Extended Nearest Centroid Neighbor Method With Training Set Reduction For Classification

Lean manufacturing seeks Kaizen in terms of quality, cost and cycle time. A robust problem-solving often extends to external parties such as vendors, to draw in their unique technology resources and knowledge. The perusal of contemporary peer-reviewed literature reveals limited academic investigation onto such form of partnership; particularly vendor engagement having elements of properly defined risk and reward sharing. In this premise, Vendor Risk and Reward Sharing – Kaizen (VRRS-Kaizen) framework was proposed as a generic and holistic prescriptive system to guide personnel to duly deal with vendors. The objective of the framework is to ensure systematic and effective practice. Plan-Do-Check-Act underpins the framework and dichotomises the relevant stages of Kaizen. VRRS-Kaizen commences with the identification by Kaizen Team for the need of calling in vendors for countermeasure development. Lean tools, proof of concept and multi-criteria scoring methods were used for assessments in the framework. Framework verification was performed through three case studies at an electronic measurement system company in Penang. Their scopes involve 100% elimination of device under test high internal temperature failures (Case Study One), reduction of high workstation electricity by 60.9% and maintenance charges by 55.6% (Case Study Two) and mitigation of high freight charges of Packaging Assembly 64A by 24% (Case Study Three). Evidently different in nature, these three cases have been successfully deployed following the framework. In total, these were translated into RM 204,105.86 in return (between 2017 to 2018), of which 45.52% was shared with vendors as financial reward sharing. The research objectives have been achieved

Performance Evaluation of User Independent Score Normalization Based Quadratic Function in Multimodal Biometric

Normalization is an essential step in multimodal biometric system that involves various nature and scale of outputs from different modalities before employing any fusion techniques. This paper proposes score normalization technique based on mapping function to increase the separation of score at overlap region and reduce the effect of overlap region on fusion algorithm. The effect of the proposed normalization technique on recognition system performance for different fusion methods is examined. Experiments on three different NIST databases suggest that integrating the proposed normalization technique with the classical simple rule fusion strategies (sum, min and max) and SVM-based fusion results significant improvement compared to other baseline normalization techniques used in this work

A 93.36 dB, 161 MHz CMOS Operational Transconductance Amplifier (OTA) for a 16 Bit Pipeline Analog-to-Digital Converter (ADC)

A gain modified CMOS Operational Transconductance Amplifier (OTA) for a 16 bit pipeline Analog-to-Digital Converter (ADC) is presented. The circuit is designed to be used for a high resolution and low sampling rate ADC. Gain boosting technique is implemented in the design to achieve high DC gain and settling time as required. Post layout simulations for a 5 pF load capacitance shows that OTA achieves a gain bandwidth of 161 MHz at a phase margin 93.14o with 93.27 dB DC gain. The settling time for an OTA is 163 ns for 0.1 % accuracy to achieve final value and consume power about 4.88 mW from 5 V power supply.Keywords: ADC; common mode feedback; CMOS Operational Amplifier; fully differential folded cascadeDOI:http://dx.doi.org/10.11591/ijece.v2i1.12

Finger Vein Recognition Using Principle Component Analysis and Adaptive k-Nearest Centroid Neighbor Classifier

The k-nearest centroid neighbor kNCN classifier is one of the non-parametric classifiers which provide a powerful decision based on the geometrical surrounding neighborhood. Essentially, the main challenge in the kNCN is due to slow classification time that utilizing all training samples to find each nearest centroid neighbor. In this work, an adaptive k-nearest centroid neighbor (akNCN) is proposed as an improvement to the kNCN classifier. Two new rules are introduced to adaptively select the neighborhood size of the test sample. The neighborhood size for the test sample is changed through the following ways: 1) The neighborhood size, k will be adapted to j if the centroid distance of j-th nearest centroid neighbor is greater than the predefined boundary. 2) There is no need to look for further nearest centroid neighbors if the maximum number of samples of the same class is found among jth nearest centroid neighbor. Thus, the size of neighborhood is adaptively changed to j. Experimental results on theFinger Vein USM (FV-USM) image database demonstrate the promising results in which the classification time of the akNCN classifier is significantly reduced to 51.56% in comparison to the closest competitors, kNCN and limited-kNCN. It also outperforms its competitors by achieving the best reduction ratio of 12.92% whilemaintaining the classification accuracy