Academics

Academic hours: 39
ECTS: 4.5
Semester: B

The course offers students an Introduction to design and analysis of algorithms. Students will learn the basic principles of the algorithms design and the methods of algorithms analysis.

In the first part of the course the lectures will be given by a lector. Afterword, scientific papers will be distributed among the participants and the students will make presentations in the class.

Academic hours: 39
ECTS: 4.5
Semester: A + B

The course is an introduction to Deep learning, concepts and algorithms of this field of machine learning and its implementation using advanced modern neural networks. Subjects of this course: Introduction to python, Computer vision, neural networks, architectures, objects detection, visualization, and image classification.

Academic hours: 39
ECTS: 4.5
Semester:  B

The seminar focuses on algorithmics in distributed systems and networks. The aim is to study basic topics and techniques related to design and analysis of AI algorithms in non-faulty networks, and algorithms and impossibility results in faulty networks. Within faulty networks the seminar also considers self-stabilizing systems. In addition, basic concepts concerning online algorithms and approximation algorithms in networking problems are addressed.

Academic hours: 39
ECTS: 4.5
Semester: A

Randomized algorithms are algorithms which use randomness for making certain decisions. Randomized algorithms are in use in all fields of computer science and software engineering, and they often allow us to solve certain problems simply and efficiently.
The course consists of two main parts: In the first part, the lecturer will give several introductory lectures on the subject. In the second part, the students will be given relevant literature and, under the guidance of the supervisor, will present it to the class.

Academic hours: 39
ECTS: 4.5
Semester: B

This seminar covers several methods for automatic verification of hardware and software.
The seminar topics include Formal specifications, Hoare proof system, Linear temporal logic, Branching time temporal logic, algorithms for verifying temporal logic properties, BDDs, symbolic algorithms, bounded model-checking. The seminar is partially based on student presentations.

Academic hours: 39
ECTS: 4.5
Semester: A+B

The course is an introduction to machine learning and deep learning concepts and algorithms. Today, Convolution Neural Networks (CNN) are in great use in many systems and are developed for classification and regression purposes.

Subjects of this course include: supervised learning, generalization and overfitting, optimization methods, computer vision, CNN basics, CNN architectures and current advanced topics.

The course will include lectures and seminars given by students on papers from leading scientific journals.

Academic hours: 39
ECTS: 4.5
Semester: A + B

The Capstone Project encompasses utilizing a variety of methods, tools and techniques in software engineering and computer science, with the intention to apply the knowledge and engineering capabilities acquired throughout the course of learning thus far. The Capstone Project includes a research component which focuses on either a theoretical area (Computer Science), technological area (engineering, innovation, etc.), or application domain. This course is the first out of two semestrial phases of the Capstone Project.

Academic hours: 39
ECTS: 4.5
Semester: A + B

The Capstone Project encompasses utilizing a variety of methods, tools and techniques in software engineering and computer science, with the intention to apply the knowledge and engineering capabilities acquired throughout the course of learning thus far. The Capstone Project includes a research component which focuses on either a theoretical area (Computer Science), technological area (engineering, innovation, etc.), or application domain. This course is the second phase of the Capstone Project, in which the outcome of the first phase is further developed, including application, implementation and evaluation of the software which was built to achieve the project’s goals.

Academic hours: 39
ECTS: 4.5
Semester: B

The seminar will provide a combination of fundamental concepts and advanced techniques in the field of artificial intelligence. A wide range of decision making problems and environments are covered in this course involving agents and multi-agent systems. Problem solving through search methods and reasoning in uncertain and constrained environments is discussed. The Markov decision problem (MDP) and Q learning are also examined. The seminars use state-of-the-art papers from leading journals.

Academic hours: 26
ECTS: 3
Semester: B

Practice in the use, written and spoken, of technical, scientific and business English. The course includes basic and essential English grammar and vocabulary, summary writing, scientific and technical reporting, meeting agenda composition, execution and minute taking, audience-directed language (Register), company presentation, composition of scientific protocols, plagiarism, verbal and written presentation of original research/business proposals.

Academic hours: 39
ECTS: 3
Semester: B

Study of a range of technologies for the fractionation and purification of proteins and peptides, their identification and uses. Emphasis on the physicochemical properties of proteins and peptides, properties that determine the choice and application of technologies for their fractionation and purification. Advanced methods of recombinant protein and peptide production. The influence of Genomics on the identification of functional proteins. The principles and practice of Proteomics in the discovery, characterization and production of proteins. Applications and implications of High Throughput technologies.

Academic hours: 26
ECTS: 4.5
Semester: A + B

Basic concepts of Immunology. The Innate and Acquired immune systems. Cells and tissues of the immune response. Antibody genetics and structure. Antibody classes and their specific functions. The T-cell receptor, its recognition of self and non-self antigens. The Major Histocompatibility Complex (MHC), its recognition of antigens and cooperation with the T-cell receptor. Humoral and cellular immunity. Cytokines. Mechanism of immune reactions against pathogens and tumor-specific antigens. Regulation of the immune response and autoimmunity.

Academic hours: 52
ECTS: 3.7
Semester: B

The course is crafted for mechanical engineering students, with the objective of equipping them with the advanced skills and profound insights necessary to thrive in the rapidly evolving landscape of IoT technologies. This curriculum has been developed as a proactive response to the growing industrial demand for mechanical engineers who are not only masters of their traditional expertise but are also proficient in the cutting-edge technologies that are pivotal to the Fourth Industrial Revolution (Industry 4.0). To this end, the course offers a comprehensive blend of lectures, interactive laboratory sessions, and a collaborative team project, all designed to provide students with the skills needed to address the collaborative and interdisciplinary challenges that define contemporary engineering practices.
Through an integrated approach that combines theoretical knowledge with practical application, students will acquire a holistic understanding of how IoT solutions can be applied innovatively within the engineering domain as a response to users’ requirements and needs.

Academic hours: 39
ECTS: 3.7
Semester: B

The goal of the course is to introduce the students to flight sciences. The course provides background and knowledge if mechanics of flight vehicles, and utilizes tools acquired in the basic dynamics and fluid mechanics courses to introduce a complex mechanical application – the aircraft.

Academic hours: 39
ECTS: 1.5
Semester: B

This laboratory provides the student with an opportunity to explore topics learned in Fluid Mechanics and Heat Transfer. As part of a team, the student must prepare, conduct and document a series of 12 classical experiments employing a variety of instruments for the collection of data. Data are processed and reported, along with a discussion comparing empirical results with theoretical calculations and identifying sources of uncertainty. The hands-on experience serves to deepen the student’s understanding of the subject material.

Academic hours: 65
ECTS: 6
Semester: A 

This is an innovative hybrid course to enhance the design skills of Mechanical Engineering Students.
The course offers real experience of working with doctors to design practical solutions to actual problems that affect surgeons, their patients and their trainees every day. By the end of the course, students will have a physical project prototype to present, test and use and show to a patent lawyer who will explain the process of patenting.

Academic hours: 39
ECTS: 4.5
Semester: A + B

The course gives the student basic understanding of the following subjects:

• Practical aspects of materials engineering and their implementation.
• Acquaintance with various manufacturing processes starting with selection and ordering materials to final product manufacturing.
• Exposure to techno-economic considerations and production in a competitive environment.
• Planning manufacturing operations and acceptance testing.

Academic hours: 52
ECTS: 4.5
Semester:  B

The main objective is to become acquainted
with various aspects related to “Industry
4.0”, including efficiency and productivity,
process chains, optimization, sensors and
automation, sustainability, biologicalisation,
energy and resources, digitization, and the
use of data in the world of new manufacturing
systems, conventional processes, and advanced
technologies like AM, EDM, ECM, Waterjet, and
Laser in industry. In addition, students will
acquire up-to-date knowledge on industrial and
scientific developments, new materials, powder
materials, composite, and micro-machining

Academic hours: 52
ECTS: 4.5
Semester:  B

The course is focus on the approximation of real-valued continuous functions by some simpler class of functions, such as algebraic polynomials.

Some of the topics that will be investigated are:

• Chebyshev Polynomials
• Least square problems
• Projection methods
• Interpolation (for example: Lagrange, Chebyshev, Hermite)
• Remez’s algorithm
• Padé approximant

The above and more related topics will be practiced Chebfun (an open-source package for computing).

Academic hours: 52
ECTS: 4.5
Semester:  B

This course is centered around acquiring knowledge of algebraic structures, including groups, rings and fields. These areas are central to mathematics and are widely
used in applications, especially in computer science.

Academic hours: 52
ECTS: 4.5
Semester: A 

A practical course on signal processing applications and similar areas using MATLAB coding environment. During the course students will get assignments and projects on the topics of signals and systems in the time and frequency domains as well as digital signal processing in time and in frequency. The students will be required to implement projects that are related, but not limited to: audio signal processing, RF communication, biological signal processing.

Academic hours: 52
ECTS: 4.5
Semester: A + B

Students will be able to understand how modern operating systems work, and how to interact with them efficiently and utilize the available resource in an optimal manner.

The second part of the course will introduce the concurrent programming concept, enabling the programs to utilize all the available resources of a computer system, speeding the work.

We will learn both theoretical and practical aspects in class and in the homework projects. In order to gain deeper understanding we will implement part of the solutions. Therefore, the course will include a substantial programming component using the Python language.

Academic hours: 52
ECTS: 4.5
Semester: A + B

The course will provide the concepts in the Object Oriented Paradigm, and will go over practical tools used for Object Oriented Programming. The topics of the course will include the following concepts: abstract data types (ADT), overloading, encapsulation, classes, objects, Inheritance, multiple inheritance, polymorphism, generic programming, casting and more. We will also discuss practical programming skills such as efficient programming, libraries, and top-down design.

Academic hours: 52
ECTS: 4.5
Semester: A + B

Python language for quick and easy development of algorithms and programs:
in this course, you will learn an Object Oriented Programming language, based on the C language. Data structures used in the language, input/output, modules, packages,
special libraries, GUI, advanced material,
packaging, version control system.

Academic hours: 52
ECTS: 4.5
Semester: A + B

Based on prior knowledge from previous courses, learn data structures and advanced algorithms used in engineering and electrical engineering in particular. Covering complexity costs, general search and sort algorithms, strings and different data structures and algorithms utilizing these structures. Advanced data structures: trees, graphs and algorithms like minimum path, spanning trees and more. Pattern matching and text compression, greedy algorithms.

Academic hours: 78
ECTS: 4.5
Semester: A + B

This course provides an introduction to micro-processor based systems, inside architecture of 16 bit processor (Intel 8086). Principles of micro-processor programming in Machine Code, Assembly 8086 language and Modular programming. Principle operation of RISC and CISC processors. Programing for Windows OS, based on DLL files   Advanced architecture of modern processors “Intel 32bit”, Pentium4- dual core, Pentium– pro and inside architecture of “Intel 64”, Itanium. Fundamentals of development of a micro-processor based system, Pentium- Main Memory Organization, Virtual Memory, Paging Mechanism, Cache Memory Organization.  Principles of serial communication, RS-232, USB. Detailed studies of computer I/O and interrupt techniques, timers, parallel and serial interfaces. Laboratory activities provide the student with experience in developing the hardware and software required to incorporate microprocessors into systems in ASM86 language.    PC peripherals including – keyboard, screen, drives, serial port and mouse.

Academic hours: 78
ECTS: 4.5
Semester: A + B

Analog CMOS integrated circuits design focuses on the basic building blocks including current source/mirror, single stage amplifiers, differential stage amplifier. The lab experiments involve hands-on design using state of the art CAD tools. Lectures complement the experiments providing theoretical background. The course follows the design cycle: from specification definitions, through architecture selection and basic design, to fine-tuning providing precise simulations. Simulation employing CAD tools of performance parameters such as gain, frequency response, stability, voltage span, operating point, slew rate and offset. To summarize the course, the students will be given independent design tasks (mini- projects) to implement the techniques studied.

Academic hours: 39
ECTS: 4.5
Semester:  B

Basic analog and digital signals. Examples of medical signals (ECG, EEG, EMG, ERG, PPG). “Arduino Due” board as software defined signal generator controlled by UART command. “EasyStart Kit – PIC32MX7” board as fast prototype board for RT-DSP algorithms test.

Practical aspects of the signal’ acquisition by using ADC: pre-amplifiers, anti-aliasing filters, usage of timers and interrupts. Usage of TFT screen to present graphs of the signals and textual information. Basic DSP algorithms and their practical implementation: filtration by convolution and by using FFT, normalized correlation, autocorrelation, median filtration. In the frames of the course “Arduino Due” board and “EasyStart Kit – PIC32MX7” board are used to create working prototypes of RT-DSP systems: “Spectrum Analyzer”, “Medical signals smart monitor”, “Filtration of audio signals” and others.

Academic hours: 84
ECTS: 4.5
Semester: A + B

Basic properties of the human visual system. Pixel. Computer presentation of the Gray and RGB images as arrays. Creating a set of synthetic test images by using C and C++. Contrast and Brightness. Pixel-to- Pixel operations: Contrast stretch, Automatic Min-Max  contrast stretch, Histogram Equalization. Usage of LUT and pointers for fast implementation of pixel-to-pixel operations. Geometrical Transformations: scaling, rotation, affine Transform. Image registration. Median filtration. Filtration by convolution. Gaussian filter.

Usage of FFT for Image Processing. Unsharp Masking. Edge detectors. Usage of MATLAB for fast prototyping Image Processing systems. Design and properties of digital camera. In the frames of the course, laboratory, students implement a selection of the Image Processing algorithms by using Visual Studio (C, C++, C#, .NET).

Academic Hours: 52
ECTS: 4.5
Semester: A + B

The subject matter of this course covers two distinct but interlinked areas of knowledge or expertise: dynamical system modelling and numerical simulation of dynamical systems. The students will learn to derive mathematical models by applying the ‘law of conservation’ to various common processes with lumped parameters. The students will analyse the transient behaviour of these models in a laboratory type environment, where they will use numerical simulation methods to solve a model’s non-linear state differential
equations.

Academic hours: 39
ECTS: 4.5
Semester: A + B

This course covers specific aspects that put Marketing at the leading edge of the modern firm’s activities: understanding customer’s needs and designing a comprehensive approach aiming to fulfill these special needs. The students will be exposed to the basic principles, perspectives, concepts, theories and models that have been crystallized into the contemporary science of Marketing.

Academic hours: 39
ECTS: 4.5
Semester:  B

This course is about integrating three concepts in a real-world context: problem-solving, creativity, and modeling. Problem-solving is a critical skill to develop and nurture. Not to mention creativity that due to the increasing complexity of challenges has been becoming a necessity rather than an advantage. To fully utilize the two in a systematic way, modeling is adopted. Via real-world case studies, we will identify, formulate, analyze,
and validate models to solve the challenges stemming from these case studies. Modeling approach will be the vehicle through which we capture the essence of the dynamics of the major problem at hand and creativity will aid in developing and soliciting innovative solutions at the various stages of the problem-solving process.
Advanced Excel will be the platform via which these models will be developed.

Academic hours: 39
ECTS: 4.5
Semester:  B

Behavioral science is a branch of the science, which is concerned with the study of human behavior. Behavioral science looks at individuals and their behavior along with the behavior of societies and groups, and processes which can contribute to specific behaviors. Learning behavioral science is an important part of becoming team or project manager. More and more, professional
and organizations are explicitly endorsing the necessity of engineers being skilled and well trained in these areas. This course will focus on human personality, motivation and other work attitudes, learning, perception, stereotypes and discrimination, burnout and stress. The theories and insights of major studies will be discussed, while emphasis their relevancy to organizations and
industry.

Academic hours: 26
ECTS: 4.5
Semester: B

This course is an online course entitled Embracing Diversity that targets undergraduate and graduate students, and aims to expose students to diversity in four modules:

1. Multiculturalism
2. Disability and Accessibility
3. Facial Appearance
4. Gender and Sexual Orientation

Students will be exposed to diversity’s different dimensions using diverse technological tools.