Friedrichsenhandberg9615

Several emerging areas like the Internet of Things, sensor networks, healthcare and distributed networks feature resource-constrained devices that share secure and privacy-preserving data to accomplish some goal. The majority of standard cryptographic algorithms do not fit with these constrained devices due to heavy cryptographic components. In this paper, a new block cipher, BRISK, is proposed with a block size of 32-bit. The cipher design is straightforward due to simple round operations, and these operations can be efficiently run in hardware and suitable for software. Another major concept used with this cipher is dynamism during encryption for each session; that is, instead of using the same encryption algorithm, participants use different ciphers for each session. Professor Lars R. Knudsen initially proposed dynamic encryption in 2015, where the sender picks a cipher from a large pool of ciphers to encrypt the data and send it along with the encrypted message. The receiver does not know about the encryption technique used before receiving the cipher along with the message. However, in the proposed algorithm, instead of choosing a new cipher, the process uses the same cipher for each session, but varies the cipher specifications from a given small pool, e.g., the number of rounds, cipher components, etc. Therefore, the dynamism concept is used here in a different way.Advances in robotics are part of reducing the burden associated with manufacturing tasks in workers. For example, the cobot could be used as a "third-arm" during the assembling task. Thus, the necessity of designing new intuitive control modalities arises. This paper presents a foot gesture approach centered on robot control constraints to switch between four operating modalities. This control scheme is based on raw data acquired by an instrumented insole located at a human's foot. Navoximod concentration It is composed of an inertial measurement unit (IMU) and four force sensors. Firstly, a gesture dictionary was proposed and, from data acquired, a set of 78 features was computed with a statistical approach, and later reduced to 3 via variance analysis ANOVA. Then, the time series collected data were converted into a 2D image and provided as an input for a 2D convolutional neural network (CNN) for the recognition of foot gestures. Every gesture was assimilated to a predefined cobot operating mode. The offline recognition rate appears to be highly dependent on the features to be considered and their spatial representation in 2D image. We achieve a higher recognition rate for a specific representation of features by sets of triangular and rectangular forms. link2 These results were encouraging in the use of CNN to recognize foot gestures, which then will be associated with a command to control an industrial robot.Frequent inspections are essential for drains to maintain proper function to ensure public health and safety. Robots have been developed to aid the drain inspection process. However, existing robots designed for drain inspection require improvements in their design and autonomy. This paper proposes a novel design of a drain inspection robot named Raptor. The robot has been designed with a manually reconfigurable wheel axle mechanism, which allows the change of ground clearance height. Design aspects of the robot, such as mechanical design, control architecture and autonomy functions, are comprehensively described in the paper, and insights are included. Maintaining the robot's position in the middle of a drain when moving along the drain is essential for the inspection process. Thus, a fuzzy logic controller has been introduced to the robot to cater to this demand. Experiments have been conducted by deploying a prototype of the design to drain environments considering a set of diverse test scenarios. Experiment results show that the proposed controller effectively maintains the robot in the middle of a drain while moving along the drain. Therefore, the proposed robot design and the controller would be helpful in improving the productivity of robot-aided inspection of drains.Neuro-muscular disorders and diseases such as cerebral palsy and Duchenne Muscular Dystrophy can severely limit a person's ability to perform activities of daily living (ADL). Exoskeletons can provide an active or passive support solution to assist these groups of people to perform ADL. This study presents an artificial neural network-trained adaptive controller mechanism that uses surface electromyography (sEMG) signals from the human forearm to detect hand gestures and navigate an in-house-built wheelchair-mounted upper limb robotic exoskeleton based on the user's intent while ensuring safety. To achieve the desired position of the exoskeleton based on human intent, 10 hand gestures were recorded from 8 participants without upper limb movement disabilities. Participants were tasked to perform water bottle pick and place activities while using the exoskeleton, and sEMG signals were collected from the forearm and processed through root mean square, median filter, and mean feature extractors prior to training a scaled conjugate gradient backpropagation artificial neural network. The trained network achieved an average of more than 93% accuracy, while all 8 participants who did not have any prior experience of using an exoskeleton were successfully able to perform the task in less than 20 s using the proposed artificial neural network-trained adaptive controller mechanism. These results are significant and promising thus could be tested on people with muscular dystrophy and neuro-degenerative diseases.We conducted experiments on SnO2 thin layers to determine the dependencies between the stoichiometry, electrochemical properties, and structure. This study focused on features such as the film structure, working temperature, layer chemistry, and atmosphere composition, which play a crucial role in the oxygen sensor operation. We tested two kinds of resistive SnO2 layers, which had different grain dimensions, thicknesses, and morphologies. Gas-sensing layers fabricated by two methods, a rheotaxial growth and thermal oxidation (RGTO) process and DC reactive magnetron sputtering, were examined in this work. The crystalline structure of SnO2 films synthesized by both methods was characterized using XRD, and the crystallite size was determined from XRD and AFM measurements. Chemical characterization was carried out using X-ray photoelectron (XPS) and Auger electron (AES) spectroscopy for the surface and the near-surface film region (in-depth profiles). We investigated the layer resistance for different oxygen concentrations within a range of 1-4%, in a nitrogen atmosphere. Additionally, resistance measurements within a temperature range of 423-623 K were analyzed. We assumed a flat grain geometry in theoretical modeling for comparing the results of measurements with the calculated results.In the last few years, many works have addressed Predictive Maintenance (PdM) by the use of Machine Learning (ML) and Deep Learning (DL) solutions, especially the latter. The monitoring and logging of industrial equipment events, like temporal behavior and fault events-anomaly detection in time-series-can be obtained from records generated by sensors installed in different parts of an industrial plant. However, such progress is incipient because we still have many challenges, and the performance of applications depends on the appropriate choice of the method. This article presents a survey of existing ML and DL techniques for handling PdM in the railway industry. This survey discusses the main approaches for this specific application within a taxonomy defined by the type of task, employed methods, metrics of evaluation, the specific equipment or process, and datasets. Lastly, we conclude and outline some suggestions for future research.Research on brain-computer interfaces (BCIs) has become more democratic in recent decades, and experiments using electroencephalography (EEG)-based BCIs has dramatically increased. The variety of protocol designs and the growing interest in physiological computing require parallel improvements in processing and classification of both EEG signals and bio signals, such as electrodermal activity (EDA), heart rate (HR) or breathing. If some EEG-based analysis tools are already available for online BCIs with a number of online BCI platforms (e.g., BCI2000 or OpenViBE), it remains crucial to perform offline analyses in order to design, select, tune, validate and test algorithms before using them online. Moreover, studying and comparing those algorithms usually requires expertise in programming, signal processing and machine learning, whereas numerous BCI researchers come from other backgrounds with limited or no training in such skills. Finally, existing BCI toolboxes are focused on EEG and other brain signals but usually do not include processing tools for other bio signals. Therefore, in this paper, we describe BioPyC, a free, open-source and easy-to-use Python platform for offline EEG and biosignal processing and classification. Based on an intuitive and well-guided graphical interface, four main modules allow the user to follow the standard steps of the BCI process without any programming skills (1) reading different neurophysiological signal data formats, (2) filtering and representing EEG and bio signals, (3) classifying them, and (4) visualizing and performing statistical tests on the results. We illustrate BioPyC use on four studies, namely classifying mental tasks, the cognitive workload, emotions and attention states from EEG signals.The compensation of magnetic and electromagnetic interference generated by drones is one of the main problems related to drone-borne magnetometry. The simplest solution is to suspend the magnetometer at a certain distance from the drone. However, this choice may compromise the flight stability or introduce periodic data variations generated by the oscillations of the magnetometer. We studied this problem by conducting two drone-borne magnetic surveys using a prototype system based on a cesium-vapor magnetometer with a 1000 Hz sampling frequency. First, the magnetometer was fixed to the drone landing-sled (at 0.5 m from the rotors), and then it was suspended 3 m below the drone. These two configurations illustrate endmembers of the possible solutions, favoring the stability of the system during flight or the minimization of the mobile platform noise. Drone-generated noise was filtered according to a CWT analysis, and both the spectral characteristics and the modelled source parameters resulted analogously to that of a ground magnetic dataset in the same area, which were here taken as a control dataset. This study demonstrates that careful processing can return high quality drone-borne data using both flight configurations. The optimal flight solution can be chosen depending on the survey target and flight conditions.In this paper we analyze the performance of QUIC as a transport alternative for Internet of Things (IoT) services based on the Message Queuing Telemetry Protocol (MQTT). QUIC is a novel protocol promoted by Google, and was originally conceived to tackle the limitations of the traditional Transmission Control Protocol (TCP), specifically aiming at the reduction of the latency caused by connection establishment. QUIC use in IoT environments is not widespread, and it is therefore interesting to characterize its performance when in over such scenarios. link3 We used an emulation-based platform, where we integrated QUIC and MQTT (using GO-based implementations) and compared their combined performance with the that exhibited by the traditional TCP/TLS approach. We used Linux containers as end devices, and the ns-3 simulator to emulate different network technologies, such as WiFi, cellular, and satellite, and varying conditions. The results evince that QUIC is indeed an appropriate protocol to guarantee robust, secure, and low latency communications over IoT scenarios.