## 5 Discussion  
Although the integration of sensing tools and communication systems has been recognized as a pivotal area, there is a deficiency in comprehensive exploration aimed at simplifying these models to achieve heightened efficiency and reduced resource consumption. There are limited comprehensive studies showcasing the effective amalgamation of these components.  
This paper aims to integrate sensing tools with communication systems to facilitate efficient data transmission and processing. It addresses the challenge of simplifying intricate machine learning models, specifically the YOLO v4-tiny model, in order to improve performance, while minimizing resource utilization. Additionally, the research focuses  
\begin{table}
\begin{tabular}{c c} \hline \hline
**YOLO-v4 Tiny Architecture** & **mAP(60.50)** \\ \hline Original & 0.562309 \\ \hline Reduce the Resblock body 1 to 1 convolution operation & 0.170274 \\ \hline Reduce the Resblock body 2 to 1 convolution operation & 0.477842 \\ \hline Reduce the Resblock body 3 to 2 convolution operation & 0.548834 \\ \hline Reduce the Resblock body 2 and 3 to 3 convolution operation & 0.421765 \\ \hline \hline \end{tabular}
\end{table}
Table 6: mAP changes after the convolutional layer replaces the Resblock body.  
Figure 12: GPU vs. CPU only (YOLO v4-tiny).  
on the development of hardware-based quantization techniques designed to convert data from floating-point to fixed-point number formats. This endeavor is intended to expedite computation processes and reduce storage requirements.  
Simplified machine learning models and hardware-based quantization techniques can benefit scientists and researchers by providing efficient methods for processing data and reducing computational resources, thereby accelerating the pace of research in machine learning and related fields. Moreover, for society, these advancements can lead to the creation of more efficient and accurate systems for healthcare, security, and surveillance, contributing to improved safety measures, healthcare monitoring, and technological advancements that benefit society at large.  
We analyze the effects of the proposed quantization method to be used in YOLO models, which are computed on PC CPU-only (tagged as CPU) stations and CPU + GPU (tagged as GPU) stations. Figure 9 illustrates that computing performance with the proposed quantization method on CPU is lower by a time difference of 280 ms compared with GPU in YOLO-v4 models. Figure 10 shows that computing performance with the proposed quantization method on CPU is higher by a time difference of 153 ms compared with GPU in YOLO v4-tiny models. Following the above results, the proposed quantization method is an important mechanism for the computing of the YOLO-v4 model. In particular, the YOLO v4-tiny model with the proposed quantization method on CPU has excellent performance compared with GPU. This result proves that the proposed quantization method is suitable for use on the YOLO v4-tiny model in the embedded systems to create a smart IOT system. According to the evaluation results, we can replace Resblock body 2 and Resblock body 3 with convolutional layers to simplify the number of layers. In this way, the total reduced convolution operations are 5 layers and the computing performance of the YOLO v4-tiny model will be improved by up to 20%.  
Currently, performance testing relies primarily on C language for estimating the prediction time of images. In the future, the aim is to implement the entire framework onto a chip and establish a complete computing architecture. Regarding the utilization of YOLO-v4 in this paper, there is a hope to adjust the architecture to design a learning model that better suits the goals of this research with enhanced performance.