PIN generation using EEG : a stability study

In a previous study, it has been shown that brain activity, i.e. electroencephalogram (EEG) signals, can be used to generate personal identification number (PIN). The method was based on brain–computer interface (BCI) technology using a P300-based BCI approach and showed that a single-channel EEG was sufficient to generate PIN without any error for three subjects. The advantage of this method is obviously its better fraud resistance compared to conventional methods of PIN generation such as entering the numbers using a keypad. Here, we investigate the stability of these EEG signals when used with a neural network classifier, i.e. to investigate the changes in the performance of the method over time. Our results, based on recording conducted over a period of three months, indicate that a single channel is no longer sufficient and a multiple electrode configuration is necessary to maintain acceptable performances. Alternatively, a recording session to retrain the neural network classifier can be conducted on shorter intervals, though practically this might not be viable

Benefits of Sustainable Supply Chain Management in Singapore’s Retail Sector.

In chapter 1, the introduction chapter states about the sustainability of the supply chain management in the retail industry of Singapore. It determines the aims, objectives, and research questions on which the whole research is based on independently. The retail industry of Singapore is highly sustainable as it offers products to the consumers that are based on environmental concerns. It states that the retail industry of Singapore is dependent on the supply chain management system so that it can obtain high efficiency in the market. In chapter 2, that is the Literature Review section; several aspects of SCM have been discussed. Consumer Buying Behaviour and several factors influencing the buying intention of consumers have been also discussed in this. Several aspects such as Stakeholder Pressure, Technological Integration, and Consumers' perception of SSCM have been also discussed in this chapter. Knowledge-based theory and Behavioural-decision Theory have been used to gain an understanding of integrated SSCM. In chapter 3, the conceptual framework is engaged in making the discussions related to the two effective theories such as behavioural decision theory as well as the nowledge-based theory that is highly focussed to attain the success of the retail industry among the competitors in the competitive market in an effective manner. In chapter 4, the overall chapter has been exclaiming about the research methods which are being used in this research. In this regard, the descriptive design, inductive approach, and positivism philosophy has been used by the researcher. However, the primary and secondary qualitative method has been used to collect and analyse the data eventually. In chapter 5, the primary qualitative analysis along with the secondary analysis has been conducted in the findings and analysis chapter. The qualitative analysis is the conduction of the interview which has been done with the online presence. However, the secondary analysis has been focused on the case scenarios related to the companies of Singapore. Thus, the Isetan, Henkel, and Zara company’s case studies have been determined in the following chapter. In chapter 6, the conclusion and recommendation show the SSCM as to be analysed that proper management plays an important role in the retail industry to conduct sustainable supply chain management for extracting the necessary advantages that will be beneficial to the industry for gaining the competitive advantage in a significant manner

Approximate entropy as an indicator of non-linearity in self paced voluntary finger movement EEG

This study investigates the indications of non-linear dynamic structures in electroencephalogram signals. The iterative amplitude adjusted surrogate data method along with seven non-linear test statistics namely the third order autocorrelation, asymmetry due to time reversal, delay vector variance method, correlation dimension, largest Lyapunov exponent, non-linear prediction error and approximate entropy has been used for analysing the EEG data obtained during self paced voluntary finger-movement. The results have demonstrated that there are clear indications of non-linearity in the EEG signals. However the rejection of the null hypothesis of non-linearity rate varied based on different parameter settings demonstrating significance of embedding dimension and time lag parameters for capturing underlying non-linear dynamics in the signals. Across non-linear test statistics, the highest degree of non-linearity was indicated by approximate entropy (APEN) feature regardless of the parameter settings

Paying With Our Health: The Real Cost of Freight Transport in California

Examines the health costs of asthma and other illnesses, and the level of economic opportunity provided to affected communities, with the transport of goods in California. Estimates the cost of state-recommended pollution controls to transporters

Parameter Optimization of the CNC Wire-Cut EDM Process for Machining Aluminium 6063-B4C Metal Matrix Composites

In the competitive manufacturing environment, conventional monolithic materials cannot compete with composite materials in the ever growing market because of their inherent limitations. Consequently, composite materials are preferred globally in major industries. CNC wire-cut electrical discharge machining is one of the non-traditional machining (NTM) processes, which are used to cut ferrous and non-ferrous metals with properties ranging from low hardness to high hardness and especially to cut high hardness materials and complicated profiles and shapes of all engineering and aerodynamic products. The process parameter setting is critical in the wire-cut electric discharge machining (WEDM) as it has a direct impact on performance characteristics. This paper addresses the preparation of aluminium metal matrix composites (AMMCs) and the optimization of the WEDM process parameters for machining AMMCs to improve the key performance characteristics, namely the metal removal rate (MRR), the surface roughness (SR), and the kerf width (KW). The optimization of the WEDM process parameters is multi-objective in nature. The prime objective of this study was to obtain optimal WEDM process parameters for machining AMMCs with the maximum MRR and the minimum SR and KW. The grey-based Taguchi method was applied to choose an optimal parameter combination to achieve the above said performance characteristics. AMMCs with the base metal Al6063 and the reinforcement of boron carbide (B4C) in three different percentages (3%, 6% and 9%) were obtained by using the stir casting method. WEDM experiments were conducted and the optimal process parameters were found to be as follows: servo voltage (SV)-26V, pulse on time (Ton)-122µs, pulse off time (Toff)-52 µs, B4C-6%, wire feed-3 m/min, and wire tension-49.05N. The key findings from this study reveal that the MRR was increased from 35.759 mm3/min to 42.229 mm3/min, the SR decreased from 4.500µm to 4.382µm and the KW decreased from 371 micron to 364 micron

On the stimulus duty cycle in steady state visual evoked potential

Brain-computer interfaces (BCI) are useful devices that allow direct control of external devices using thoughts, i.e. brain's electrical activity. There are several BCI paradigms, of which steady state visual evoked potential (SSVEP) is the most commonly used due to its quick response and accuracy. SSVEP stimuli are typically generated by varying the luminance of a target for a set number of frames or display events. Conventionally, SSVEP based BCI paradigms use magnitude (amplitude) information from frequency domain but recently, SSVEP based BCI paradigms have begun to utilize phase information to discriminate between similar frequency targets. This paper will demonstrate that using a single frame to modulate a stimulus may lead to a bi-modal distribution of SSVEP as a consequence of a user attending both transition edges. This incoherence, while of less importance in traditional magnitude domain SSVEP BCIs becomes critical when phase is taken into account. An alternative modulation technique incorporating a 50% duty cycle is also a popular method for generating SSVEP stimuli but has a unimodal distribution due to user's forced attention to a single transition edge. This paper demonstrates that utilizing the second method results in significantly enhanced performance in information transfer rate in a phase discrimination SSVEP based BCI

Using EEG and NIRS for brain-computer interface and cognitive performance measures: a pilot study

This study addresses two important problem statements, namely, selection of training datasets for online Brain-Computer Interface (BCI) classifier training and determination of participant concentration levels during an experiment. The work also attempted a pilot study to integrate electroencephalograms (EEGs) and Near Infra Red Spectroscopy (NIRS) for possible applications such as the BCI and for measuring cognitive levels. Two experiments are presented, the first being a mathematical task interleaved with rest states using NIRS only. In the next, integration of the EEG-NIRS with reference to P300-based BCI systems as well as the experimental conditions designed to elicit the concentration levels (denoted as ON and OFF states here) during the paradigm, are presented. The first experiment indicates that NIRS can be used to differentiate a concentrated (i.e., mental activity) level from the rest. However, the second experiment reveals statistically significant results using the EEG only. We present details about the equipment used, the participants as well as the signal processing and machine learning techniques implemented to analyse the EEG and NIRS data. After discussing the results, we conclude by describing the research scope as well as the possible pitfalls in this work from a NIRS viewpoint, which presents an opportunity for future research exploration for BCI and cognitive performance measures