160 research outputs found
Three-dimensional and nonlinear metamaterials at terahertz frequencies
No full textThesis (Ph.D.)--Boston UniversityPLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at [email protected]. Thank you.During the past decade metamaterials have emerged as a unifying theme across a large swath of the electromagnetic spectrum. Scale invariance of the underlying equations enables translation of phenomena realized in one region of the spectrum to others. To date, the majority of metamaterials studies focus in the microwave, and infrared to visible regimes, while leaving a span in between. This region, called terahertz regime from 0.3 to 10 terahertz, is of particular interest because of its increasing technological importance, which includes as examples, security screening and embedded imaging. This lag in development of metamaterials is due to enormous challenges with two most important being fabrication strategies and available terahertz sources and detectors. This, in turn, restricts multifunctional responses of metamaterials that are particularly important for implementing dynamic devices at terahertz frequencies. The object of this thesis is to describe our progress on developing a fabrication strategy to construct three-dimensional metamaterials and taking advantage of recent advances in high field terahertz generation to realize nonlinear metamaterials.
The first part of this thesis details the developed multilayer electroplating technique for fabrication of stand-up metamaterials on rigid and conformally flexible substrate. The strong resonance resulting from the coupling to the incident magnetic field indicates a significant magnetic response and negative permeability of metamaterials at terahertz frequencies. Extending our fabrication technique, we also experimentally demonstrated broadband three-dimensional metamaterials with dynamic tuning range over 30%. Through photoexcitation of active medium of silicon that is incorporated in the metamaterial active region, the resonant frequency can be effectively tuned. Next, with the judicious incorporation of field enhancement of metamaterials with state-of-the-art technique of high field terahertz generation, field-dependent nonlinear metamaterials fabricated on semiconductors are presented. Modeling and numerical simulations indicate that the origin of nonlinearity arises from nonequilibrium carrier transport within the capacitive regions of resonators. With increasing field, the retrieved off-resonance permittivity exhibits tinting between negative and positive values. Our innovative work opens up numerous possibilities for nonlinear metamaterials at terahertz frequencies. This thesis provides a route forward to create novel metamaterial-based devices for sensing and manipulating electromagnetic waves.2999-01-0
Guang bai chuan xue hai: [shi ji]. v.1
No full text[V.1]. 甲集. 聖學範圍圖說 / 嶽元聲 -- 戊申立春考 / [邢雲路] -- 正朔考 / 魏了翁 -- 龍興慈記 / 王文祿 -- 在田錄 / 張定 -- 逐鹿記 / 王褘 -- 東朝紀 / 王泌 -- 壟起雜事 / 楊儀 -- 椒宮舊事 / 王達 -- 造邦賢勳錄畧 / 王褘 -- [v.2]. 掾曹名臣錄 / 王凝齋 -- 明良錄畧 / 沈士謙 -- 乙集. 聖君初政記 / 沈文 -- 致身錄 / 史仲彬 -- 殉身錄 / 亡名氏 -- 備遺錄 / 張芹 -- [v.3.] 平夏錄 / 黃標 -- 復辟錄 / 亡名氏 -- 使高麗錄 / 徐兢 -- 玉堂漫筆 / 陸深 -- 金臺紀聞 / 陸深 -- 制府雜錄 / 楊一清 -- [v.4]. 杜陽雜編 : [三卷] / 蘇鶚 -- 丙集. 翦勝野聞 / 徐禎卿 -- [v.5]. 觚不觚錄 / 王世貞 -- 溪山餘話 / 陸深 -- 清暑筆談 / 陸樹聲 -- 吳中故語 / 楊循吉 -- 甲乙剩言 / 胡應麟 -- 三朝野史 / 吳萊 -- [v.6]. 熙朝樂事 / 田汝成 -- 委巷叢談 / 田汝成 -- 蜩笑偶言 / 鄭瑗 -- 玉笑零音 / 田藝蘅 -- 春雨雜述 / 解縉 -- 病榻寤言 / 陸樹聲 -- 褚氏遺書 / 翟褚澄.[v.7]. 丁集. 瀟湘錄 / 李隱 -- 清尊錄 / 廉宣 -- 昨夢錄 / 康譽之 -- 就日錄 / 耐得翁 -- 已瘧編 / 劉玉記 -- 耳目記 / 張鷟 -- 括異志 / 魯應龍 -- [v.8]. 戊集. 枕譚 / 陳繼儒 -- 猥談 / 祝允明 -- 語怪 / 祝允明 -- 異林 / 徐禎卿 -- 群碎錄 / 陳繼儒 -- [v.9]. 物異考 / 方鳳 -- 真靈位業圖 / 陶弘景 -- 己集. 空同子 / 李夢陽 -- 冥寥子游 / 屠隆 -- [v.10]. 廣莊 / 袁宏道 -- 貧士傳 : [二卷] / 黃姬水 -- 長者言 / 陳繼儒 -- [v.11]. 香案牘 / 陳繼儒 -- 清言 / 屠隆 -- 續清言 / 屠隆 -- 歸有園塵談 / 徐太室 -- 偶譚 / 李鼎 -- 韋弦佩 / 屠本畯 -- 金石契 / 祝肇 -- [v.12]. 庚集. 岩棲幽事 / 陳繼儒 -- 友論 / 利瑪竇集 -- 農說 / 馬一龍 -- 山棲志 / 慎蒙 -- 林水錄 / 彭年 -- [v.13]. 吳社編 / 王穉登 -- 客越志 / 王穉登 -- 雨航紀 / 王穉登 -- 荊溪疏 / 王穉登 -- 大嶽志 / 方升 -- 辛集. 蜀都雜鈔 / 陸深 -- [v.14]. 雙溪雜記 / 王瓊 -- 泉南雜誌 / 陳懋仁 -- 武夷游記 / 吳拭 -- 海槎餘錄 / 顧岕 -- 瀛涯勝覽 / 馬觀 -- 滇載記 / 楊慎 -- [v.15]. 閩部疏 / 王世懋 -- 吳中勝記 / 華鑰 -- 田家五行 / 婁元禮 -- 明月編 / 王穉登.[v.16]. 壬集. 丹青志 / 王穉登 -- 書畫史 / 陳繼儒 -- 畫說 / 莫是龍 -- 畫塵 / 沈顥 -- 畫禪 / 蓮儒 -- 竹泒 / 蓮儒 -- 詞旨 / 陸輔之 -- 曲豔品 / 潘之恒 -- 樂府指迷 / 張玉田 -- [v.17]. 陽關三疊圖譜 / 田藝蘅 -- 藝圃擷餘 / 王世懋 -- 學古編 : 附錄 / 吾丘衍 -- 古今印史 / 徐官 -- [v.18]. 古奇器錄 / 陸深 -- 硯譜 / 沈仕 -- 癸集. 奕律計四十條 / 王思任 -- 葉子譜 / 潘之恒 -- 茶疏 / 吳次忬 -- 羅岕茶記 / 熊明遇 -- 觴政 / 袁宏道 -- 瓶史 / 袁宏道 -- 鉼花譜 / 張謙德 -- [v.19]. 草花譜 / 高濂 -- 藝菊 / 黃省曾 -- 蘭譜 / 高濂 -- 種樹書 / 郭槖駝 -- 學圃雜疏 : 花疏, 果疏, 瓜蔬疏 / 王世懋 -- [v.19-20]. 野蔌品 / 高濂 -- [v.20]. 稻品 / 黃省曾撰 -- 蠶經 / 黃省曾 -- 魚品 /遯園居士 -- 獸經 / 黃省曾 -- 虎苑 : [二卷] / 王穉登.[V.1]. Jia ji. Sheng xue fan wei tu shuo / Yue Yuansheng -- Wu shen li chun kao / [Xing Yunlu] -- Zheng shuo kao / Wei Liaoweng -- Long xing ci ji / Wang Wenlu -- Zai tian lu / Zhang Ding -- Zhu lu ji / Wang Hui -- Dong zhao ji / Wang Mi -- Long qi za shi / Yang Yi -- Jiao gong jiu shi / Wang Da -- Zao bang xian xun lu lü / Wang Hui -- [v.2]. Yuan cao ming chen lu / Wang Ningzhai -- Ming liang lu lü / Shen Shiqian -- Yi ji. Sheng jun chu zheng ji / Shen Wen -- Zhi shen lu / Shi Zhongbin -- Xun shen lu / Wang Mingshi -- Bei yi lu / Zhang Qin -- [v.3.] Ping xia lu / Huang Biao -- Fu pi lu / Wang Mingshi -- Shi gao li lu / Xu Jing -- Yu tang man bi / Lu Shen -- Jin tai ji wen / Lu Shen -- Zhi fu za lu / Yang Yiqing -- [v.4]. Du yang za bian : [san juan] / Su E -- Bing ji. Jian sheng ye wen / Xu Zhenqing -- [v.5]. Gu bu gu lu / Wang Shizhen -- Xi shan yu hua / Lu Shen -- Qing shu bi tan / Lu Shusheng -- Wu zhong gu yu / Yang Xunji -- Jia yi sheng yan / Hu Yinglin -- San zhao ye shi / Wu Lai -- [v.6]. Xi zhao yue shi / Tian Rucheng -- Wei xiang cong tan / Tian Rucheng -- Tiao xiao ou yan / Zheng Yuan -- Yu xiao ling yin / Tian Yiheng -- Chun yu za shu / Jie Jin -- Bing ta wu yan / Lu Shusheng -- Chu shi yi shu / Zhai Chucheng.[v.7]. Ding ji. Xiao xiang lu / Li Yin -- Qing zun lu / Lian Xuan -- Zuo meng lu / Kang Yuzhi -- Jiu ri lu / Nai Deweng -- Yi nve bian / Liu Yuji -- Er mu ji / Zhang Zhuo -- Kuo yi zhi / Lu Yinglong -- [v.8]. Wu ji. Zhen tan / Chen Jiru -- Wei tan / Zhu Yunming -- Yu guai / Zhu Yunming -- Yi lin / Xu Zhenqing -- Qun sui lu / Chen Jiru -- [v.9]. Wu yi kao / Fang Feng -- Zhen ling wei ye tu / Tao Hongjing -- Ji ji. Kong tong zi / Li Mengyang -- Ming liao zi you / Tu Long -- [v.10]. Guang zhuang / Yuan Hongdao -- Pin shi zhuan : [er juan] / Huang Jishui -- Chang zhe yan / Chen Jiru -- [v.11]. Xiang an du / Chen Jiru -- Qing yan / Tu Long -- Xu qing yan / Tu Long -- Gui you yuan chen tan / Xu Taishi -- Ou tan / Li Ding -- Wei xian pei / Tu Benjun -- Jin shi qie / Zhu Zhao -- [v.12]. Geng ji. Yan qi you shi / Chen Jiru -- You lun / Limadou ji -- Nong shuo / Ma Yilong -- Shan qi zhi / Shen Meng -- Lin shui lu / Peng Nian -- [v.13]. Wu she bian / Wang Zhideng -- Ke yue zhi / Wang Zhideng -- Yu hang ji / Wang Zhideng -- Jing xi shu / Wang Zhideng -- Da yue zhi / Fang Sheng -- Xin ji. Shu du za chao / Lu Shen -- [v.14]. Shuang xi za ji / Wang Qiong -- Quan nan za zhi / Chen Maoren -- Wu yi you ji / Wu Shi -- Hai cha yu lu / Gu Jie -- Ying ya sheng lan / Ma Guan -- Tian zai ji / Yang Shen -- [v.15]. Min bu shu / Wang Shimao -- Wu zhong sheng ji / Hua Yao -- Tian jia wu xing / Lou Yuanli -- Ming yue bian / Wang Zhideng.[v.16]. Ren ji. Dan qing zhi / Wang Zhideng -- Shu hua shi / Chen Jiru -- Hua shuo / Mo Shilong -- Hua chen / Shen Hao -- Hua chan / Lian Ru -- Zhu gu / Lian Ru -- Ci zhi / Lu Fuzhi -- Qu yan pin / Pan Zhiheng -- Yue fu zhi mi / Zhang Yutian -- [v.17]. Yang guan san die tu pu / Tian Yiheng -- Yi pu xie yu / Wang Shimao -- Xue gu bian : fu lu / Wu Qiuyan -- Gu jin yin shi / Xu Guan -- [v.18]. Gu qi qi lu / Lu Shen -- Yan pu / Shen Shi -- Gui ji. Yi lü ji si shi tiao / Wang Siren -- Ye zi pu / Pan Zhiheng -- Cha shu / Wu Ciyu -- Luo jie cha ji / Xiong Mingyu -- Shang zheng / Yuan Hongdao -- Ping shi / Yuan Hongdao -- Ping hua pu / Zhang Qiande -- [v.19]. Cao hua pu / Gao Lian -- Yi ju / Huang Shengzeng -- Lan pu / Gao Lian -- Zhong shu shu / Guo Tuotuo -- Xue pu za shu : hua shu, guo shu, gua shu shu / Wang Shimao -- [v.19-20]. Ye su pin / Gao Lian -- [v.20]. Dao pin / Huang Shengzeng zhuan -- Can jing / Huang Shengzeng -- Yu pin / Dun Yuanju shi -- Shou jing / Huang Shengzeng -- Hu yuan : [er juan] / Wang Zhideng.[馮可賓撰序].綫裝, 二函.框19.4x14.2公分, 9行20字, 小字雙行同, 白口, 單白魚尾, 左右雙邊, 版心上鐫小題名, 下鐫葉次.分甲, 乙, 丙, 丁, 戊, 巳, 庚, 辛, 壬, 癸集.Xian zhuang, er han.Kuang 19.4 x 14.2 gong fen, 9 hang 20 zi, xiao zi shuang hang tong, bai kou, dan bai yu wei, zuo you shuang bian, ban xin shang juan xiao ti ming, xia juan ye ci.Fen jia, yi, bing, ding, wu, si, geng, xin, ren, gui ji.[Feng Kebin zhuan xu]
A New Collaborative Filtering Recommendation Approach Based on Naive Bayesian Method
No full textRecommendation is a popular and hot problem in e-commerce. Recommendation systems are realized in many ways such as content-based recommendation, collaborative filtering recommendation, and hybrid approach recommendation. In this article, a new collaborative filtering recommendation algorithm based on naive Bayesian method is proposed. Unlike original naive Bayesian method, the new algorithm can be applied to instances where conditional independence assumption is not obeyed strictly. According to our experiment, the new recommendation algorithm has a better performance than many existing algorithms including the popular k-NN algorithm used by Amazon.com especially at long length recommendation.Computer Science, Artificial IntelligenceComputer Science, Theory & MethodsEICPCI-S(ISTP)
A realtime locomotion mode recognition method for an active pelvis orthosis
No full textThis paper presents a realtime locomotion mode recognition method for an active pelvis orthosis. Five locomotion modes, including sitting, standing still, level-ground walking, ascending stairs, and descending stairs, are taken into consideration. The recognition is performed with locomotion information measured by the onboard hip angle sensors and the pressure insoles. These five modes are firstly divided into static modes and dynamic modes, and the two kinds are classified by monitoring the variation of the relative hip angles of the two legs within a pre-defined period. Static states are further classified into sitting and standing still based on the absolute hip angle. As for dynamic modes, a fuzzy-logic based method is proposed for the recognition. Two event-based locomotion features, including the hip joint angle at the first foot-strike and the center of foot pressure at the first foot-strike are used to calculate the membership of different modes based on the membership function, and the mode with the maximal membership is selected as the target mode. Experimental results with three subjects achieve an average recognition accuracy of 99.87% and average recognition delay of 18.12% of one gait cycle
Enhancing Depth Estimation in Adverse Lighting Scenarios for Autonomous Driving
No full textThe author has granted permission for their work to be available to the general public.The accurate determination of object depth from cameras or sensors mounted on autonomous vehicles is a significant challenge in the field of autonomous driving. While monocular or stereo cameras have been extensively used to improve depth estimation, recent advances in deep neural networks have significantly increased their accuracy. However, these state-of-the-art methods do not consider two special cases: a) radiometric differences in stereo images. b) monocular depth estimation in dynamic scenes. Meanwhile, most of them are mainly designed for daytime scenarios, limiting their effectiveness in low-light or nighttime conditions. The poor performance of neural network-based depth estimation in nighttime scenarios is primarily due to several factors: a) the lack of precise ground truth depth data for training, b) the decreased visibility of objects in images due to adverse lighting conditions, and c) elevated image noise resulting from insufficient light sources during camera capture. Addressing these challenges is crucial for improving the accuracy and practicality of depth estimation, allowing deep neural networks to function effectively in both day and night scenarios. In the first part of my research, I consider the radiometric differences in stereo images from the viewpoint of the Bidirectional Reflectance Distribution Function (BRDF).I propose a novel approach for removing these radiometric differences to perform stereo matching effectively. The approach estimates irradiance images based on the Bidirectional Reflectance Distribution Function (BRDF) which describes the ratio of radiance to irradiance for a given image. I demonstrate that to compute an irradiance image I only need to estimate the light source direction and the object's roughness. I consider an approximation that the dot product of the unknown light direction parameters follows a Gaussian distribution and I use that to estimate the light source direction. The object's roughness is estimated by calculating the pixel intensity variance using a local window strategy. By applying the above steps independently on the original stereo images, I obtain the illumination invariant irradiance images that can be used as input to stereo matching methods. Experiments conducted on well-known stereo estimation datasets demonstrate that my proposed approach significantly reduces the error rate of stereo matching methods. In the second part of my research, I propose a novel method for monocular depth estimation in dynamic scenes. I first explore the arbitrariness of object's movement trajectory in dynamic scenes theoretically. To overcome the arbitrariness, I assume that points move along a straight line over short distances and then summarize it as a triangular constraint loss in two dimensional Euclidean space. This triangular loss function is used as part of my proposed pixel movement prediction network, PMPNet, to estimate a dense depth map from a single input image. To overcome the depth inconsistency problem around the edges, I propose a deformable support window module that learns features from different shapes of objects, making depth value more accurate around edge area. The proposed model is trained and tested on two outdoor datasets - KITTI and Make3D, as well as an indoor dataset - NYU Depth V2. The quantitative and qualitative results reported on these datasets demonstrate the success of my proposed model when compared against other approaches. Ablation study results on the KITTI dataset also validate the effectiveness of the proposed pixel movement prediction module as well as the deformable support window module. In the third part of my research, I propose a self-supervised model to address the lack of ground-truth data for depth estimation. To achieve this, I introduce a novel prior called the red channel attention prior, which models the relationship between each channel in the image using Rayleigh scattering. This prior generates an attention map that is used in an attention mechanism, giving the red channel in each pixel greater weight in the neural network to improve the final accuracy of depth estimation. In my fourth study, I have reevaluated the connection between image enhancement and monocular depth estimation, and have proposed a new approach called "enhancement parameter prior." This approach leverages the inverse relationship between depth and visual quality to create a self-supervised signal that can effectively train depth networks. However, simply enhancing the brightness of each pixel may improve image clarity for human vision but not necessarily for neural networks. This results in minimal improvement in depth estimation. To address this issue, I have taken inspiration from and proposed the use of the Gaussian Cumulative Distribution-Curve (GCD-Curve) for image enhancement. By using the enhancement parameter map, red channel attention, and geometry constraints between sequential inputs, my proposed monocular depth estimation network can effectively estimate the depth value from a single image without ground truth labels. To evaluate the effectiveness of my proposed model, I have tested it on four datasets: RobotCar-Night, nuScenes-Night, RobotCar-Day, and KITTI. The results, both quantitative and qualitative, demonstrate the success of my approach when compared to 14 other approaches. In my fifth study, I present a novel approach to training an effective image denoising model using only noisy images. The recent advancements in neural networks have greatly contributed to the field of image denoising, but the need for large amounts of noisy-clean image pairs for supervision remains a constraint. To overcome this limitation, my method leverages the concept of Gaussian distribution and physics-based noise modeling to generate training pairs from a single noisy image. First, I show that a neural network can denoise an image using two different images, as long as the difference between them follows a Gaussian distribution. Second, I propose a physics-based sub-sampling strategy to generate the training image pairs. This strategy models the sub-sampling probability based on the physics of the camera pipeline, ensuring that the paired pixels are neighbors and have the same probability distribution as the original noisy image. Finally, the denoising network is trained on the sub-sampled training pairs, leading to improved performance, even in adverse lighting conditions such as nighttime scenarios. Additionally, by adhering to the physics-based noise modeling, my model has a wider range of real-world applicability. Furthermore, I propose an original and novel theory to prove mathematically that this self-supervised technique is effective in nighttime scenarios. This new theorem enables the neural network to be perceived as having a solid foundation rather than being a black box. Overall, my research makes several contributions to the field of computer vision: A Computer Graphics perspective is provided for removing the radiometric differences in stereo images by modeling it with the Bidirectional Reflectance Distribution Function (BRDF). Irradiance image estimation is proposed for radiometric difference removal, which is robust to lighting conditions and camera exposure. The light source direction is approximated using a Gaussian distribution and object roughness is estimated using local window-based pixel intensity variance. I propose a novel deep neural network architecture called PMPNet. It consists of a Pixel Movement Prediction Module which provides two pixel movement predictions and a third straight-line prediction. The relation between pixel movements and straight-line is summarized into a novel Triangular Constraint Loss Function. I propose a novel deep neural network for accurate monocular depth estimation, especially in low-light conditions, using two priors - enhancement parameters from image enhancement and red channel attention from Rayleigh scattering. I propose the use of the Gaussian Cumulative Distribution-Curve (GCD-Curve) to enhance the input image, resulting in improved depth estimation performance. I propose a novel and effective self-supervised image denoising model based on the simple U-Net architecture. This model can be trained using training image pairs generated from a single noisy input image, thereby reducing the need for large amounts of noisy-clean image pairs for supervision. I provide a solid theoretical foundation for the proposed image denoising model, demonstrating the validity of my self-supervised image denoising framework, especially in low-light conditions.Computer Scienc
Fuzzy-logic-based hybrid locomotion mode classification for an active pelvis orthosis: Preliminary results
No full textIn this paper, we present a fuzzy-logic-based hybrid locomotion mode classification method for an active pelvis orthosis. Locomotion information measured by the onboard hip joint angle sensors and the pressure insoles is used to classify five locomotion modes, including two static modes (sitting, standing still), and three dynamic modes (level-ground walking, ascending stairs, and descending stairs). The proposed method classifies these two kinds of modes first by monitoring the variation of the relative hip joint angle between the two legs within a specific period. Static states are then classified by the time-based absolute hip joint angle. As for dynamic modes, a fuzzy-logic based method is proposed for the classification. Preliminary experimental results with three able-bodied subjects achieve an off-line classification accuracy higher than 99.49%
ChemInform Abstract: Titanium‐Mediated Reductive Cross‐Coupling Reactions of Imines with Nitriles: An Efficient Route for the Synthesis of α‐Aminoketones or 1,2‐Diketones.
No full text
Experimental realization of a terahertz all-dielectric metasurface absorber
No full textMetamaterial absorbers consisting of metal, metal-dielectric, or dielectric materials have been realized across much of the electromagnetic spectrum and have demonstrated novel properties and applications. However, most absorbers utilize metals and thus are limited in applicability due to their low melting point, high Ohmic loss and high thermal conductivity. Other approaches rely on large dielectric structures and / or a supporting dielectric substrate as a loss mechanism, thereby realizing large absorption volumes. Here we present a terahertz (THz) all dielectric metasurface absorber based on hybrid dielectric waveguide resonances. We tune the metasurface geometry in order to overlap electric and magnetic dipole resonances at the same frequency, thus achieving an experimental absorption of 97.5%. A simulated dielectric metasurface achieves a total absorption coefficient enhancement factor of FT=140, with a small absorption volume. Our experimental results are well described by theory and simulations and not limited to the THz range, but may be extended to microwave, infrared and optical frequencies. The concept of an all-dielectric metasurface absorber offers a new route for control of the emission and absorption of electromagnetic radiation from surfaces with potential applications in energy harvesting, imaging, and sensing. © 2017 Optical Society of America
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