Tutorial 1

Technical Breadth and Depth of LoRa Technology

Tallal Elshabrawy (German University in Cairo, Egypt)

In the new era of the Internet-of-Things (IoT), communication technologies have become an integral constituent of a multitude of applications within smart cities, such as environmental monitoring, waste management, traffic control, and smart metering, just to name a few. Recently, Low Power Wide Area Networks (LP-WAN) have been evolving to become a key enabler for the realization of such types of city-wide applications with dense IoT devices. LP-WAN are typically configured as star-of-star networks. Within each underlying star network, thousands of power-constrained IoT end-devices communicate directly with an IoT gateway over long distances at the scale of kilometers. LoRa has been exhibiting tremendous commercial growth to establish itself among the front runners of emerging LP-WAN, where the number of countries with LoRaWAN deployments has surpassed more than 140 countries. LoRa is pillared on its patented chirp spread spectrum modulation that supports energy-efficient/reliable long-range communication.

The aim of this tutorial is to provide an introductory practical insight to the LoRa technology. Within the scope of the tutorial, the fundamentals of the patented LoRa chirp spread spectrum modulation will be explained. This is followed by an explanation of the practical transmission and reception of LoRa signals using software defined radio.

Tutorial 2

Implementation of Median Filter and 2DFIR FILTER algorithms Using SDSoC

Mohamed Goumih (ENSIAS RABAT, Morocco)

Average filter- Average filter is one of the linear filters. Most linear filter algorithms are developed by performing some linear combination of the pixel values present in the neighborhood and updating the pixel in the middle according to that. As discussed earlier that noise mainly appears in the neighborhood as maximum or minimum intensity value i.e. large deviation from the mean. The average filter reduces the diversity of pixel intensities by using the mean value of the pixel values of the neighborhood and replacing that with the middle element of the window. The issue with the averaging filter is that its output is not that smooth and especially at edges the image gets blurry. To avoid this problem a new approach is proposed which is used in the Median Filter algorithm. Here in this handout we will mainly talk about the Median Filter and 2DFIR FILTER. We will discuss the algorithms that have been used in the hardware and software implementation and compare their respective performances.

Tutorial 3

Artificial Intelligence of Things: The Opinion of Things

Amr T. Abdel-Hamid (German University in Cairo, Egypt)

IoT and AI are two independent technologies that made a significant impact on our daily life. IoT can be considered nervous system where data is collected and sent to the brain (AI) in the cloud to take decisions and act upon. Unfortunately, such combination is still not adequate to some problems. Data dense application, such as cameras which is supposed to send every frame to the cloud. Yet, by adding an AI layer at the end node will allow the system to send only meaningful frames upon detecting certain objects. The combination of AI and IoT brings AIoT (Artificial Intelligence of Things) promises a more economical, more intelligent and smartly connected systems. Edge computing tackles this problem by handling more data at the edge. This way devices analyzes the data and may act upon them and finally, determine what needs to be sent to the cloud. The concept simply means moving computational power out to the “edge”. Edges got limited power and processing capabilities, also most of the data analysis models and techniques are designed with the cloud huge resources in mind. In this talk, we are discussing the AIOT paradigm, main challenges faced by different applications, and proposed solutions and architectures. To make this more beneficial, we tried to abridge the gap between research and development, and discuss such challenges based on real proposed and developed applications showing the main approaches taken to solve such challenges.

Tutorial 4

How to Simulate Ternary logic Circuits from A to Z Using CNFET and Implemented in HSPICE Simulator?

RAMZI A. JABER (Beirut Arab University, Lebanon)

Multiple-Valued Logic (MVL) has more than two-valued logic to decrease the interconnections and energy consumption. Also, the market has seen a significant increase in portable electronics and embedded systems, which depend on batteries. Therefore, in this tutorial, the audiences, the students, and the authors will learn how to simulate the Ternary Inverters, Unary operators, and Ternary Half-Adder circuits from A to Z using Stanford 32 nm channel Carbon Nano-Tube Field-Effect Transistor (CNFET) and implemented in HSPICE simulator for different temperatures and voltages.