~~~ 𝑻𝒉𝒆 π‘Ίπ’†π’“π’Šπ’†π’” 𝒐𝒇 π‘©π’Šπ’ˆ 𝑢 ~~~𝑷𝒂𝒓𝒕 – 𝑰𝑽

Polynomial time complexity in algorithms causes tasks to become increasingly complex with each additional input, much like managing a family WhatsApp group. Joining the group is O(n), deciphering messages is O(n^2), and managing disputes is O(n^3). Nested loops and multiple operations on the entire dataset indicate polynomial time complexity, such as O(n^2) or O(n^3).

Read More ~~~ 𝑻𝒉𝒆 π‘Ίπ’†π’“π’Šπ’†π’” 𝒐𝒇 π‘©π’Šπ’ˆ 𝑢 ~~~𝑷𝒂𝒓𝒕 – 𝑰𝑽

~~~ 𝑻𝒉𝒆 π‘Ίπ’†π’“π’Šπ’†π’” 𝒐𝒇 π‘©π’Šπ’ˆ 𝑢 ~~~𝑷𝒂𝒓𝒕 – 𝑰𝑰𝑰

In a linear time scenario, the time taken to complete a task grows with the size of the input. Like avoiding pesky questions at a big family gathering, each relative must be checked before finding the relative of interest. Similarly, in algorithms, checking each element in a list one by one creates a linear relationship. As datasets grow, efficient algorithms become crucial.

Read More ~~~ 𝑻𝒉𝒆 π‘Ίπ’†π’“π’Šπ’†π’” 𝒐𝒇 π‘©π’Šπ’ˆ 𝑢 ~~~𝑷𝒂𝒓𝒕 – 𝑰𝑰𝑰

~~~ 𝑻𝒉𝒆 π‘Ίπ’†π’“π’Šπ’†π’” 𝒐𝒇 π‘©π’Šπ’ˆ 𝑢 ~~~𝑷𝒂𝒓𝒕 – 𝑰𝑰

O(logn) indicates logarithmic time complexity, where the algorithm’s efficiency grows as the problem size reduces. For instance, in a sorted list search, you progressively eliminate half the list at each step, akin to finding a word in a dictionary by halving the search space. Various examples illustrate logarithmic time complexity, emphasizing its efficiency for large datasets.

Read More ~~~ 𝑻𝒉𝒆 π‘Ίπ’†π’“π’Šπ’†π’” 𝒐𝒇 π‘©π’Šπ’ˆ 𝑢 ~~~𝑷𝒂𝒓𝒕 – 𝑰𝑰

~~~ 𝑻𝒉𝒆 π‘Ίπ’†π’“π’Šπ’†π’” 𝒐𝒇 π‘©π’Šπ’ˆ 𝑢 ~~~𝑷𝒂𝒓𝒕 – 𝑰

Big O Notation provides a way to describe how the performance of an algorithm changes with input size, offering a worst-case scenario. O(1) represents constant time, where the algorithm takes the same amount of time regardless of input size. Examples include accessing array elements by index, swapping numbers, and inserting nodes in a linked list.

Read More ~~~ 𝑻𝒉𝒆 π‘Ίπ’†π’“π’Šπ’†π’” 𝒐𝒇 π‘©π’Šπ’ˆ 𝑢 ~~~𝑷𝒂𝒓𝒕 – 𝑰

𝑾𝒉𝒂𝒕 π’Šπ’” 𝒂 π‘Ίπ’π’π’π’‘π’Šπ’π’ˆ 𝒄𝒂𝒄𝒉𝒆 𝒄𝒐𝒉𝒆𝒓𝒆𝒏𝒄𝒆 𝒑𝒓𝒐𝒕𝒐𝒄𝒐𝒍?

As the demand for high-performance and energy-efficient computation rises, multiprocessor architectures like MPSoCs are increasingly used in embedded systems. Snooping Cache Coherence protocol ensures data consistency and real-time response for shared memory locations. It involves broadcasted transactions and different cache line states. There are two primary protocols: Write-Invalidate and Write-Update (or Write-Broadcast).

Read More 𝑾𝒉𝒂𝒕 π’Šπ’” 𝒂 π‘Ίπ’π’π’π’‘π’Šπ’π’ˆ 𝒄𝒂𝒄𝒉𝒆 𝒄𝒐𝒉𝒆𝒓𝒆𝒏𝒄𝒆 𝒑𝒓𝒐𝒕𝒐𝒄𝒐𝒍?

~~~ 𝑾𝒉𝒂𝒕 π’Šπ’” π‘«π’Šπ’—π’Šπ’”π’Šπ’ƒπ’π’† 𝑳𝒐𝒂𝒅 π‘Ίπ’„π’‰π’†π’…π’–π’π’Šπ’π’ˆ? ~~~

Divisible Load Scheduling (DLS) is a method for efficiently distributing computational work across processors or nodes. It involves considering factors like processing power and communication time. This ensures simultaneous task completion, making it useful for real-time systems, multi-core processors, distributed embedded systems, and load balancing in sensor networks.

Read More ~~~ 𝑾𝒉𝒂𝒕 π’Šπ’” π‘«π’Šπ’—π’Šπ’”π’Šπ’ƒπ’π’† 𝑳𝒐𝒂𝒅 π‘Ίπ’„π’‰π’†π’…π’–π’π’Šπ’π’ˆ? ~~~

Linux IPC’s

Linux IPCs are essential tools for processes to communicate in the operating system. They include various methods like Pipes, Message Queues, Shared Memory, Sockets, Semaphores, and File-based communication. Each method serves specific communication needs, from simple data transfer to complex synchronization and coordination among processes.

Read More Linux IPC’s

𝑻𝒓𝒂𝒑 π‘Ίπ’šπ’”π’•π’†π’Ž π’Šπ’ 𝑨𝑼𝑹𝑰𝑿 𝑻π‘ͺπŸ‘πŸ•πŸ“π‘³π‘²

The AURIX TC375LK architecture (Tricore TC162P) system experiences traps from events like Non-Maskable Interrupts, instruction exceptions, etc. These traps cannot be disabled by software. There are eight trap classes distinguished as synchronous or asynchronous, hardware or software. This ensures reliable and secure real-time operations. When a trap occurs, specific actions are taken to handle it effectively.

Read More 𝑻𝒓𝒂𝒑 π‘Ίπ’šπ’”π’•π’†π’Ž π’Šπ’ 𝑨𝑼𝑹𝑰𝑿 𝑻π‘ͺπŸ‘πŸ•πŸ“π‘³π‘²

π‘Ίπ’π’‡π’•π’˜π’‚π’“π’† 𝑢𝒗𝒆𝒓 𝑻𝒉𝒆 π‘¨π’Šπ’“ (𝑺𝑢𝑻𝑨)Β 

Summary:
Software Over-The-Air (SOTA) allows remote updates for AURIX TC3xx microcontrollers, with dual PFLASH banks ensuring secure and reliable updates. The active and inactive banks are mapped for CPU execution, and address mapping is switched without data copying during updates. Configuration parameters are set in the User Configuration Block. The hardware settings update during System Reset.

Read More π‘Ίπ’π’‡π’•π’˜π’‚π’“π’† 𝑢𝒗𝒆𝒓 𝑻𝒉𝒆 π‘¨π’Šπ’“ (𝑺𝑢𝑻𝑨)Β