電腦科學與資訊工程

ChatGPT 問世後，許多問題皆已能由其回答。然而在邏輯方面的問題，ChatGPT 免費版有著明顯的不足，時常出現似是而非的答案。為了解決此問題，本研究利用連線 ChatGPT API，使用四種 Chain-of-thought prompt 的方式，將問題分解成若干個子問題，利用子問題們提供原問題較多的資訊以降低在解題過程出現的錯誤。最後分析四種方法的優劣，四種方法各有優缺，並無一方法於所有題目皆正確率最高。

網際網路高速發展時代，為解決身處於無蜂窩訊號涵蓋範圍下，可提供通訊服務及緊急呼叫的手持無線電及衛星服務昂貴不普及，因此本研究採用遠距離、低成本、ISM免執照頻段的LoRa物聯網無線電技術，透過其啁啾調頻技術(Chirp Spread Spectrum)、高鏈路預算等優勢，以「人人皆為基地台，亦為客戶端」理念，設計P2P去中心化網狀網路(Mesh Net work)節點傳輸協定和低廉可負擔的通訊裝置，應用於SOS緊急救難呼叫、短文通訊、定位回報等。在固有網路系統不可用時也得以獨立組網，無須高昂的修建維護成本。也可透過搜救無人機、高空氣球等，快速部署Gateway於高山極地、無訊號區、第三世界國家等情境場所，促進人類福祉達成聯合國SDGs永續理念，未來將技術結合低軌道衛星，較其他衛星技術更經濟、環保，並覆蓋全球範圍達成無死角通訊。

Stroke is a very common disease, almost a national disease. In terms of stroke frequency, 匈牙利 ranks second in the world. Every year, 40-50 thousand people become paralyzed or permanently injured as a result of cerebrovascular disorders. This number is three to four times higher than in developed countries. Almost every Hungarian family is affected! Of course, saving the life of someone who has a stroke is the most important thing, but rehabilitation is also very important, since only with the help of a physiotherapist will the patient be able to live a full life.

A custom Local Positioning System designed for applications in supply chain logistics, inventory tracking, collision avoidance, and precision systems. Responsive UI allows for real-time management and dynamic parameter adjustments.

AI (Artificial Intelligence) technology has developed very rapidly in recent years, to the point where it can make fake videos or photos called "deepfake''. According to Sumsub Identity Fraud Report 2023 just in the past year, in the APAC region the number of deepfakes has grown 1530%, in the philippines an astounding 4500% and in 馬來西亞 a 1000% increase, these numbers will continue to rise without a proper defense against them, With this rapidly developing technology, there are several threats from misuse deepfake, namely making fraud via video calls, fake videos to blame innocent people, and so on. Therefore, in this research project, an algorithm architecture will be created, namely a system and method used to detect "deepfake" images. The architecture of this algorithm involves convolution functions, neural networks, convolutional neural networks, data normalization functions, namely ReLu and SoftMax, and pooling. This architecture will then be trained over and over with 140,000 scrambled images, which then will make the architecture ready to be used. By researching and combining this algorithm architecture, a system is produced without a cost and with a final result of up to 90% accuracy and detection of 32 images faster than a human can blink.

This project had one purpose: creating almost unbreakable encryption by breaking a Caesar – and Vigenère Cipher and getting familiar with how they work. Created a program to encrypt and decrypt messages with a Caesar Cipher and Vigenère Cipher encryption. Breaking these encryptions in these programs will help to identify the factors that contribute to strong and weak encryption systems. A program was created to encrypt messages using Caesar Cipher with a key from 1 to 25 and decrypt messages without knowing the original key by doing different types of “attacks” on the system: a brute force and frequency analysis attack. Created another program to encrypt messages using Vigenère Cipher with a keyword or keyphrase and decrypted messages whilst knowing that original keyword. Tested and compared the two different cyphers when being attacked. This helped identify factors that influenced the strength of encryption and identified the advantages and disadvantages of each Cipher as well as the weaknesses in each attack. Through testing and breaking a Caesar and Vigenère Cipher successfully, multiple factors were identified that influenced the strength of the encryption system. These were used to ensure the new encryption created will be as strong as can be. Comparing the success rate of the different attacks on each Cipher, the similarities, weaknesses and strengths in the Brute Force and Frequency Analysis attacks were found.

在今日，電路板被廣泛運用各大領域，而電路板上錫球在焊接的過程中有可能會產生瑕疵，因此我們需要找到一個方式來檢驗電路板上錫球是否有瑕疵。有很多方式可以進行，例如: ART (Algebraic Reconstruction Technique)、SART (Simultaneous Algebraic Reconstruction Technique)和 FBP（Filtered Back Projection），ART、SART 為疊代型的方法，疊代型比較準確但花費較多時間，與之相比，FBP 利用反投影法，能節省許多時間。本實驗嘗試利用 FBP 得到電路板上錫球的269張切片圖，利用 ImageJ 將所有圖片疊起來以得到3D 圖並探討錫球是否有缺陷，之後採用不同的種類濾波(filter)進行測試並利用 ImageJ 分析比較各 filter 的特色，高通濾波器如 Ramp filter 主要強化圖像邊緣，低通濾波器如 Hann filter 主要強化圖像中低頻的部分，使影像對比度變高，分析比較後嘗試自己建立 filter，此 filter 結合了 high pass filter 與low pass filter 的優點，影像的對比度變高的同時，錫球的輪廓也更明顯。

影像對位(Image Registration)為重要影像處理工具，目前已經廣泛運用各個領域，從影像醫學偵測與診斷、工業製造、品管檢測、臉部辨識都是電腦對位的應用。本次研究將探討機器人影像對位，分別透過不同影像感測器來擷取外部影像資訊，透過對位系統進行目標 物瞄準。在準確性分析研究發現不論是單一定點或是連續三點分析 結果顯示感應器的Cpk 未達1.33。此外，分析過程中發現Vision sensor 的 Cpk 值高於 GPS，顯示 Vision sensor 在影像對位準確性表現性上優於 GPS。在時效性分析方面，感應器的種類與底盤齒輪組對於影像對位時間無顯著差異，在連續三點實驗發現時效性與偵測的地點達 顯著差異。研究結果顯示在未來自駕車的設計可以將影像對位與辨 識功能結合，透過感測器觀察外界影像資訊，結合機器學習與演算法， 提高辨識率及時提供行車資訊，提高道路安全與成員安全。

汽、機車呼嘯而過的排氣噪音經常造成周遭民眾的困擾，為杜絕這種狀況，環保署於2021 年開始推動「聲音照相科技執法」，透過包含魚眼攝影機、車牌辨識攝影機等設備的整合，來紀錄行經車輛產生的噪音是否超過管制標準，不過此系統目前仍存在一些限制，如:當多台車輛同時經過時無法辨別確切的噪音來源，而需要人工判定。本研究旨在利用聲源定位原理結合影像辨識技術，實作出一套能夠追蹤聲源（噪音）並辨識聲音來源（車輛）的聲音照相系統，期日後能實際應用並解決道路上多車輛時無法辨識噪音來源的困擾。研究中提出一套能透過遠端控制程式，將收音裝置、攝影設備和資料分析軟體等各項軟硬體設備，串聯成自動化追蹤聲源的系統設計方案。

Throughout our generations, a traditional system has been implemented for registering student attendance, in which the teacher is responsible for carrying out said activity, investing an average time of 15 to 20 minutes, which are part of the time of class. The objective of this project is to optimize this process, thus achieving effective class times, promoting the use of digital tools and innovation in teaching practice, in addition to generating security and confidence in tutors through the use of a service of message, which will notify the student's attendance in real time. Through a survey of the teaching staff of the CONALEP 338 Córdoba campus, it was detected that each teacher has academic loads equivalent to 3 to 5 modules per day, with an average of more than 40 students assigned to each module. Based on this information, the use of technological tools will be promoted and this process of teaching practice will be innovated with zero costs.