\r\n\t(i) Quantum dots of very high-quality optical applications, Quantum dot light-emitting diodes (QD-LED) and ‘QD-White LED’, Quantum dot photodetectors (QDPs), Quantum dot solar cells (Photovoltaics).
\r\n\r\n\t(ii) Quantum Computing (quantum bits or ‘qubits’), (vii) The Future of Quantum Dots (broad range of real-time applications, magnetic quantum dots & graphene quantum dots), Superconducting Loop, Quantum Entanglement, Quantum Fingerprints.
\r\n\r\n\t(iii) Biomedical and Environmental Applications (to study intracellular processes, tumor targeting, in vivo observation of cell trafficking, diagnostics and cellular imaging at high resolutions), Bioconjugation, Cell Imaging, Photoelectrochemical Immunosensor, Membranes and Bacterial Cells, Resonance Energy-Transfer Processes, Evaluation of Drinking Water Quality, Water and Wastewater Treatment, Pollutant Control.
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Thirumalai received his Ph.D. from Alagappa University, Karaikudi, He was also awarded the Post-doctoral Fellowship from Pohang University of Science and Technology (POSTECH), the Republic of Korea. His research interests focus on luminescence, self-assembled nanomaterials, and thin-film optoelectronic devices. He has published more than 60 SCOPUS/ISI indexed papers and 11 book chapters, edited 4 books, and member of several national and international societies like RSC, OSA, etc. His h-index is 19.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"99242",title:"Prof.",name:"Jagannathan",middleName:null,surname:"Thirumalai",slug:"jagannathan-thirumalai",fullName:"Jagannathan Thirumalai",profilePictureURL:"https://mts.intechopen.com/storage/users/99242/images/system/99242.png",biography:"Dr. J. Thirumalai received his Ph.D. from Alagappa University, Karaikudi in 2010. 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The field of application ranges from research to automation, from industry to home automation, basically everything that in some way must be performed without human supervision.
Data acquisition systems are primarily used to measure physical phenomena such as: temperature, voltage, current, strain and pressure, shock and vibration, distance and displacement, RPM, angle and discrete events, and weight.
When the engineer is interested in controlling a physical process (light intensity, sound analysis, mass measure, position check, velocity, PID control, etc.) his first problem is to acquire the right information coming from one or more sensors, in some cases we talk about sensor strings or distributed sensors.
The goal is to acquire data that are consistent over time and that correctly describe the shaping of the physical process. All this allows both the correct processing of data and a fast action on the control system through its actuators (motors, LEDs, speakers, etc.).
“Data acquisition” means data exchange in both directions: from the process to the system and vice versa. In all control systems the “heart” of the process is the data acquisition that plays a main role but at the same time it must be accompanied by a simple and intuitive user interface, the HMI-Human Machine Interface. Data acquisition systems are generally referred to by the acronym DAQ (Data AcQuisition).
Figure 1 shows the electronic chain to acquire an analog signal. The sensor is the device sensitive to the physical feature, the analog-to-digital conversion system, and the computer on which the SW architecture for managing the information is developed. Both feedback and actuators are missing in this figure as they are not the subject of this chapter.
(Acquisition chain) [
This chapter is designed to be a guide for beginners, programming amateurs and students who wish to approach the world of automation with LabView using low-cost third-party DAQs such as Arduino.
Arduino is a “machine” capable of working in Stand Alone, it can perform simple industrial control tasks.
In a SCADA (Supervisory Control And Data Acquisition) system there is a Master and many Slaves. The Master device carries out the configuration, supervision and control of the slaves. The slave, a local device very close to the process, is equipped with a processor and a system of ports to interface with the sensors and actuators. In this chapter we will write some code to have LabView in the role of Master and Arduino in the role of Slave.
We speak about Data Acquisition process, DAQ , when we refer to the process of making measurements of physical phenomena with a PC (tablet, smartphone, workstation, etc). The signals, to be processed, are converted from the analog domain to the digital domain. Only after the digital acquisition we can process the data acquired (recording, visualization, analysis). For this purpose, an A/D (Analog to Digital) subsystem is used to convert the signal.
We report, below, some theoretical hints of the components visible in Figure 2.
Detail of the complete acquisition scheme.
At the sensor output, the electronic chain includes a “signal conditioning circuit”, a multiplexer, the sampling circuit and finally the A/D converter.
The measurement of a physical phenomenon, such as temperature, sound level, vibration of motion oscillatory, or wind speed, begins with a sensor. A sensor is a device that converts the physical phenomenon into a measurable electrical signal.
For example, an elevator gets to the floor through the installation of positioning sensors; a washing machine is equipped with a sensor that measures the rpm of the motor or the water level in the drum; a twilight light; a TV remote control. The classic mercury thermometer is also a type of sensor that is used to measure temperature. In this case, however, the measure is expressed directly on a graduated scale readable by man and not by the machine: we speak in this case of
The sensors can produce several kind of electrical outputs such as voltage, current, resistance, or other electrical characteristics modulated from physical phenomenon. When the signal coming from the sensor or from the transmission line is noisy or the ground reference is not at 0 volts (as it should) is preferable to use an isolation system.
In “signal conditioning circuit” we propose a section with electrical isolation that allows the separation of the signal from other electrical sources. This aspect is also essential for the measurement of signals with very small amplitude in which external electrical potentials can affect the quality of the signal considerably, providing incorrect results.
The
Signal conditioning, filtering and amplification.
Example of a 4-input multiplexer.
The multiplexer (commonly called MUX) is a selector of data lines (analog or digital) able to select different input signals: once selected the channel, the corresponding signal is collected and sent on the output line. There are some particularly performing and expensive devices that do not use the MUX but they have a complete acquisition chain for each input.
The S&H system is the circuit part that performs the sampling of the signal (sampling phase). Sampling, in signal theory, is a technique that consists in converting a continuous signal in time into a discrete signal, evaluating its amplitude at regular time intervals. Therefore, considering that the physical quantity attributed to the physical phenomenon varies continuously over time without any interruption, it is necessary to decide with which time interval to interrogate the sensor in order to have meaningful data for our measurement.
From the definition of the sampling interval (Tc) for the scan we derive the sampling rate:
The effect of the circuit in Figure 5 is to store the analog value taken at a given time (sample phase) and keep it constant for as long as it takes the converter to perform the conversion (hold phase).
S&H circuit and example.
But how fast should the sampling rate be? Clearly it depends on the phenomenon we are observing. See two examples below:
We want to monitor the temperature of a room to stabilize it at a value of Tset ± error. Considering the inertia of the room and the radiators it makes sense to acquire the temperature every second i.e. fc = 1 Hz.
Question:
How high has to be the sampling rate if we would like to create automatic braking for anti-collision car system? Assume that max velocity, for small/medium sized car, is 180 km/h.
Answer:
Let us assume that the control system reacts in such a time that the car still travels at maximum for 10 cm (
So, if we make some calculations, the time between one reading and the next one of the vision sensor must be less 2 ms. These involves
The proposed cases are at the antipodes: while in the first one we do not have any criticality, in the second one there is a big responsibility due to the need to manage the stop of the car before the impact.
In the real world, according to the mathematician J. Fourier, an analogue signal can be represented by linear combination of sinusoidal functions (called
The first harmonic, called fundamental, has the same frequency as the input signal, while the following harmonics will have a frequency multiple of the fundamental.
The transition from the analogue to the digital domain, therefore discrete, leads us to acquire one of these harmonics, of course the first one, therefore a suitable sampling frequency will be the key to a good acquisition of the analogue signal, preserving its main characteristic, its frequency.
In order to have a correct sampling (without loss of information) we must to choose a correct frequency of sample rate. Supposing that the frequency signal (first harmonic) is
If, in addition to the frequency, we would like to storage also the shape of the signal we need:
The sampling rate is normally expressed in Sample Rate and the unit of measure is number of samples per second [#S/s].
To understand better how the theorem works in the Figure 6 we report a sequence of acquiring with several sampling rate. The software used is developed for university student’s lectures [2].
Signal sampled with different fs.
Figure 6 shows how the sampling frequency acts. We start with a 440 Hz source signal (resonance frequency of a conventional tuning fork) which is visible in the first waveform graph of the sequence. In the following sequences the following sampling frequencies were used: 440 Hz, 600 Hz, 880 Hz and 2200 Hz.
In the first and second cases the fs is not adequate, in fact we have an under-sampling. In the third case we have a result that preserves the frequency of the input signal. Finally, in the last case, we have reconstructed quite faithfully the profile of the original signal.
The last sequence in the DAQ chain (Figure 2) consists of the operations performed by the A/D converter: quantization and encoding. First we need to introduce the concept of signal dynamics. The dynamics of the signal indicates the maximum excursion of the signal and, therefore, also the maximum and minimum values it can reach, the range of Vin (also defined as the Full Scale value):
(we have assumed a voltage signal)
The input signal, being continuous in time, can by definition take on an infinity of values. As well as the sampler has discretized the signal in time (X axis) we now need another circuit which discretizes the values of the physical quantity which represents the information (Y axis). So the technique is to approximate the value acquired in the sampling phase to a discrete value. The number of discrete values available for these approximations is given by a very simple calculation. If we choose
At this point we have to define the unit of quantization that we call quantum or quantization step, that is the smallest approximation interval that we use to compare the sampled signal to discretize it.
Q is called quantization step. It is possible to assert, at this point, that a higher bit number and a smaller VFS interval implies the greater number of intervals available. This means that the size of the interval will tend to be an extremely small value with increasingly accurate measure.
The simplest coding (commonly used for unipolar signals, i.e. always positive ones), natural binary code (straight binary), consists in making each quantization interval correspond to a progressive binary number, starting from 0 (corresponding to the lowest level) up to 2n-1.
In Figure 7 we show what we have said, on the X-axis we put the intervals between Vmax and Vmin and beside them the bit combinations. The first level consists of all bit to zero, so the word 000…00 corresponds to Vmin while the last level is given by the word with all ones 111….11 i.e. Vmax.
Quantization and coding.
A different number of resolution bits clearly produces different quantization ranges, some data is shown in Figure 8.
Resolution example.
Clearly the measurement of Q is affected by error and corresponds precisely to Q/2 and is defined as quantization error.
Recapitulate, in order to perform a correct measurement through a DAQ system, the following points must be satisfied:
Prefer a sensor with a linear response and that the maximum and minimum values are compatible with the dynamics of the DAQ;
Choose an appropriate sampling rate;
Choose an appropriate resolution;
The premises made so far are useful to better understand the code written for the Master unit and the slave unit. In this chapter we propose an cheap and open source prototyping board for which we will write some code to transform it into a DAQ . The proposed board is Arduino UNO rev.3. In the next paragraph, the Arduino technology will be presented [3].
Arduino Uno (Figure 9) is a microcontroller board (Italian open source project) based on the ATmega328P (resolution @10 bit; input range 0÷5 V). It has 14 digital input/output pins (of which 6 can be used as PWM outputs), 6 analog inputs, a 16 MHz like internal clock (sample rate = ~10 kS/s), a USB high speed connection, a power jack 9 Volt input, an ICSP header, reset button and several states LED like Tx/Rx serial communication.
Arduino UNO rev.3.
It contains all interfaces needed to support the microcontroller and its functionality; You can use prototype board with your Uno without worrying about doing something wrong, worst case you can replace chip with a new one and start over again. The Uno board is the first USB Arduino boards, today are available several models of it: with wifi o ethernet, compact or large model, wearable, etc.
Wiring is an open-source programming framework for microcontrollers C/C++ based.
The developer, under conditions of classical use, writes code for Arduino in order to have a “machine” that works in Stand Alone, in Figure 10 is shown his working scheme, the code runs on Arduino, through the code reads the sensors and produces actions on the physical world. In the next paragraph will be discussed the code to transform Arduino from Master to Slave.
Arduino-stand alone mode.
The new role of Arduino will be to be used in LabView environment as a real data acquisition system (Figure 11).
Control hierarchy with LabView-Arduino.
Among of programmer “sketch” is the name that Arduino’s programmer uses for a program. It’s of code written in like C, compiled and, then, uploaded on the board. After it is possible to run on an Arduino board the code. There are two distinct functions available in Arduino sketch:
The
The
Normally in the setup() section there is the sequence of instructions to configure all the Arduino peripherals and features that will be used in the project such as: Analog input, PWM, i2c. In loop(), instead, is written all the control algorithm that will be characterized by an infinite loop.
In this paragraph we propose the development of a code from a different perspective, Arduino will be used as a DAQ system. So inside the setup() there will be a pre-cycle in which the Arduino waits for the USB connection to LabView and waits for the ASCII character sequence to configure the Arduino ports as desired.
The ASCII code, we call op-code from now, to send for configuration are printable characters, so you can always test the Arduino code from any serial terminal or using the serial monitor of the IDE.
For example, to configure the Analog Input channel zero (A0) just send the code “a”. Arduino will remain in the setup() section until the master sends the character “z” on the serial which will end the setup cycle to execute the code in the loop().
The code proposes a scenario in which analog inputs A0÷A5, DIO pin2 and pin4 and a PWM channel on pn3 are configurable. Clearly it is possible to extend the “offer” by adding other input or output lines. The complete management of a sensor through Arduino libraries could also be included.
Regarding the sampling time Ts it is possible to define through the ASCII codes A,B,C,D a time delay equal respectively to 100 msec, 10 msec, 1 msec, 500 μsec. If it is omitted the acquisition time is 1000 msec.
The code developed in the loop() section collects data from the previously configured input line ports, maps them to the following format #A0#A1#A2#A3#A4#A5$D0$D1 and sends the message continuously to the USB port. The message will contain as many strings as there are lines configured. In the syntax #Ai (i = 0…5) the value of Ai corresponds to the decimal decoding of the combination of the 10 bits, so there will be 2n combinations. At value 0 will correspond 0 (zero) Volt and at value 1023 will correspond 5 Volt.
WE suggest to the reader to test own system velocity before to set 500 μsec of sample rate. Usually, for my experience, it is very rare to follow with a LabView (not real time) Loop code that velocity. In case the system is not fast enough, one way of not losing data could be the following: change the Arduino’s code to collect the msg (measured value) in a vector of 100 elements and send it to LabView each 50 msec. You can choose different size of vector but you have avoid to saturate the Arduino memory.
The op-code (operation code) we have written does not belong to any standard communication protocol. We have invented a sequence of simple ASCIII strings to be sent over serial. So the Master will have at his disposal a set of instructions, which can be extended by the reader, to change the status of a digital output: D0_ON\\n, D0_OFF\\n, D1_ON\\n, D1_OFF\\n.
In order to avoid a slowdown loop() for sensors reading, due at continuous polling on the receipt of messages from the Master, an event-driven solution has been considered.
The reception on the serial line of a request from the Master is triggered by the event generated by the chip that manages the USB communication. When a byte arrives on RX an event is generated and triggered by a software procedure. When this occurs the Master message will be read (Figure 12).
Code- SerialEvent() and blinking().
In the end we can send a message to set a Analog output by pin3 in PWM mode.
The
In electronics it is used to change the voltage, and therefore the power, on a generic load. For example, to change the speed of a direct current electric motor, to vary the brightness of light bulbs, especially LEDs. A useful duty cycle of 0% indicates a pulse of zero duration, in practice no signal (Vout = 0 volts), while a value of 100% indicates that the pulse ends when the next one begins (Vout = Vcc). To use this technique with Arduino is very simple, with the analogWrite (PIN, VALUE) function it is possible to modulate the work cycle. The PIN corresponds at PWM pins and VALUE is scale from 0 to 255. For example analogWrite (pin, 255) corresponds to a 100% duty cycle and analogWrite (191) is a 75% duty cycle (Figure 13).
PWM example.
In the following boxes (Figure 15) we’ll show some code that you can use to create a communication Master–Slave from LabView and Arduino [5].
In Figure 14 it is possible to understand the functionally of declarations reading the comments.
CODE-variable declaration.
In Figure 15 is possible to verify the
Code-slave mode setup().
The code written in the “WHILE LOOP” could be redesigned to treat the Arduino channels dynamically. We want to say that configuration strings (like
Example code for dynamic configuration.
At this point we show the code about the blinking procedure and the Serial events procedure, respectively both in Figure 12.
In the end we report the loop() code, Figure 17. The code is very simple, the final message is made-up by concatenating the message in each “if” statement.
Loop() code.
In this section we show you the architecture that we use to run LabView code in Mater mode. We have chosen the
The Producer/Consumer design pattern (Figure 18) is based on the Master/Slave pattern, and is geared towards enhanced data sharing between multiple loops running at different rates. The Producer/Consumer pattern is commonly used when acquiring multiple sets of data to be processed in order. Suppose you want to write an application that accepts data while processing them in the order they were received. Because queuing up (producing) this data is much faster than the actual processing (consuming), the Producer/Consumer design pattern is best suited for this application. In our project we can set a high sample rate (up to fs = 10 kHz) so in this can we can occur in a data loss case. With Producer/Consumer is sure that we are implementing a data lossless LabView architecture. But we have considerated also an architecture
Event structure in producer/consumer design pattern.
In Figure 19 we show the front panel developed in LabView [7].
LabView front panel.
On the left side of front panel are present a several controls to configure the DAQ (Arduino in Slave mode) in according with previous paragraphs. Instead on the right side we found a control to set the PWM value (analog output) and the digital output state. We use Waveform chart like oscilloscope to view the six signal.
After defined the configuration you have to send a message at the serial VISA communication by the pressing of “SEND CONFIGURATION” button.
After that the cycle Producer/Consumer starts and the sensor reading is shown on the Waveform Chart.
Inside the LabView Code (block diagram) there are three nodes. The first one composed by “While Loop” (Figure 20a) that waiting for user’s hardware configuration. In this Loop we create a Boolean array with all hardware instance, at the end of the configuration the user pushes the button and send the array to subvi “open and configure.vi” (second node). It makes a rights sequence of op-code, open the Serial Port communication (in this case
First node and second node in block diagram (a and b).
From Figure 20b is possible to verify that the serial port velocity is 2Mbps.
In this way the communication between Master and Slave does not make interference with acquiring. In fact one character, in ASCII encoding (1 byte), from Arduino to LabView is sent in 4 μsec. If we would configure all analog inputs (6) and all digital inputs (14) the maximum number of characters would be = 6 prefixes (#) + 6*4 (digits of value among 0÷1023) + 14 prefixes (&) + 14 digital states = 58 bytes.
Maximum time to transmit the entire message is 58 Byte * 4 μsec = 232 μsec. This time is half of the minimum sampling time set in the code, that is 500 μsec. You could also reach 100 μsec of sampling rate that corresponds to 10 kHz of sampling frequency, in this case you have to merge the bits of the digital input, so it is possible to save 26 bytes but it is not enough. We have to modify the syntax of sending analog input values to reach at least 80 μsec of transmission time. This modification to the Arduino code we leave to the reader as an exercise.
In last one node, Figure 21, we can see the Producer Loop and the Consumer Loop. Both are connected by the queue, in queue process we read the Arduino’s message at maximum frequency and by consumer loop we process the data. The Event-Driven statement is configurated with the following
Producer/consumer event-driven LabView CODE.
The timeout terminal of “Event Structure” is connected, of course, at local variable”Ts (Sampling Rate)” in according with sample rate configurated in Arduino in node 1. In this “case” we read with “msg read from ARDUINO.vi” the Arduino’s message from serial (Figure 22). It is very simple code. The data are available on serial port (hardware) and the code read it using a
Block diagram of msg read from ARDUINO.Vi.
in this case we send a message to Arduino by serial port, remember that Arduino reads the message with a SerialEvent() function. Here we make a message with a word PWM followed with “ANALOG OUTPUT [PWM]” control knob converted in ASCII code (Figure 23).
Analog output [PWM] code.
In this “case” (Figure 24) we build the message to send Arduino by serial port to change the digital pin state, remember that Arduino reads the message with a SerialEvent() function Figure 12.
Pin2 & pin4 event.
Now we go back at Figure 21 where we have to talk about the Consumer Loop. Through the enqueue function we read the data from the head of the queue with the FIFO method (first in first out). If we have not error the data read are processed with the subvi “data extraction from Arduino message.vi”.
In Figure 25 there is the screen code. The code scan the message, check if present special ID char (#) or (&) and collect the data by
Data extraction from Arduino message.vi.
The subvi “data extraction from Arduino message.vi” returns the status of the digital inputs and the numerical values of the analogue inputs, if configured.
To convert the integer values reads from analog ports we need to perform a simple conversion. According to what we have studied in the previous paragraphs having a 10 bit ADC and a dynamic of 5 volts we obtain:
At this point, in the consumer loop, before displaying the analogue signals on the Waveform chart we multiply the output by the value 0.00488.
In this chapter we have seen one of the many ways of how LabView can be used with third parties hardware. The idea is to have an inexpensive tool not for industrial use but for High School applications where it is possible with a few euros to set up a laboratory for the analysis of an RC/RLC circuit, voltage divider, diode/transistor characterization. With a cheap sensors, connected at Analogue inputs, you can prepare laboratory experiments such as the pendulum oscillation, spring characterization, measurements of angles in uniform angular motion, etc.
In the end you could organize LabView CORE I and CORE II training courses in e-learning where the DAQ board is very cheap and easily purchased on the web from the students.
The authors declare no conflict of interest.
Dedicated to My wife and my daughters for encouraging and supporting me.
I would like to thank, my friend, the Director of the Department of Mathematics and Physics at my University, Prof. Lucio Gialanella, for supporting my initiative and for his precious advice.
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\\n\\nWe aim at improving the quality and availability of scholarly communication by promoting and practicing:
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The Open Access publishing movement started in the early 2000s when academic leaders from around the world participated in the formation of the Budapest Initiative. They developed recommendations for an Open Access publishing process, “which has worked for the past decade to provide the public with unrestricted, free access to scholarly research—much of which is publicly funded. Making the research publicly available to everyone—free of charge and without most copyright and licensing restrictions—will accelerate scientific research efforts and allow authors to reach a larger number of readers” (reference: http://www.budapestopenaccessinitiative.org)
\n\nIntechOpen’s co-founders, both scientists themselves, created the company while undertaking research in robotics at Vienna University. Their goal was to spread research freely “for scientists, by scientists’ to the rest of the world via the Open Access publishing model. The company soon became a signatory of the Budapest Initiative, which currently has more than 1000 supporting organizations worldwide, ranging from universities to funders.
\n\nAt IntechOpen today, we are still as committed to working with organizations and people who care about scientific discovery, to putting the academic needs of the scientific community first, and to providing an Open Access environment where scientists can maximize their contribution to scientific advancement. By opening up access to the world’s scientific research articles and book chapters, we aim to facilitate greater opportunity for collaboration, scientific discovery and progress. We subscribe wholeheartedly to the Open Access definition:
\n\n“By “open access” to [peer-reviewed research literature], we mean its free availability on the public internet, permitting any users to read, download, copy, distribute, print, search, or link to the full texts of these articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose, without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself. The only constraint on reproduction and distribution, and the only role for copyright in this domain, should be to give authors control over the integrity of their work and the right to be properly acknowledged and cited” (reference: http://www.budapestopenaccessinitiative.org)
\n\nOAI-PMH
\n\nAs a firm believer in the wider dissemination of knowledge, IntechOpen supports the Open Access Initiative Protocol for Metadata Harvesting (OAI-PMH Version 2.0). Read more
\n\nLicense
\n\nBook chapters published in edited volumes are distributed under the Creative Commons Attribution 3.0 Unported License (CC BY 3.0). IntechOpen upholds a very flexible Copyright Policy. There is no copyright transfer to the publisher and Authors retain exclusive copyright to their work. All Monographs/Compacts are distributed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). Read more
\n\nPeer Review Policies
\n\nAll scientific works are Peer Reviewed prior to publishing. Read more
\n\nOA Publishing Fees
\n\nThe Open Access publishing model employed by IntechOpen eliminates subscription charges and pay-per-view fees, enabling readers to access research at no cost. In order to sustain operations and keep our publications freely accessible we levy an Open Access Publishing Fee for manuscripts, which helps us cover the costs of editorial work and the production of books. Read more
\n\nDigital Archiving Policy
\n\nIntechOpen is committed to ensuring the long-term preservation and the availability of all scholarly research we publish. We employ a variety of means to enable us to deliver on our commitments to the scientific community. Apart from preservation by the Croatian National Library (for publications prior to April 18, 2018) and the British Library (for publications after April 18, 2018), our entire catalogue is preserved in the CLOCKSS archive.
\n\nOpen Science is transparent and accessible knowledge that is shared and developed through collaborative networks.
\n\nOpen Science is about increased rigour, accountability, and reproducibility for research. It is based on the principles of inclusion, fairness, equity, and sharing, and ultimately seeks to change the way research is done, who is involved and how it is valued. It aims to make research more open to participation, review/refutation, improvement and (re)use for the world to benefit.
\n\nOpen Science refers to doing traditional science with more transparency involved at various stages, for example by openly sharing code and data. It implies a growing set of practices - within different disciplines - aiming at:
\n\nWe aim at improving the quality and availability of scholarly communication by promoting and practicing:
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On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. 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After finishing his P. hD degree in 1992, he served in the Industry as a Scientific Officer and continued his academic career as a visiting scholar for a number of educational institutions. In 1996 he joined National University of Science & Technology Pakistan (NUST) as an Associate Professor; NUST is one of the top few universities in Pakistan. In 1999 he joined an International Company Lineo Inc, Canada as Manager Compiler Group, where he headed the group for developing Compiler Tool Chain and Porting of Operating Systems for the BLACKfin processor. The processor development was a joint venture by Intel and Analog Devices. In 2002 Lineo Inc., was taken over by another company, so he joined Aalborg University Denmark as an Assistant Professor.\nProfessor Akbar has truly a multi-disciplined career and he continued his legacy and making progress in many areas of his interests both in teaching and research. 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The motor of the society is the industry and the research of this topic has to be empowered in order to increase and improve the quality of our lives.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/22.jpg",keywords:"Machine Learning, Intelligence Algorithms, Data Science, Artificial Intelligence, Applications on Applied Intelligence"},{id:"23",title:"Computational Neuroscience",scope:"Computational neuroscience focuses on biologically realistic abstractions and models validated and solved through computational simulations to understand principles for the development, structure, physiology, and ability of the nervous system. This topic is dedicated to biologically plausible descriptions and computational models - at various abstraction levels - of neurons and neural systems. This includes, but is not limited to: single-neuron modeling, sensory processing, motor control, memory, and synaptic plasticity, attention, identification, categorization, discrimination, learning, development, axonal patterning, guidance, neural architecture, behaviors, and dynamics of networks, cognition and the neuroscientific basis of consciousness. Particularly interesting are models of various types of more compound functions and abilities, various and more general fundamental principles (e.g., regarding architecture, organization, learning, development, etc.) found at various spatial and temporal levels.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/23.jpg",keywords:"Single-Neuron Modeling, Sensory Processing, Motor Control, Memory and Synaptic Pasticity, Attention, Identification, Categorization, Discrimination, Learning, Development, Axonal Patterning and Guidance, Neural Architecture, Behaviours and Dynamics of Networks, Cognition and the Neuroscientific Basis of Consciousness"},{id:"24",title:"Computer Vision",scope:"The scope of this topic is to disseminate the recent advances in the rapidly growing field of computer vision from both the theoretical and practical points of view. Novel computational algorithms for image analysis, scene understanding, biometrics, deep learning and their software or hardware implementations for natural and medical images, robotics, VR/AR, applications are some research directions relevant to this topic.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",keywords:"Image Analysis, Scene Understanding, Biometrics, Deep Learning, Software Implementation, Hardware Implementation, Natural Images, Medical Images, Robotics, VR/AR"},{id:"25",title:"Evolutionary Computation",scope:"Evolutionary computing is a paradigm that has grown dramatically in recent years. This group of bio-inspired metaheuristics solves multiple optimization problems by applying the metaphor of natural selection. It so far has solved problems such as resource allocation, routing, schedule planning, and engineering design. Moreover, in the field of machine learning, evolutionary computation has carved out a significant niche both in the generation of learning models and in the automatic design and optimization of hyperparameters in deep learning models. This collection aims to include quality volumes on various topics related to evolutionary algorithms and, alternatively, other metaheuristics of interest inspired by nature. For example, some of the issues of interest could be the following: Advances in evolutionary computation (Genetic algorithms, Genetic programming, Bio-inspired metaheuristics, Hybrid metaheuristics, Parallel ECs); Applications of evolutionary algorithms (Machine learning and Data Mining with EAs, Search-Based Software Engineering, Scheduling, and Planning Applications, Smart Transport Applications, Applications to Games, Image Analysis, Signal Processing and Pattern Recognition, Applications to Sustainability).",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",keywords:"Genetic Algorithms, Genetic Programming, Evolutionary Programming, Evolution Strategies, Hybrid Algorithms, Bioinspired Metaheuristics, Ant Colony Optimization, Evolutionary Learning, Hyperparameter Optimization"},{id:"26",title:"Machine Learning and Data Mining",scope:"The scope of machine learning and data mining is immense and is growing every day. It has become a massive part of our daily lives, making predictions based on experience, making this a fascinating area that solves problems that otherwise would not be possible or easy to solve. This topic aims to encompass algorithms that learn from experience (supervised and unsupervised), improve their performance over time and enable machines to make data-driven decisions. It is not limited to any particular applications, but contributions are encouraged from all disciplines.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/26.jpg",keywords:"Intelligent Systems, Machine Learning, Data Science, Data Mining, Artificial Intelligence"},{id:"27",title:"Multi-Agent Systems",scope:"Multi-agent systems are recognised as a state of the art field in Artificial Intelligence studies, which is popular due to the usefulness in facilitation capabilities to handle real-world problem-solving in a distributed fashion. The area covers many techniques that offer solutions to emerging problems in robotics and enterprise-level software systems. Collaborative intelligence is highly and effectively achieved with multi-agent systems. Areas of application include swarms of robots, flocks of UAVs, collaborative software management. Given the level of technological enhancements, the popularity of machine learning in use has opened a new chapter in multi-agent studies alongside the practical challenges and long-lasting collaboration issues in the field. It has increased the urgency and the need for further studies in this field. We welcome chapters presenting research on the many applications of multi-agent studies including, but not limited to, the following key areas: machine learning for multi-agent systems; modeling swarms robots and flocks of UAVs with multi-agent systems; decision science and multi-agent systems; software engineering for and with multi-agent systems; tools and technologies of multi-agent systems.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/27.jpg",keywords:"Collaborative Intelligence, Learning, Distributed Control System, Swarm Robotics, Decision Science, Software Engineering"}],annualVolumeBook:{},thematicCollection:[],selectedSeries:{title:"Artificial Intelligence",id:"14"},selectedSubseries:null},seriesLanding:{item:{id:"7",title:"Biomedical Engineering",doi:"10.5772/intechopen.71985",issn:"2631-5343",scope:"Biomedical Engineering is one of the fastest-growing interdisciplinary branches of science and industry. The combination of electronics and computer science with biology and medicine has improved patient diagnosis, reduced rehabilitation time, and helped to facilitate a better quality of life. Nowadays, all medical imaging devices, medical instruments, or new laboratory techniques result from the cooperation of specialists in various fields. The series of Biomedical Engineering books covers such areas of knowledge as chemistry, physics, electronics, medicine, and biology. This series is intended for doctors, engineers, and scientists involved in biomedical engineering or those wanting to start working in this field.",coverUrl:"https://cdn.intechopen.com/series/covers/7.jpg",latestPublicationDate:"May 7th, 2022",hasOnlineFirst:!0,numberOfOpenTopics:3,numberOfPublishedChapters:96,numberOfPublishedBooks:12,editor:{id:"50150",title:"Prof.",name:"Robert",middleName:null,surname:"Koprowski",fullName:"Robert Koprowski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTYNQA4/Profile_Picture_1630478535317",biography:"Robert Koprowski, MD (1997), PhD (2003), Habilitation (2015), is an employee of the University of Silesia, Poland, Institute of Computer Science, Department of Biomedical Computer Systems. For 20 years, he has studied the analysis and processing of biomedical images, emphasizing the full automation of measurement for a large inter-individual variability of patients. Dr. Koprowski has authored more than a hundred research papers with dozens in impact factor (IF) journals and has authored or co-authored six books. Additionally, he is the author of several national and international patents in the field of biomedical devices and imaging. Since 2011, he has been a reviewer of grants and projects (including EU projects) in biomedical engineering.",institutionString:null,institution:{name:"University of Silesia",institutionURL:null,country:{name:"Poland"}}},subseries:[{id:"7",title:"Bioinformatics and Medical Informatics",keywords:"Biomedical Data, Drug Discovery, Clinical Diagnostics, Decoding Human Genome, AI in Personalized Medicine, Disease-prevention Strategies, Big Data Analysis in Medicine",scope:"Bioinformatics aims to help understand the functioning of the mechanisms of living organisms through the construction and use of quantitative tools. The applications of this research cover many related fields, such as biotechnology and medicine, where, for example, Bioinformatics contributes to faster drug design, DNA analysis in forensics, and DNA sequence analysis in the field of personalized medicine. Personalized medicine is a type of medical care in which treatment is customized individually for each patient. Personalized medicine enables more effective therapy, reduces the costs of therapy and clinical trials, and also minimizes the risk of side effects. Nevertheless, advances in personalized medicine would not have been possible without bioinformatics, which can analyze the human genome and other vast amounts of biomedical data, especially in genetics. The rapid growth of information technology enabled the development of new tools to decode human genomes, large-scale studies of genetic variations and medical informatics. The considerable development of technology, including the computing power of computers, is also conducive to the development of bioinformatics, including personalized medicine. In an era of rapidly growing data volumes and ever lower costs of generating, storing and computing data, personalized medicine holds great promises. Modern computational methods used as bioinformatics tools can integrate multi-scale, multi-modal and longitudinal patient data to create even more effective and safer therapy and disease prevention methods. Main aspects of the topic are: Applying bioinformatics in drug discovery and development; Bioinformatics in clinical diagnostics (genetic variants that act as markers for a condition or a disease); Blockchain and Artificial Intelligence/Machine Learning in personalized medicine; Customize disease-prevention strategies in personalized medicine; Big data analysis in personalized medicine; Translating stratification algorithms into clinical practice of personalized medicine.",annualVolume:11403,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/7.jpg",editor:{id:"351533",title:"Dr.",name:"Slawomir",middleName:null,surname:"Wilczynski",fullName:"Slawomir Wilczynski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035U1loQAC/Profile_Picture_1630074514792",institutionString:null,institution:{name:"Medical University of Silesia",institutionURL:null,country:{name:"Poland"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"5886",title:"Dr.",name:"Alexandros",middleName:"T.",surname:"Tzallas",fullName:"Alexandros Tzallas",profilePictureURL:"https://mts.intechopen.com/storage/users/5886/images/system/5886.png",institutionString:"University of Ioannina, Greece & Imperial College London",institution:{name:"University of Ioannina",institutionURL:null,country:{name:"Greece"}}},{id:"257388",title:"Distinguished Prof.",name:"Lulu",middleName:null,surname:"Wang",fullName:"Lulu Wang",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRX6kQAG/Profile_Picture_1630329584194",institutionString:null,institution:{name:"Shenzhen Technology University",institutionURL:null,country:{name:"China"}}},{id:"225387",title:"Prof.",name:"Reda",middleName:"R.",surname:"Gharieb",fullName:"Reda Gharieb",profilePictureURL:"https://mts.intechopen.com/storage/users/225387/images/system/225387.jpg",institutionString:"Assiut University",institution:{name:"Assiut University",institutionURL:null,country:{name:"Egypt"}}}]},{id:"8",title:"Bioinspired Technology and Biomechanics",keywords:"Bioinspired Systems, Biomechanics, Assistive Technology, Rehabilitation",scope:'Bioinspired technologies take advantage of understanding the actual biological system to provide solutions to problems in several areas. Recently, bioinspired systems have been successfully employing biomechanics to develop and improve assistive technology and rehabilitation devices. The research topic "Bioinspired Technology and Biomechanics" welcomes studies reporting recent advances in bioinspired technologies that contribute to individuals\' health, inclusion, and rehabilitation. Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',annualVolume:11404,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",editor:{id:"144937",title:"Prof.",name:"Adriano",middleName:"De Oliveira",surname:"Andrade",fullName:"Adriano Andrade",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRC8QQAW/Profile_Picture_1625219101815",institutionString:null,institution:{name:"Federal University of Uberlândia",institutionURL:null,country:{name:"Brazil"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"49517",title:"Prof.",name:"Hitoshi",middleName:null,surname:"Tsunashima",fullName:"Hitoshi Tsunashima",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTP4QAO/Profile_Picture_1625819726528",institutionString:null,institution:{name:"Nihon University",institutionURL:null,country:{name:"Japan"}}},{id:"425354",title:"Dr.",name:"Marcus",middleName:"Fraga",surname:"Vieira",fullName:"Marcus Vieira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003BJSgIQAX/Profile_Picture_1627904687309",institutionString:null,institution:{name:"Universidade Federal de Goiás",institutionURL:null,country:{name:"Brazil"}}},{id:"196746",title:"Dr.",name:"Ramana",middleName:null,surname:"Vinjamuri",fullName:"Ramana Vinjamuri",profilePictureURL:"https://mts.intechopen.com/storage/users/196746/images/system/196746.jpeg",institutionString:"University of Maryland, Baltimore County",institution:{name:"University of Maryland, Baltimore County",institutionURL:null,country:{name:"United States of America"}}}]},{id:"9",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",annualVolume:11405,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",institutionString:null,institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"35539",title:"Dr.",name:"Cecilia",middleName:null,surname:"Cristea",fullName:"Cecilia Cristea",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYQ65QAG/Profile_Picture_1621007741527",institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"40735",title:"Dr.",name:"Gil",middleName:"Alberto Batista",surname:"Gonçalves",fullName:"Gil Gonçalves",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYRLGQA4/Profile_Picture_1628492612759",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"211725",title:"Associate Prof.",name:"Johann F.",middleName:null,surname:"Osma",fullName:"Johann F. Osma",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSDv7QAG/Profile_Picture_1626602531691",institutionString:null,institution:{name:"Universidad de Los Andes",institutionURL:null,country:{name:"Colombia"}}},{id:"69697",title:"Dr.",name:"Mani T.",middleName:null,surname:"Valarmathi",fullName:"Mani T. Valarmathi",profilePictureURL:"https://mts.intechopen.com/storage/users/69697/images/system/69697.jpg",institutionString:"Religen Inc. | A Life Science Company, United States of America",institution:null},{id:"205081",title:"Dr.",name:"Marco",middleName:"Vinícius",surname:"Chaud",fullName:"Marco Chaud",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSDGeQAO/Profile_Picture_1622624307737",institutionString:null,institution:{name:"Universidade de Sorocaba",institutionURL:null,country:{name:"Brazil"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"profile.detail",path:"/profiles/344190",hash:"",query:{},params:{id:"344190"},fullPath:"/profiles/344190",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()