Low Voltage Amplification Using Self Starting Voltage Regulator For Storage System

This thesis presents a storage system design based on energy harvesting to achieve battery-less use for low power application. This thesis basically deals with the effectiveness of DC to DC converter to boost the low input voltage of the harvested energy for energy storage system. This storage system’s function is to store the harvested energy collected from the environment surroundings such as vibration,salinity, RF energy and many more. As generally known, the output voltage of the harvested energy is insufficient for most applications and only generates extremely low power. In the case of wireless sensor network, for example, the sensor node would require energy only during transmitting and receiving data whereas during standby mode or sleep mode, the amount of energy required would be very small. Therefore, the storage system will make use of this standby time or sleep mode of the sensor node to store as much energy as possible. Moreover, a converter must be designed to boost up low input voltage harvested through vibration energy to the higher dc voltage. The method discussed in this thesis gives a promising solution to boost the low input voltage which comes from the rectified voltage of energy harvesting sources that is known to be extremely low voltage. The proposed approach is using MOSFET with the capability of fast switching to perform as the main switch and also as a switching regulator. The MOSFET will be driven by the Pulse Width Modulation (PWM) generated by oscillating circuit which is able to work even at low input voltage by using JFET component. The designed boost converter must be capable and sufficient to charge the super capacitor as an energy storage device and also provides power to energize the load application. The simulation and experimental results showed that the circuit is able to boost the input voltage as low as 0.1 V up to 0.75 V with the range of power efficiency within 82 % to 90 %. Even though the output results from the hardware experiment was lower than the simulation results where the efficiency of simulation can achieved up to 90 % but the experimental result only can achieved maximum 87 %, this is expected as there will be power losses at the component circuit especially in oscillation path, series resistance, diode and also the MOSFET

Electrophysiological Degrading Correlates For Driving Attention Loss Threshold Determination

Statistics by Malaysian Institute of Road Safety Research (MIROS) showed that attention loss significantly lead to road accidents. Hence, the area of research on attention detection for driver safety is becoming more important. There have been a number of studies that displayed the possibility of identifying drivers’ attention using electroencephalography (EEG) signal. The studies obtained the Event Related Potential (ERP) waveform from a small pool of samples. However, the data obtained were insufficient to significantly characterize attentiveness and inattentiveness due to the unique characteristic of each individual. Therefore, the aim of this research is to define the attentiveness state of each subject from large number of samples in controlled parameters to minimize the variability gap of the ERP peak between each individual. The experiment has been conducted using driving simulator to obtain the EEG data from two groups of subjects which were categorized as attentive and inattentive state by using two distinct stimulations i.e., listening to radio and no stimulation. The obtained results show significant boundary and similarity patterns for the level of attentiveness in both groups. Due to these patterns, a hybrid mean-fuzzy (HMF) technique was proposed to analyze the peak of N170 ERP decrement value versus the accident score based on the driving performance and attention threshold was determined accordingly. Three levels of attention namely ‘attentive’, ‘the beginning of inattentiveness’ and ‘inattentive’ state were presented within a new framework scale in the form of a fish bone diagram known as Attention Degradation Scale (ADS). In order to validate the feasibility of the proposed ADS for both groups, the analysis of the data has been done with and without ADS. Based on the outcome, 52% of the subjects were detected as attentive whilst 56% were in inattentive state which is significant as the percentage obtained with ADS was more than without it. Finally, a prototype application has been implemented to prove the theoretical data of attention level prediction. The prototype has successfully warned the subjects of potential accidents whenever the attention level was below the threshold value. Therefore, the findings of this research can be a promising foundation for alarm system which based on attention recognition technique that potentially would be able to reduce road accidents specifically with the proposed ADS

Focus Loss While Driving Detection By Using Prior Stage ERP As Baseline

Driving demands a full focus on the road to avoid any kind of unfortunate events. However, it is common to loss focus over time especially on the road with less traffic and long journey. Studies have shown that quiet number of accidents happened due to loss of focus. Hence, this on-going study is to develop a device to detect focus loss while driving. In our preliminary papers, we have shown that loss of focus is associated to the declining of evoked response potential (erp) amplitude over time. However, to determine the significant decline of amplitude is challenging due to inter-variability of individuals. Hence, in this paper, we propose novelty detection approach by using prior stage of recording as baseline to extract focus loss in single trials of erp of respective individuals. Erps of 20 subjects were recorded while driving a simulator car. The obtained results suggest that the proposed approach detects the attention loss successfully but with a delay as few seconds are needed to obtain the baseline. Novelty detection by using prior stage of recording as baseline is promising but improvement need to be done to apply it in real time

Loudness Perception Differentiation Using Repeating Sinus Rhythm

During the setting of hearing aid device process, the most comfortable loud (MLC) level is the most difficult level to determine with by existing methods i.e., verbal or behavioral technique. This is because the hearing aid device users should evaluate their own given the volume level. The users might find it confusing as this level is next to uncomfortable loud level (UC). UC level is not considered for the determination of the loudness scaling because in a long period it will damage the user’s hearing. The setting process will be more challenging if the user does not have any listening experience such as a child. Thus, the relationship in between the decline of the N100 wave peak and loudness perception is studied to distinguish between the MLC and UC for an objective loudness scaling measurement. As a result, the percentage decrease in N100 peak decreased with increasing volume is observed. From the results, it was found that for the level UC, the percentage decrease in peak N100 does not exceed 1.95% by 3 out of 7 subjects. While MLC showed the percentage decrease in N100 peak higher than UC and lower than the MEDIUM. In conclusion, the loudness perception can be measured by the percentage decrease in peak N100 and this method can be used to adjust the hearing aid device objectively

The Feasibility Of Music And Talk Radio Program As A Focus Stimulant For Driver

Long distance driver tends to listen to the music in order to help them awake on the road. However, several studies have reported that music has repetition element which is a catalyst to attention deficit phenomenon. This study evaluated the feasibility of music and talk radio program as a stimulant to keep our focus behind the wheel. N1 waves of 11 participants were studied and the results have suggested that music is not a good stimulant for driving partner compared to talk radio program. All the participants showed a great attention when listened to talk program compared to music of their choice

Event-Related Potential N100 Vs. N170 Wave Results Comparison On Driving Alertness

Driver’s attention, especially during long rides, is very crucial to avoid road accidents, which may lead to injuries and fatalities on the roadway. However, with modern technologies, the decline in driver’s attention can be investigated through the electrical activity of the brain. Event-Related Potential (ERP) is the electrophysiological brain response measurement that related to the sensory, cognitive and motor events. By using simple averaging techniques, ERPs can give reliable result to measure the attentiveness of the driver during driving. In this paper, N100 and N170 wave results were presented, which obtained from temporal and occipital lobes respectively and been compared to measure the driver’s attention. In term of attentiveness difference percentage, it is 0.09% and 38% differences were observed from N100 and N170 wave results respectively. From the results, it clearly can be seen that using N170 wave from occipital lobe is more significant to measure the driver’s alertness compared to N100 wave that recorded from the temporal lobe

The Design Of Self Starting Regulator Using Step-Up Converter Topology For WSN Application

Continuous monitoring is very important for chronic patient, elderly or who was under supervision for recovery from an acute event or surgical. For this Wireless Sensor Network give a solution for continuous health monitoring and able to wirelessly monitoring patient conditions at any time. It is able to generate early warning if received unwanted signal from the patient as well. As known, Wireless Sensor Network is only consumes a little power to turn it on and energy harvesting is able to power up this devices without using the batteries. Continuous monitoring needs a continuous and uninterruptable power source. Hence, energy harvesting is one of the options of the solutions. However, up till now the energy harvesting still develop a low output voltage which is not enough to power on the wireless sensor network . Therefore, this paper proposed a new technique called a self starting DC to DC converter which is able to boost up the input voltage as low as 0.4V to the output voltage of 5.1V. The circuit efficiency is up to 92% which is verified by simulation using LTspice tools. Hardware implementation will be done in future work

Design and Implementation of Multiplexed and Obfuscated Physical Unclonable Function

Model building attack on Physical Unclonable Functions (PUFs) by using machine learning (ML) techniques has been a focus in the PUF research area. PUF is a hardware security primitive which can extract unique hardware characteristics (i.e., device-specific) by exploiting the intrinsic manufacturing process variations during integrated circuit (IC) fabrication. The nature of the manufacturing process variations which is random and complex makes a PUF realistically and physically impossible to clone atom-by-atom. Nevertheless, its function is vulnerable to model-building attacks by using ML techniques. Arbiter-PUF is one of the earliest proposed delay-based PUFs which is vulnerable to ML-attack. In the past, several techniques have been proposed to increase its resiliency, but often has to sacrifice the reproducibility of the Arbiter-PUF response. In this paper, we propose a new derivative of Arbiter-PUF which is called Mixed Arbiter-PUF (MA-PUF). Four Arbiter-PUFs are combined and their outputs are multiplexed to generate the final response. We show that MA-PUF has good properties of uniqueness, reliability, and uniformity. Moreover, the resilient of MA-PUF against ML-attack is 15% better than a conventional Arbiter-PUF. The predictability of MA-PUF close to 65% could be achieved when combining with challenge permutation technique

Efficient Low Voltage Amplification Using Self Starting Voltage Regulator for Storage System

Abstract-This paper presents a storage system design based on energy harvesting to achieve batteryless for Wireless Sensor Network (WSN) application. The storage system is part of the Wireless Sensor Energy Harvesting to store and amplify the energy harvested from the surroundings. Finding a new sources of renewable energy has becomes a fashionable among researchers nowadays in particular harvesting the energy from the surrounding. However the challenge raised is to boost up the energy that known are very low. Thus the proposed method must be consumes very little power and suitable for ambient environmental sources such as vibration, wind and RF energy and be able to boost up the energy for storage system. The output of the harvested voltage is insufficient for most applications, therefore the system will boost up the input voltage level using DC to DC converter topology to higher dc voltage.The DC to DC converter shall be designed to suit the types of storage required. The output voltage of this DC converter should be sufficient to charge either capacitor or supercapacitor that will be use in this system as the energy storage system. The supercapacitor will provide power to energize any system such as in this case wireless sensor network[1]. In the case of wireless sensor network for example, the node would require the energy during transmitting and receiving data only whereas during standby mode or sleep mode, the amount of energy required would be very smal