title: Efficiency Improvement of LDO Output Based Linear Regulator With Supercapacitor Energy Recovery – A versatile new technique with an example of a 5V to 1.5V version

authors: Zainul Abidin, Saiful Riza

subjects: Supercapacitor

abstract: Supercapacitors are used in various industrial applications and the supercapacitors technology is gradually progressing into a mature state. Common applications of supercapacitors are in electric vehicles, hybrid electric vehicles, uninterruptible power supply (UPS) and in portable devices such as cellular phones and laptops. The capacitance values range from fractional Farads to few thousand Farads and their continuos DC voltage ratings are from 2V to 6V. At University of Waikato, a team works on using supercapacitors for improving the efficiency of linear voltage regulators. In particular, this patented technique aims at combining off the shelfs LDO ICs and a supercapacitor array for improving end to end efficiency of linear regulator. My work is aimed at developing the theoretical background and designing prototype circuitry for a voltage regulator for the case of unregulated input supply is more than 3 times of the minimum input voltage requirement of the LDO which is applicable for a 5V to 1.5V regulator. Experimental results are indicated with future suggestions for improvement.

text: Efficiency Improvement of LDO Output Based Linear Regulator With Supercapacitor Energy Recovery – A versatile new technique with an example of a 5V to 1.5V version Supercapacitors are used in various industrial applications and the supercapacitors technology is gradually progressing into a mature state. Common applications of supercapacitors are in electric vehicles, hybrid electric vehicles, uninterruptible power supply (UPS) and in portable devices such as cellular phones and laptops. The capacitance values range from fractional Farads to few thousand Farads and their continuos DC voltage ratings are from 2V to 6V. At University of Waikato, a team works on using supercapacitors for improving the efficiency of linear voltage regulators. In particular, this patented technique aims at combining off the shelfs LDO ICs and a supercapacitor array for improving end to end efficiency of linear regulator. My work is aimed at developing the theoretical background and designing prototype circuitry for a voltage regulator for the case of unregulated input supply is more than 3 times of the minimum input voltage requirement of the LDO which is applicable for a 5V to 1.5V regulator. Experimental results are indicated with future suggestions for improvement.

year: 2011

title: ‘It’s on the tip of my Google’: Intra-active performance and the non-totalising learning environment

authors: Snake-Beings, Emit

subjects: Assemblage, decentralised learning environment, interactive performance, noise music, non-totalising, sound culture

abstract: Technologies that expand the learning environment to include interactions outside of the physical space of the classroom, such as the use of Google as an aid to memory, represent one aspect of learning that occurs within several seemingly decentralised spaces. On the other hand, it can be argued that such interactive technologies are enclosed in what Bruno Latour calls a ‘Black-box’: a ‘totalising’ enclosure that delimits interaction and channels users towards yet another form of centralised learning space. Used as a starting point, the focus of this article rapidly shifts from the constraints of the ‘Black-box’ towards a type of engagement that embraces material agency: an engagement with materials and fragments of knowledge that emerge from the ‘non-totalising’ assemblage. To assist in this trajectory, Karen Barad’s concept of intra-activity is employed, where agency is seen as distributed between human and non-human actants. The space in which this engagement between human and materials occurs, as a non-totalising learning space, is the concern of this article, which uses an interactive audio/visual performance event called Bingodisiac as a case study to examine various ways in which we can learn to move beyond the constraints of totalising structures. Bingodisiac is a project initiated by the researcher in 2002, as an informal collection of musicians who are assembled for a one-off improvised performance. This article draws upon interviews and journal notes collected at the time of the performances to explore the analogy of ‘noise music’ and how this can be related to ways in which the learning space of the classroom and the types of knowledge produced have become decentralised.

text: ‘It’s on the tip of my Google’: Intra-active performance and the non-totalising learning environment Technologies that expand the learning environment to include interactions outside of the physical space of the classroom, such as the use of Google as an aid to memory, represent one aspect of learning that occurs within several seemingly decentralised spaces. On the other hand, it can be argued that such interactive technologies are enclosed in what Bruno Latour calls a ‘Black-box’: a ‘totalising’ enclosure that delimits interaction and channels users towards yet another form of centralised learning space. Used as a starting point, the focus of this article rapidly shifts from the constraints of the ‘Black-box’ towards a type of engagement that embraces material agency: an engagement with materials and fragments of knowledge that emerge from the ‘non-totalising’ assemblage. To assist in this trajectory, Karen Barad’s concept of intra-activity is employed, where agency is seen as distributed between human and non-human actants. The space in which this engagement between human and materials occurs, as a non-totalising learning space, is the concern of this article, which uses an interactive audio/visual performance event called Bingodisiac as a case study to examine various ways in which we can learn to move beyond the constraints of totalising structures. Bingodisiac is a project initiated by the researcher in 2002, as an informal collection of musicians who are assembled for a one-off improvised performance. This article draws upon interviews and journal notes collected at the time of the performances to explore the analogy of ‘noise music’ and how this can be related to ways in which the learning space of the classroom and the types of knowledge produced have become decentralised.

year: 2017

title: Mana Motuhake Ringa: The non-invasive neural interface based artificial hand

authors: Owen, Mahonri W.

subjects: Prosthetics

abstract: Ten million people on the earth at any given time suffer from the loss or lack of a limb. Three million of these are upper extremity amputees. No matter the cause or reason for amputation the amputees’ life is never the same again. Independence can be lost and never regained. The loss of a limb can be an overpowering event that effects the mental, social, and physical well-being of a person. Although there has been many attempts to restore hand function through neural prosthetics there is little to no credible solutions to regain it. This thesis presents methods to restore hand function based on the human grasping system and addresses key challenges in the practical application of those methods. The contribution of this work lies in improving artificial hand function by mimicking the human grasping system. The human grasping system consists of the brain, the nervous system and the hand. The methods used investigate: biomechanical design principles for artificial hands, the limitations of machine learning in non-invasive neural interfaces (NI’s) and the development of an autonomous hand control framework for artificial hand control. 

The application of the Support Vector Machine (SVM) to a non-invasive neural interface is principally limited to binary applications in the real world. In order to overcome this limitation an autonomous approach is developed to achieve better response and accuracy from NI control frameworks. In binary applications the SVM performed well with a classification accuracy of around 80% accompanied by a response time of just under 5 seconds. When applied to multiple classifications the SVM decreased in accuracy and response dramatically. The accuracy of the autonomous hand control framework had a classification accuracy of 95% with a response time comparable to that of the SVM. This outcome has the potential to move non-invasive NI research into the real world and to restore hand function in an intuitive and meaningful way. A key learning of this research is that machine learning methods for non-invasive neural interfaces are not the best way to restore hand function for amputees. Hand function is best restored through the combining of machine learning and machine vision into a single NI based control framework. Additionally, this work is based in Mātauranga Māori with a western science approach. By so doing, amputees will be empowered, have increased independence and enjoy a better quality of life.

The scope of this work is limited to the consideration of hand function restoration for upper extremity amputees. In particular trans-radial amputees. Focus is given to non-invasive methods of control, therefore, investigations are limited to the electroencephalography (EEG) signal. Mimicking the human grasping system is a complex and difficult process, therefore, the attempts in this work to restore hand function only consider mechanisms and processes related directly to the motor control of the human hand.

text: Mana Motuhake Ringa: The non-invasive neural interface based artificial hand Ten million people on the earth at any given time suffer from the loss or lack of a limb. Three million of these are upper extremity amputees. No matter the cause or reason for amputation the amputees’ life is never the same again. Independence can be lost and never regained. The loss of a limb can be an overpowering event that effects the mental, social, and physical well-being of a person. Although there has been many attempts to restore hand function through neural prosthetics there is little to no credible solutions to regain it. This thesis presents methods to restore hand function based on the human grasping system and addresses key challenges in the practical application of those methods. The contribution of this work lies in improving artificial hand function by mimicking the human grasping system. The human grasping system consists of the brain, the nervous system and the hand. The methods used investigate: biomechanical design principles for artificial hands, the limitations of machine learning in non-invasive neural interfaces (NI’s) and the development of an autonomous hand control framework for artificial hand control. 

The application of the Support Vector Machine (SVM) to a non-invasive neural interface is principally limited to binary applications in the real world. In order to overcome this limitation an autonomous approach is developed to achieve better response and accuracy from NI control frameworks. In binary applications the SVM performed well with a classification accuracy of around 80% accompanied by a response time of just under 5 seconds. When applied to multiple classifications the SVM decreased in accuracy and response dramatically. The accuracy of the autonomous hand control framework had a classification accuracy of 95% with a response time comparable to that of the SVM. This outcome has the potential to move non-invasive NI research into the real world and to restore hand function in an intuitive and meaningful way. A key learning of this research is that machine learning methods for non-invasive neural interfaces are not the best way to restore hand function for amputees. Hand function is best restored through the combining of machine learning and machine vision into a single NI based control framework. Additionally, this work is based in Mātauranga Māori with a western science approach. By so doing, amputees will be empowered, have increased independence and enjoy a better quality of life.

The scope of this work is limited to the consideration of hand function restoration for upper extremity amputees. In particular trans-radial amputees. Focus is given to non-invasive methods of control, therefore, investigations are limited to the electroencephalography (EEG) signal. Mimicking the human grasping system is a complex and difficult process, therefore, the attempts in this work to restore hand function only consider mechanisms and processes related directly to the motor control of the human hand.

year: 2019