ALBERT

Description: This study presents a 2-D lidar odometry based on an ICP (iterative closest point) variant used in a simple and straightforward platform that achieves real-time and low-drift performance. With a designated multi-scale feature extraction procedure, the lidar cloud information can be utilized at multiple levels and the speed of data association can be accelerated according to the multi-scale data structure, thereby achieving robust feature extraction and fast scan-matching algorithms. First, on a large scale, the lidar point cloud data are classified according to the curvature into two parts: smooth collection and rough collection. Then, on a small scale, noise and unstable points in the smooth or rough collection are filtered, and edge points and corner points are extracted. Then, the proposed tangent-vector-pairs based on edge and corner points are applied to evaluate the rotation term, which is significant for producing a stable solution in motion estimation. We compare our performance with two excellent open-source SLAM algorithms, Cartographer and Hector SLAM, using collected and open-access datasets in structured indoor environments. The results indicate that our method can achieve better accuracy.

Description: Volcanic ash fall-out represents a serious hazard for air and road traffic. The forecasting models used to predict its time–space evolution require information about the core characteristics of volcanic particles, such as their granulometry. Typically, such information is gained by the spot direct observation of the ash collected at the ground or by using expensive instrumentation. In this paper, a vision-based methodology aimed at the estimation of ash granulometry is presented. A dedicated image processing paradigm was developed and implemented in LabVIEW™. The methodology was validated experimentally using digital reference images resembling different operating conditions. The outcome of the assessment procedure was very encouraging, showing an accuracy of the image processing algorithm of 1.76%.

Description: Fault tolerance in IoT systems is challenging to overcome due to its complexity, dynamicity, and heterogeneity. IoT systems are typically designed and constructed in layers. Every layer has its requirements and fault tolerance strategies. However, errors in one layer can propagate and cause effects on others. Thus, it is impractical to consider a centralized fault tolerance approach for an entire system. Consequently, it is vital to consider multiple layers in order to enable collaboration and information exchange when addressing fault tolerance. The purpose of this study is to propose a multi-layer fault tolerance approach, granting interconnection among IoT system layers, allowing information exchange and collaboration in order to attain the property of dependability. Therefore, we define an event-driven framework called FaTEMa (Fault Tolerance Event Manager) that creates a dedicated fault-related communication channel in order to propagate events across the levels of the system. The implemented framework assist with error detection and continued service. Additionally, it offers extension points to support heterogeneous communication protocols and evolve new capabilities. Our empirical results show that introducing FaTEMa provided improvements to the error detection and error resolution time, consequently improving system availability. In addition, the use of Fatema provided a reliability improvement and a reduction in the number of failures produced.

Description: Voids in polymer matrix composites are one of the most common manufacturing defects, which may influence the mechanical properties and structural behavior of the final parts made of composites by various manufacturing methods. Therefore, numerous non-destructive testing (NDT) techniques were developed and applied for quality control and in-service testing of such structures. In this paper, the authors analyzed various alternatives to the reference technique, X-ray computed tomography (XCT) NDT, which is used for industrial testing of composite disks having defects in the form of the lower density areas. Different candidates, namely: vibration-based testing, infrared thermography, vibro-thermography, as well as ultrasonic testing were analyzed in terms of their sensitivity and technical feasibility. The quality of the results, the complexity of the testing procedure, time and labor consumption, and the cost of the equipment were analyzed and compared with the reference technique. Based on the conducted research the authors finally proposed a hybrid approach to quality control, using a combination of two NDT techniques–infrared thermography (for initial scanning and detection of near-surface defects) and ultrasonic testing (for a more detailed analysis of products that pass the first testing procedure). It allowed for replacing the costly XCT diagnostics with a much cheaper, but almost equally effective, alternative.

Description: For the sound field reconstruction of large conical surfaces, current statistical optimal near-field acoustic holography (SONAH) methods have relatively poor applicability and low accuracy. To overcome this problem, conical SONAH based on cylindrical SONAH is proposed in this paper. Firstly, elementary cylindrical waves are transformed into those suitable for the radiated sound field of the conical surface through cylinder-cone coordinates transformation, which forms the matrix of characteristic elementary waves in the conical spatial domain. Secondly, the sound pressure is expressed as the superposition of those characteristic elementary waves, and the superposition coefficients are solved according to the principle of superposition of wave field. Finally, the reconstructed conical pressure is expressed as a linear superposition of the holographic conical pressure. Furthermore, to overcome ill-posed problems, a regularization method combining truncated singular value decomposition (TSVD) and Tikhonov regularization is proposed. Large singular values before the truncation point of TSVD are not processed and remaining small singular values representing high-frequency noise are modified by Tikhonov regularization. Numerical and experimental case studies are carried out to validate the effectiveness of the proposed conical SONAH and the combined regularization method, which can provide reliable evidence for noise monitoring and control of mechanical systems.

Description: Lane and road marker segmentation is crucial in autonomous driving, and many related methods have been proposed in this field. However, most of them are based on single-frame prediction, which causes unstable results between frames. Some semantic multi-frame segmentation methods produce error accumulation and are not fast enough. Therefore, we propose a deep learning algorithm that takes into account the continuity information of adjacent image frames, including image sequence processing and an end-to-end trainable multi-input single-output network to jointly process the segmentation of lanes and road markers. In order to emphasize the location of the target with high probability in the adjacent frames and to refine the segmentation result of the current frame, we explicitly consider the time consistency between frames, expand the segmentation region of the previous frame, and use the optical flow of the adjacent frames to reverse the past prediction, then use it as an additional input of the network in training and reasoning, thereby improving the network’s attention to the target area of the past frame. We segmented lanes and road markers on the Baidu Apolloscape lanemark segmentation dataset and CULane dataset, and present benchmarks for different networks. The experimental results show that this method accelerates the segmentation speed of video lanes and road markers by 2.5 times, increases accuracy by 1.4%, and reduces temporal consistency by only 2.2% at most.

Description: Monitoring physical activity in medical and clinical rehabilitation, in sports environments or as a wellness indicator is helpful to measure, analyze and evaluate physiological parameters involving the correct subject’s movements. Thanks to integrated circuit (IC) technologies, wearable sensors and portable devices have expanded rapidly in monitoring physical activities in sports and tele-rehabilitation. Therefore, sensors and signal acquisition devices became essential in the tele-rehabilitation path to obtain accurate and reliable information by analyzing the acquired physiological signals. In this context, this paper provides a state-of-the-art review of the recent advances in electroencephalogram (EEG), electrocardiogram (ECG) and electromyogram (EMG) signal monitoring systems and sensors that are relevant to the field of tele-rehabilitation and health monitoring. Mostly, we focused our contribution in EMG signals to highlight its importance in rehabilitation context applications. This review focuses on analyzing the implementation of sensors and biomedical applications both in literature than in commerce. Moreover, a final review discussion about the analyzed solutions is also reported at the end of this paper to highlight the advantages of physiological monitoring systems in rehabilitation and individuate future advancements in this direction. The main contributions of this paper are (i) the presentation of interesting works in the biomedical area, mainly focusing on sensors and systems for physical rehabilitation and health monitoring between 2016 and up-to-date, and (ii) the indication of the main types of commercial sensors currently being used for biomedical applications.

Description: This paper investigates the hybrid source localization problem using differential received signal strength (DRSS) and angle of arrival (AOA) measurements. The main advantage of hybrid measurements is to improve the localization accuracy with respect to a single sensor modality. For sufficiently short wavelengths, AOA sensors can be constructed with size, weight, power and cost (SWAP-C) requirements in mind, making the proposed hybrid DRSS-AOA sensing feasible at a low cost. Firstly the maximum likelihood estimation solution is derived, which is computationally expensive and likely to become unstable for large noise levels. Then a novel closed-form pseudolinear estimation method is developed by incorporating the AOA measurements into a linearized form of DRSS equations. This method eliminates the nuisance parameter associated with linearized DRSS equations, hence improving the estimation performance. The estimation bias arising from the injection of measurement noise into the pseudolinear data matrix is examined. The method of instrumental variables is employed to reduce this bias. As the performance of the resulting weighted instrumental variable (WIV) estimator depends on the correlation between the IV matrix and data matrix, a selected-hybrid-measurement WIV (SHM-WIV) estimator is proposed to maintain a strong correlation. The superior bias and mean-squared error performance of the new SHM-WIV estimator is illustrated with simulation examples.

Description: The microwave drying process has a wide application in industry, including drying polymer foams after the impregnation process for sealings in the construction industry. The objective of the drying process is to reach a certain moisture in the foam by adjusting the power levels of the microwave sources. A moisture controller can be designed to achieve this goal; however, a process model is required to design model-based controllers. Since complex physics governs the microwave drying process, system identification tools are employed in this paper to exploit the process input and output information and find a simplified yet accurate model of the process. The moisture content of the foam that is the process output is measured using a designed electrical capacitance tomography (ECT) sensor. The ECT sensor estimates the 2D permittivity distribution of moving foams, which correlates with the foam moisture. Experiments are conducted to collect the ECT measurements while giving different inputs to the microwave sources. A state-space model is estimated using one of the collected datasets and is validated using the other datasets. The comparison between the model response and the actual measurements shows that the model is accurate enough to design a controller for the microwave drying process.

Description: Vehicular communication has been envisioned to support a myriad of essential fifth-generation and beyond use-cases. However, the increasing proliferation of smart and intelligent vehicles has generated a lot of design and infrastructure challenges. Of particular interest are the problems of spectrum scarcity and communication security. Consequently, we considered a cognitive radio-enabled vehicular network framework for accessing additional radio spectrum and exploit physical layer security for secure communications. In particular, we investigated the secrecy performance of a cognitive radio vehicular network, where all the nodes in the network are moving vehicles and the channels between them are modeled as double-Rayleigh fading. Furthermore, adopting an underlay approach, the communication between secondary nodes can be performed by employing two interference constraint strategies at the primary receiver; (1) Strategy I: the secondary transmitter power is constrained by the interference threshold of the primary receiver, and (2) Strategy II: the secondary transmitter power is constrained by both the interference threshold of the primary receiver and the maximum transmit power of the secondary network. Under the considered strategies, we derive the exact secrecy outage probability (SOP) and ergodic secrecy capacity (ESC) expressions over double-Rayleigh fading. Moreover, by analyzing the asymptotic SOP behavior, we show that a full secrecy diversity of 1 can be achieved, when the average channel gain of the main link goes to infinity with a fixed average wiretap channel gain. From the ESC analysis, it is revealed that the ESC follows a scaling law of ΘlnΩm2Ωe2 for large Ωm and Ωe, where Ωm and Ωe are the average channel gains of the main link and wiretap link. The numerical and simulation results verify our analytical findings.