The document discusses different data structures for maps or dictionaries, including linked lists, sorted arrays, balanced binary search trees, and arrays indexed by keys. It then focuses on hashing, describing how to map keys to array indices using hash functions. Two common approaches for handling collisions are open addressing, where elements are placed in the next available slot, and external chaining, where each slot contains a linked list. The performance of different hashing schemes depends on the load factor.
The document discusses hashing techniques and collision resolution methods for hash tables. It covers:
- Hashing maps keys of variable length to smaller fixed-length values using a hash function. Hash tables use hashing to efficiently store and retrieve key-value pairs.
- Collisions occur when two keys hash to the same value. Common collision resolution methods are separate chaining, where each slot points to a linked list, and open addressing techniques like linear probing and double hashing.
- Bucket hashing groups hash table slots into buckets to improve performance. Records are hashed to buckets and stored sequentially within buckets or in an overflow bucket if a bucket is full. This reduces disk accesses when the hash table is stored
This document provides information about dictionaries and hash tables. It defines dictionaries as dynamic sets that support operations like insertion, deletion, and searching. Hash tables are described as an efficient implementation of dictionaries that map keys to array positions using a hash function. The document discusses hash functions, collisions, open and closed addressing techniques to handle collisions, and qualities of good hash functions.
This document discusses different searching methods like sequential, binary, and hashing. It defines searching as finding an element within a list. Sequential search searches lists sequentially until the element is found or the end is reached, with efficiency of O(n) in worst case. Binary search works on sorted arrays by eliminating half of remaining elements at each step, with efficiency of O(log n). Hashing maps keys to table positions using a hash function, allowing searches, inserts and deletes in O(1) time on average. Good hash functions uniformly distribute keys and generate different hashes for similar keys.
Data Structure and Algorithms: What is Hash Table pptJUSTFUN40
Outline:
What is a HASH TABLE?
What is a HASH FUNCTION?
What is a HASH COLLISION?
Implementation of a Hash Table
Discussion on collision resolution methods in particular SEPARATE CHAINING and OPEN ADDRESSING
Separate Chaining Implementation
Open Addressing Implementation
Linear Probing
Quadratic Probing
Double Hashing
presentation on important DAG,TRIE,Hashing.pptxjainaaru59
Directed acyclic graph (DAG) is used to represent the flow of values between basic blocks of code. A DAG is a directed graph with no cycles. It is generated during intermediate code generation. DAGs determine common subexpressions and the flow of names and computed values between blocks of code. An algorithm is described to construct a DAG by creating nodes for operands and adding edges between nodes and operator nodes. Examples show how expressions are represented by a DAG. The complexity of a DAG depends on its width and depth. Applications of DAGs include determining common subexpressions, names used in blocks, and which statements' values may be used outside blocks.
Hashing is an algorithm that maps keys of variable length to fixed-length values called hash values. A hash table uses a hash function to map keys to values for efficient search and retrieval. Linear probing is a collision resolution technique for hash tables where open addressing is used. When a collision occurs, linear probing searches sequentially for the next empty slot, wrapping around to the beginning if reaching the end. This can cause clustering where many collisions occur in the same area. Lazy deletion marks deleted slots as deleted instead of emptying them.
The document discusses different data structures for maps or dictionaries, including linked lists, sorted arrays, balanced binary search trees, and arrays indexed by keys. It then focuses on hashing, describing how to map keys to array indices using hash functions. Two common approaches for handling collisions are open addressing, where elements are placed in the next available slot, and external chaining, where each slot contains a linked list. The performance of different hashing schemes depends on the load factor.
The document discusses hashing techniques and collision resolution methods for hash tables. It covers:
- Hashing maps keys of variable length to smaller fixed-length values using a hash function. Hash tables use hashing to efficiently store and retrieve key-value pairs.
- Collisions occur when two keys hash to the same value. Common collision resolution methods are separate chaining, where each slot points to a linked list, and open addressing techniques like linear probing and double hashing.
- Bucket hashing groups hash table slots into buckets to improve performance. Records are hashed to buckets and stored sequentially within buckets or in an overflow bucket if a bucket is full. This reduces disk accesses when the hash table is stored
This document provides information about dictionaries and hash tables. It defines dictionaries as dynamic sets that support operations like insertion, deletion, and searching. Hash tables are described as an efficient implementation of dictionaries that map keys to array positions using a hash function. The document discusses hash functions, collisions, open and closed addressing techniques to handle collisions, and qualities of good hash functions.
This document discusses different searching methods like sequential, binary, and hashing. It defines searching as finding an element within a list. Sequential search searches lists sequentially until the element is found or the end is reached, with efficiency of O(n) in worst case. Binary search works on sorted arrays by eliminating half of remaining elements at each step, with efficiency of O(log n). Hashing maps keys to table positions using a hash function, allowing searches, inserts and deletes in O(1) time on average. Good hash functions uniformly distribute keys and generate different hashes for similar keys.
Data Structure and Algorithms: What is Hash Table pptJUSTFUN40
Outline:
What is a HASH TABLE?
What is a HASH FUNCTION?
What is a HASH COLLISION?
Implementation of a Hash Table
Discussion on collision resolution methods in particular SEPARATE CHAINING and OPEN ADDRESSING
Separate Chaining Implementation
Open Addressing Implementation
Linear Probing
Quadratic Probing
Double Hashing
presentation on important DAG,TRIE,Hashing.pptxjainaaru59
Directed acyclic graph (DAG) is used to represent the flow of values between basic blocks of code. A DAG is a directed graph with no cycles. It is generated during intermediate code generation. DAGs determine common subexpressions and the flow of names and computed values between blocks of code. An algorithm is described to construct a DAG by creating nodes for operands and adding edges between nodes and operator nodes. Examples show how expressions are represented by a DAG. The complexity of a DAG depends on its width and depth. Applications of DAGs include determining common subexpressions, names used in blocks, and which statements' values may be used outside blocks.
Hashing is an algorithm that maps keys of variable length to fixed-length values called hash values. A hash table uses a hash function to map keys to values for efficient search and retrieval. Linear probing is a collision resolution technique for hash tables where open addressing is used. When a collision occurs, linear probing searches sequentially for the next empty slot, wrapping around to the beginning if reaching the end. This can cause clustering where many collisions occur in the same area. Lazy deletion marks deleted slots as deleted instead of emptying them.
A hash table maps keys to values by applying a hash function to the keys, which then indexes into an array of buckets or slots. There are two main types: open-addressing hash tables, where keys are stored directly in the array slots, and separate chaining, where each slot contains a linked list of key-value entries. Some example applications include databases, symbol tables for compilers, and network processing algorithms.
This document discusses hashing techniques for implementing abstract data types like tables. It begins by describing tables as data structures with fields that can be searched using a key. Different implementations of tables are then examined, including unsorted and sorted arrays, linked lists, and binary trees. The document focuses on hashing as a way to enable fast search (O(1) time) by using a hash function to map keys to array indices. It covers hash table implementation using arrays with collision resolution via separate chaining or open addressing. Factors like hash functions, collision handling, and table size that influence hashing performance are also summarized.
Hash tables provide constant time insertion, deletion and search by using a hash function to map keys to indexes in an array. Collisions occur when different keys hash to the same index. Separate chaining resolves collisions by storing keys in linked lists at each index. Open addressing resolves collisions by probing to the next index using functions like linear probing. The load factor and choice of hash function impact performance.
This document provides an outline and overview of hashing and hash tables. It defines hashing as a technique for storing data to allow for fast insertion, retrieval, and deletion. A hash table uses an array and hash function to map keys to array indices. Collision resolution techniques like linear probing are discussed. The document also summarizes the Hashtable class in .NET, which uses buckets and load factor to avoid collisions. Examples of hash functions and using the Hashtable class are provided.
Unit – VIII discusses searching and hashing techniques. It describes linear and binary searching algorithms. Linear search has O(n) time complexity while binary search has O(log n) time complexity for sorted arrays. Hashing is also introduced as a technique to allow O(1) access time by mapping keys to array indices via a hash function. Separate chaining and open addressing like linear probing and quadratic probing are described as methods to handle collisions during hashing.
This document provides an overview of hash tables and collision resolution techniques for hash tables. It discusses separate chaining and open addressing as the two broad approaches for resolving collisions in hash tables. For separate chaining, items with the same hash are stored in linked lists. For open addressing, techniques like linear probing, quadratic probing and double hashing use arrays to resolve collisions by probing to different index locations. The document outlines common hashing functions, applications of hash tables, and situations where hash tables may not be suitable. It also includes examples and pseudocode.
Hashing notes data structures (HASHING AND HASH FUNCTIONS)Kuntal Bhowmick
A Hash table is a data structure used for storing and retrieving data very quickly. Insertion of data in the hash table is based on the key value. Hence every entry in the hash table is associated with some key.
HASHING AND HASH FUNCTIONS, HASH TABLE REPRESENTATION, HASH FUNCTION, TYPES OF HASH FUNCTIONS, COLLISION, COLLISION RESOLUTION, CHAINING, OPEN ADDRESSING – LINEAR PROBING, QUADRATIC PROBING, DOUBLE HASHING
The document discusses different techniques for storing and searching data, including sequential search, binary search, and hashing. It provides details on open hashing and closed hashing, describing that closed hashing stores elements within buckets and can cause collisions when multiple elements are mapped to the same bucket. The document also outlines characteristics of good hash functions and different hashing methods like division, mid-square, folding, digit analysis, length dependent, algebraic coding, and multiplicative hashing.
Hashing is a technique for storing data in an array-like structure that allows for fast lookup of data based on keys. It improves upon linear and binary search by avoiding the need to keep data sorted. Hashing works by using a hash function to map keys to array indices, with collisions resolved through techniques like separate chaining or open addressing. Separate chaining uses linked lists at each index while open addressing resolves collisions by probing to alternate indices like linear, quadratic, or double hashing.
Hashing is a technique for mapping data to array indices to allow for fast insertion and search operations in O(1) time on average. It works by applying a hash function to a key to obtain an array index, which may cause collisions that require resolution techniques like separate chaining or open addressing. Open addressing resolves collisions by probing alternative indices using functions like linear probing, quadratic probing, or double hashing to find the next available empty slot.
Data to be Signed: Suppose you have a message or data that you want to sign to prove its authenticity and integrity.
Hashing: First, this message is subjected to a cryptographic hash function (commonly SHA-1, SHA-256, or similar). The purpose of this step is to produce a fixed-length hash value, which represents the unique fingerprint of the data. The hash value is considerably shorter than the original data.
Signing: The DSA algorithm then uses this hash value to create a digital signature. The signature is generated using the private key of the signer and some mathematical operations.
Verification: The recipient of the data, who has access to the corresponding public key, can use the same cryptographic hash function to generate a hash value from the received data. They then use the sender's public key to verify the digital signature.
Hashing is a technique that maps large amounts of data to smaller data structures using a hashing function. A hash function takes inputs of any size and maps them to a fixed-size table called a hash table. To handle collisions where two keys map to the same slot, separate chaining uses linked lists attached to each slot while open addressing resolves collisions by probing to the next slot using techniques like linear probing, quadratic probing, or double hashing. As the hash table fills up, rehashing may be needed to recalculate hashcodes and move entries to a larger table.
This document discusses hashing and its applications. It begins by describing dictionary operations like search, insert, delete, minimum, maximum, and their implementations using different data structures. It then focuses on hash tables, explaining how they work using hash functions to map keys to array indices. The document discusses collisions, good and bad hash functions, and performance of hash table operations. It also describes how hashing can be used for substring pattern matching and other applications like document fingerprinting.
This presentation provides an overview of hash functions and how they are used. It discusses:
- The history and applications of hashing, including hash tables and databases.
- How hash functions work by mapping data to a fixed size and introducing the concept of collisions.
- Common hash function algorithms like separate chaining and linear probing and how they handle collisions.
- An example implementation in C of a hash table with functions for insertion, searching, and display.
- The time complexity of hash table operations is better than logarithmic search but not constant like ideal hash functions.
Arrays allow storing and manipulating a collection of related data elements. They can hold groups of integers, characters, or other data types. Declaring individual variables becomes difficult when storing many elements, so arrays provide an efficient solution. Arrays are declared with a datatype and size, and elements can be initialized, accessed, inserted, deleted, and manipulated using their index positions. Common array operations include displaying values, finding maximum/minimum values, calculating sums, and passing arrays to functions. Multi-dimensional arrays extend the concept to store elements in a table structure accessed by row and column indexes.
The document outlines various data structures and algorithms for implementing dictionaries and hash tables, including:
- Separate chaining, which handles collisions by storing elements that hash to the same value in a linked list. Find, insert, and delete take average time of O(1).
- Open addressing techniques like linear probing and quadratic probing, which handle collisions by probing to alternate locations until an empty slot is found. These have faster search but slower inserts and deletes.
- Double hashing, which uses a second hash function to determine probe distances when collisions occur, reducing clustering compared to linear probing.
The document describes the process of depth-first search (DFS) on a graph using an adjacency list representation. It shows the recursive DFS algorithm by stepping through an example graph with 8 nodes. At each step, it marks the currently visited node as visited, marks the predecessors, and makes recursive calls to visit neighboring unvisited nodes. This traces out the DFS tree, showing how the structure captures the recursive calls. It concludes that DFS finds valid paths in the graph and runs in O(V+E) time like breadth-first search when using an adjacency list.
Hashing In Data Structure Download PPT icajiwol341
1. The document discusses hashing techniques for implementing dictionaries and search data structures, including separate chaining and closed hashing (linear probing, quadratic probing, and double hashing).
2. Separate chaining uses a linked list at each index to handle collisions, while closed hashing searches for empty slots using a collision resolution function.
3. Double hashing is described as the best closed hashing technique, as it uses a second hash function to spread keys out and avoid clustering completely.
This document discusses hashing techniques for storing data in tables. Hashing allows for fast O(1) insertion, deletion and search times by mapping keys to table indices via hash functions. Collisions occur when different keys hash to the same index, and are resolved using separate chaining (linked lists at each index) or open addressing techniques like linear probing. The load factor determines performance, and tables may need rehashing if they become too full.
Trees are hierarchical data structures that consist of nodes connected by edges. They are used to store and access information efficiently. Binary trees are a type of tree where each node has at most two children. Graphs model relationships between objects using nodes connected by edges. Hash tables store key-value pairs and allow for very fast lookup, insertion, and deletion of data using hash functions, but collisions can decrease efficiency.
A hash table maps keys to values by applying a hash function to the keys, which then indexes into an array of buckets or slots. There are two main types: open-addressing hash tables, where keys are stored directly in the array slots, and separate chaining, where each slot contains a linked list of key-value entries. Some example applications include databases, symbol tables for compilers, and network processing algorithms.
This document discusses hashing techniques for implementing abstract data types like tables. It begins by describing tables as data structures with fields that can be searched using a key. Different implementations of tables are then examined, including unsorted and sorted arrays, linked lists, and binary trees. The document focuses on hashing as a way to enable fast search (O(1) time) by using a hash function to map keys to array indices. It covers hash table implementation using arrays with collision resolution via separate chaining or open addressing. Factors like hash functions, collision handling, and table size that influence hashing performance are also summarized.
Hash tables provide constant time insertion, deletion and search by using a hash function to map keys to indexes in an array. Collisions occur when different keys hash to the same index. Separate chaining resolves collisions by storing keys in linked lists at each index. Open addressing resolves collisions by probing to the next index using functions like linear probing. The load factor and choice of hash function impact performance.
This document provides an outline and overview of hashing and hash tables. It defines hashing as a technique for storing data to allow for fast insertion, retrieval, and deletion. A hash table uses an array and hash function to map keys to array indices. Collision resolution techniques like linear probing are discussed. The document also summarizes the Hashtable class in .NET, which uses buckets and load factor to avoid collisions. Examples of hash functions and using the Hashtable class are provided.
Unit – VIII discusses searching and hashing techniques. It describes linear and binary searching algorithms. Linear search has O(n) time complexity while binary search has O(log n) time complexity for sorted arrays. Hashing is also introduced as a technique to allow O(1) access time by mapping keys to array indices via a hash function. Separate chaining and open addressing like linear probing and quadratic probing are described as methods to handle collisions during hashing.
This document provides an overview of hash tables and collision resolution techniques for hash tables. It discusses separate chaining and open addressing as the two broad approaches for resolving collisions in hash tables. For separate chaining, items with the same hash are stored in linked lists. For open addressing, techniques like linear probing, quadratic probing and double hashing use arrays to resolve collisions by probing to different index locations. The document outlines common hashing functions, applications of hash tables, and situations where hash tables may not be suitable. It also includes examples and pseudocode.
Hashing notes data structures (HASHING AND HASH FUNCTIONS)Kuntal Bhowmick
A Hash table is a data structure used for storing and retrieving data very quickly. Insertion of data in the hash table is based on the key value. Hence every entry in the hash table is associated with some key.
HASHING AND HASH FUNCTIONS, HASH TABLE REPRESENTATION, HASH FUNCTION, TYPES OF HASH FUNCTIONS, COLLISION, COLLISION RESOLUTION, CHAINING, OPEN ADDRESSING – LINEAR PROBING, QUADRATIC PROBING, DOUBLE HASHING
The document discusses different techniques for storing and searching data, including sequential search, binary search, and hashing. It provides details on open hashing and closed hashing, describing that closed hashing stores elements within buckets and can cause collisions when multiple elements are mapped to the same bucket. The document also outlines characteristics of good hash functions and different hashing methods like division, mid-square, folding, digit analysis, length dependent, algebraic coding, and multiplicative hashing.
Hashing is a technique for storing data in an array-like structure that allows for fast lookup of data based on keys. It improves upon linear and binary search by avoiding the need to keep data sorted. Hashing works by using a hash function to map keys to array indices, with collisions resolved through techniques like separate chaining or open addressing. Separate chaining uses linked lists at each index while open addressing resolves collisions by probing to alternate indices like linear, quadratic, or double hashing.
Hashing is a technique for mapping data to array indices to allow for fast insertion and search operations in O(1) time on average. It works by applying a hash function to a key to obtain an array index, which may cause collisions that require resolution techniques like separate chaining or open addressing. Open addressing resolves collisions by probing alternative indices using functions like linear probing, quadratic probing, or double hashing to find the next available empty slot.
Data to be Signed: Suppose you have a message or data that you want to sign to prove its authenticity and integrity.
Hashing: First, this message is subjected to a cryptographic hash function (commonly SHA-1, SHA-256, or similar). The purpose of this step is to produce a fixed-length hash value, which represents the unique fingerprint of the data. The hash value is considerably shorter than the original data.
Signing: The DSA algorithm then uses this hash value to create a digital signature. The signature is generated using the private key of the signer and some mathematical operations.
Verification: The recipient of the data, who has access to the corresponding public key, can use the same cryptographic hash function to generate a hash value from the received data. They then use the sender's public key to verify the digital signature.
Hashing is a technique that maps large amounts of data to smaller data structures using a hashing function. A hash function takes inputs of any size and maps them to a fixed-size table called a hash table. To handle collisions where two keys map to the same slot, separate chaining uses linked lists attached to each slot while open addressing resolves collisions by probing to the next slot using techniques like linear probing, quadratic probing, or double hashing. As the hash table fills up, rehashing may be needed to recalculate hashcodes and move entries to a larger table.
This document discusses hashing and its applications. It begins by describing dictionary operations like search, insert, delete, minimum, maximum, and their implementations using different data structures. It then focuses on hash tables, explaining how they work using hash functions to map keys to array indices. The document discusses collisions, good and bad hash functions, and performance of hash table operations. It also describes how hashing can be used for substring pattern matching and other applications like document fingerprinting.
This presentation provides an overview of hash functions and how they are used. It discusses:
- The history and applications of hashing, including hash tables and databases.
- How hash functions work by mapping data to a fixed size and introducing the concept of collisions.
- Common hash function algorithms like separate chaining and linear probing and how they handle collisions.
- An example implementation in C of a hash table with functions for insertion, searching, and display.
- The time complexity of hash table operations is better than logarithmic search but not constant like ideal hash functions.
Arrays allow storing and manipulating a collection of related data elements. They can hold groups of integers, characters, or other data types. Declaring individual variables becomes difficult when storing many elements, so arrays provide an efficient solution. Arrays are declared with a datatype and size, and elements can be initialized, accessed, inserted, deleted, and manipulated using their index positions. Common array operations include displaying values, finding maximum/minimum values, calculating sums, and passing arrays to functions. Multi-dimensional arrays extend the concept to store elements in a table structure accessed by row and column indexes.
The document outlines various data structures and algorithms for implementing dictionaries and hash tables, including:
- Separate chaining, which handles collisions by storing elements that hash to the same value in a linked list. Find, insert, and delete take average time of O(1).
- Open addressing techniques like linear probing and quadratic probing, which handle collisions by probing to alternate locations until an empty slot is found. These have faster search but slower inserts and deletes.
- Double hashing, which uses a second hash function to determine probe distances when collisions occur, reducing clustering compared to linear probing.
The document describes the process of depth-first search (DFS) on a graph using an adjacency list representation. It shows the recursive DFS algorithm by stepping through an example graph with 8 nodes. At each step, it marks the currently visited node as visited, marks the predecessors, and makes recursive calls to visit neighboring unvisited nodes. This traces out the DFS tree, showing how the structure captures the recursive calls. It concludes that DFS finds valid paths in the graph and runs in O(V+E) time like breadth-first search when using an adjacency list.
Hashing In Data Structure Download PPT icajiwol341
1. The document discusses hashing techniques for implementing dictionaries and search data structures, including separate chaining and closed hashing (linear probing, quadratic probing, and double hashing).
2. Separate chaining uses a linked list at each index to handle collisions, while closed hashing searches for empty slots using a collision resolution function.
3. Double hashing is described as the best closed hashing technique, as it uses a second hash function to spread keys out and avoid clustering completely.
This document discusses hashing techniques for storing data in tables. Hashing allows for fast O(1) insertion, deletion and search times by mapping keys to table indices via hash functions. Collisions occur when different keys hash to the same index, and are resolved using separate chaining (linked lists at each index) or open addressing techniques like linear probing. The load factor determines performance, and tables may need rehashing if they become too full.
Trees are hierarchical data structures that consist of nodes connected by edges. They are used to store and access information efficiently. Binary trees are a type of tree where each node has at most two children. Graphs model relationships between objects using nodes connected by edges. Hash tables store key-value pairs and allow for very fast lookup, insertion, and deletion of data using hash functions, but collisions can decrease efficiency.
Similar to Data Structures and Agorithm: DS 24 Hash Tables.pptx (20)
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.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
Design and optimization of ion propulsion dronebjmsejournal
Electric propulsion technology is widely used in many kinds of vehicles in recent years, and aircrafts are no exception. Technically, UAVs are electrically propelled but tend to produce a significant amount of noise and vibrations. Ion propulsion technology for drones is a potential solution to this problem. Ion propulsion technology is proven to be feasible in the earth’s atmosphere. The study presented in this article shows the design of EHD thrusters and power supply for ion propulsion drones along with performance optimization of high-voltage power supply for endurance in earth’s atmosphere.
Software Engineering and Project Management - Software Testing + Agile Method...Prakhyath Rai
Software Testing: A Strategic Approach to Software Testing, Strategic Issues, Test Strategies for Conventional Software, Test Strategies for Object -Oriented Software, Validation Testing, System Testing, The Art of Debugging.
Agile Methodology: Before Agile – Waterfall, Agile Development.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
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
AI for Legal Research with applications, toolsmahaffeycheryld
AI applications in legal research include rapid document analysis, case law review, and statute interpretation. AI-powered tools can sift through vast legal databases to find relevant precedents and citations, enhancing research accuracy and speed. They assist in legal writing by drafting and proofreading documents. Predictive analytics help foresee case outcomes based on historical data, aiding in strategic decision-making. AI also automates routine tasks like contract review and due diligence, freeing up lawyers to focus on complex legal issues. These applications make legal research more efficient, cost-effective, and accessible.
Digital Twins Computer Networking Paper Presentation.pptxaryanpankaj78
A Digital Twin in computer networking is a virtual representation of a physical network, used to simulate, analyze, and optimize network performance and reliability. It leverages real-time data to enhance network management, predict issues, and improve decision-making processes.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Data Structures and Agorithm: DS 24 Hash Tables.pptx
1. International Islamic University H-10, Islamabad, Pakistan
Data Structure
Lecture No. 24
Hash Tables
Engr. Rashid Farid Chishti
http://youtube.com/rfchishti
http://sites.google.com/site/chishti
2. Hash table is a data structure that allows very fast retrieval of data no matter
how much data there is
For that reason hash tables are widely used in database indexing, cashing,
program compilation, error checking and much more.
Consider a simple 1 Dimensional Array
To Find an item in the list you can apply a linear search approach. For a very
big array this could take a very long time.
Now suppose you want to retrieve a value from an element in an array and
you already know the index number of that element. You can lookup the value
very quickly indeed and it does not depend on size of an array or position of
an element.
Introduction
0 1 2 3 4 5 6 7 8 9 10
Jan Jim Mia Sam Leo Ted Bea Lou Ada Max Zoe
3. Each index number can be calculated by the value itself so that the index
number is a some way related to the data.
We get each letter of the word and get the ASCII code. E.g. for a Word Ada
Its ASCII Code is: A = 65, d = 100, a = 97
Now get sum of these ASCII codes: sum = 65+100+97= 262
Now divide it by size of any array and take reminded: 262 % 11 = 9
So Ada will be stored at index number 9 of an array.
Similarly all the words will be stores in an array.
Suppose we want to search Ada, we will add its ASCII codes and then we will
divide it’s sum by size of an array and will take reminder which is index no. 9.
We can perform a fast array lookup using index no.9.
Introduction
0 1 2 3 4 5 6 7 8 9 10
Ada
4. Hash Table
Word Letter 1 ASCII 1 Letter 2 ASCII 2 Letter 3 ASCII 3 Sum Mod 11
Ada A 65 d 100 a 97 262 9
Bea B 66 e 101 a 97 264 0
Jan J 74 a 97 n 110 281 6
Leo L 76 e 101 o 111 288 2
Lou L 76 o 111 u 117 304 7
Max M 77 a 97 x 120 294 8
Mia M 77 i 105 a 97 279 4
Sam S 83 a 97 m 109 289 3
Ted T 84 e 101 d 100 285 10
Tim T 84 i 105 m 109 298 1
Zoe Z 90 o 111 e 101 302 5
0 1 2 3 4 5 6 7 8 9 10
Bea Tim Leo Sam Mia Zoe Jan Lou Max Ada Ted
5. Hash Function
0 1 2 3 4 5 6 7 8 9 10
Bea Tim Leo Sam Mia Zoe Jan Lou Max Ada Ted
int hash(const string &key, int tableSize) {
int hasVal = 0;
for (int i = 0 ; i < key.length(); i++)
hashVal += key[i];
return hashVal % tableSize;
}
6. Rather that just storing individual item of data in an array. Hash tables are
often used store key value pairs
For example Ada name would be the key which will be used to calculate index.
Date of birth and other data would be her corresponding values.
Using an array of Objects you can store as much data as you like for each key.
A Hashing Algorithm also known as a Hash Function is the calculation applied
to the key which may be a very large number or a very large string.
A Calculation is applied to a key to transform it into an address
Hash Table
0 1 2 3 4 5 6 7 8 9 10
Bea Tim Leo Sam Mia Zoe Jan Lou Max Ada Ted
27-01-1941 08-06-1955 31-12-1945 27-04-1791 20-02-1986 19-06-1978 13-02-1956 27-12-1822 23-04-1858 10-12-1815 17-06-1937
English English American American Russian American Polish French German English American
Astronomer Inventor Mathematician Inventor Space Station Actress Logician Biologist Physicist Mathematician Philosopher
7. For numeric keys, divide the key by the number of available addresses (n) and
take the remainder. Address = key Mod n
For alphanumeric keys, divide the sum of ASCII codes in a key by the number
of available addresses (n) and take the remainder.
Folding method divides key into equal parts then adds the parts together
E.g. The telephone number 01452 8345654, becomes 01 +45 + 28 +34 +56 + 54 = 218
Depending on size of table, may then divide by some constant and take remainder
There are lot of different hash algorithms to choose from. Some are more
appropriate then others depending on nature of your data.
Hash Table
8. Some times if you apply a hash function to two different keys, it generates the
same index number for both. But both items can not go to same place.
This is known as a collision and need to resolve it.
There are several methods for dealing with this:
Close addressing
Separate chaining
Open addressing
Linear Probing
Lets load the array with different set of data.
Collision
9. In linear probing, collisions are resolved by sequentially scanning an array
(with wraparound) until an empty cell is found.
Open Addressing
Word Letter ASCII Letter ASCII Letter ASCII Sum Mod 11 New index
Mia M 77 i 105 a 97 279 4 4
Tim T 84 i 105 m 109 298 1 1
Bea B 66 e 101 a 97 264 0 0
Zoe Z 90 o 111 e 101 302 5 5
Sue S 83 u 117 e 101 301 4 6
Len L 76 e 101 n 110 287 1 2
Moe M 77 o 111 e 101 289 3 3
Lou L 76 o 111 u 117 304 7 7
Rae R 82 a 97 e 101 280 5 8
Max M 77 a 97 x 120 294 8 9
Tod T 84 o 111 d 100 295 9 10
0 1 2 3 4 5 6 7 8 9 10
Bea Tim Len Moe Mia Zoe Sue Lou Rae Max Tod
Hash (“Mia”, 11) = 4
Hash (“Sue”, 11) = 4
Hash (“Tim”, 11) = 1
Hash (“Len”, 11) = 1
10. The idea is to keep a list of all elements that hash to the same value.
The array elements are pointers to the first nodes of the lists.
A new item is inserted to the front of the list.
Advantages:
Better space utilization for large items.
Simple collision handling: searching linked list.
Overflow: we can store more items than the hash table size.
Deletion is quick and easy: deletion from the linked list.
Separate Chaining
12. Separate Chaining
To Find Rae
Rae = 82 + 97 + 101 = 280
280 % 11 = 5
Now Search from Linked
List starting from index 5
of an array
13. It should minimize the collision so that less time is spent in collision
resolution. And the data retrieval will be quicker.
Ideally it should give uniform distribution of hash values. Therefore data will
be spread across the hash table.
The hash function should be easy to calculate.
It should include a suitable method to resolve any collision that do occur.
Objectives of Hash Function
14. Hash Tables are used to index large amount of data.
The address of each key is calculated using the key itself.
resolution. And the data retrieval will be quicker.
Collision is resolved with either open or closed addressing.
Hashing is widely used in database indexing, compilers, cashing, password
authentication and more.
Insertion, Deletion and retrieval of data from hash table occur in a constant
time.
Summary