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European Police Forces and Law Enforcement in the First World War

為了解決Rear light的問題，作者 這樣論述：

This book offers a global history of civilian, military and gendarmerie-style policing around the First World War. Whilst many aspects of the Great War have been revisited in light of the centenary, and in spite of the recent growth of modern policing history, the role and fate of police forces in t

he conflict has been largely forgotten. Yet the war affected all European and extra-European police forces. Despite their diversity, all were confronted with transnational factors and forms of disorder, and suffered generally from mass-conscription. During the conflict, societies and states were fac

ed with a crisis situation of unprecedented magnitude with mass mechanised killing on the battle field, and starvation, occupation, destruction, and in some cases even revolution, on the home front. Based on a wide geographical and chronological scope - from the late nineteenth century to the interw

ar years - this collection of essays explores the policing of European belligerent countries, alongside their empires, and neutral countries. The book's approach crosses traditional boundaries between neutral and belligerent nations, centres and peripheries, and frontline and rear areas. It focuses

on the involvement and wartime transformations of these law-enforcement forces, thus highlighting underlying changes in police organisation, identity and practices across this period. Jonas Campion is Visiting Lecturer at UCLouvain, Belgium, Assistant Lecturer at the University of Lille, France,

and Researcher at the IRHIS institute, France. Laurent López is Research and Teaching Fellow at the French Defence History Service, Vincennes, France. Guillaume Payen is Lecturer at Sorbonne University and Researcher at the Centre d’Histoire du XIXè Siècle, France.

Rear light進入發燒排行的影片

基於語意之輪廓表示法及全連結捲積類神經網路之單晶片多車輛辨識系統

為了解決Rear light的問題，作者徐雋航 這樣論述：

鑒於現今智慧車輛發展迅速，前方車輛辨識及車距檢測為先進駕駛輔助系統 (Advanced Driver Assistance Systems, ADAS) 設計中相當重要的一環，此項技術通常藉由攝影鏡頭擷取前方影像，並透過影像辨識技術來判斷前方是否存在車輛、障礙物等等，進而控制車輛減速以保持安全距離。而這些複雜的圖形辨識技術往往需要透過高功耗之大型運算系統來實現，並且，若將傳統電腦安裝於車內常需要克服體積過大、耐震性不佳等缺點。因此，本研究專注於如何將車輛辨識及車距檢測演算法實現於單晶片，以達到高性能、低功耗，以及體積小之目的。為實現前方車輛辨識及車距檢測，本研究透過單一彩色相機模組收集前方影

像資訊，並於單一現場可程式邏輯閘陣列 (Field Programmable Gate Array, FPGA) 晶片中以最精簡之硬體電路實現白平衡 (White Balance)、影像對比度強化技術 (Image Contrast Technique)、物體邊緣檢測、利用基於模糊語意影像描述 (Semantics-based Vague Image Representation, SVIR) 改良之基於語義之輪廓表示法 (Semantic-based Contour Representation, SCR) 特徵表達物體、再透過不同的卷積核 (Convolution Kernel) 重釋SC

R特徵並交由全連接類神經網路(Fully Connected Neural Network, FCN) 進行車輛辨識。最後，以多個邊界框 (Bounding Box) 同時檢測前方多台車輛，達到單頁多目標辨識 (Single Shot MultiBox Detector，SSD) 之功能，而邊界框之座標可以透視法 (Perspective View) 計算前車相對距離。根據本研究之實驗結果，在相機以每秒90張影像攝影速度以及影像解析度在640×480像素的條件下，本研究僅須3.61us即可完成單台車輛辨識，車輛辨識率可達到94%，且車輛與非車輛至少保持38%以上之分離度，有效減少感測錯誤的情況

發生。因此，實現一真正高性能、低功耗以及體積小之前方車輛辨識晶片。

The Devil Is a Part-timer!, Light Novel

為了解決Rear light的問題，作者Wagahara, Satoshi/ Oniku (COR) 這樣論述：

Do you want fries with your hellfire?Shenanigans ensue in this collection of short stories about Maou and company Emilia bravely crossed through space and time from the world of Ente Isla in pursuit of the Devil-but all her searching in this strange land of "Tokyo" has turned up nothing. Then, af

ter sneaking in through an open window to a luxury apartment in Eifukucho, the Hero meets a human for the first time. Later on, Emilia and Chiho strike up a friendship over shared sushi, while MgRonald store manager Kisaki gets a blast from the past when a childhood "frenemy" shows up And when the

Devil goes out to work, he discovers something is wrong with his rear...In other words, just another day in Tokyo

以主路徑分析法探討智慧交通系統之學術發展

為了解決Rear light的問題，作者許家瑜 這樣論述：

日新月異的科技帶給世人進步的生活，隨處可見的智慧裝置、智慧系統，人們對於「雲端」已不再陌生。由世界各地所發起的智慧城市，乃至各國政府無不積極打造的智慧國家，顯示生活與資訊科技環環相扣、密不可分。而在智慧城市中，「智慧交通」一直是為世人關心、與人們最息息相關的，逐漸發展成為「智慧議題」中的一門顯學。本研究透過Scopus文獻資料庫進行文獻搜索，檢索關鍵詞為“Smart Transportation System” or “Intelligent Transportation System”。將搜索結果之所有文獻整理過後，透過Main Path找出智慧交通系統的發展軌跡與歷年較具代表性之文獻，並

接著以Global Main Path、Key-route Main Path進行研究，最後使用 Pajek將路徑圖像化，再透過集群分析梳理出智慧交通系統中主要探討的領域。 透過主路徑分析法可得知智慧交通系統之發展軌跡，並可從路徑上發現發展過程中關鍵的學術文獻；而透過集群分析可以得知智慧交通系統中不同的應用領域，共二十群，取前五大群如下：短期交通流量預測、全球定位系統與地圖匹配法、車載隨意行動網路、以動態規劃進行車輛能源優化、自駕車之自動控制系統與防碰撞機制。