This paper introduces shape deformation as a new approach to the computer-aided design (CAD) of high frequency components. We show that geometry deformation opens up new design possibilities and offers additional degrees of freedom in the 3D modeling of microwave structures. Such design flexibility is highly desirable if the full potential of Additive Manufacturing (AM) is to be exploited in the fabrication of RF & microwave devices. The use of deformation techniques in the design of high-frequency components allows the attainment of improved electrical parameters, such as high-quality factors for cavity resonators and wide higher-order mode separation. In this work, shape deformation with radial basis functions (RBFs) is integrated with an electromagnetic field simulator based on the 3D finite-element method (FEM), allowing the semiautomated optimization of microwave components, such as cavity resonators and filters. The proposed strategy is used for the design of high Q-factor cavity resonators, cavity bandpass filters with improved spurious mode separation and of a compact twisted waveguide filter. Three designs of waveguide cavity filters with complex geometry are experimentally verified using 3D-printed prototypes fabricated with the selective laser melting (SLM) technology.

A surveillance system is an important security aid. In most criminal cases, they become crucial evidence. However, traditional surveillance systems are incapable of preventing loss or crime. Because they only record it, unless someone keeps monitoring it. The human action recognition in this paper overcomes the limitation. Wall climbing, punching, kicking, and falling down were all recognized with a good accuracy rate in the experiment. This technology is ready to support whole-size security systems. It will send messages about detected abnormal action and the location to security staff to take action before it's too late. Furthermore, it can be implemented on an edge-based device at a low cost and does not necessitate a large amount of bandwidth.

A technique for registering and relating events that cause an observable and definable system state is proposed. Discrete events of system state transfer are expressed by event tracking and clustering in the form of contiguous quanta of data. This approach is capable of describing typical processes in industrial systems in a chain of codes that contain system input/output parameters. The constituent nodes of the Markovian Processes chain form a series akin to genes in the DNA, repeatable and predictable.

This paper discusses about researching an automated robot car using artificial intelligence; training its neural network using AlexNet model, using YOLO (you only look once algorithm) for object detection phase and for practical deduction and judging component we have used Open Neural Network Exchange (ONNX) format. Our Robot Car model is agile and cost efficient. It detects objects efficiently in front of it and movement of it is smooth. It moves through sensors in motors which makes it different than other models in the world.

Dual-energy computed tomography (DECT) is an advanced CT scanning technique enabling material characterization not possible with conventional CT scans. It allows the reconstruction of energy decay curves at each 3D image voxel, representing varying image attenuation at different effective scanning energy levels. In this paper, we develop novel functional data analysis (FDA) techniques and adapt them to the analysis of DECT decay curves. More specifically, we construct functional mixture models that integrate spatial context in mixture weights, with mixture component densities being constructed upon the energy decay curves as functional observations. We design unsupervised clustering algorithms by developing dedicated expectation maximization (EM) algorithms for the maximum likelihood estimation of the model parameters. To our knowledge, this is the first article to adapt statistical FDA tools and model-based clustering to take advantage of the full spectral information provided by DECT. We evaluate our methods on 91 head and neck cancer DECT scans. Institutional review board approval was obtained for this study with waiver of informed consent. We compare our unsupervised clustering results to tumor contours traced manually by radiologists, as well as to several baseline algorithms. Given the inter-rater variability even among experts at delineating head and neck tumors, and given the potential importance of tissue reactions surrounding the tumor itself, our proposed methodology has the potential to add value in downstream machine learning applications for clinical outcome prediction based on DECT data in head and neck cancer.

We have processed EEG signals of 18 subjects and then classified them into three classes. Our main aim of this project is to implement hybrid BCI system using lightweight data.

Melody extraction is a challenging task in music information retrieval that enables many down-stream applications. In this paper we propose a simple dilated convolutional model for melody extraction. It takes variable-q transforms as inputs. It first uses consecutive layers of convolution to capture local temporal-frequency patterns. Afterward, it relies only a single layer of dilated convolution for capturing global frequency patterns formed by the pitches and harmonics of active notes. This model is effective in that it achieves the-state-of-the-art performance on most datasets, for both general and vocal melody extraction. In addition, it gets the best performance with the least training data.

Invasion of locust swarms has affected the crops in many countries in Africa and Asia, which is a significant threat to food security. Therefore, different approaches are adopted to monitor and control the locust swarms to save the crops. Furthermore, it has been proved in various studies that technology can help in agriculture through drones, real-time data monitoring, or teaching the farmers with the latest tools. Following the UN sustainability goals for food security, this research has presented a Virtual Reality(VR) based educational application to teach sustainable locust management strategies. Using hand tracking technology in the Oculus Quest lets users learn how farmers can deal with locusts without pesticides. Based on a storytelling approach, the methods presented are profitable for the farmers and free of any harm to crops regarding food security. This application can help motivate the adoption of these sustainable locust control strategies in broader interventions for environmental recovery.

The increased complexity and intelligence of automation systems require the development of intelligent fault diagnosis (IFD) methodologies. By relying on the concept of a suspected space, this study develops explainable data-driven IFD approaches for nonlinear dynamic systems. More in detail, we parameterize nonlinear systems through a generalized kernel representation used for system modeling and the associated fault diagnosis. An important result obtained is a unified form of kernel representations, applicable to both unsupervised and supervised learning. More importantly, through a rigorous theoretical analysis we discover the existence of \emph{a bridge} (i.e., a bijective mapping) between some supervised and unsupervised learning-based entities. Notably, the designed IFD approaches achieve the same performance by the use of this bridge. In order to have a better understanding of the results obtained, unsupervised and supervised neural networks are chosen as the learning tools to identify generalized kernel representations and design the IFD schemes; an invertible neural network is then employed to build the bridge between them. This study is a perspective article, whose contribution lies in proposing and detailing the fundamental concepts for explainable intelligent learning methods, contributing to system modeling and data-driven IFD designs for nonlinear dynamic systems.

Virtual reality (VR) offers a robust platform for  human behavioral neuroscience, granting unprecedented  experimental control over every aspect of an immersive and  interactive visual environment. VR experiments have already  integrated non-invasive neural recording modalities such as  EEG and functional MRI to explore the neural correlates of  human behavior and cognition. Integration with implanted  electrodes would enable significant increase in spatial and  temporal resolution of recorded neural signals and the option of  direct brain stimulation for neurofeedback. In this paper, we  discuss the first such implementation of a VR platform with  implanted electrocorticography (ECoG) and stereo?electroencephalography (sEEG) electrodes in human, in-patient  subjects. Noise analyses were performed to evaluate the effect of  the VR headset on neural data collected in two VR-naïve  subjects, one child and one adult, including both ECOG and  sEEG electrodes. Results demonstrate an increase in line noise  power (57-63Hz) while wearing the VR headset that is mitigated  effectively by common average referencing (CAR), and no  significant change in the noise floor bandpower (125-240Hz). To  our knowledge, this study represents first demonstrations of VR  immersion during invasive neural recording with in-patient  human subjects.

Clustering has important applications in many fields. However, there are not enough benchmark datasets with rich characteristics for the development and evaluation of clustering algorithms, so the clustering performance cannot be truly evaluated. Neither real data nor manually synthetic data can solve this problem. We propose a new data generator, Artificial Cluster (AC, http://ac.fwgenetics.org), that can thoroughly customize the cluster characteristics that affect clustering, such as sample size, density, overlap and shape. The randomization of the default parameters enables AC to efficiently generate benchmark datasets with different characteristic combinations that can be used to evaluate the robustness of clustering algorithms. We evaluated nine popular clustering algorithms using an example benchmark dataset generated by AC. From the results, the advantages and disadvantages of these algorithms can be clearly seen. AC is expected to provide sufficient data support for clustering research.

Measurements from an in-service immersed tunnel. These data are used to design a Wireless Sensor Node for Structural Health Monitoring (SHM) system.

Wireless fully-passive neural recording systems are demonstrated with heterogeneous integration of sensing, mixing and communication components in flexible polymer dielectric films. The recording neurosensor has an antenna, antiparallel diode, and a by-pass capacitor to modulate an incoming carrier signal with the neuropotentials and backscatter the mixed signal to an external reader circuit. Planar antennas are realized on Liquid Crystal Polymer (LCP) flexible substrates with low dielectric constant. The designed antenna topology is realized with a single metal layer and eliminates the dependency on substrate thickness and leads to thinner sensors. This is a major advance compared to prior passive neural recording with rigid and thicker substrates and components. Approximately 80% thickness reduction and 20% volume reduction are achieved with similar performance compared to earlier studies. Our approach thus leads to low-cost and disposable wireless neurosensors on a flexible platform with heterogenous integration.

Renewable energies, smart loads, energy storage, and new market behavior are adding new sources of uncertainty to power systems. Therefore, planning in real-time and developing high-quality models is crucial to adapt to uncertainties. Model validation based on actual measurements is necessary for obtaining accurate representations of power systems dynamics with system uncertainties. This paper presents a new measurement-based method to calibrate the parameters of a synchronous generator by deep learning method based on the long-short term memory (LSTM) network. First, critical parameters are determined regarding the active/reactive behavior of the generator. Then, a parallel multimodal LSTM (PM-LSTM) is designed with flexible input time steps to capture important features of temporal patterns from time-series measurements. The extracted features are then fed into a dense layer to capture the joint representation of inputs. The simulations conducted for a hydro generator under different events show that the proposed method can estimate the model parameters accurately and efficiently.

A document by Benish Sharfeen . Click on the document to view its contents.

A new active anti-disturbance control strategy based on a combination of Proportional Integral Observer, an Uncertainty and Disturbance Estimator, and a conventional predictor is developed for a class of unknown delayed LTI systems with disturbances and uncertainties in state and control input matrices.

Link layer network technology is evolving to take advantage of dynamic software-defined addressing (SDA). IEEE 802 standards have begun to support such an evolution by providing interoperable assignment of local addresses. This paper explores address assignment using the Block Address Registration and Claiming (BARC) protocol, under consideration in the P802.1CQ standardization project. Application examples illustrate the use of BARC and its potential to support new link layer networking capabilities.

Healthcare data are inherently multimodal. Almost all data generated and acquired during a patient's life can be hypothesized to contain information relevant to providing optimal personalized healthcare. Data sources such as ECGs, doctor's notes, histopathological and radiological images all contribute to inform a physician's treatment decision. However, most machine learning methods in healthcare focus on single-modality data. This becomes particularly apparent within the field of radiology, which, due to its information density, accessibility, and computational interpretability, constitutes a central pillar in the healthcare data landscape and traditionally has been one of the key target areas of medically-focused machine learning. Computer-assisted diagnostic systems of the future should be capable of simultaneously processing multimodal data, thereby mimicking physicians, who also consider a multitude of resources when treating patients. Before this background, this review offers a comprehensive assessment of multimodal machine learning methods that combine data from radiology and other medical disciplines. It establishes a modality-based taxonomy, discusses common architectures and design principles, evaluation approaches, challenges, and future directions. This work will enable researchers and clinicians to understand the topography of the domain, describe the state-of-the-art, and detect research gaps for future research in multimodal medical machine learning.