Register transfer and microoperations part 1Prasenjit Dey
Register transfer language, hardware implementation of bus transfer using multiplexer and three state buffer, hardware implementation of memory transfer e.g., memory read and memory write.
IMPLEMENTATION OF SDC - SDF ARCHITECTURE FOR RADIX-4 FFT VLSICS Design
Very large scale integration and Digital signal processing are the very crucial technologies from the last
few decades. DSP applications require high performance, low area and low power VLSI circuits. This
paper is discussing about FFT which is one of the vital component in the digital signal processing. In this
Paper, we propose a single path delay commutator–feedback (SDC-SDF) Architecture for Radix-4 FFT
and presented its simulation and synthesis results. The Radix-4 FFT architecture consists of log4 N-1 SDC
Stages and 1 SDF stage. Previously, the radix-2 SDC-SDF (Single path delay commutator-feedback) FFT
architecture was includes log2 N-1 SDC Stages and 1 SDF stage. The proposed Radix-4 SDC-SDF
architecture reduces the number of multiplications and additions as well as number of stages which
achieves reduced area and low power. The resultant architecture is simulated using Modelsim, design
verification and synthesis results are done using Xilinx ISE. The proposed architecture is compared with
Radix-2 SDC-SDF FFT and it can achieve less area as well as low power
Register transfer and microoperations part 1Prasenjit Dey
Register transfer language, hardware implementation of bus transfer using multiplexer and three state buffer, hardware implementation of memory transfer e.g., memory read and memory write.
IMPLEMENTATION OF SDC - SDF ARCHITECTURE FOR RADIX-4 FFT VLSICS Design
Very large scale integration and Digital signal processing are the very crucial technologies from the last
few decades. DSP applications require high performance, low area and low power VLSI circuits. This
paper is discussing about FFT which is one of the vital component in the digital signal processing. In this
Paper, we propose a single path delay commutator–feedback (SDC-SDF) Architecture for Radix-4 FFT
and presented its simulation and synthesis results. The Radix-4 FFT architecture consists of log4 N-1 SDC
Stages and 1 SDF stage. Previously, the radix-2 SDC-SDF (Single path delay commutator-feedback) FFT
architecture was includes log2 N-1 SDC Stages and 1 SDF stage. The proposed Radix-4 SDC-SDF
architecture reduces the number of multiplications and additions as well as number of stages which
achieves reduced area and low power. The resultant architecture is simulated using Modelsim, design
verification and synthesis results are done using Xilinx ISE. The proposed architecture is compared with
Radix-2 SDC-SDF FFT and it can achieve less area as well as low power
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
An Approach to Detecting Writing Styles Based on Clustering Techniquesambekarshweta25
An Approach to Detecting Writing Styles Based on Clustering Techniques
Authors:
-Devkinandan Jagtap
-Shweta Ambekar
-Harshit Singh
-Nakul Sharma (Assistant Professor)
Institution:
VIIT Pune, India
Abstract:
This paper proposes a system to differentiate between human-generated and AI-generated texts using stylometric analysis. The system analyzes text files and classifies writing styles by employing various clustering algorithms, such as k-means, k-means++, hierarchical, and DBSCAN. The effectiveness of these algorithms is measured using silhouette scores. The system successfully identifies distinct writing styles within documents, demonstrating its potential for plagiarism detection.
Introduction:
Stylometry, the study of linguistic and structural features in texts, is used for tasks like plagiarism detection, genre separation, and author verification. This paper leverages stylometric analysis to identify different writing styles and improve plagiarism detection methods.
Methodology:
The system includes data collection, preprocessing, feature extraction, dimensional reduction, machine learning models for clustering, and performance comparison using silhouette scores. Feature extraction focuses on lexical features, vocabulary richness, and readability scores. The study uses a small dataset of texts from various authors and employs algorithms like k-means, k-means++, hierarchical clustering, and DBSCAN for clustering.
Results:
Experiments show that the system effectively identifies writing styles, with silhouette scores indicating reasonable to strong clustering when k=2. As the number of clusters increases, the silhouette scores decrease, indicating a drop in accuracy. K-means and k-means++ perform similarly, while hierarchical clustering is less optimized.
Conclusion and Future Work:
The system works well for distinguishing writing styles with two clusters but becomes less accurate as the number of clusters increases. Future research could focus on adding more parameters and optimizing the methodology to improve accuracy with higher cluster values. This system can enhance existing plagiarism detection tools, especially in academic settings.
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
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.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
2. INSTRUCTION CLASSIFICATION
• Data Transfer group
• Arithmetic group
• Logical group
• Branch group
• Stack, I/O and Machine Control group
3. DATA TRANSFER
GROUPMOV 𝑟1, 𝑟2 Move content of 𝑟2 to 𝑟1 T-states: 4 Flags affected: None M-
cycle-1
MOV r, M Move content of [H-L] to r T-states: 7 Flags affected: None M-
cycle-2
MOV M,r Move content of r to [H-L] T-states: 7 Flags affected: None M-
cycle-2
MVI r,data Move immediate data to register T-states: 7 Flags affected: None M-
cycle-2
MVI M,data Move immediate data to memory T-states: 10 Flags affected: None M-
cycle-3
LXI rp, data Load register pair immediate with T-states:10 Flags affected: None M-
cycle-3
16 bit data
Example: LXI H,2500H will load 16 bit data 2500H into H-L pair. In code form it is written
as 21,00,25, where
1st byte of the instruction 21 is the opcode for LXI H. The 2nd byte 00 is 8 LSBs of the data
loaded in L and 25 is the 3rd byte of the instruction which is 8 bit MSBs loaded in H.
LDA addr Load accumulator direct T-states: 13 Flags affected: None
M-cycle-4
4. SHLD addr Store H-L pair direct T-states: 16 Flags affected: None
M-cycle-5
Example: SHLD 2500 will store the content of L in memory location 2500H
and the content of H is stored in 2501H
LDAX rp Load accumulator indirect T-states: 7 Flags affected: none
M-cycle: 2
Example: LDAX B will load the content of the memory location, whose
address is in the B-C pair, into the accumulator. This instruction is used only for
B-C and D-E pairs.
STAX rp Store accumulator indirect T-states: 7 Flags affected: none
M-cycle: 2
Example: STAX D will store the content of the accumulator in the memory
location whose address is in the D-E pair. This instruction is used only for
B-C and D-E pairs.
XCHG Exchange the contents of T-states: 4 Flags affected: none
M-cycle: 1
DATA TRANSFER
GROUP
5. EXECUTION OF MVI
B,05H
T-states: 7, M-cycle: 2
• In T1-state, the high order address {10H} is placed
on the bus A15 ⇔ A8 and low-order address {00H}
on the bus AD7 ⇔ AD0 and ALE = 1.
• In T2 -state, the RD line goes low, and the data 06H
from memory location 1000H are placed on the
data bus. The fetch cycle becomes complete in T3-
state.The instruction is decoded in the T4-state.
• During T4-state, the contents of the bus are
unknown. With the change in the status signal, IO/
M = 0, S1 = 1 and S0 = 0, the
2nd machine cycle is identified as the memory read.
• The address is 1001H and the data byte [05H] is
fetched via the data bus. Both M1 and M2 perform
memory read operation, but the M1 is called op-
code fetch i.e., the 1st machine cycle of each
instruction is identified as the opcode
fetch cycle
6. EXECUTION TIME OF MVI B,05H
Execution time for MVI B,05H i.e., memory read machine cycle
and instruction cycle:
As we know, clock frequency of 8085 is 3.125 MHz
Time (T) for one clock is 1/3.125= 0.32µs
Time for memory read=3T=3*0.32µs=0.96µs
Total execution time= 7T= 7*0.32µs=2.24µs
7. ASSIGNMENT-2
MARKS-5
1. Write a program to get 05H in register A, then MOV it to
register B.
2. Write a program to load the content of the memory location
FC50H directly to the accumulator, then transfer it to register
B. The content of the memory location FC50H is 05.
SUBMISSION DATE: 26/07/17