Smart Traction Control Systems for Electric Vehicles Using Acoustic Road-type Estimation

The application of traction control systems (TCS) for electric vehicles (EV) has great potential due to easy implementation of torque control with direct-drive motors. However, the control system usually requires road-tire friction and slip-ratio values, which must be estimated. While it is not possible to obtain the first one directly, the estimation of latter value requires accurate measurements of chassis and wheel velocity. In addition, existing TCS structures are often designed without considering the robustness and energy efficiency of torque control. In this work, both problems are addressed with a smart TCS design having an integrated acoustic road-type estimation (ARTE) unit. This unit enables the road-type recognition and this information is used to retrieve the correct look-up table between friction coefficient and slip-ratio. The estimation of the friction coefficient helps the system to update the necessary input torque. The ARTE unit utilizes machine learning, mapping the acoustic feature inputs to road-type as output. In this study, three existing TCS for EVs are examined with and without the integrated ARTE unit. The results show significant performance improvement with ARTE, reducing the slip ratio by 75% while saving energy via reduction of applied torque and increasing the robustness of the TCS.Comment: Accepted to be published by IEEE Trans. on Intelligent Vehicles, 22 Jan 201

High-resolution palynological analysis in Lake Sapanca as a tool to detect recent earthquakes on the North Anatolian Fault

High-resolution palynological analysis of a 38-cm-long core collected from Lake Sapanca, northwest Turkey, reveals large earthquakes that occurred during the second half of the 20th century along the North Anatolian Fault Zone. Four events have disturbed the lacustrine sedimentary sequence. Three of the four events are historical earthquakes in 1999 in Izmit, 1967 in Mudurnu and 1957 in Abant. These events are recorded in the core by turbiditic deposits and reworked sediment and by low overall palynomorph concentrations but high values of thick-exined pollen, fern spores and fungal spores. Palynomorphs in the event beds have been grouped based on their associations in modern moss, river and lake samples. The inferred mechanisms of transport and sources for the palynomorphs are: 1- lake sediment displaced by slump, 2- collapsed shoreline sediment owing to seiche, waves and sudden lake level changes, 3- subsidence of deltas and 4- river-transported soil and sediment from upland areas. The 1999 Izmit earthquake is only weakly recorded by palynomorphs, probably due to recent engineering control on the rivers. The 1967 Mudurnu earthquake had the strongest effect on the lake, introducing successive packages of sediment to the centre of the lake from underwater slopes, the lakeshore and rivers

Theory of a Directive Optical Leaky Wave Antenna Integrated into a Resonator and Enhancement of Radiation Control

We provide for the first time the detailed study of the radiation performance of an optical leaky wave antenna (OLWA) integrated into a Fabry-P\'erot resonator. We show that the radiation pattern can be expressed as the one generated by the interference of two leaky waves counter-propagating in the resonator leading to a design procedure for achieving optimized broadside radiation, i.e., normal to the waveguide axis. We thus report a realizable implementation of the OLWA made of semiconductor and dielectric regions. The theoretical modeling is supported by full-wave simulation results, which are found to be in good agreement. We aim to control the radiation intensity in the broadside direction via excess carrier generation in the semiconductor regions. We show that the presence of the resonator can provide an effective way of enhancing the radiation level modulation, which reaches values as high as 13.5 dB, paving the way for novel promising control capabilities that might allow the generation of very fast optical switches, as an example.Comment: 10 pages, 14 figure

Old but Gold: Secondary School Students’ Communication Attitude Scale

Communication as a skill is not something new; however, it has become much more important than ever before as the way and with whom we communicate have changed. That is why educators need to have valid and reliable tools to understand how this skill is perceived by students. This study aims to provide a valid and reliable secondary school students’ communication attitude scale (SSSCAS). A draft form for the scale was prepared with 32 items depending on the relevant literature and reviewed by experts in the field. The revised form was applied to 397 students at a state secondary school in Aksaray city of Turkey in the 2020-2021 academic year. First, explanatory factor analysis that tests construct validity was carried out and three items were extracted from further analysis due to insufficient factor loadings. The remaining 29 items are placed under four sub-dimensions called openness to communication, body language and preferences, self-confidence, and obstacles. The structure was tested through confirmatory factor analysis and the validity of the scale with the four sub-dimensions was found to be appropriate. The Cronbach’s alpha values of all sub-dimensions and the scale were above the required level. As a result, the developed scale is a valid and reliable scale to assess communication attitudes of secondary school students

Türkiye’de Popülizm ve Demokrasi: Cumhuriyet Halk Partisi Örneği

This study will examine the main opposition party in Turkey, the Republican People’s Party (CHP) and discusses the constitutive role of populism in the party’s discourse. Therefore, this study will highlight and compare the major cornerstones of the CHP’s populist discourse and its current manifestation. For this purpose, after some opening remarks on the historiography of populism in Turkey, this article will move on to analyze the three historical periods that have shaped the party’s populist appeal: the single-party era of 1923–1946; the 1970s, which saw the rise of left-populism in the party; and the social-democratic opening of the party in the late 1980s under the name of, first, the Social Democratic Party (SODEP) and subsequently the Social Democratic Populist Party (SHP). The final section will provide an analysis of the party’s current performance and its discourse on contemporary Turkish political issues, to offer a critical debate on the continuity and ruptures within the CHP’s populist discourse and the potential for democratic left-populism in Turkish politics.Bu çalışma, ana muhalefet partisi olan Cumhuriyet Halk Partisi’nin (CHP) söyleminde kurucu rol oynayan popülizm temasını ele almaktadır. Bu amaçla ilk olarak CHP’nin popülist söyleminin tarihsel gelişiminin önemli aşamaları ve şu andaki performansı incelenmektedir.Bu çerçevede, Türkiye’de popülizm literatürüne dair başlangıç tespitlerinden sonra, CHP’nin popülist söyleminin tarihsel uğrakları tek partili dönem (1923-1946), sol-popülizmin hakim olduğu 1970’ler ve 1980’lerin ortasında Sosyal Demokrat Parti (SODEP) ve sonlarında Sosyal Demokrat Halk Partisi (SHP) performansı çerçevesinde haritalandırılıyor. Sonrasında ise partinin mevcut performansı ve Türkiye’nin güncel meseleleri üzerine geliştirdiği yaklaşımlar ele alınarak, partinin popülist söyleminde demokratik sol-popülizmin imkanları ve sınırları üzerine bir tartışma yapılıyor

Human Action Localization And Recognition In Unconstrained Videos

As imaging systems become ubiquitous, the ability to recognize human actions is becoming increasingly important. Just as in the object detection and recognition literature, action recognition can be roughly divided into classification tasks, where the goal is to classify a video according to the action depicted in the video, and detection tasks, where the goal is to detect and localize a human performing a particular action. A growing literature is demonstrating the benefits of localizing discriminative sub-regions of images and videos when performing recognition tasks. In this thesis, we address the action detection and recognition problems. Action detection in video is a particularly difficult problem because actions must not only be recognized correctly, but must also be localized in the 3D spatio-temporal volume. We introduce a technique that transforms the 3D localization problem into a series of 2D detection tasks. This is accomplished by dividing the video into overlapping segments, then representing each segment with a 2D video projection. The advantage of the 2D projection is that it makes it convenient to apply the best techniques from object detection to the action detection problem. We also introduce a novel, straightforward method for searching the 2D projections to localize actions, termed TwoPoint Subwindow Search (TPSS). Finally, we show how to connect the local detections in time using a chaining algorithm to identify the entire extent of the action. Our experiments show that video projection outperforms the latest results on action detection in a direct comparison. Second, we present a probabilistic model learning to identify discriminative regions in videos from weakly-supervised data where each video clip is only assigned a label describing what action is present in the frame or clip. While our first system requires every action to be manually outlined in every frame of the video, this second system only requires that the video be given a single highlevel tag. From this data, the system is able to identify discriminative regions that correspond well iii to the regions containing the actual actions. Our experiments on both the MSR Action Dataset II and UCF Sports Dataset show that the localizations produced by this weakly supervised system are comparable in quality to localizations produced by systems that require each frame to be manually annotated. This system is able to detect actions in both 1) non-temporally segmented action videos and 2) recognition tasks where a single label is assigned to the clip. We also demonstrate the action recognition performance of our method on two complex datasets, i.e. HMDB and UCF101. Third, we extend our weakly-supervised framework by replacing the recognition stage with a twostage neural network and apply dropout for preventing overfitting of the parameters on the training data. Dropout technique has been recently introduced to prevent overfitting of the parameters in deep neural networks and it has been applied successfully to object recognition problem. To our knowledge, this is the first system using dropout for action recognition problem. We demonstrate that using dropout improves the action recognition accuracies on HMDB and UCF101 datasets

Silicon Nitride Waveguides for Plasmon Optical Trapping and Sensing Applications

We demonstrate a silicon nitride trench waveguide deposited with bowtie antennas for plasmonic enhanced optical trapping. The sub-micron silicon nitride trench waveguides were fabricated with conventional optical lithography in a low cost manner. The waveguides embrace not only low propagation loss and high nonlinearity, but also the inborn merits of combining micro-fluidic channel and waveguide together. Analyte contained in the trapezoidal trench channel can interact with the evanescent field from the waveguide beneath. The evanescent field can be further enhanced by plasmonic nanostructures. With the help of gold nano bowtie antennas, the studied waveguide shows outstanding trapping capability on 10 nm polystyrene nanoparticles. We show that the bowtie antennas can lead to 60-fold enhancement of electric field in the antenna gap. The optical trapping force on a nanoparticle is boosted by three orders of magnitude. A strong tendency shows the nanoparticle is likely to move to the high field strength region, exhibiting the trapping capability of the antenna. Gradient force in vertical direction is calculation by using a point-like dipole assumption, and the analytical solution matches the full-wave simulation well. The investigation indicates that nanostructure patterned silicon nitride trench waveguide is suitable for optical trapping and nanoparticle sensing applications

Electric field enhancement with plasmonic colloidal nanoantennas excited by a silicon nitride waveguide

We investigate the feasibility of CMOS-compatible optical structures to develop novel integrated spectroscopy systems. We show that local field enhancement is achievable utilizing dimers of plasmonic nanospheres that can be assembled from colloidal solutions on top of a CMOS-compatible optical waveguide. The resonant dimer nanoantennas are excited by modes guided in the integrated silicon nitride waveguide. Simulations show that 100 fold electric field enhancement builds up in the dimer gap as compared to the waveguide evanescent field amplitude at the same location. We investigate how the field enhancement depends on dimer location, orientation, distance and excited waveguide modes

Analysis of feature detector and descriptor combinations with a localization experiment for various performance metrics

The purpose of this study is to provide a detailed performance comparison of feature detector/descriptor methods, particularly when their various combinations are used for image-matching. The localization experiments of a mobile robot in an indoor environment are presented as a case study. In these experiments, 3090 query images and 127 dataset images were used. This study includes five methods for feature detectors (features from accelerated segment test (FAST), oriented FAST and rotated binary robust independent elementary features (BRIEF) (ORB), speeded-up robust features (SURF), scale invariant feature transform (SIFT), and binary robust invariant scalable keypoints (BRISK)) and five other methods for feature descriptors (BRIEF, BRISK, SIFT, SURF, and ORB). These methods were used in 23 different combinations and it was possible to obtain meaningful and consistent comparison results using the performance criteria defined in this study. All of these methods were used independently and separately from each other as either feature detector or descriptor. The performance analysis shows the discriminative power of various combinations of detector and descriptor methods. The analysis is completed using five parameters: (i) accuracy, (ii) time, (iii) angle difference between keypoints, (iv) number of correct matches, and (v) distance between correctly matched keypoints. In a range of 60{\deg}, covering five rotational pose points for our system, the FAST-SURF combination had the lowest distance and angle difference values and the highest number of matched keypoints. SIFT-SURF was the most accurate combination with a 98.41% correct classification rate. The fastest algorithm was ORB-BRIEF, with a total running time of 21,303.30 s to match 560 images captured during motion with 127 dataset images.Comment: 11 pages, 3 figures, 1 tabl