We propose an asynchronous acoustic chirp slope keying to map short bit sequences on single or multiple bands without preamble or error correction coding on the physical layer. We introduce a symbol detection scheme in the demodulator that uses the superposed matched filter results of up and down chirp references to estimate the symbol timing, which removes the requirement of a preamble for symbol synchronization. Details of the implementation are disclosed and discussed, and the performance is verified in a pool measurement on laboratory scale, as well as the simulation for a channel containing Rayleigh fading and Additive White Gaussian Noise. For time-bandwidth products (TB) of 50 in single band mode, a raw data rate of 100 bit/s is simulated to achieve bit error rates (BER) below 0.001 for signal-to-noise ratios above −6 dB. In dual-band mode, for TB of 25 and a data rate of 200 bit/s, the same bit error level was achieved for signal-to-noise ratios above 0 dB. The simulated packet error rates (PER) follow the general behavior of the BER, but with a higher error probability, which increases with the length of bits in each packet. Full article

Air pollution is a widespread problem due to its impact on both humans and the environment. Providing decision makers with artificial intelligence based solutions requires to monitor the ambient air quality accurately and in a timely manner, as AI models highly depend on the underlying data used to justify the predictions. Unfortunately, in urban contexts, the hyper-locality of air quality, varying from street to street, makes it difficult to monitor using high-end sensors, as the cost of the amount of sensors needed for such local measurements is too high. In addition, development of pollution dispersion models is challenging. The deployment of a low-cost sensor network allows a more dense cover of a region but at the cost of noisier sensing. This paper describes the development and deployment of a low-cost sensor network, discussing its challenges and applications, and is highly motivated by talks with the local municipality and the exploration of new technologies to improve air quality related services. However, before using data from these sources, calibration procedures are needed to ensure that the quality of the data is at a good level. We describe our steps towards developing calibration models and how they benefit the applications identified as important in the talks with the municipality. Full article

We report on a prototypical study of the detection of microplastic embedded in table salts by using terahertz time-domain spectroscopy. In the experiment, high-density polyethylene (HDPE) of sizes from 150 to 400 μm are used as a representative microplastic and mixed with table salts. Analyzing terahertz transmittance with an effective medium model, we extract various optical properties such as refractive index, absorption coefficient, and real/imaginary parts of the dielectric constant of the mixture. Consequently, the optical properties exhibit volume-ratio-dependence in 0.1–0.5 THz regimes. Especially, the refractive index and the real part of the dielectric constant possess monotonic frequency dependence, meaning that the quantities can be relevant indicators for the detection of the microplastic in terms of practical applications. Our work proves that terahertz time-domain spectroscopy can pave a way to recognize microplastic mixed with salts and be expanded for detecting various micro-sized particles. Full article

Parking in heavily populated areas has been considered one of the main challenges in the transportation systems for the past two decades given the limited parking resources, especially in city centres. Drivers often waste long periods of time hunting for an empty parking spot, which causes congestion and consumes energy during the process. Thus, finding an optimal parking spot depends on several factors such as street traffic congestion, trip distance/time, the availability of a parking spot, the waiting time on the lot gate, and the parking fees. Designing a parking spot allocation algorithm that takes those factors into account is crucial for an efficient and high-availability parking service. We propose a smart routing and parking algorithm to allocate an optimal parking space given the aforementioned limiting factors. This algorithm supports choosing the appropriate travel route and parking lot while considering the real-time street traffic and candidate parking lots. A multi-objective function is introduced, with varying weights of the five factors to produce the optimal parking spot with the least congested route while achieving a balanced utilization for candidate parking lots and a balanced traffic distribution. A queueing model is also developed to investigate the availability rate in candidate parking lots while considering the arrival rate, departure rate, and the lot capacity. To evaluate the performance of the proposed algorithm, simulation scenarios have been performed for different cases of high and low traffic intensity rates. We have tested the algorithm on in-city parking facility in the city of Al Madinah as a case study. The results show that the proposed algorithm is effective in achieving a balanced utilization of the parking lots, reducing traffic congestion rates on all routes to candidate parking lots, and minimizing the driving time to the assigned parking spot. Additionally, the proposed algorithm outperforms the MADM algorithm in terms of the selected three metrics for the five periods. Full article

Leaf pigment content retrieval is an essential research field in remote sensing. However, retrieval studies on anthocyanins are quite rare compared to those on chlorophylls and carotenoids. Given the critical physiological significance of anthocyanins, this situation should be improved. In this study, using the reflectance, partial least squares regression (PLSR) and Gaussian process regression (GPR) were sought to retrieve the leaf anthocyanin content. To our knowledge, this is the first time that PLSR and GPR have been employed in such studies. The results showed that, based on the logarithmic transformation of the reflectance (log(1/R)) with 564 and 705 nm, the GPR model performed the best (R²/RMSE (nmol/cm²): 0.93/2.18 in the calibration, and 0.93/2.20 in the validation) of all the investigated methods. The PLSR model involved four wavelengths and achieved relatively low accuracy (R²/RMSE (nmol/cm²): 0.87/2.88 in calibration, and 0.88/2.89 in validation). GPR apparently outperformed PLSR. The reason was likely that the non-linear property made GPR more effective than the linear PLSR in characterizing the relationship for the absorbance vs. content of anthocyanins. For GPR, selected wavelengths around the green peak and red edge region (one from each) were promising to build simple and accurate two-wavelength models with R² > 0.90. Full article

Adhesively bonded dissimilar materials have attracted high interest in the aerospace and automotive industries due to their ability to provide superior structural characteristics and reduce the weight for energy savings. This work focuses on the improvement of disbond-type defect detectability using the immersion pulse-echo ultrasonic technique and an advanced post-processing algorithm. Despite the extensive work done for investigation, it is still challenging to locate such defects in dissimilar material joints due to the large differences in the properties of metals and composites as well as the multi-layered structure of the component. The objective of this work is to improve the detectability of defects in adhesively bonded aluminum and carbon fiber-reinforced plastic (CFRP) by the development of an advanced post-processing algorithm. It was determined that an analysis of multiple reflections has a high potential to improve detectability according to results received by inspection simulations and the evaluation of boundary characteristics. The impact of a highly influential parameter such as the sample curvature can be eliminated by the alignment of arrival time of signals reflected from the sample. The processing algorithm for the improvement of disbond detectability was developed based on time alignment followed by selection of the time intervals with a significant amplitude change of the signals reflected from defective and defect-free areas and shows significant improvement of disbond detectability. Full article

Diabetes patients are at risk of having chronic wounds, which would take months to years to resolve naturally. Chronic wounds can be countered using the electrical stimulation technique (EST) by dielectrophoresis (DEP), which is label-free, highly sensitive, and selective for particle trajectory. In this study, we focus on the validation of polystyrene particles of 3.2 and 4.8 μm to predict the behavior of keratinocytes to estimate their crossover frequency (f_XO) using the DEP force (F_DEP) for particle manipulation. MyDEP is a piece of java-based stand-alone software used to consider the dielectric particle response to AC electric fields and analyzes the electrical properties of biological cells. The prototypic 3.2 and 4.8 μm polystyrene particles have f_XO values from MyDEP of 425.02 and 275.37 kHz, respectively. Fibroblast cells were also subjected to numerical analysis because the interaction of keratinocytes and fibroblast cells is essential for wound healing. Consequently, the predicted f_XO from the MyDEP plot for keratinocyte and fibroblast cells are 510.53 and 28.10 MHz, respectively. The finite element method (FEM) is utilized to compute the electric field intensity and particle trajectory based on DEP and drag forces. Moreover, the particle trajectories are quantified in a high and low conductive medium. To justify the simulation, further DEP experiments are carried out by applying a non-uniform electric field to a mixture of different sizes of polystyrene particles and keratinocyte cells, and these results are well agreed. The alive keratinocyte cells exhibit N_DEP force in a highly conductive medium from 100 kHz to 25 MHz. 2D/3D motion analysis software (DIPP-MotionV) can also perform image analysis of keratinocyte cells and evaluate the average speed, acceleration, and trajectory position. The resultant N_DEP force can align the keratinocyte cells in the wound site upon suitable applied frequency. Thus, MyDEP estimates the Clausius–Mossotti factors (CMF), FEM computes the cell trajectory, and the experimental results of prototypic polystyrene particles are well correlated and provide an optimistic response towards keratinocyte cells for rapid wound healing applications. Full article

Audio signal acquisition using a laser speckle image is an appealing topic since it provides an accurate and non-contact solution for vibration measurement. However, due to the limitation of camera frame rate and image processing speed, previous research could not achieve real time reconstruction of an audio signal. In this manuscript, we use a one-dimensional laser speckle image to measure the acoustic vibration of sound source and propose a fast and sub-pixel accuracy algorithm to estimate the displacement of captured one-dimensional laser speckle images. Compared with previous research, the proposed method is faster and more accurate in displacement estimation. Owing to this, the frequency bandwidth and the robustness are significantly increased. Experiment results show that the proposed system can achieve 20k samples-per-second sampling rate, and the audio signal can be reconstructed with high quality in real time. Full article

Multi-Object Tracking (MOT) is an integral part of any autonomous driving pipelines because it produces trajectories of other moving objects in the scene and predicts their future motion. Thanks to the recent advances in 3D object detection enabled by deep learning, track-by-detection has become the dominant paradigm in 3D MOT. In this paradigm, a MOT system is essentially made of an object detector and a data association algorithm which establishes track-to-detection correspondence. While 3D object detection has been actively researched, association algorithms for 3D MOT has settled at bipartite matching formulated as a Linear Assignment Problem (LAP) and solved by the Hungarian algorithm. In this paper, we adapt a two-stage data association method which was successfully applied to image-based tracking to the 3D setting, thus providing an alternative for data association for 3D MOT. Our method outperforms the baseline using one-stage bipartite matching for data association by achieving 0.587 Average Multi-Object Tracking Accuracy (AMOTA) in NuScenes validation set and 0.365 AMOTA (at level 2) in Waymo test set. Full article

Image demosaicking has been an essential and challenging problem among the most crucial steps of image processing behind image sensors. Due to the rapid development of intelligent processors based on deep learning, several demosaicking methods based on a convolutional neural network (CNN) have been proposed. However, it is difficult for their networks to run in real-time on edge computing devices with a large number of model parameters. This paper presents a compact demosaicking neural network based on the UNet++ structure. The network inserts densely connected layer blocks and adopts Gaussian smoothing layers instead of down-sampling operations before the backbone network. The densely connected blocks can extract mosaic image features efficiently by utilizing the correlation between feature maps. Furthermore, the block adopts depthwise separable convolutions to reduce the model parameters; the Gaussian smoothing layer can expand the receptive fields without down-sampling image size and discarding image information. The size constraints on the input and output images can also be relaxed, and the quality of demosaicked images is improved. Experiment results show that the proposed network can improve the running speed by 42% compared with the fastest CNN-based method and achieve comparable reconstruction quality as it on four mainstream datasets. Besides, when we carry out the inference processing on the demosaicked images on typical deep CNN networks, Mobilenet v1 and SSD, the accuracy can also achieve 85.83% (top 5) and 75.44% (mAP), which performs comparably to the existing methods. The proposed network has the highest computing efficiency and lowest parameter number through all methods, demonstrating that it is well suitable for applications on modern edge computing devices. Full article

Cadmium zinc telluride (CdZnTe) detectors are known to suffer from polarization effects under high photon flux due to poor hole transport in the crystal material. This has led to the development of a high-flux capable CdZnTe material (HF-CdZnTe). Detectors with the HF-CdZnTe material have shown promising results at mitigating the onset of the polarization phenomenon, likely linked to improved crystal quality and hole carrier transport. Better hole transport will have an impact on charge collection, particularly in pixelated detector designs and thick sensors (>1 mm). In this paper, the presence of charge sharing and the magnitude of charge loss were calculated for a 2 mm thick pixelated HF-CdZnTe detector with 250 μm pixel pitch and 25 μm pixel gaps, bonded to the STFC HEXITEC ASIC. Results are compared with a CdTe detector as a reference point and supported with simulations from a Monte-Carlo detector model. Charge sharing events showed minimal charge loss in the HF-CdZnTe, resulting in a spectral resolution of 1.63 ± 0.08 keV Full Width at Half Maximum (FWHM) for bipixel charge sharing events at 59.5 keV. Depth of interaction effects were shown to influence charge loss in shared events. The performance is discussed in relation to the improved hole transport of HF-CdZnTe and comparison with simulated results provided evidence of a uniform electric field. Full article

Consumer-level 3D printers are becoming increasingly prevalent in home settings. However, research shows that printing with these desktop 3D printers can impact indoor air quality (IAQ). This study examined particulate matter (PM) emissions generated by 3D printers in an indoor domestic setting. Print filament type, brand, and color were investigated and shown to all have significant impacts on the PM emission profiles over time. For example, emission rates were observed to vary by up to 150-fold, depending on the brand of a specific filament being used. Various printer settings (e.g., fan speed, infill density, extruder temperature) were also investigated. This study identifies that high levels of PM are triggered by the filament heating process and that accessible, user-controlled print settings can be used to modulate the PM emission from the 3D printing process. Considering these findings, a low-cost home IAQ sensor was evaluated as a potential means to enable a home user to monitor PM emissions from their 3D printing activities. This sensing approach was demonstrated to detect the timepoint where the onset of PM emission from a 3D print occurs. Therefore, these low-cost sensors could serve to inform the user when PM levels in the home become elevated significantly on account of this activity and furthermore, can indicate the time at which PM levels return to baseline after the printing process and/or after adding ventilation. By deploying such sensors at home, domestic users of 3D printers can assess the impact of filament type, color, and brand that they utilize on PM emissions, as well as be informed of how their selected print settings can impact their PM exposure levels. Full article

Accurate brain tissue segmentation of MRI is vital to diagnosis aiding, treatment planning, and neurologic condition monitoring. As an excellent convolutional neural network (CNN), U-Net is widely used in MR image segmentation as it usually generates high-precision features. However, the performance of U-Net is considerably restricted due to the variable shapes of the segmented targets in MRI and the information loss of down-sampling and up-sampling operations. Therefore, we propose a novel network by introducing spatial and channel dimensions-based multi-scale feature information extractors into its encoding-decoding framework, which is helpful in extracting rich multi-scale features while highlighting the details of higher-level features in the encoding part, and recovering the corresponding localization to a higher resolution layer in the decoding part. Concretely, we propose two information extractors, multi-branch pooling, called MP, in the encoding part, and multi-branch dense prediction, called MDP, in the decoding part, to extract multi-scale features. Additionally, we designed a new multi-branch output structure with MDP in the decoding part to form more accurate edge-preserving predicting maps by integrating the dense adjacent prediction features at different scales. Finally, the proposed method is tested on datasets MRbrainS13, IBSR18, and ISeg2017. We find that the proposed network performs higher accuracy in segmenting MRI brain tissues and it is better than the leading method of 2018 at the segmentation of GM and CSF. Therefore, it can be a useful tool for diagnostic applications, such as brain MRI segmentation and diagnosing. Full article

Underwater wireless sensor networks (UWSNs) have emerged as a promising technology to monitor and explore the oceans instead of traditional undersea wireline instruments. Traditional routing protocols are inefficient for UWSNs due to the specific nature of the underwater environment. In contrast, Opportunistic Routing (OR) protocols establish an online route for each transmission, which can well adapt with time-varying underwater channel. Cross-layer design is an effective approach to combine the metrics from different layers to optimize an OR routing in UWSNs. However, typical cross-layer OR routing protocols that are designed for UWSNs suffer from congestion problem at high traffic loads. In this paper, a Cross-Layer-Aided Opportunistic Routing Protocol (CLOR) is proposed to reduce the congestion in multi-hop sparse UWSNs. The CLOR consists of a negotiation phase and transmission phase. In the negotiation phase, the cross-layer information in fuzzy logic is utilized to attain an optimal forwarder node. In the transmission phase, to improve the transmission performance, a burst transmission strategy with network coding is exploited. Finally, we perform simulations of the proposed CLOR protocol in a specific sea region. Simulation results show that CLOR significantly improves the network performances at various traffic rates compared to existing protocols. Full article

Decision-making is an important part of human life and particularly in any engineering process related to a complex product. New sensors and actuators based on MEMS technologies are increasingly complex and quickly evolving into products. New biomedical implanted devices may benefit from system engineering approaches, previously reserved to very large projects, and it is expected that this need will increase in the future. Here, we propose the application of Model Based Systems Engineering (MBSE) to systematize and optimize the trade-off analysis process. The criteria, their utility functions and the weighting factors are applied in a systematic way for the selection of the best alternative. Combining trade-off with MBSE allow us to identify the more suitable technology to be implemented to transfer energy to an implanted biomedical micro device. Full article