Abstract

An Electric Vehicle (EV) charger, also called Electric Vehicle Charging Station (EVCS) is an infrastructure element that supplies electrical energy for recharging EVs. The paper focuses on the design and fabrication of two-wheeler (escooter) electric vehicle charging equipment. Considering the future scenario of mass privatization of EV two-wheelers on Indian roads, the outline discusses the product design based on fabricability, affordability, and ability to mass manufacture. The proposed architecture follows the Level 2 charging standards (240 Volts), and is based on the open charge point protocol (OCPP). The suggested EVCS is constructed considering the safety prerequisites of system administrators, installers, consumers, government agencies and others. The design of EVCS links to three industries: equipment manufacturers, software industry and electric power networks. This paper presents design considerations by elaborating the hardware, software, and protocols followed to design the Level 2 charging standard EVCS.

Abstract

Accelerometers are among the most mature sensor technologies with a broad range of applications in multiple fields and industries. They represent the most widely used microelectromechanical system (MEMS) devices with excellent and reliable performance. MEMS acceleration sensors established dominance mainly in navigation and control applications. In recent years, however, recent technologies and materials have emerged that introduce novel sensing mechanisms, improve performance, enable customization, reduce cost, and reduce fabrication complexity. Herein, the recent advances in accelerometers based on MEMS and recent emerging technologies are reviewed. This work provides a comprehensive review of accelerometers’ sensing mechanisms and the main characteristics and features of each type of sensor, material, and fabrication strategies used to fabricate them. From the aspect of sensor application, this work focuses on reviewing applications that demonstrate the use of accelerometers manufactured using unconventional technologies and materials in prevailing fields such as healthcare monitoring, automotive industry, navigation, building, and structural monitoring. Moreover, challenges and future efforts needed to be addressed in this field are summarized

Abstract

Point clouds, being the simple and compact representation of surface geometry of 3D objects, have gained increasing popularity with the evolution of deep learning networks for classification and segmentation tasks. Unlike human, teaching the machine to analyze the segments of an object is a challenging task and quite essential in various machine vision applications. In this paper, we address the problem of segmentation and labelling of the 3D point clouds by proposing a inception based deep network architecture called PIG-Net, that effectively characterizes the local and global geometric details of the point clouds. In PIG-Net, the local features are extracted from the transformed input points using the proposed inception layers and then aligned by feature transform. These local features are aggregated using the global average pooling layer to obtain the global features. Finally, feed the concatenated local and global features to the convolution layers for segmenting the 3D point clouds. We perform an exhaustive experimental analysis of the PIG-Net architecture on two state-of-the-art datasets, namely, ShapeNet [1] and PartNet [2]. We evaluate the effectiveness of our network by performing ablation study.

Abstract

For decades, the revolution in design and fabrication methodology of flexible capacitive pressure sensors using various inorganic/organic materials has significantly enhanced the field of flexible and wearable electronics with a wide range of applications in aerospace, automobiles, marine environment, robotics, healthcare, and consumer/portable electronics. Mathematical modelling, finite element simulations, and unique fabrication strategies are utilized to fabricate diverse shapes of diaphragms, shells, and cantilevers which function in normal, touch, or double touch modes, operation principles inspired from microelectromechanical systems (MEMS) based capacitive pressure sensing techniques. The capacitive pressure sensing technique detects changes in capacitance due to the deformation/deflection of a pressure sensitive mechanical element that alters the separation gap of the capacitor. Due to advancement in state-of-the-art fabrication technologies, the performance and properties of capacitive pressure sensors are enhanced. In this review paper, recent progress in flexible capacitive pressure sensing techniques in terms of design, materials, and fabrication strategies is reported. The mechanics and fabrication steps of paper-based low-cost MEMS/flexible devices are also broadly reported. Lastly, the applications of flexible capacitive pressure sensors, challenges, and future perspectives are discussed.

Abstract

Earthquake response control of base-isolated bridges using supplementary passive dampers is presented. A benchmark highway overcrossing located in California is selected to indicate the effectiveness of such hybrid response control schemes. The bridge is modeled as a lumped mass three-dimensional stick model. The coupled differential equations of motion for the system are derived and solved using Newmark’s step-by-step iteration method. Considering a bidirectional interaction of the seismic forces, the bridge responses are investigated using various combinations of base isolators and damper systems, called hybrid systems. Further, a parametric study is carried out to investigate the dynamic response of the highway bridge by changing the characteristic parameters of the control devices and the isolation period of the structure for different hybrid systems under various real earthquake ground motions. The variations in seismic responses, such as bearing displacement and base shear in the bridge pier, are computed and compared to study the effectiveness of the various damper-isolator combinations. The present study concludes better performance of the fluid viscous damper when used as supplementary dampers with the lead-core rubber bearing or friction pendulum system for the base isolation, leading to reduced bearing displacements and pier base shear.

Abstract

Surface normal estimation is an essential component of several computer and robot vision pipelines. While this problem has been extensively studied, most approaches are geared towards indoor scenes and often rely on multiple modalities (depth, multiple views) for accurate estimation of normal maps. Outdoor scenes pose a greater challenge as they exhibit significant lighting variation, often contain occluders, and structures like building facades are often ridden with numerous windows and protrusions. Conventional supervised learning schemes excel in indoor scenes, but do not exhibit competitive performance when trained and deployed in outdoor environments. Furthermore, they involve complex network architectures and require many more trainable parameters. To tackle these challenges, we present an adversarial learning scheme that regularizes the output normal maps from a neural network to appear more realistic, by using a small number of precisely annotated examples. Our method presents a lightweight and simpler architecture, while improving performance by at least 1.5x across most metrics. We evaluate our approaches against the state-of-the-art on normal map estimation, on a synthetic and a real outdoor dataset, and observe significant performance enhancements.

Abstract

Explainable AI aims to build systems that not only give high performance but also are able to provide insights that drive the decision making. However, deriving this explanation is often dependent on fully annotated (class label and local annotation) data, which are not readily available in the medical domain. Approach: This paper addresses the above-mentioned aspects and presents an innovative approach to classifying a lung nodule in a CT volume as malignant or benign, and generating a morphologically meaningful explanation for the decision in the form of attributes such as nodule margin, sphericity, and spiculation. A deep learning architecture that is trained using a multi-phase training regime is proposed. The nodule class label (benign/malignant) is learned with full supervision and is guided by semantic attributes that are learned in a weakly supervised manner. Results: Results of an extensive evaluation of the proposed system on the LIDC-IDRI dataset show good performance compared with state-of-the-art, fully supervised methods. The proposed model is able to label nodules (after full supervision) with an accuracy of 89.1% and an area under curve of 0.91 and to provide eight attributes scores as an explanation, which is learned from a much smaller training set. The proposed system's potential to be integrated with a sub-optimal nodule detection system was also tested, and our system handled 95% of false positive or random regions in the input well by labeling them as benign, which underscores its robustness. Conclusions: The proposed approach offers a way to address computer-aided diagnosis system design under the constraint of sparse availability of fully annotated images.

Abstract

Skin is one of the very important and largest organs of the human body that helps in regulating the body temperature and is responsible for sensations such as feel, touch, hot and cold. These days, skin problems are very common in day-to-day life. There may be many reasons for skin diseases: unbalanced and impure diet, several types of pollutions, or maybe family heredity. However, if skin disease after a long treatment does not show any sign of improvement or the skin cells grow abnormally, this may lead to skin cancer. There are many forms of skin cancer. For early and timely diagnosis of skin cancer, an efficient technique is required at utmost importance. Many people across the globe lost their lives due to the late diagnosis. Therefore, a technique that is cost-effective, quicker, and easily accessible needs a higher demand. These days for the classification of images, machine learning, and deep learning techniques proved to be the most efficient approach. In this paper, the dataset of several images of a benign and malignant tumor was taken and pre-processed. Once all the images were pre-processed, they are ready to fed in several CNN models. These models extract the features and pass the images to several machine learning classifiers for the classification of moles as benign or malignant. The results verify by using the classification approach it becomes very much easy for the dermatologist to easily detect the lesions and provide the appropriate treatment to the patient to save the life.

Abstract

The newly detected virus also called coronavirus spreads the disease Covid19. World Health Organization (WHO) confirmed this virus as a worldwide pandemic as it has infected millions of people and has taken away many lives across the globe. An infection caused by Covid19 disease majorly destroys the respiratory tract of human beings that ends with multiple organ failures or death in the worst case. In the present work, chest radiographs were provided as input to various deep learning CNN architectures for the purpose of feature extraction. After extracting the features, the images were provided as the input to various machine learning classifiers that classify the chest radiographs as Covid-19 positive, pneumonia infection, or healthy scans.

Abstract

We develop a theory of linear witnesses for detecting non-Markovianity, based on the geometric structure of the set of Choi states for all Markovian evolutions having Lindblad-type generators. We show that the set of all such Markovian Choi states form a convex and compact set under the small time interval approximation. Invoking geometric Hahn–Banach theorem, we construct linear witnesses to separate a given non-Markovian Choi state from the set of Markovian Choi states. We present examples of such witnesses for dephasing channel and Pauli channel in case of qubits. Furthermore, we have devised another method of detection NM of various qubit channels, by using projective measurements in the Bell basis. This can be done without the knowledge of what specific kind of channel we are dealing with. This gives us a huge operational advantage for NM detection. We further investigate the geometric structure of the Markovian Choi states to find that they do not form a polytope. This presents a platform to consider nonlinear improvement of non-Markovianity witnesses.