電腦科學與資訊工程

Our vision aims to raise the quality of life and we had planned smart cities from scratch but what about the current cities the residents of Riyadh suffer from extreme traffic and spend hours circling the block searching for a open park which wastes time money and is bad for the environment. 30-50% of traffic is causing by not being able to park and due to Riyadh lack of proper city planning and radid increase in inhabitants especially after allowing women to drive and as the car being the main way of transportation finding a open park could be a nightmare for some. We have approached this problem from the technological perspective by developing a free application for Riyadhs inhabitants that's main goal is to navigate each driver from their current location to the best open park possible in the shortest time possible but what dirstinguishes us from similar apps in the literature is that we provide the time of departure for each park as well as the ability to book suck parks even if it is ahead of time via a interactive live map. The technology's that we used are the cellar censor that will track the users location and the ultrasonic sensor to track the occupancy of the parking in case the driver doesn't have the app but in which case will case will not be able to provide booking features. We have struggled in the lack of expertise and experience and in motivating the drivers to input correct data about there time of departure we also didn't have enough time to validate our project For future work we will validate our project and we plan on making the detection of the time of departure automatic as well as vobering all kinds of parks. We plan on expanding the scope of target users to include institutes as well because with time the app will have collected enough data to help institutions provide better parking such as ruch hours parking scope percentage of booked parking etc we also plan on benefiting more from the cellular secsor to link data with the persons phone like certain access to private parks like disabled parking or home parking or private hotel offices parking etc

發展安全且可靠的身份辨識技術是當今的重要議題，而指靜脈因其高安全性及難以偽造特性成為我們的主題。本研究提出一種基於Transformer模型架構的指靜脈辨識模型稱為GLA-FD，旨在解決現有技術對指靜脈影像特徵表示與提取的局限性。透過開發特徵解耦與重建模組(FDRM)，模型能夠有效區分指靜脈的背景資訊與紋理特徵，並將其重新組合以提升辨識準確度。此外，本研究開發的全域-局部注意力模組(GLAM)能同時捕捉影像的全域與局部特徵，進一步強化模型對指靜脈特徵的理解。GLA-FD在FV-USM、PLUSVein-FV3、MMCBNU-6000、UTFVP、NUPT-FPV 資料集中的正確辨識率(CIR)達到100%、98.47%、99.75%、96.11%、99.82%，展現卓越的穩定性與泛化能力。此外，本模型在處理不同年齡層、國籍與影像模糊度的資料下，仍能保持高辨識準確度，顯示其在需要高安全性辨識的應用場景中具備廣泛的實用性。

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.

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.

現今醫療中，黃疸的早期偵測對肝臟疾病的預防與治療至關重要，但多數人難以在症狀輕微時察覺。我們希望藉由智慧手機影像結合機器學習進行黃疸檢測，提升民眾自我監測的能力。Su 等人（2021）曾使用深度學習和機器學習進行黃疸預測，但其方法依賴專業色卡進行色彩校正，成本高且限制應用範圍。本研究提出以白平衡演算法中的白色補丁法與灰界演算法，搭配深度學習模型 DCCNM1和2 取代色卡，提升黃疸檢測的普及性與便利性。經黃疸偵測效果評估顯示，DCCNM2 在無色卡模型中表現最佳，雖然各指標略低於色卡校正，但其展現出優異的穩定性和準確性，證明其作為無色卡黃疸篩檢方案的可行性。本方法將能提供便捷的居家黃疸檢測途徑，尤其對偏鄉地區居民而言，不僅提升早期發現的機會，還能有效減輕醫護人員的負擔，推動大眾健康管理。

「在格狀平面中用矩形以互不重疊的方式鋪滿(2D rectangle tiling problem)」為一NP-complete問題(Dani`ele Beauquier et al ,1995)，目前多項式時間只能求出盡可能覆蓋最大面積的近似解。本研究所創的階梯演算法 stair algorithm 透過改變動態規劃紀錄狀態的方式，使狀態數大幅減少，進而改善求準確解的時間複雜度，也成功證明此演算法的正確性。本研究的演算法可被應用於平行計算中的負載平衡、積體電路設計等方面。隨後，本研究寫了一個互動展示品清楚呈現此演算法的功能。且以階梯演算法成功檢驗並比較 RTILE PROBLEM 的 7/3-approximation algorithm (Krzysztof Lorys and Katarzyna E. Paluch,2000 [4]) 與 11/5-approximation algorithm (Piotr Berman et al,2001[7])進行比較與分析。

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.

在臺灣手語辨識，先前研究所使用的監督式學習需要大量標記樣本而限制可辨識詞彙量。為此，本研究借鑒自然語言處理領域中BERT 的遮罩想法，將未標記手語影片隨機遮蓋部分幀數，並讓模型學習預測被遮蓋的幀數以學習臺灣手語的特徵，並透過遷移學習來訓練辨識模型，此作法可克服現有臺灣手語資料缺少的問題。經過實驗，本研究訓練之詞彙辨識模型達成了242 個詞彙量，94.8%的準確率。 此外，先前研究皆未在手語句子翻譯上有成果。因此本研究基於預訓練模型，整合設計手語翻譯的系統，實驗中，系統在100 個句子的翻譯表現達到88%的準，且BLEU-4 分數取得20.98，證明自監督學習的方式在手語辨識、翻譯上是有效的。並展現出樣本需求少與辨識詞彙量可輕易擴大的潛力。

臺灣的許多人要使用電腦打字時皆會選擇「注音輸入法」作為輸入的方法。然而要使用注音輸入法輸入中文時若無切換輸入法則有可能會誤用到英文輸入法。如要輸入「今天天氣好」五字，使用英文輸入法輸入時會輸出「rup wu0 wu0 fu4cl3」。這種文字不易理解。本研究的目的即為研究將未切換到注音輸入法而打出的英數符號混和字元翻譯為漢字的方法。 本研究使用「PTT 中文語料」與「維基百科中文資料庫」訓練 GRU、BiGRU、LSTM 和 Transformer 以及計算維特比演算法，並與 Google 輸入工具的進行比較。整體來看以 PTT 中文語料計算的維特比演算法的 BLEU4 分數最高，在準確率以及 BLEU4 的評分皆高於Google 輸入工具，分數分別為 0.94 與 88.3 分。 本研究之成果在應用方面極廣，可應用於線上翻譯或聊天軟體的即時翻譯。本研究使用之程式碼開源於 GitHub 頁面，除了可讓使用者下載使用外，使用者也可訓練自己的模型。