This document provides an overview of Python data structures, focusing on lists and tuples. It discusses how lists and tuples store and organize data, how to define, access, update, and manipulate elements within lists and tuples using various Python functions and methods. Lists are described as mutable sequences that can contain elements of different data types, while tuples are described as immutable sequences. The document provides examples of using lists and tuples for tasks like stacks, queues, and storing records. It also covers list and tuple operations like slicing, filtering, mapping, and reducing.
This document discusses using lists in Python to represent and manipulate collections of data. It provides examples of calculating statistics like mean, median, and standard deviation on lists of numbers. Functions are defined to get numbers from the user into a list, calculate the mean of a list, calculate the standard deviation of a list given the mean, and find the median of a sorted list. The document shows how lists can store complex objects like student records and be sorted. Lists allow programs to efficiently organize and analyze entire datasets.
Data structure is an arrangement of data in computer's memory. It makes the data quickly available to the processor for required operations.It is a software artifact which allows data to be stored, organized and accessed.
This document discusses linear and non-linear data structures. Linear data structures like arrays, stacks, and queues store elements sequentially. Static linear structures like arrays have fixed sizes while dynamic structures like linked lists can grow and shrink. Non-linear structures like trees and graphs store elements in a hierarchical manner. Common abstract data types (ADTs) include stacks, queues, and lists, which define operations without specifying implementation. Lists can be implemented using arrays or linked lists.
This document discusses data structures and their implementation in C++. It begins by defining the objectives of understanding data structures, their types, and operations. It then defines data and data structures, and describes how data is represented in computer memory. The document classifies data structures as primitive and non-primitive, and describes common operations on each. It provides examples of linear and non-linear data structures like arrays, stacks, queues, and trees. The document concludes by explaining arrays in more detail, including their representation in memory and basic operations like traversing, searching, and sorting.
The document discusses data structures and linked lists. It defines data structures as logical ways of organizing and storing data in computer memory for efficient use. Linked lists are introduced as a linear data structure where elements are connected via pointers and can grow/shrink dynamically. Algorithms for traversing, inserting, and deleting nodes from singly linked lists using both recursion and iteration are provided with pseudocode.
This document summarizes a massive open online course on Udemy about fundamental data structures and algorithms using the C language. The 15-hour course covers key topics like stacks, queues, linked lists, trees, recursion, and analyzing algorithm efficiency. It aims to help students strengthen programming skills and prepare for technical interviews at top companies. The course consists of 14 sections and includes weekly quizzes on the Udemy platform.
This document discusses various common data structures, including their definitions, examples, and basic operations. It begins by defining an abstract data type and explaining that a data structure provides one way to implement an ADT by organizing data. Then it provides examples of common data structures like queues, stacks, binary search trees, lists, arrays, linked lists, graphs, and hashing. For each it briefly explains what it is, potential applications, and some basic operations that can be performed on it.
This document discusses using lists in Python to represent and manipulate collections of data. It provides examples of calculating statistics like mean, median, and standard deviation on lists of numbers. Functions are defined to get numbers from the user into a list, calculate the mean of a list, calculate the standard deviation of a list given the mean, and find the median of a sorted list. The document shows how lists can store complex objects like student records and be sorted. Lists allow programs to efficiently organize and analyze entire datasets.
Data structure is an arrangement of data in computer's memory. It makes the data quickly available to the processor for required operations.It is a software artifact which allows data to be stored, organized and accessed.
This document discusses linear and non-linear data structures. Linear data structures like arrays, stacks, and queues store elements sequentially. Static linear structures like arrays have fixed sizes while dynamic structures like linked lists can grow and shrink. Non-linear structures like trees and graphs store elements in a hierarchical manner. Common abstract data types (ADTs) include stacks, queues, and lists, which define operations without specifying implementation. Lists can be implemented using arrays or linked lists.
This document discusses data structures and their implementation in C++. It begins by defining the objectives of understanding data structures, their types, and operations. It then defines data and data structures, and describes how data is represented in computer memory. The document classifies data structures as primitive and non-primitive, and describes common operations on each. It provides examples of linear and non-linear data structures like arrays, stacks, queues, and trees. The document concludes by explaining arrays in more detail, including their representation in memory and basic operations like traversing, searching, and sorting.
The document discusses data structures and linked lists. It defines data structures as logical ways of organizing and storing data in computer memory for efficient use. Linked lists are introduced as a linear data structure where elements are connected via pointers and can grow/shrink dynamically. Algorithms for traversing, inserting, and deleting nodes from singly linked lists using both recursion and iteration are provided with pseudocode.
This document summarizes a massive open online course on Udemy about fundamental data structures and algorithms using the C language. The 15-hour course covers key topics like stacks, queues, linked lists, trees, recursion, and analyzing algorithm efficiency. It aims to help students strengthen programming skills and prepare for technical interviews at top companies. The course consists of 14 sections and includes weekly quizzes on the Udemy platform.
This document discusses various common data structures, including their definitions, examples, and basic operations. It begins by defining an abstract data type and explaining that a data structure provides one way to implement an ADT by organizing data. Then it provides examples of common data structures like queues, stacks, binary search trees, lists, arrays, linked lists, graphs, and hashing. For each it briefly explains what it is, potential applications, and some basic operations that can be performed on it.
This document provides an overview of the course "Data Structures and Applications" including the module topics, definitions, and classifications of data structures. The first module covers introduction to data structures, including definitions of primitive and non-primitive data structures, data structure operations, arrays, structures, stacks, and queues. Key concepts like dynamic memory allocation and various data structure implementations are also summarized.
The document discusses lists in Python. It begins by defining lists as mutable sequences that can contain elements of any data type. It describes how to create, access, manipulate, slice, traverse and delete elements from lists. It also explains various list methods such as append(), pop(), sort(), reverse() etc. and provides examples of their use. The document concludes by giving some programs on lists including finding the sum and maximum of a list, checking if a list is empty, cloning lists, checking for common members between lists and generating lists of square numbers.
This document discusses different data structures and algorithms. It provides examples of common data structures like arrays, linked lists, stacks, queues, trees, and graphs. It describes what each data structure is, how it stores and organizes data, and examples of its applications. It also discusses abstract data types, algorithms, and how to analyze the time and space complexity of algorithms.
This document provides an overview of key concepts for data science in Python, including popular Python packages like NumPy and Pandas. It introduces Python basics like data types, operators, and functions. It then covers NumPy topics such as arrays, slicing, splitting and reshaping arrays. It discusses Pandas Series and DataFrame data structures. Finally, it covers operations on missing data and combining datasets using merge and join functions.
This document provides an overview of the course "Data Structures and Applications" which covers various data structures like arrays, stacks, queues, trees, and graphs. It discusses primitive data structures like integers and non-primitive structures like linked lists. Operations on data structures like creation, searching, and deletion are also summarized. Common implementations of stacks and queues using arrays and pointers are mentioned.
This document introduces several common data structures used in computer science, including arrays, linked lists, stacks, queues, trees, and graphs. Arrays store a collection of elements of the same type in a linear order. Linked lists consist of nodes that contain data and links to other nodes, allowing efficient insertion and removal. Stacks and queues are linear data structures where elements can only be added or removed from one end, with stacks following last-in first-out order and queues following first-in first-out order. Trees store hierarchical relationships between elements, and graphs represent relationships between elements without a defined hierarchy.
The document provides information about data structures, including definitions of key terms, examples of different data structure types, and operations that can be performed on data structures.
It begins by defining a data structure as a collection of elements and operations on those elements. Linear data structures like stacks, queues, and linked lists are described, where elements are arranged sequentially. Non-linear structures like trees and graphs are also mentioned.
Common operations on data structures include creation, insertion, deletion, searching, sorting, and reversing elements. Abstract data types are defined, and several applications of data structures in areas like operating systems, databases, and artificial intelligence are listed. Specific data structure types like linked lists, stacks, and queues are then defined
The document discusses data structures and algorithms. It defines arrays as a series of objects of the same size and type, where each object is an element that can be accessed via an index. Algorithms are described as finite sequences of instructions to solve problems, with analysis of algorithms determining the resources like time and storage required.
Data structures and algorithms short note (version 14).pdNimmi Weeraddana
The document provides an introduction to data structures and algorithms. It discusses why data structures are needed, common measurements of efficiency, and the costs of algorithms. It defines abstract and concrete data structures, and provides examples of stacks and queues as abstract data types that can be implemented using concrete data structures like arrays. It also discusses algorithms for operations like sorting, searching, insertion and removal. Overall, the document serves as an introductory overview of key concepts in data structures and algorithms.
The document discusses different types of list data structures. It describes lists as sequential data structures that can be implemented as arrays or linked lists. Linked lists have nodes that are linked together via pointers. There are different types of linked lists including singly linked lists where each node has a next node pointer, circular lists where nodes form a ring, and doubly linked lists where each node has pointers to the next and previous nodes. The document provides examples and illustrations of implementing various list operations for each type of linked list.
In this PDF, MarsDevs presents you with the technical information on Python list, its syntax, examples, and different operations on python list.
Click here to read more: https://www.marsdevs.com/blogs/everything-technical-on-list-in-python
This document provides an introduction to Python fundamentals, covering key topics such as data types, compound data structures, conditionals and loops, functions, and object-oriented programming. The main topics covered include integers, floats, strings, booleans, lists, tuples, dictionaries, if/else statements, while loops, functions, classes, objects, and the __init__ method. Examples are provided to demonstrate indexing strings, typecasting, list operations, and using input() to take user input.
The document discusses various data structures used in programming, including arrays, lists, linked lists, stacks, queues, and dictionaries. It provides definitions and summaries of each data structure, including their common operations and time complexities. For example, it notes that arrays provide O(1) direct access by index but fixed size, while lists are dynamically sized but insertion/deletion at non-end positions is O(n).
This document provides an introduction to data structures. It defines data structures as representations of logical relationships between data elements. Data structures can be primitive, like integers and floats, or non-primitive, like lists, stacks, queues, trees and graphs. Non-primitive data structures are built from primitive structures and emphasize structuring groups of homogeneous or heterogeneous data. The document describes common data structures like arrays, lists, stacks, queues and trees, and explains their properties and implementations.
This chapter discusses different data structures and their applications. It begins with classifying data structures as primitive and non-primitive. Common linear data structures like stacks, queues, and linked lists are described. Stacks follow LIFO while queues follow FIFO. Linked lists use nodes connected by pointers. The chapter provides examples of implementing operations for these structures.
This document provides a practical manual on data structures for computer science students. It was prepared by Mr. Naveen Choudhary and Dr. Dharm Singh of the Computer Science and Engineering department at Maharana Pratap University of Agriculture and Technology in Udaipur. The 138-page manual contains exercises and solutions to help students understand data structures from an applied perspective. It covers topics like stacks, queues, linked lists, trees, and sorting and searching algorithms.
A data structure is a way of storing data in computer memory so that it can be retrieved and manipulated efficiently. There are two main categories of data structures: linear and non-linear. Linear data structures include arrays, stacks, and queues where elements are stored in a linear order. Non-linear structures include trees and graphs where elements are not necessarily in a linear order. Common operations on data structures include traversing, searching, insertion, deletion, sorting, and merging. Algorithms use data structures to process and solve problems in an efficient manner.
The document discusses key concepts related to data structures and algorithms. It defines data as values or sets of values that can be organized hierarchically into fields, records, and files. Entities have attributes that can be assigned values. Related entities form entity sets. Data structures organize data through fields, records, and files while supporting operations like searching, insertion, and deletion. Algorithms are step-by-step processes to solve problems in a finite number of steps. The efficiency of algorithms is measured by time and space complexity.
This document provides an overview of the course "Data Structures and Applications" including the module topics, definitions, and classifications of data structures. The first module covers introduction to data structures, including definitions of primitive and non-primitive data structures, data structure operations, arrays, structures, stacks, and queues. Key concepts like dynamic memory allocation and various data structure implementations are also summarized.
The document discusses lists in Python. It begins by defining lists as mutable sequences that can contain elements of any data type. It describes how to create, access, manipulate, slice, traverse and delete elements from lists. It also explains various list methods such as append(), pop(), sort(), reverse() etc. and provides examples of their use. The document concludes by giving some programs on lists including finding the sum and maximum of a list, checking if a list is empty, cloning lists, checking for common members between lists and generating lists of square numbers.
This document discusses different data structures and algorithms. It provides examples of common data structures like arrays, linked lists, stacks, queues, trees, and graphs. It describes what each data structure is, how it stores and organizes data, and examples of its applications. It also discusses abstract data types, algorithms, and how to analyze the time and space complexity of algorithms.
This document provides an overview of key concepts for data science in Python, including popular Python packages like NumPy and Pandas. It introduces Python basics like data types, operators, and functions. It then covers NumPy topics such as arrays, slicing, splitting and reshaping arrays. It discusses Pandas Series and DataFrame data structures. Finally, it covers operations on missing data and combining datasets using merge and join functions.
This document provides an overview of the course "Data Structures and Applications" which covers various data structures like arrays, stacks, queues, trees, and graphs. It discusses primitive data structures like integers and non-primitive structures like linked lists. Operations on data structures like creation, searching, and deletion are also summarized. Common implementations of stacks and queues using arrays and pointers are mentioned.
This document introduces several common data structures used in computer science, including arrays, linked lists, stacks, queues, trees, and graphs. Arrays store a collection of elements of the same type in a linear order. Linked lists consist of nodes that contain data and links to other nodes, allowing efficient insertion and removal. Stacks and queues are linear data structures where elements can only be added or removed from one end, with stacks following last-in first-out order and queues following first-in first-out order. Trees store hierarchical relationships between elements, and graphs represent relationships between elements without a defined hierarchy.
The document provides information about data structures, including definitions of key terms, examples of different data structure types, and operations that can be performed on data structures.
It begins by defining a data structure as a collection of elements and operations on those elements. Linear data structures like stacks, queues, and linked lists are described, where elements are arranged sequentially. Non-linear structures like trees and graphs are also mentioned.
Common operations on data structures include creation, insertion, deletion, searching, sorting, and reversing elements. Abstract data types are defined, and several applications of data structures in areas like operating systems, databases, and artificial intelligence are listed. Specific data structure types like linked lists, stacks, and queues are then defined
The document discusses data structures and algorithms. It defines arrays as a series of objects of the same size and type, where each object is an element that can be accessed via an index. Algorithms are described as finite sequences of instructions to solve problems, with analysis of algorithms determining the resources like time and storage required.
Data structures and algorithms short note (version 14).pdNimmi Weeraddana
The document provides an introduction to data structures and algorithms. It discusses why data structures are needed, common measurements of efficiency, and the costs of algorithms. It defines abstract and concrete data structures, and provides examples of stacks and queues as abstract data types that can be implemented using concrete data structures like arrays. It also discusses algorithms for operations like sorting, searching, insertion and removal. Overall, the document serves as an introductory overview of key concepts in data structures and algorithms.
The document discusses different types of list data structures. It describes lists as sequential data structures that can be implemented as arrays or linked lists. Linked lists have nodes that are linked together via pointers. There are different types of linked lists including singly linked lists where each node has a next node pointer, circular lists where nodes form a ring, and doubly linked lists where each node has pointers to the next and previous nodes. The document provides examples and illustrations of implementing various list operations for each type of linked list.
In this PDF, MarsDevs presents you with the technical information on Python list, its syntax, examples, and different operations on python list.
Click here to read more: https://www.marsdevs.com/blogs/everything-technical-on-list-in-python
This document provides an introduction to Python fundamentals, covering key topics such as data types, compound data structures, conditionals and loops, functions, and object-oriented programming. The main topics covered include integers, floats, strings, booleans, lists, tuples, dictionaries, if/else statements, while loops, functions, classes, objects, and the __init__ method. Examples are provided to demonstrate indexing strings, typecasting, list operations, and using input() to take user input.
The document discusses various data structures used in programming, including arrays, lists, linked lists, stacks, queues, and dictionaries. It provides definitions and summaries of each data structure, including their common operations and time complexities. For example, it notes that arrays provide O(1) direct access by index but fixed size, while lists are dynamically sized but insertion/deletion at non-end positions is O(n).
This document provides an introduction to data structures. It defines data structures as representations of logical relationships between data elements. Data structures can be primitive, like integers and floats, or non-primitive, like lists, stacks, queues, trees and graphs. Non-primitive data structures are built from primitive structures and emphasize structuring groups of homogeneous or heterogeneous data. The document describes common data structures like arrays, lists, stacks, queues and trees, and explains their properties and implementations.
This chapter discusses different data structures and their applications. It begins with classifying data structures as primitive and non-primitive. Common linear data structures like stacks, queues, and linked lists are described. Stacks follow LIFO while queues follow FIFO. Linked lists use nodes connected by pointers. The chapter provides examples of implementing operations for these structures.
This document provides a practical manual on data structures for computer science students. It was prepared by Mr. Naveen Choudhary and Dr. Dharm Singh of the Computer Science and Engineering department at Maharana Pratap University of Agriculture and Technology in Udaipur. The 138-page manual contains exercises and solutions to help students understand data structures from an applied perspective. It covers topics like stacks, queues, linked lists, trees, and sorting and searching algorithms.
A data structure is a way of storing data in computer memory so that it can be retrieved and manipulated efficiently. There are two main categories of data structures: linear and non-linear. Linear data structures include arrays, stacks, and queues where elements are stored in a linear order. Non-linear structures include trees and graphs where elements are not necessarily in a linear order. Common operations on data structures include traversing, searching, insertion, deletion, sorting, and merging. Algorithms use data structures to process and solve problems in an efficient manner.
The document discusses key concepts related to data structures and algorithms. It defines data as values or sets of values that can be organized hierarchically into fields, records, and files. Entities have attributes that can be assigned values. Related entities form entity sets. Data structures organize data through fields, records, and files while supporting operations like searching, insertion, and deletion. Algorithms are step-by-step processes to solve problems in a finite number of steps. The efficiency of algorithms is measured by time and space complexity.
The document discusses classes and objects in Python programming. It covers key concepts like defining classes, creating objects, assigning attributes to objects, passing objects as arguments and returning objects from functions. It provides examples to illustrate these concepts like defining a Point class to represent coordinate points, creating Rectangle class with a Point object as one of its attributes. The document also discusses concepts like aliasing of objects and how to create a copy of an object instead of alias.
The document discusses Python lists and their key features. It covers how lists are ordered sequences that can contain elements of different types. Lists are mutable and can be accessed using indexes. Common list operations include slicing, concatenation, repetition, sorting, and using various list methods like append(), extend(), index(), reverse() etc. Tuples are immutable sequences similar to lists. Dictionaries are another data type that store elements as key-value pairs. The document also briefly introduces regular expressions for text parsing and extraction.
The document discusses different types of loops in Python including while loops, for loops, and infinite loops. It provides examples of using while loops to iterate until a condition is met, using for loops to iterate over a set of elements when the number of iterations is known, and how to terminate loops early using break or skip iterations using continue. It also discusses using the range() function to generate a sequence of numbers to iterate over in for loops.
This document discusses why learning to program is useful, provides an overview of Python programming, and covers installing and getting started with Python. Key points include:
- Programs are written to solve problems computationally and humans tend to get bored with repetitive tasks, so programming allows computers to act as assistants.
- Python is a programming language that can be used on many devices and supports different data structures and algorithms.
- To communicate with the CPU to solve problems, one must write a set of instructions called a program. Installing Python or the Anaconda distribution allows you to start programming with Python.
Python for Data Analysis_ Data Wrangling with Pandas, Numpy, and Ipython ( PD...R.K.College of engg & Tech
This document provides an overview and table of contents for the book "Python for Data Analysis" by Wes McKinney. The book covers using Python and essential Python data analysis libraries like NumPy, pandas, matplotlib, and others for data wrangling, exploration, and modeling. It includes chapters on data structures and manipulation, data loading and storage, data cleaning, aggregation, visualization, and more. The second edition was published in 2017 and builds on the first edition.
This document discusses Python functions and modules. It explains that Python allows programmers to break programs into independent segments called functions. Functions enable easier understanding, coding, testing and maintenance of programs. The document then covers various aspects of defining, calling and using functions in Python like function declaration, parameters, local and global variables, return values, recursion and importing from modules.
This document discusses Python functions and modules. It explains that Python allows programmers to break programs into independent segments called functions. Functions enable easier understanding, coding, testing and maintenance of programs. The document covers function declaration, definition, calls, parameters, local and global variables, return statements, default arguments, anonymous functions, recursion and importing from modules.
This document discusses control statements in Python programming. It covers sequential, selection, and iterative control statements including if, if-else, nested if, if-elif-else, while loops, for loops, break, continue, pass, and else statements used with loops. Control statements allow programmers to control the flow and execution of code by making decisions or repeating actions.
The document provides an overview of the Python programming language. It discusses features of Python like being simple, easy to learn, versatile, free and open source. It also covers limitations of Python such as being slower than C/C++ for computation heavy tasks. The document describes applications of Python like web development, scientific computing, games development. It then explains basics of Python programming like writing the first program, data types, variables, operators, expressions and lists. Finally, it discusses type conversion and type coercion in Python.
The document discusses how to define an interface in Java that specifies methods for generating different types of number series. An interface named Series is defined with three methods - getNext() to return the next number, reset() to restart the series, and setStart() to set the starting value. Classes can then implement the Series interface and provide implementations of these three methods to generate specific types of number series, like one that returns even numbers starting from 2.
Packages in Java help organize code by providing a layer of encapsulation and control. Packages group related classes and interfaces together, and allow classes to be made private to a package. To define a package, use the package statement at the top of a file with the package name. Multiple files can belong to the same package. Packages are hierarchical, with names separated by periods. Directories mirror package hierarchies.
The document provides examples of multilevel inheritance in Java. In multilevel inheritance, a subclass inherits from an intermediate derived class, which itself inherits from a base class. This allows subclasses to inherit properties from all parent classes up the inheritance chain. Three programs are given as examples, demonstrating how subclasses can call constructors and methods of parent classes in the inheritance hierarchy.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Low power architecture of logic gates using adiabatic techniquesnooriasukmaningtyas
The growing significance of portable systems to limit power consumption in ultra-large-scale-integration chips of very high density, has recently led to rapid and inventive progresses in low-power design. The most effective technique is adiabatic logic circuit design in energy-efficient hardware. This paper presents two adiabatic approaches for the design of low power circuits, modified positive feedback adiabatic logic (modified PFAL) and the other is direct current diode based positive feedback adiabatic logic (DC-DB PFAL). Logic gates are the preliminary components in any digital circuit design. By improving the performance of basic gates, one can improvise the whole system performance. In this paper proposed circuit design of the low power architecture of OR/NOR, AND/NAND, and XOR/XNOR gates are presented using the said approaches and their results are analyzed for powerdissipation, delay, power-delay-product and rise time and compared with the other adiabatic techniques along with the conventional complementary metal oxide semiconductor (CMOS) designs reported in the literature. It has been found that the designs with DC-DB PFAL technique outperform with the percentage improvement of 65% for NOR gate and 7% for NAND gate and 34% for XNOR gate over the modified PFAL techniques at 10 MHz respectively.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia