Converting infrared radiation in the form of heat into electricity is one of the interesting energy conversion approaches. This can be simply accomplished through thermoelectric effect with the well-known device called thermoelectric cooler (TEC). In this paper, we briefly overview TEC-based concepts, demonstrations, and products for converting heat into electricity. We then propose our own portable TEC-based heat-to-electricity converting module. Experimental proof of concept is also highlighted showing a promising output voltage of 5VDC and 0.224A suitable for low voltage applications. Future work relates to design optimization, engineering improvement, and testing in real world scenario.
A farmer usually uses a cheap chemical material called chlorine to destroy the cell structure of unwanted organisms and remove some plant effluents in a baby shrimp farm. A color changing of the reaction between chlorine and chemical indicator is used to monitor the residue chlorine in water before releasing a baby shrimp into a pond. To get rid of the error in color reading, our previous works showed how a smartphone can be functioned as a color reader for estimating the chlorine concentration in water. In this paper, we show the improvement of interior configuration of our prototype and the distribution to several baby shrimp farms. In the future, we plan to make it available worldwide through the online market as well as to develop more application programs for monitoring other chemical substances.
The embryo or germ of a rice seed is growing to the shoot and the root parts of a seedling. In the early stage, the
germinated embryo directly receives food from the endosperm. How healthy of the seedling can be physically predicted
by measuring the areas of the embryo and endosperm. In this work, we show for the first time how the embryo and
endosperm areas of a brown rice can be spatially measured. Our key design is based on the utilization of a tablet
equipped with our lens module for capturing the rice seed image under white light illumination. Our Windows-based
program is developed to analyze and separate the image of the whole brown rice into the embryo and endosperm parts
within 2 seconds per seed. Our tablet-based system is just 30×30×6 cm<sup>3</sup>
with 1 kilogram in weight, capable to easily
carry to perform in the field.
Dimensions of grains are important factors in evaluating the physical quality of the grains. In this work, we show for the first time that the thickness, the width, and the length of rice grains can be simultaneously measured. Rather than imaging rice grains only above from a two-dimensional plane, our key idea is to insert a tilt reflective surface on the measuring plane such that the side view of the rice grains can be observed at the same time. Demonstration from our prototype shows a very promising result in determining the thickness, the width, and the length of the rice grain with maximum values of 2.20 mm, 3.65 mm, and 10.27 mm, respectively. It offers a very high average resolution of 22 μm and a measured response time of 205 ms. Additional key features include low cost, low components count, and ease of implementation.
Some green fruits do not change their color from green to yellow when being ripe. As a result, ripeness estimation via color and fluorescent analytical approaches cannot be applied. In this article, we propose and show for the first time how a thermal imaging camera can be used to two-dimensionally classify fruits into different ripeness levels. Our key idea relies on the fact that the mature fruits have higher heat capacity than the immature ones and therefore the change in surface temperature overtime is slower. Our experimental proof of concept using a thermal imaging camera shows a promising result in non-destructively identifying three different ripeness levels of mangoes <i>Mangifera indica L</i>.
As the color level of the rice leaf corresponds to the nitrogen status of rice in the field, farmers use a leaf color chart
(LCC) to identify the color level of the rice leaf in order to estimate the amount of N fertilizer needed for the rice field.
However, the ability of the farmers and degeneration of the LCC color affect the accuracy in reading the rice leaf color
level. In this paper, we propose a mobile device-based rice leaf color analyzer called “BaiKhao” (means rice leaf in
Thai). Our key idea is to simultaneously capture and process the two-dimensional (2-D) data scattered and reflected from
the rice leaf and its surrounding reference, thus eliminating expensive external components and alleviating the
environmental fluctuation but yet achieving a high accuracy. Our field tests using an Android-based mobile phone show
that all important leaf color levels of 1, 2, 3, and 4 can be correctly identified. Additional key features include low cost
and ease of implementation with highly efficient distribution through the internet.
Nitrogen status is an important factor for evaluating the growth of rice or the amount of nitrogen fertilizers needed per
rice field. It can be done easily and cheaply by using a leaf color chart. However, the accuracy of the resulting color level
depends on the ability of the farmer to compare the leaf color with the reference chart as well as on the direction of Sun
light. With this issue in mind, this paper proposes a low-cost light-emitting-diode (LED) based leaf color meter that can
be used to estimate the nitrogen level needed in the rice field. In particular, we show how we integrate an off-the-shelf
green 562-nm wavelength LED, a silicon photodiode, an 8-bit microcontroller, and a 6×1 LED panel in a compact
packaging style for the implementation of this needed leaf color analyzer. The total cost is only USD39. Field test results
confirm that key leaf color levels of 2, 3, and 4 can be identified. Other key features are ease of use and upgradability for
different color levels.
Since the deployment of the credit card, the number of credit card fraud cases has grown rapidly with a huge amount of
loss in millions of US dollars. Instead of asking more information from the credit card's holder or taking risk through
payment approval, a nondestructive and data-non-intrusive credit card verifier is highly desirable before transaction
begins. In this paper, we review optical techniques that have been proposed and invented in order to make the genuine
credit card more distinguishable than the counterfeit credit card. Several optical approaches for the implementation of
credit card verifiers are also included. In particular, we highlight our invention on a hyperspectral-imaging based
portable credit card verifier structure that offers a very low false error rate of 0.79%. Other key features include low
cost, simplicity in design and implementation, no moving part, no need of an additional decoding key, and adaptive
We propose and experimentally demonstrate an angle-multiplexing based optical structure for verifying a credit card.
Our key idea comes from the fact that the fine detail of the embossed hologram stamped on the credit card is hard to
duplicate and therefore its key color features can be used for distinguishing between the real and counterfeit ones. As the
embossed hologram is a diffractive optical element, we choose to shine one at a time a number of broadband
lightsources, each at different incident angle, on the embossed hologram of the credit card in such a way that different
color spectra per incident angle beam is diffracted and separated in space. In this way, the number of pixels of each color
plane is investigated. Then we apply a feed forward back propagation neural network configuration to separate the
counterfeit credit card from the real one. Our experimental demonstration using two off-the-shelf broadband white light
emitting diodes, one digital camera, a 3-layer neural network, and a notebook computer can identify all 69 counterfeit
credit cards from eight real credit cards.