Biosensors and Bioelectronics (2024/04)

電機工程系 蔡宗亨 ( 通訊作者) / JIF:10.7 / Rank:0.028302 (3/106) CHEMISTRY, ANALYTICAL

Herein we developed an integrated microfluidic system (IMS) for rapid quantification of four cardiovascular diseases biomarkers, including N-terminal pro B-type natriuretic peptide (NT-proBNP), fibrinogen, cardiac troponin I (cTnI), and C-reactive protein (CRP)- via aptamer-coated interdigitated electrodes (IDE) with integrated circuits (IC) and a self-driven IMS for sample treatment.

IEEE Transactions on Wireless Communications (2024/05)

電機工程系 王蒞君 (通訊作者,共同作者(作者順序 4 ) ) / JIF:10.451 / Rank:13/352 = 0.036932 ENGINEERING, ELECTRICAL & ELECTRONIC

本研究探討在不同技術（如可重構智能表面（RIS）和解碼轉發（DF）中繼）下，URLLC（超可靠低延遲通信）和eMBB（增強型行動寬頻）多工系統的延遲和積壓行為。研究提出了基於鞅理論的分析方法，透過將到達和服務過程轉換為指數形式的矩生成函數，評估系統的延遲和積壓界限，並推導了兩跳異質網絡中的閉合形式表達式。 數值結果顯示，該方法在精確性和有效性上優於經典隨機網絡演算法，揭示了RIS和DF中繼技術對系統性能的影響。這些發現提供了新的視角來分析和改進多工通訊系統，具有重要意義。

IEEE Transactions on Power Electronics (2024/07)

電子所 陳柏宏 (通訊作者) / JIF:6.7 / Rank:39/352

本論文提出閘極電壓偵測的技術，僅需偵測碳化矽功率元件的閘極電壓即可對其進行主動式閘極驅動，使閘極驅動器的偵測與控制電路簡單化，讓偵測電路、控制器、輸出級得以整合在單一晶片上，並且在驅動不同型號的碳化矽功率元件時都可以達成主動式閘極驅動，降低功率元件切換過程中電壓/電流過衝現象和能量損耗之間的權衡關係，以及高電壓/電流斜率帶來的電磁干擾。

Biosensors and Bioelectronics (2024/04 available online)

生醫所 李博仁 (通訊作者) / JIF:10.7 / Rank:3/77=3.8%

男性不育症是全球普遍的生殖挑戰，主要歸因於精液質量的下降。為了解決這個問題，輔助生殖技術中對精子篩選的關注度不斷增加。本研究介紹了一項突破性發展，即一種熱趨性和流趨性微流體（TRMC）裝置，能在短短15分鐘內高效篩選出運動精子。TRMC裝置模擬輸卵管的自然精子篩選機制，選擇運動性和DNA完整性優越的精子。實驗結果顯示，在溫度從38°C下降到35°C後，篩選後的前向運動精子百分比顯著提高，從3.90%激增至96.11%。值得注意的是，精子運動性提高了69%。TRMC裝置表現出令人欽佩的回收率，達到60.93%，超過了現行的臨床要求。此外，篩選後的精子顯示出DNA碎片指數顯著降低至6.94%，表示DNA完整性提高了90%。這一突破性進展使TRMC裝置成為體外受精（IVF）和卵胞漿內單精子注射（ICSI）應用的理想選擇，為解決男性不育問題提供了有希望的解決方案。

IEEE Journal of Translational Engineering in Health and Medicine (2024/03/21)

生醫所 孫家偉 (通訊作者) / JIF:3.7 / Rank:46/122=37.7%

Osteoporosis is a prevalent chronic disease worldwide, particularly affecting the aging population. The gold standard diagnostic tool for osteoporosis is Dual-energy X-ray Absorptiometry (DXA). However, the expensive cost of the DXA machine and the need for skilled professionals to operate it restrict its accessibility to the general public. This paper builds upon previous research and proposes a novel approach for rapidly screening bone density. The method involves utilizing near-infrared light to capture local body information within the human body. Deep learning techniques are employed to analyze the obtained data and extract meaningful insights related to bone density. Our initial prediction, utilizing multi-linear regression, demonstrated a strong correlation (r = 0.98, p-value = 0.003**) with the measured Bone Mineral Density (BMD) obtained from Dual-energy X-ray Absorptiometry (DXA). This indicates a highly significant relationship between the predicted values and the actual BMD measurements. A deep learning-based algorithm is applied to analyze the underlying information further to predict bone density at the wrist, hip, and spine. The prediction of bone densities in the hip and spine holds significant importance due to their status as gold-standard sites for assessing an individual’s bone density. Our prediction rate had an error margin below 10% for the wrist and below 20% for the hip and spine bone density.

Biomedical Optics Express (2024/04)

生醫所 孫家偉 (通訊作者) / JIF:2.9 / Rank:60/204=29.4%

In neurosurgery, accurately identifying brain tumor tissue is vital for reducing recurrence. Current imaging techniques have limitations, prompting the exploration of alternative methods. This study validated a binary hierarchical classification of brain tissues: normal tissue, primary central nervous system lymphoma (PCNSL), high-grade glioma (HGG), and low-grade glioma (LGG) using transfer learning. Tumor specimens were measured with optical coherence tomography (OCT), and a MobileNetV2 pre-trained model was employed for classification. Surgeons could optimize predictions based on experience. The model showed robust classification and promising clinical value. A dynamic t-SNE visualized its performance, offering a new approach to neurosurgical decision-making regarding brain tumors.

Nature Communications (2024/04)

光電工程學系 陳姿伶 (第一作者) / JIF:14.7 / Rank:8/134

我們開發了一種簡單的方法，在無需奈米製程的情況下，利用特殊圓二色性（Apparent Circular Dichorism）薄膜實現了平面Fabry–Pérot微腔中左右圓偏振光的非對稱穿透。這一創新技術打破了傳統需要法拉第旋轉器或藉由新穎材料反轉鏡面特性的限制，並顯著提高了腔模態的非對稱性，將來有望應用於自旋電子學、極化子學和旋性雷射光等領域。

Journal of Lightwave Technology (2024/06/01)

光電工程學系 鄒志偉 (第一作者,通訊作者) / JIF:4.1(2023) / Rank:26/119 (Q1 @ OPTICS)

該論文作為特邀教程 Invited Tutorial 發表在 IEEE/OPTICA Journal of Lightwave Technology (2024) [1]，總結了我們在光無線通訊 (OWC) 和感測領域的最新進展和未來展望。為滿足無線通訊頻寬的持續增長，作為射頻通訊補充技術的OWC因而應運而生。OWC支援了點對點 (P2P)、點對多點 (P2MP)，囊括了陸地、無人機、衛星、水下通訊等，提供更廣的覆蓋範圍及更大的容量傳輸。 我們在OWC領域所取得的成就包括如下： (1) 展示了基於空間光調變器 (SLM) 的OWC系統，該控系統使用非正交多工 (NOMA) 實現光束掃描 (Beam Steering) 和同時多人通訊 (圖一)。 (2) 提出多種的滾動式快門 (Rolling Shutter) 解碼演算法，實現可以應用於無人機的高性能OWC (圖二)。 (3) 展示了基於接收訊號強度 (RSS) 的可見光定位系統 (VLP)，並利用 AI / ML 演算法，實現精度 < 10 cm之室內精準定位 (圖三)。

IEEE Transactions on Nanotechnology (2024/04)

電信所 李義明 (通訊作者) / JIF:2.1 / Rank:54.5% (192/352 in ENGINEERING, ELECTRICAL & ELECTRONIC in SCIE edition)

本研究主要研究了製程變異效應以及金屬閘極功函數擾動對於三維度垂直堆疊全閘極矽互補場效電晶體（CFET）的直流/交流特性擾動的影響。結果顯示製程變異效應對元件對於截止電流的變異性特別顯著。由於鰭式底部寄生通道之故，與N型電晶體相比，P型元件的截止電流擾動超過200%。當金屬閘極為非晶型金屬晶粒時其引起的特性變異性最小。人工神經網路模型判定係數的估計值顯示建立的模型可正確掌握資料主要資訊。此人工神經網路模型可用於下世代新興CFET積體電路設計、模擬與最佳化。

Journal of NeuroEngineering and Rehabilitation (2024/06)

電控工程研究所 柯立偉 (通訊作者) / JIF:5.2 / Rank:4/169 = top 2.3% (Q1) in REHABILITATION

This is the first study to investigate the temporally synchronized activities of gait, MRCP, PFCC, and bipedal classification. In post-stroke rehabilitation, functional connectivity (FC), motor-related cortical potential (MRCP), and gait activities are common measures of recovery outcomes. However, the interrelationship between FC, MRCP, gait activities, and bipedal distinguishability has yet to be investigated. This study proposed a classification model based on functional connectivity to distinguish bipedal activity that demonstrated promising accuracy in healthy subjects. Ten participants were equipped with EEG devices and inertial measurement units (IMUs) while performing lower limb motor preparation (MP) and motor execution (ME) tasks. MRCP, FCs, and bipedal distinguishability were extracted from the EEG signals, while the change in knee degree during the ME phase was calculated from the gait data. FCs were analyzed with pairwise Pearson’s correlation, and the brain-wide FC was fed into a support vector machine (SVM) for bipedal classification. Parietal–frontocentral connectivity (PFCC) disconnection and MRCP desynchronization were related to the MP and ME phases, respectively. Hemiplegic limb movement exhibited higher PFCC strength than nonhemiplegic limb movement. Bipedal classification had a short-lived peak of 75.1% in the pre-movement phase. These results contribute to a better understanding of the neurophysiological functions during motor tasks concerning localized MRCP and nonlocalized FC activities. The difference in PFCCs between both limbs could be a marker for understanding the motor function of the brain of post-stroke patients. This study discovered that PFCCs are temporally dependent on lower limb gait movement and MRCP. The PFCCs are also related to post-stroke patients' lower limb motor performance. The detection of motor intentions allows the development of bipedal brain-controlled exoskeletons to rehabilitate the lower limbs.

IEEE Transactions on Neural Systems and Rehabilitation Engineering (2024/05)

電控工程研究所 柯立偉 (通訊作者) / JIF:4.8 / Rank:5/169 = top 2.3% (Q1) in REHABILITATION

Sleep is vital to our daily activity. Lack of proper sleep can impair functionality and overall health. While stress is known for its detrimental impact on sleep quality, the precise effect of pre-sleep stress on subsequent sleep structure remains unknown. This study introduced a novel approach to study the pre-sleep stress effect on sleep structure, specifically slow-wave sleep (SWS) deficiency. To achieve this, we selected forehead resting EEG immediately before and upon sleep onset to extract stress-related neurological markers through power spectra and entropy analysis. These markers include beta/delta correlation, alpha asymmetry, fuzzy entropy (FuzzEn) and spectral entropy (SpEn). Fifteen subjects were included in this study. Our results showed that subjects lacking SWS often exhibited signs of stress in EEG, such as an increased beta/delta correlation, higher alpha asymmetry, and increased FuzzEn in frontal EEG. Conversely, individuals with ample SWS displayed a weak beta/delta correlation and reduced FuzzEn. Finally, we employed several supervised learning models and found that the selected neurological markers can predict subsequent SWS deficiency. Our investigation demonstrated that the classifiers could effectively predict varying levels of slow-wave sleep (SWS) from pre-sleep EEG segments, achieving a mean balanced accuracy surpassing 0.75. The SMOTE-Tomek resampling method could improve the performance to 0.77. This study suggests that stress-related neurological markers derived from pre-sleep EEG can effectively predict SWS deficiency. Such information can be integrated with existing sleep-improving techniques to provide a personalized sleep forecasting and improvement solution.