電腦科學與資訊工程

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。

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

「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。

Medication errors in patients are a global problem. They can negatively affect patients and be costly for hospitals and medical clinics. In 2021, a 28-year-old man with heart problems was admitted to a hospital in Porto Alegre. Due to a pharmacy error and insufficient monitoring in the administration, he received a dose 10 times higher than prescribed. This caused serious and probably irreversible damage to the patient. Reading the news and following the case in the media has encouraged research in scientific databases, searching for information and data on medication errors, as well as emerging technologies to reduce the occurrence of adverse medication events. Based on the findings of an English study that proved that errors occur at the drug prescription stage, the first stage of this research focused on drug dosage errors. The aim of this study is to develop an application based on artificial intelligence that can recognise these errors and help prevent them. The application uses a neural network to analyse prescriptions and warn of possible cases of incorrect dosage. The computer program was developed using a neural network and the drug dosage error recognition system using Python and Keras. The system was trained with 10 drugs and correct and incorrect dosage cases. A graphical interface was created to input and display new case data. Neural networks with different configurations were tested to obtain high accuracy with the training and validation data. A confusion matrix was used to assess the accuracy of the network for cases not used for training. The accuracy was approximately 96%, but problems were found in certain intervals. The errors are due to the need for more training, higher processing capacity and a cloud server. The results of the first stage of the research indicate the feasibility of using a neural network to recognise medication dosage errors and thus preventing the associated risks. Such a method could prevent cases like the one in Porto Alegre. Future studies could incorporate more types of drugs, allergies, drug interactions, pre-existing illnesses and other relevant factors into the system.

"Blindness keeps you from things, deafness keeps you from people" (Helen Keller) Wibrazz is a communication tool that can be placed inside sportswear. Two versions have been developed. The simpler one allows hearing-impaired footballers to compete in the league with other athletes. The referee is given an additional device to give a signal when he blows his whistle. The hearing-impaired footballer then senses the signal from the device he is wearing and knows that he must pay attention to the referee. The complex version speeds up communication between the coach and the players during training sessions. It allows the coach to send simple messages to his players using his smart device. The athlete senses the signal from the device and acts on what has been previously discussed (e.g. a long signal means, "Everyone come to me!") With over 70 million deaf people worldwide, and 2-4 out of 1000 people in the United States who are functionally deaf, this can affect an individual's mental and physical well-being, and it is therefore a pressing issue to provide these athletes with the means to develop their talents in a traditional team environment. In addition to the organisations within countries, the ICSD is present on the international stage. Their importance is demonstrated by the fact that the 2023 Deaf Football World Cup featured teams from countries such as the United States, Germany, England and Japan.

在現今社會，個人越來越依賴自主學習以提升技能和知識，而舞蹈學習尤其受到關注。然而，在沒有專業指導的情況下，學員往往難以掌握舞蹈動作的細節，也難以清楚地評估自己的表現與標準示範之間的差距。 為了應對這一挑戰，本研究利用人體姿態識別演算法OpenPose，捕捉舞蹈者的關節點。通過這項技術，針對舞蹈的標準動作、力度、流暢度等方面，成功地開發出一款自動評分系統。 通過人體姿態識別技術，我們能夠深入分析舞蹈動作的細節，讓學員與標準舞蹈動作進行比較，以確認學習上的差異。我們希望通過這項研究，學員能在沒有專業指導的情況下，利用網路平台創建更有效且有趣的自主學習環境。

本研究中，我們對模型逆向攻擊在語音辨識系統中的影響及風險進行深入分析。隨著Siri、Google Home等智能助理設備在日常生活中的廣泛使用，其語者辨識系統的安全隱患引起了我們的注意。本研究目的在於深入理解模型逆向攻擊的運作機制，並探討其對語音辨識系統的攻擊效果。我們透過實施多樣化的攻擊策略，對不同的模型架構和數據處理方法進行了評估，並對人聲與非人聲的數據集進行了攻擊效果的比較。此外，我們亦實現了基於差分隱私的防禦算法，在多數模型架構下達到接近50%的防禦效果，顯著提高攻擊代價。研究整體揭示了語音辨識系統在面對模型逆向攻擊時的脆弱性，並藉由實驗分析推論出可能的防禦策略，期待能通過策略來增強模型的安全性。

睡眠相關問題常見於現代緊張的社會，傳統睡眠分析方法需要腦電圖（EEG）、肌電圖（EMG）、眼電圖（EOG）等信號，量測複雜度高。本研究透過 Python 程式語言以深度學習和階層式投票機器學習方法，開發一套自動分析程式，僅透過心電圖（ECG）信號分析睡眠階段。並結合睡眠評估標準，製訂一可量化的睡眠品質評估表，提供臨床醫師判讀睡眠品質的指標。本研究的優點是僅透過一種信號便能準確、客觀、快速分析，且操作介面簡易。研究結果顯示，本研究清醒和睡眠狀態之辨識準確率高達約90%，與其他類似睡眠品質評估研究的論文比較，準確率高出10～17%，整體睡眠階段分析準確度高達87%。本研究方法未來可應用於臨床醫療，協助醫師做精準的患者睡眠品質診斷。

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。