
    ; ; ;
    Supri A. Ghani
    The major compounds in honey are carbohydrates such as monosaccharides and disaccharides. The same compounds are found in cane-sugar concentrates. Unfortunately when sugar concentrate is added to honey, laboratory assessments are found to be ineffective in detecting this adulteration. Unlike tracing heavy metals in honey, sugar adulterated honey is much trickier and harder to detect, and traditionally it has been very challenging to come up with a suitable method to prove the presence of adulterants in honey products. This paper proposes a combination of array sensing and multi-modality sensor fusion that can effectively discriminate the samples not only based on the compounds present in the sample but also mimic the way humans perceive flavours and aromas. Conversely, analytical instruments are based on chemical separations which may alter the properties of the volatiles or flavours of a particular honey. The present work is focused on classifying 18 samples of different honeys, sugar syrups and adulterated samples using data fusion of electronic nose (e-nose) and electronic tongue (e-tongue) measurements. Each group of samples was evaluated separately by the e-nose and e-tongue. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were able to separately discriminate monofloral honey from sugar syrup, and polyfloral honey from sugar and adulterated samples using the e-nose and e-tongue. The e-nose was observed to give better separation compared to e-tongue assessment, particularly when LDA was applied. However, when all samples were combined in one classification analysis, neither PCA nor LDA were able to discriminate between honeys of different floral origins, sugar syrup and adulterated samples. By applying a sensor fusion technique, the classification for the 18 different samples was improved. Significant improvement was observed using PCA, while LDA not only improved the discrimination but also gave better classification. An improvement in performance was also observed using a Probabilistic Neural Network classifier when the e-nose and e-tongue data were fused.
      1  87
  • Publication
    A 1.5 V, 0.85-13.35 GHZ MMIC low noise amplifier design using optimization technique
    ( 2009)
    Arjuna Marzuki
    ;
    ;
    This paper describes how a broadband, 1.5 V, 0.85-13.35 GHz low noise amplifier in 0.15 μm 85 GHz PHEMT process is synthesized to simultaneously meet multiple design specifications such as bandwidth, noise figure, power gain and power consumption. Power-constrained synthesis technique is used to design the broadband amplifier. The simulated peak S21 is 19.8 dB, maximum noise Figure is 2.5 dB, 3-dB bandwidth is 12.5 GHz and power consumption is 73.5 mW. The calculated Figure of merit (FOM) is better than many reported broadband low noise amplifier (LNA).
      8  20
  • Publication
    Development of a scalable testbed for mobile olfaction verification
    The lack of information on ground truth gas dispersion and experiment verification information has impeded the development of mobile olfaction systems, especially for real-world conditions. In this paper, an integrated testbed for mobile gas sensing experiments is presented. The integrated 3 m × 6 m testbed was built to provide real-time ground truth information for mobile olfaction system development. The testbed consists of a 72-gas-sensor array, namely Large Gas Sensor Array (LGSA), a localization system based on cameras and a wireless communication backbone for robot communication and integration into the testbed system. Furthermore, the data collected from the testbed may be streamed into a simulation environment to expedite development. Calibration results using ethanol have shown that using a large number of gas sensor in the LGSA is feasible and can produce coherent signals when exposed to the same concentrations. The results have shown that the testbed was able to capture the time varying characteristics and the variability of gas plume in a 2 h experiment thus providing time dependent ground truth concentration maps. The authors have demonstrated the ability of the mobile olfaction testbed to monitor, verify and thus, provide insight to gas distribution mapping experiment.
      1  16
  • Publication
    Monitoring of milk quality with disposable taste sensor
    ( 2003)
    Maxsim Sim
    ;
    Teo Jau Shya
    ;
    Mohd Noor Ahmad
    ;
    ;
    Abdul Othman
    ;
    Muhammad Hitam
    A disposable screen-printed multi channel taste sensor composed of several types of lipid as transducers and a computer as data analyzer could detect taste in a manner similar to human gustatory sensation. The disposable taste sensor was used to measure the electrical potential resulted from the interaction between lipid membranes and taste substances. In the present study, two types of packaged commercial milk, the ultra high temperature (UHT) and the pasteurized milk were tested. It was found that the disposable taste sensor is capable to discriminate reliably between fresh and spoiled milk and to follow the deterioration of the milk quality when it is stored at room temperature based on a pattern recognition principle namely Principle Component Analysis (PCA). This research could provide a new monitoring method ideally for simple and cheap decentralized testing for controlling the quality of milk, which may be of great use in the dairy industries.
      2  18
  • Publication
    Classifying sources influencing Indoor Air Quality (IAQ) using Artificial Neural Network (ANN)
    Monitoring indoor air quality (IAQ) is deemed important nowadays. A sophisticated IAQ monitoring system which could classify the source influencing the IAQ is definitely going to be very helpful to the users. Therefore, in this paper, an IAQ monitoring system has been proposed with a newly added feature which enables the system to identify the sources influencing the level of IAQ. In order to achieve this, the data collected has been trained with artificial neural network or ANN—a proven method for pattern recognition. Basically, the proposed system consists of sensor module cloud (SMC), base station and service-oriented client. The SMC contain collections of sensor modules that measure the air quality data and transmit the captured data to base station through wireless network. The IAQ monitoring system is also equipped with IAQ Index and thermal comfort index which could tell the users about the room’s conditions. The results showed that the system is able to measure the level of air quality and successfully classify the sources influencing IAQ in various environments like ambient air, chemical presence, fragrance presence, foods and beverages and human activity.
      1  21
  • Publication
    Implementation of LNS addition and subtraction function with co-transformation in positive and negative region: A comparative analysis
    The European Logarithmic Microprocessor (ELM) had been an outstanding breakthrough in logarithmic number system (LNS) research history. The processor successfully reaches the par ability of floating-point (FLP) processor with its rapid and accurate design towards FLP. The design was able to improve the LNS addition and subtraction procedure, which are the drawbacks of any implementation of LNS arithmetic. ELM's subtraction operation had adopted a unique approach, which is the first-order co-transformation to overcome the singularity-to-zero issue of the non-linear function in negative region. Therefore, this research had been introduced to extensively compare and analyze the ELM-based addition and subtraction procedures with the same co-transformation technique implemented in positive region. In achieving this, two aspects are considered, which are the accuracy towards FLP and the memory consumption of both procedures in both regions. Conclusively, the exact ELM-based implementation in positive region of both operations could be realized and achieved comparable accuracy and memory area with a slight modification of the operation procedure. The outcome of this analysis could benefit further investigation of optimizing the LNS design for hardware implementation.
      1  20
  • Publication
    A hybrid sensing approach for pure and adulterated honey classification
    ( 2012)
    Norazian Subari
    ;
    Junita Mohamad Saleh
    ;
    ;
    This paper presents a comparison between data from single modality and fusion methods to classify Tualang honey as pure or adulterated using Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) statistical classification approaches. Ten different brands of certified pure Tualang honey were obtained throughout peninsular Malaysia and Sumatera, Indonesia. Various concentrations of two types of sugar solution (beet and cane sugar) were used in this investigation to create honey samples of 20%, 40%, 60% and 80% adulteration concentrations. Honey data extracted from an electronic nose (e-nose) and Fourier Transform Infrared Spectroscopy (FTIR) were gathered, analyzed and compared based on fusion methods. Visual observation of classification plots revealed that the PCA approach able to distinct pure and adulterated honey samples better than the LDA technique. Overall, the validated classification results based on FTIR data (88.0%) gave higher classification accuracy than e-nose data (76.5%) using the LDA technique. Honey classification based on normalized low-level and intermediate-level FTIR and e-nose fusion data scored classification accuracies of 92.2% and 88.7%, respectively using the Stepwise LDA method. The results suggested that pure and adulterated honey samples were better classified using FTIR and e-nose fusion data than single modality data.
      2  8
  • Publication
    Arithmetic addition and subtraction function of logarithmic number system in positive region: An investigation
    Logarithmic number system or LNS has become an optimal choice in digital image processing instead of floating point (FP) system based on latest researches in LNS. Digital image processing which deals with a lot of complex operations such as multiplication and division, makes LNS as a great choice of implementation. However, the implementation had been restricted by the addition and subtraction function in LNS arithmetic as these functions entail complex procedures and circuitry. As its huge potential to be a substitution of FP, there is an urgent need for LNS to improve the performance of both operations. Hence, various studies had been conducted in this area, however most of the research concern the implementation of these operations in the negative region. Therefore, this study is conducted with the objective on the exploration of both LNS addition and subtraction operations in the positive region and highlights the potential areas for design modifications and improvements. Then, these enhancements will be combined with other arithmetic functions in creating an optimum LNS design to be utilized in any digital image processing system.
      16  20