隨著人工智慧和自然語言處理技術的發展，客戶端小型語言模型在各種應用中扮演著日益重要的角色，例如智能手機、物聯網設備和邊緣計算裝置等。這些小型 語言模型需要在有限的計算資源和存儲空間下實現高效的自然語言處理能力。在這種情況下，訓練資料量的大小對於客戶端小型語言模型的性能至關重要。 過去的研究已經表明，大規模的訓練資料對於建立高性能的語言模型至關重要， 但對於客戶端小型語言模型而言，資源的限制使得無法直接應用這些方法。因此，我們需要探討訓練資料量對於客戶端小型語言模型的影響，以找到最佳的平衡點。本研究的結果將有助於指導客戶端小型語言模型的設計和訓練，從而更好地滿足現實世界中的應用需求，同時充分利用有限的計算資源和存儲空間。

Over 30 million souls live in a world of darkness, a number greater than the populations of both Norway and Sweden combined. Every individual deserves the chance to embark on a journey across our magnificent blue planet. Yet, regrettably, little has been done to assist them. With this project, we’re lighting the way for the blind to explore our beautiful world independently, breaking free from dependence and embracing boundless horizons. In order to put our theory of the project into practice & explore the use of artificial intelligence & computer science, we started by collecting the required materials for our project such as micro-controllers, sensors, a pair of glasses, a laptop, and a miniature camera. Then we moved onto creating the project itself in which the digital software programmed onto the hardware plays the key-role, as the sensors and the camera will record the details and information from the surroundings and send it to the laptop for further processing. The camera would be the backbone of our project, as it will stream real-time footage to the laptop which will be analyzed by an open-source object detection model ‘YOLOv8’ for identifying objects. After finishing the base model of our project, we tested it in-front of objects such as toy cars, bikes, people, etc, and the results of the object-detection would be shown on the laptop. To observe this data, we created a device which has different modules and integrations for different functions. For example, we will use our camera and then stream it onto a laptop so the reading and the data can be processed on the laptop by AI using YOLOv8. As mentioned in the start, many people do not possess the ability to see, to assist them we have thought of this device which uses all readings and its analytical skills to analyze data and help them navigate, travel or simply, live a better life.

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

本研究開發了一種基於影像辨識的水果品質分類系統，採用最適合同時處理局部及全局資訊的 Swin-Transformer 模型，透過分析水果外觀來判斷其品質，並多次實驗以調整參數、訓練多個模型以辨識不同水果種類與品質。 使用者上傳水果影像後，系統即能識別水果種類和品質，並提供新鮮度建議與食用方式。此系統相較於傳統的檢測方法，具備非破壞式檢測優勢，且降低成本，適用於農業生產與消費市場。 模型測試結果顯示，水果類型分類器準確率為99.0%，蘋果品質分類器為85.04%，橘子品質分類器為97.67%，顯示該系統在分類與品質評估上具有較高的準確性，對水果檢測及提升食品管理有重要意義。

本研究旨在解決肢體障礙或單手使用者在操作電腦時所面臨的不便，設計一款融合鍵盤與滑鼠功能的單手操作裝置。透過自製PCB電路板、客製化鍵帽與鍵軸配置、3D列印人體工學外殼，以及整合滑鼠感測器與QMK韌體技術，實現單手即可同時完成鍵入與游標控制的操作方式。我們的實驗結果顯示，單手使用時相較於傳統鍵鼠組合，本裝置能大幅減少手腕移動的距離與次數，有效提升使用效率並降低疲勞感。產品設計不僅針對身障者，也適用於特定單手使用情境，具備良好的通用性與實用價值。未來將持續優化硬體與軟體，期望推廣至更廣泛的使用族群，實踐科技輔具的社會意義。

本研究透過結合局部敏感雜湊（Locality-Sensitive Hashing, LSH）與知識圖譜（Knowledge Graph, KG）以改善檢索增強生成技術（Retrieval-Augmented Generation, RAG）在檢索的時候難以在精確度與效率之間取得平衡的問題。透過使用LSH 將資料分桶，接著利用知識圖譜進一步篩選資料，以提高檢索的精確度與效率。 實驗結果顯示，結合知識圖譜與LSH後的系統，在精確度（precision）上可達到91%，相較於VectorRAG 的84%提升約8.33%，與GraphRAG的96%則僅有5.21%的差距。此外，本系統在檢索時間上較GraphRAG降低了95.38%。由此可以證實，透過結合LSH及知識圖譜能在保持高精確度的同時，顯著提高檢索效率。

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。

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.

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.

本研究試圖探索如何將資訊熵應用於Nerdle Maxi，Nerdle Maxi是一款數學益智遊戲，類似於Wordle，但要求玩家猜測出一個數學等式。遊戲的核心在於考驗玩家以有限的猜測次數，通過猜測獲得的回饋，縮小可能等式的範圍，最終猜出答案。此問題涉及對資訊的處理，讓我們聯想到資訊理論中關於不確定性的研究。根據Shannon的資訊理論，資訊熵可以用來衡量一個系統中不確定性，從而幫助我們找出減少不確定性的最佳策略。參考文獻後，我們發現有尚無人嘗試的作法，並實作、壓縮了猜測次數，保證我們能在六次內猜重。再將其與文獻中用在Wordle的做法融合後，壓縮平均猜測次數至3.38次。

延續去年的實驗做進一步的改善與測試，這次我們新增浮動式的消波塊觀測其消波的情形，實驗過程中，在無精密儀器下觀察波速與波高，是一件令人感到棘手之事，所幸目前影像分析技術大行其道，因此本實驗的觀測資料均由電腦影像分析輸出並整理。固定式消波塊的實驗結果蠻符合我們的預期，能以簡單的消波塊結構達到消波的功能，實驗數據顯示出原本岸邊的最大波高，可銳減至原有的三分之一左右，效果驚人，真的是消波～快！反之，浮動式消波塊的結果與預想有一段落差，最大原因是消波塊隨著液面升降，上方無足夠的水體產生足夠大的水壓，因此無法順利引導水流通過洩壓裝置，導致消波結果不如預期。

本研究開發了一個基於多項人工智慧技術的多模態失語症溝通系統，旨在提供失語症患者能提升表達自我和參與社交的能力的工具。系統以iOS App作為主體，接收環境影像、語音、唇語、手勢及情緒等多模態資訊後，透過大型語言模型產生完整敘述，再以語音合成播放。研究創新開發AphasiaSim-LLM生成高度擬真的模擬失語症語料，並以量化評估取代主觀評分，證實Gemini 2.5 Flash於語句還原表現最佳；此外亦建構了輕量級手勢辨識模型，並以ORB演算法優化關鍵幀提取；透過異步處理、FFmpeg影片取幀及輕量Flux文生圖模型等優化策略，最終完成能有效輔助失語症患者進行流暢溝通之系統。