Pinned Repositories
An022.github.io
My first personal website
data_strcture
Reference to Stanford University’s assignment CS106AP: Programming Methodologies in Python and CS106B: Programming Abstractions as practice, focusing topic: data structure
Default-Prediction
Predict if a customer will default in the future
event-driven-object-oriented
Reference to Stanford University’s assignment CS106AP: Programming Methodologies in Python and CS106B: Programming Abstractions as practice, focusing topics: event-driven and object-oriented
hacking-the-gender-stack-gold
image_processing
Reference to Stanford University’s assignment CS106AP: Programming Methodologies in Python and CS106B: Programming Abstractions as practice, focusing topic: image processing
IO-Scheduling
A programming lab with professor Hubertus Franke. We simulate and put into practice I/O operation scheduling and optimization in this lab. Applications send the IO subsystem their IO requests, which are kept in an IO-queue until the disk device is prepared to handle another request. A request is then chosen from the IO-queue and sent to the disk device by the IO-scheduler. In operating systems, this option is sometimes referred to as the strategy() routine and is seen in the figure below. After finishing, a different request might be pulled from the IO-queue and sent to the disk. The scheduling rules will permit some system optimization in order to cut down on disk head movement or overall wait times. Only the left side of the figure (I/O Scheduler) is provided for this lab. The implemented schedulers are FIFO I SSTF (j), LOOK (s), CLOOK (c), and FLOOK (f). The letters in brackets indicate which parameter has to be specified in the -s program flag.
Linker
A programming lab with professor Hubertus Franke. We use a two-pass linker in this lab. By resolving external symbol references (such as variables and functions) and module relative addressing by assigning global addresses after placing the modules' object code at global locations, a linker takes individually built code/object modules and builds a single executable. We assume a target machine with the following characteristics rather than dealing with intricate x86 tool chains: (1) word addressable (2) addressable memory of 512 words (3) each valid word is represented by an integer (<10000).
manipulating_string
Reference to Stanford University’s assignment CS106AP: Programming Methodologies in Python and CS106B: Programming Abstractions as practice, focusing topic: manipulating string
MMU_Virtual-Memory-Management
A programming lab with professor Hubertus Franke. In this lab, we mimic and construct the function of an operating system's virtual memory manager, which uses page table translation to translate the virtual address spaces of several processes onto physical frames. The principal counts, but the lab assumes many processes, each with a virtual address space of exactly 64 virtual pages. Paging must be used because the total number of virtual pages in all virtual address spaces may be greater than the number of physical frames in the simulated system. We support up to 128 physical page frames, but the quantity is variable and determined by a program parameter.
An022's Repositories
An022/An022.github.io
My first personal website
An022/data_strcture
Reference to Stanford University’s assignment CS106AP: Programming Methodologies in Python and CS106B: Programming Abstractions as practice, focusing topic: data structure
An022/Default-Prediction
Predict if a customer will default in the future
An022/event-driven-object-oriented
Reference to Stanford University’s assignment CS106AP: Programming Methodologies in Python and CS106B: Programming Abstractions as practice, focusing topics: event-driven and object-oriented
An022/hacking-the-gender-stack-gold
An022/image_processing
Reference to Stanford University’s assignment CS106AP: Programming Methodologies in Python and CS106B: Programming Abstractions as practice, focusing topic: image processing
An022/IO-Scheduling
A programming lab with professor Hubertus Franke. We simulate and put into practice I/O operation scheduling and optimization in this lab. Applications send the IO subsystem their IO requests, which are kept in an IO-queue until the disk device is prepared to handle another request. A request is then chosen from the IO-queue and sent to the disk device by the IO-scheduler. In operating systems, this option is sometimes referred to as the strategy() routine and is seen in the figure below. After finishing, a different request might be pulled from the IO-queue and sent to the disk. The scheduling rules will permit some system optimization in order to cut down on disk head movement or overall wait times. Only the left side of the figure (I/O Scheduler) is provided for this lab. The implemented schedulers are FIFO I SSTF (j), LOOK (s), CLOOK (c), and FLOOK (f). The letters in brackets indicate which parameter has to be specified in the -s program flag.
An022/Linker
A programming lab with professor Hubertus Franke. We use a two-pass linker in this lab. By resolving external symbol references (such as variables and functions) and module relative addressing by assigning global addresses after placing the modules' object code at global locations, a linker takes individually built code/object modules and builds a single executable. We assume a target machine with the following characteristics rather than dealing with intricate x86 tool chains: (1) word addressable (2) addressable memory of 512 words (3) each valid word is represented by an integer (<10000).
An022/manipulating_string
Reference to Stanford University’s assignment CS106AP: Programming Methodologies in Python and CS106B: Programming Abstractions as practice, focusing topic: manipulating string
An022/MMU_Virtual-Memory-Management
A programming lab with professor Hubertus Franke. In this lab, we mimic and construct the function of an operating system's virtual memory manager, which uses page table translation to translate the virtual address spaces of several processes onto physical frames. The principal counts, but the lab assumes many processes, each with a virtual address space of exactly 64 virtual pages. Paging must be used because the total number of virtual pages in all virtual address spaces may be greater than the number of physical frames in the simulated system. We support up to 128 physical page frames, but the quantity is variable and determined by a program parameter.
An022/recursion
Reference to Stanford University’s assignment CS106AP: Programming Methodologies in Python and CS106B: Programming Abstractions as practice, focusing topic: recursion
An022/Scheduler
A programming lab with professor Hubertus Franke. In this lab, we investigate how various scheduling policies are implemented and what impact they have on a group of processes or threads running on a system. Discrete event simulation is going to be used to implement the system (DES). The operation of a system is portrayed as a chronological sequence of events in discrete-event simulation. Each event takes place at a specific moment in time and represents a shift in the system's state. This suggests that rather than incrementing time continuously, the system advances in time by specifying and carrying out the events (state transitions) and by discretizing temporal progression between the events. Events are handled after being removed from the event queue in chronological order, and they may produce new events in the present or in the future. Keep in mind that DES has nothing to do with OS; it is just a fantastic, general method for simulating system behavior and time travel that may be used in a variety of system simulation scenarios.
An022/simple_calculating
Reference to Stanford University’s assignment CS106AP: Programming Methodologies in Python and CS106B: Programming Abstractions as practice, focusing topic: simple calculating
An022/smarter_transit
This report revealed the correlation of weather, transit, and crime in New York City, built on a panel of daily crime, taxi trip records, subway arrival time, and temperature, using the NYU peel cluster, Hive and Tableau as the platform. We identify the effect of weather on daily, seasonally crime by using the temperature and the number of crimes from 2006 to 2021. Looking into the relationship between transit and crime in New York City.