castle的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列懶人包和總整理

50 Gems of South Yorkshire: The History & Heritage of the Most Iconic Places

為了解決castle的問題，作者Handley, Christine,Rotherham, Ian D. 這樣論述：

The county of South Yorkshire covers Barnsley, Doncaster, Rotherham and Sheffield districts. Although each are major urban centres with their own range of civic buildings, museums and galleries, the county also ranges from low-lying farmlands in the east to high moorlands on the edge of the Peak

District National Park. It has a rich and diverse landscape including ancient woodlands, rivers and wetlands alongside the legacy of its history as an industrial powerhouse built on coal, steel and heavy engineering, including Kelham Island in Sheffield, Wortley Top Forge and Silkstone Tramway in Ba

rnsley, and Clifton Museum in Rotherham. Away from the urban centres there are picturesque market towns and villages, such as Penistone, Tickhill, Bawtry, Bradfield, Thorpe Salvin and Tankersley which still serve rural farming communities, and stunning stately homes and parkland, for example at Went

worth Woodhouse, Canon Hall and Brodsworth Hall. Earlier medieval remains survive at Roche Abbey and Monk Bretton Priory, the castle keep at Conisbrough, the Lady Chapel on the bridge in central Rotherham and Ecclesfield Church, ’the Minster of the Moors’. There are also examples of the earliest hum

an settlement and activity at Wharncliffe Crags and Wincobank Hill in Sheffield. Other places have associations with historical figures such as the sculptor Francis Chantrey and William Bradford, one of the Pilgrim Fathers. In 50 Gems of South Yorkshire authors Christine Handley and Ian Rotherham ex

plore the many places and their history that make this part of northern England so special, including many lesser known gems.

castle進入發燒排行的影片

基於FPGA單晶片及像素趨勢車道線檢測法實現車道線感測系統之研究

為了解決castle的問題，作者廖國欽 這樣論述：

車輛自動駕駛系統目前主要是由自動跟車 (Adaptive Cruise Control, ACC) 以及車道偏離警示 (Lane Departure Warning System, LDWS) 兩大系統所組成。然而，自動跟車系統在實現過程中，由於必須藉由前方車輛實現車輛跟隨功能，因此若無前方車輛時則無法實現此功能。反觀車道偏離警示系統是依據車道線軌跡來幫助車輛保持於車道內，因此具備較高實用性。在此，本研究特別針對車道感測進行研究。由於傳統的車道線感測必須仰賴高效率的電腦才能有效地完成運算，為了克服傳統車道線辨識的缺點，本研究專注於如何將車道線辨識演算法簡化，並實現在單晶片上，達到低功耗之目的

。本研究以單一數位相機及單一現場可程式邏輯閘陣列 (Field Programmable Gate Array, FPGA) 實線以精簡之硬體電路達到即時於白天及黃昏情況下進行車道線辨識。透過像素趨勢車道檢測法 (Pixel Trend Lane Detection, PTLD) 擷取特徵，並將所得之車道位置利用線性回歸 (Linear Regression, LR) 決定車道線的軌跡，再透過左右車道回歸線取得車道的中心線，藉此引導車輛穩定行駛於車道中。另外，本研究還搭配語音辨識擴充模組 (DFR0177 Voice Recognition) 來辨識由Google Map路線規劃所傳出的語音指

令。根據辨識的結果，輸出行車指令給FPGA，以此決定車輛轉彎或直線行車路線模式。根據本研究之實驗結果，在使用每秒90張畫面播放速度以及640×480影像解析度情況下，只需11 ms即可擷取車道線特徵。而由左右車道線線性回歸決定出的中心線與實際影像中的中心線，誤差僅在5個像素以內。故本研究不管在運算速度以及準確度上均符合實際運用需求，未來可以有效幫助車輛穩定行駛於車道，達成自動駕駛之目的。

Captain America: The Ghost Army (Original Graphic Novel)

為了解決castle的問題，作者Scholastic 這樣論述：

From #1 New York Times bestselling author Alan Gratz ( Refugee, Ground Zero) comes an all-new, original Captain America graphic novel!In this thrilling historical adventure set during World War II, Steve Rogers (AKA Captain America) and his young sidekick, Bucky Barnes, encounter threat like none

they’ve ever seen -- a Ghost Army. The dead of this war and wars past are coming back to life, impervious to bullets, flames, or anything else the Allies can throw at them. The armies rise from the ground in the night and seem to disappear without a trace.How can Cap and Buck fight something that’s

already dead? And just what does the mysterious Baron Mordo--sitting in his castle atop nearby Wundagore Mountain--have to do with this?Award-winning, #1 New York Times bestselling author Alan Gratz merges the worlds of historical fiction and super hero comics in this one-of-a-kind graphic novel th

at is sure to be met with major enthusiasm from fans of all ages.

卷積神經網路結合模糊演算法應用於陰道鏡檢之 子宮頸上皮內贅瘤輔助診斷

為了解決castle的問題，作者蔡智宇 這樣論述：

子宮頸癌為台灣女性好發性高的癌症之一，也因在演變成癌症前之病變徵兆不明顯，所以需仰賴定期抹片及陰道鏡檢查。抹片檢查結果有偽陰性之可能，故陰道鏡檢查變得十分重要，藉此及早發現癌情病變症狀並加以治療。因此運用深度學習的方法經由陰道鏡檢查影像來判斷子宮頸上皮內贅瘤的病變程度，再藉由病患身體相關的危害因子和陰道鏡檢概況來輔助診斷出病變程度。 本論文是以南部某醫學中心婦產科的陰道鏡檢查圖像為研究數據，並利用卷積神經網路將陰道鏡檢影像進行特徵擷取之訓練與學習，再透過指定病例影像對其組織變化概況評比相似程度，最後將相似程度和病患身體數據透過模糊演算法評估強化其子宮頸上皮內增生分級判定評估。

本研究將陰道鏡圖分別依輕度子宮頸異常增生、中度子宮頸異常增生及子宮頸原位癌三類為訓練數據，並隨機抓取三類中的陰道鏡影像為測試數據。根據實驗結果顯示子宮頸上皮內贅瘤的驗證準確率約達85.38%，透過卷積神經網路模型的病徵分級預估分析，結合病患身體數據強化預測病變程度，協助醫師避免主觀經驗判斷，也提供給經驗不足的醫生作為診斷參考，以達到輔助診斷，給予病患最適合的處置方式。