Technology News - IoT and Machine Learning

Fast Fourier Transform (FFT) is one of the most important algorithms in computer science, electronics and signal processing engineering. It is a fast solver for Discrete Fourier Transform (DFT). Basically, DFT or FFT transforms signals from time-amplitude domain to frequency-amplitude domain. The reverse form of the FFT is known as Inverse Fast Fourier Transform which converts, naturally, signals from frequency domain to time domain. FFT is heavily used in communication, radar or computer systems. For example OFDM (orthogonal frequency division multiplexing) is developed based on IFFT and FFT. Since Python is most common used scientific programming language beside Matlab, I would like to present some information about FFT and using it in Python. This blog post ( https://jakevdp.github.io/blog/2013/08/28/understanding-the-fft/ ) includes the basics of the FFT and very clear comparison of  it to DFT. Another blog post ( https://www.ritchievink.com/blog/2017/04/23/understanding-t...

I think the first question which must be answered clearly while starting teaching artificial intelligence and machine learning should be about the difference between them. AI - Artificial Intelligence is a comprehensive concept that stating that the computers can learn, think and decide what they should do by themselves in every situation. However, fully AI concept is not possible at the moment as various operations such as image recognition, playing a game, creative thinking etc. require different algorithms which are striving to solve specific problems and tasks. ML - Machine Learning is the specific application of AI, which is mostly relying on learning based on historical data to analyze future data and decide using these analyzes. It can be categorized into supervised and unsupervised learning. Former one utilizes the labelled data to train the machine learning core (brain) and the latter one uses an agent in order to solve the problems. Machine l...

Recently, the wireless communication systems have been transformed and now they have more robust communication link and higher spectral efficiency. One of the main improvement, which has been implemented into current 4G and WiFi networks, is MIMO (Multiple-Input Multiple-Output) technique. MIMO communication networks include more than one transmitter and receiver antennas in order to use multiple channel at the same time and frequency resources. The idea behind this technique is each antenna port can have a separate channel due to reflection and the scattering of the microwaves during the propagation. These channels are utilized using software based receivers and equalizer in order to simultaneously transmit data. MIMO enhances the spectral efficiency, thus the capacity of the link besides providing more communication links.  

The polarisation of electric field states the orientation and magnitude of its field vectors and their alteration through the time. Polarisation is related to the transverse electromagnetic waves (TEM), in which directions and magnitudes of both electric and magnetic fields vary by time. Polarisation of EM waves from an antenna is classified into three main categories: linear, circular or elliptical polarisations. Furthermore, the direction of polarisation may be clockwise (CW, right-hand polarisation) or counter-clockwise (CCW, left-hand polarisation). For instance, the equation indicates a circularly polarized wave which consists of two components in the x and y directions. If polarisation of the receiver antenna does not match with the polarisation of incoming waves, the amplitudes of the received waves decrease. This polarisation mismatch will cause polarisation loss and reduce the power of the received signal. On the other hand, polarisation discrepancy can be employed to trans...

Wireless sensor networks have been becomıng more important with each passing day. For example, Internet of things (IoTs) concept is developed actually based on WSN systems. There are many different potential and future applications of WNS such as in industry, defence or protecting nature One wireless sensor network node may include a wide variety of layers from physical communication layer to algorithm development. Antennas are also vital parts of the wireless sensor nodes. Due to the small size of the nodes, efficient and small antenna design is brutally challenging, many different methods, therefore, can be applied to enhance performance and efficiency of those small antenna elements.  

Firstly, I will start a list of some resources for the design of the wireless sensor networks node. Here, I will add design steps from PCB design to algorithms and software developing for a wireless sensor network nodes and system design. https://devzone.nordicsemi.com/question/41652/nrf24l01-pcb-files/ http://www.nordicsemi.com/eng/Products/2.4GHz-RF/nRF24L01P http://www.mouser.co.uk/Semiconductors/Embedded-Processors-Controllers/Microcontrollers-MCU/_/N-a85i8/?Ns=Pricing%7c0&No=25 http://arduino-for-beginners.blogspot.co.uk/2013/08/getting-nrf24l01-to-work-with-attiny84.html http://arduino-for-beginners.blogspot.co.uk/2013/04/process-of-getting-attiny85-to-work.html http://www.madebymarket.com/blog/dev/attiny-and-nrf24l01.html http://www.microchip.com/wwwproducts/en/ATtiny84 https://nathan.chantrell.net/tinytx-wireless-sensor/