Klasifikasi Otomatis Motif Tekstil Menggunakan Support Vector Machine Multi Kelas

Tekstur merupakan pola atau motif tertentu yang tersusun secara berulang-ulang pada citra. Tekstur mudah dikenali/dikelompokkan oleh manusia, tetapi sulit bagi mesin. Klasifikasi tekstur secara otomatis berguna dan dibutuhkan pada banyak bidang seperti industri tekstil, pendaratan pesawat otomatis, fotografi dan seni. Pada industri tekstil, klasifikasi tekstur otomatis dapat meningkatkan efisiensi proses desain motif. Motif tekstil terdiri dari banyak kelompok, sehingga diperlukan metode klasifikasi multi kelas untuk mengelompokkan motif-motif tersebut. Artikel ini memaparkan kinerja tiga metode Support Vector Machine (SVM) multi kelas: One Against One (OAO), Directed Acyclic Graph (DAG) dan One Against All (OAA) pada klasifikasi motif dari citra tekstil, dimana Wavelet Gabor digunakan sebagai pengekstraksi fitur. Kinerja SVM diukur berdasarkan parameter akurasi dan fitur Gabor diekstraksi dengan skala dan orientasi yang berbeda. Tujuan penelitian ini adalah menentukan kinerja SVM dan pengaruh jumlah skala dan orientasi Gabor yang digunakan pada klasifikasi motif tekstil. Pada simulasi digunakan 120 citra tekstil yang terbagi menjadi tiga kategori motif: bunga, kotak dan polkadot. Akurasi pengelompokan SVM mencapai kisaran 90%-100%, bahkan untuk citra yang terpotong. Pengujian dengan k-fold validation menunjukkan bahwa SVM DAG lebih baik daripada SVM OAO dan SVM OAA, dengan akurasi mencapai 78%. AbstractTexture is a repetition of a specific pattern concatenation in an image. The Texture can be defined as a repetition of pattern in an image.  The texture is easy for the human to classify, but it is not easy for a machine. Automatic texture classification is useful and required in many fields such as textile industry, automatic aircraft landing, photography and art. In the textile industry, automatic texture classification can enhance the efficiency of motif designing process. The textile motif is various and should be grouped into more than two classes; therefore a multiclass classification is required. This article discusses the performance of multiclass Support Vector Machine (SVM): One Against One (OAO), Directed Acyclic Graph (DAG) and One Against All (OAA) in classifying textile motifs, in which the Gabor Filter was used to extract the texture features. The SVM performance was measured in terms of accuracy, while the Gabor features were extracted in a different combination of scales and orientations. The purpose of the work is to measure the SVM performance and determine the effect of using various Gabor scales and orientations in textile motifs classification. We used 120 textile images with three motifs: flower, boxes and polka dot. The SVM accuracy of 90%-100% was achieved; even for cropped textile images. Using the k-fold validation, the accuracy of SVM DAG was 78%, higher than those of SVM OAO and SVM OA

Improving robotic grasping system using deep learning approach

Traditional robots can only move according to a pre-planned trajectory which limits the range of applications that they could be engaged in. Despite their long history, the use of computer vision technology for grasp prediction and object detection is still an active research area. However, the generating of a full grasp configuration of a target object is the main challenge to plan a successful robotic operation of the physical robotic grasp. Integrating computer vision technology with tactile sensing feedback has given rise to a new capability of robots that can accomplish various robotic tasks. However, the recently conducted studies had used tactile sensing with grasp detection models to improve prediction accuracy, not physical grasp success. Thus, the problem of detecting the slip event of the grasped objects that have different weights is addressed in this research. This research aimed to develop a Deep Learning grasp detection model and a slip detection algorithm and integrating them into one innovative robotic grasping system. By proposing a four-step data augmentation technique, the achieved grasping accuracy was 98.2 % exceeding the best-reported results by almost 0.5 % where 625 new instances were generated per original image with different grasp labels. Besides, using the twostage- transfer-learning technique improved the obtained results in the second stage by 0.3 % compared to the first stage results. For the physical robot grasp, the proposed sevendimensional grasp representations method allows the autonomous prediction of the grasp size and depth. The developed model achieved 74.8 milliseconds as prediction time, which makes it possible to use the model in real-time robotic applications. By observing the real-time feedback of a force sensing resistor sensor, the proposed slip detection algorithm indicated a quick response within 86 milliseconds. These results allowed the system to maintain holding the target objects by an immediate increase of the grasping force. The integration of the Deep Learning and slip detection models has shown a significant improvement of 18.4% in the results of the experimental grasps conducted on a SCARA robot. Besides, the utilized Zerocross-Canny edge detector has improved the robot positioning error by 0.27 mm compared to the related studies. The achieved results introduced an innovative robotic grasping system with a Grasp-NoDrop-Place scheme

Soft Biomimetic Finger with Tactile Sensing and Sensory Feedback Capabilities

The compliant nature of soft fingers allows for safe and dexterous manipulation of objects by humans in an unstructured environment. A soft prosthetic finger design with tactile sensing capabilities for texture discrimination and subsequent sensory stimulation has the potential to create a more natural experience for an amputee. In this work, a pneumatically actuated soft biomimetic finger is integrated with a textile neuromorphic tactile sensor array for a texture discrimination task. The tactile sensor outputs were converted into neuromorphic spike trains, which emulate the firing pattern of biological mechanoreceptors. Spike-based features from each taxel compressed the information and were then used as inputs for the support vector machine (SVM) classifier to differentiate the textures. Our soft biomimetic finger with neuromorphic encoding was able to achieve an average overall classification accuracy of 99.57% over sixteen independent parameters when tested on thirteen standardized textured surfaces. The sixteen parameters were the combination of four angles of flexion of the soft finger and four speeds of palpation. To aid in the perception of more natural objects and their manipulation, subjects were provided with transcutaneous electrical nerve stimulation (TENS) to convey a subset of four textures with varied textural information. Three able-bodied subjects successfully distinguished two or three textures with the applied stimuli. This work paves the way for a more human-like prosthesis through a soft biomimetic finger with texture discrimination capabilities using neuromorphic techniques that provides sensory feedback; furthermore, texture feedback has the potential to enhance the user experience when interacting with their surroundings. Additionally, this work showed that an inexpensive, soft biomimetic finger combined with a flexible tactile sensor array can potentially help users perceive their environment better

New generation of interactive platforms based on novel printed smart materials

Programa doutoral em Engenharia Eletrónica e de Computadores (área de Instrumentação e Microssistemas Eletrónicos)The last decade was marked by the computer-paradigm changing with other digital devices suddenly becoming available to the general public, such as tablets and smartphones. A shift in perspective from computer to materials as the centerpiece of digital interaction is leading to a diversification of interaction contexts, objects and applications, recurring to intuitive commands and dynamic content that can proportionate more interesting and satisfying experiences. In parallel, polymer-based sensors and actuators, and their integration in different substrates or devices is an area of increasing scientific and technological interest, which current state of the art starts to permit the use of smart sensors and actuators embodied within the objects seamlessly. Electronics is no longer a rigid board with plenty of chips. New technological advances and perspectives now turned into printed electronics in polymers, textiles or paper. We are assisting to the actual scaling down of computational power into everyday use objects, a fusion of the computer with the material. Interactivity is being transposed to objects erstwhile inanimate. In this work, strain and deformation sensors and actuators were developed recurring to functional polymer composites with metallic and carbonaceous nanoparticles (NPs) inks, leading to capacitive, piezoresistive and piezoelectric effects, envisioning the creation of tangible user interfaces (TUIs). Based on smart polymer substrates such as polyvinylidene fluoride (PVDF) or polyethylene terephthalate (PET), among others, prototypes were prepared using piezoelectric and dielectric technologies. Piezoresistive prototypes were prepared with resistive inks and restive functional polymers. Materials were printed by screen printing, inkjet printing and doctor blade coating. Finally, a case study of the integration of the different materials and technologies developed is presented in a book-form factor.A última década foi marcada por uma alteração do paradigma de computador pelo súbito aparecimento dos tablets e smartphones para o público geral. A alteração de perspetiva do computador para os materiais como parte central de interação digital levou a uma diversificação dos contextos de interação, objetos e aplicações, recorrendo a comandos intuitivos e conteúdos dinâmicos capazes de tornarem a experiência mais interessante e satisfatória. Em simultâneo, sensores e atuadores de base polimérica, e a sua integração em diferentes substratos ou dispositivos é uma área de crescente interesse científico e tecnológico, e o atual estado da arte começa a permitir o uso de sensores e atuadores inteligentes perfeitamente integrados nos objetos. Eletrónica já não é sinónimo de placas rígidas cheias de componentes. Novas perspetivas e avanços tecnológicos transformaram-se em eletrónica impressa em polímeros, têxteis ou papel. Neste momento estamos a assistir à redução da computação a objetos do dia a dia, uma fusão do computador com a matéria. A interatividade está a ser transposta para objetos outrora inanimados. Neste trabalho foram desenvolvidos atuadores e sensores e de pressão e de deformação com recurso a compostos poliméricos funcionais com tintas com nanopartículas (NPs) metálicas ou de base carbónica, recorrendo aos efeitos capacitivo, piezoresistivo e piezoelétrico, com vista à criação de interfaces de usuário tangíveis (TUIs). Usando substratos poliméricos inteligentes tais como fluoreto de polivinilideno (PVDF) ou politereftalato de etileno (PET), entre outos, foi possível a preparação de protótipos de tecnologia piezoelétrica ou dielétrica. Os protótipos de tecnologia piezoresistiva foram feitos com tintas resistivas e polímeros funcionais resistivos. Os materiais foram impressos por serigrafia, jato de tinta, impressão por aerossol e revestimento de lâmina doctor blade. Para terminar, é apresentado um caso de estudo da integração dos diferentes materiais e tecnologias desenvolvidos sob o formato de um livro.This project was supported by FCT – Fundação para a Ciência e a Tecnologia, within the doctorate grant with reference SFRH/BD/110622/2015, by POCH – Programa Operacional Capital Humano, and by EU – European Union