Electronics, Volume 10, Issue 3 (February-1 2021) – 143 articles

With the ever-advancing availability of digitized museum artifacts, the question of how to make the vast collection of exhibits accessible and explorable beyond what museums traditionally offer via their websites and exposed databases has recently gained increased attention. This research work introduces the Invisible Museum: a user-centric platform that allows users to create interactive and immersive virtual 3D/VR exhibitions using a unified collaborative authoring environment. The platform itself was designed following a Human-Centered Design approach, with the active participation of museum curators and end-users. Content representation adheres to domain standards such as International Committee for Documentation of the International Council of Museums (CIDOC-CRM) and the Europeana Data Model and exploits state-of-the-art deep learning technologies to assist the curators by generating ontology bindings for textual data. The platform enables the formulation and semantic representation of narratives that guide storytelling experiences and bind the presented artifacts with their socio-historic context. Main contributions are pertinent to the fields of (a) user-designed dynamic virtual exhibitions, (b) personalized suggestions and exhibition tours, (c) visualization in web-based 3D/VR technologies, and (d) immersive navigation and interaction. The Invisible Museum has been evaluated using a combination of different methodologies, ensuring the delivery of a high-quality user experience, leading to valuable lessons learned, which are discussed in the article. Full article

The high penetration of distributed generation in distributed energy systems causes the variation of power loss and makes the power grid become more complicated, so this paper takes various types of optimal algorithms into account and simulates the feeder reconfiguration on the IEEE-33 system as well as the Taiwan power system. The simulation verifies linear population size reduction of successful history-based adaptive differential evolution (L-SHADE) and particle swarm optimization (PSO) fitness in different systems and provides the recommended location of distributed energy. The proposed method keeps the voltage bound of 0.95 to 1.03 p.u. of Taiwan regulation. In the IEEE-33 system, we achieved a 52.57% power loss reduction after feeder reconfiguration, and a 70.55% power loss reduction after the distributed generator was implemented and feeder reconfiguration. Under the variation of load demand and power generation of the Taiwan power system, we establish the system models by forecasting one-day load demand. Then, we propose a one-day feeder switch operation strategy by considering the switches’ operation frequency with the reduction of 83.3% manual operation and recommend feeder automation to achieve feeder power loss reduction, voltage profile improvement and get regional power grid resilient configuration. Full article

Fast object recognition and classification is highly important when handling operations with robots. This article shows the design and implementation of an invariant recognition machine vision system to compute a descriptive vector called the Boundary Object Function (BOF) using the FuzzyARTMAP (FAM) Neural Network. The object recognition machine is integrated in the Zybo Z7-20 module that includes reconfigurable FPGA hardware and a RISC processor. Object encoding, description and prediction is carried out rapidly compared to the processing time devoted to video capture at the camera’s frame rate. Benefiting from parallel computing, we calculated the object’s centroid and boundary points while acquiring the progressive image frame; all that was done with the intention of readying it for neural processing. The remaining time was devoted to recognising the object, this caused low latency (1.47 ms). Our test-bed also included TCP/IP communication to send/receive part location for grasping operations with an industrial robot to evaluate the approach. Results demonstrate that the hardware integration of the video sensor, image processing, descriptor generator, and the ANN classifier for cognitive decision on a single chip can increase the speed and performance of intelligent robots designed for smart manufacturing. Full article

A great deal of information is produced daily, due to advances in telecommunication, and the issue of storing it on digital devices or transmitting it over the Internet is challenging. Data compression is essential in managing this information well. Therefore, research on data compression has become a topic of great interest to researchers, and the number of applications in this area is increasing. Over the last few decades, international organisations have developed many strategies for data compression, and there is no specific algorithm that works well on all types of data. The compression ratio, as well as encoding and decoding times, are mainly used to evaluate an algorithm for lossless image compression. However, although the compression ratio is more significant for some applications, others may require higher encoding or decoding speeds or both; alternatively, all three parameters may be equally important. The main aim of this article is to analyse the most advanced lossless image compression algorithms from each point of view, and evaluate the strength of each algorithm for each kind of image. We develop a technique regarding how to evaluate an image compression algorithm that is based on more than one parameter. The findings that are presented in this paper may be helpful to new researchers and to users in this area. Full article

Recently, with the rapid development of biomedical information, establishing secure communication and appropriate security services has become necessary to ensure a secure information exchange process. Therefore, to protect the privacy and confidentiality of personal data, in this study, we use a chaotic system, Lü system of the Lorenz-like system, to generate chaotic signals and apply them to encrypt the biomedical information. In addition, with one of the states of the chaotic system, we design a simple proportional-derivative (PD) controller to synchronize the master-slave chaotic systems for decrypting the biomedical information. Then, we encrypt the biomedical information, electrocardiography (ECG) and electromyography (EMG), measured about 30 s to 60 s to get tens of thousands of data from the subjects at the transmitting side (master) and send them to the receiving side (slave). After the receiving side receives the encrypted information, it decrypts them with the PD controller and then obtains the 1 mV to 2 mV biomedical signals. Thus, the security of the biomedical information can be ensured and realized. Full article

This paper shows an advanced method that is able to achieve accurate recognition of fear facial emotions by providing quantitative evaluation of other negative emotions. The proposed approach is focused on both a calibration computing procedure and an important feature pattern technique, which is applied to extract the most relevant characteristics on different human faces. In fact, a 3D/2D projection method is highlighted in order to deal with angular variation (AD) and orientation effects on the emotion detection. Using the combination of the principal component analysis algorithm and the artificial neural network method (PCAN), a supervised classification system is finally achieved to recognize the considered emotion data split into two categories: fear and others. The obtained results have reached an encouraging accuracy up to 20° of AD. Compared to other state-of-art and classification strategies, we recorded the highest accuracy of identified fear emotion. A statistical analysis is carried out on the whole facial emotions, which confirms the best classification performance (positive predictive values (PPV) = 95.13, negative predictive values (NPV) = 94.65, positive likelihood ratio (PLr) = 33.9, and negative likelihood ratio (NLr) = 0.054. The confidence interval for both of PPV and NPV is 92–98%. The proposed framework can be easily applied for any security domain that needs to effectively distinguish the fear cases recognition. Full article

The competitive relationship between the surface flashover of the coplanar electrodes and the body current channel was investigated. Breakdown behavior of GaAs photo-conductive semiconductor switch (PCSS) with a backside-light-receiving coplanar electrode structure was studied in this paper. GaAs PCSS was triggered by the laser pulse with an extrinsic absorption wavelength of 1064 nm. Special insulating construction was designed for GaAs PCSS, while the surface of the electrodes was encapsulated with transparent insulating adhesive. Our first set of experiments was at a bias voltage of 8 kV, and the surface flashover breakdown of GaAs PCSS was observed with 10 Hz triggering laser pulse. In the second experiment, at a bias voltage of 6 kV, the body current channel breakdown appeared on the backside of the GaAs PCSS. Compared with these results, the existence of a competitive relationship between the surface flashover breakdown and the body current channel breakdown of the GaAs PCSS was confirmed. When the bias voltage is set within a certain range (just reaching avalanche mode), GaAs PCSS with a backside-light-receiving coplanar electrode structure will undergo the body current channel breakdown. This finding is also consistent with the simulation results. Full article

Unmanned aerial vehicle (UAV) communication is regarded as a promising technology for lightweight Internet of Things (IoT) communications in narrowband-IoT (NB-IoT) systems deployed in rugged terrain. In such UAV-assisted NB-IoT systems, the optimal UAV placement and resource allocation play a critical role. Consequently, the joint optimization of the UAV placement and resource allocation is considered in this study to improve the system capacity. Because the considered optimization problem is an NP-hard problem and owing to its non-convex property, it is difficult to optimize both the UAV placement and resource allocation simultaneously. Therefore, a competitive clustering algorithm has been developed by exchanging strategies between the UAV and the adjacent IoT devices to optimize the UAV placement. With multiple iterations, the UAV and the IoT devices within the coverage area of the UAV, converge their clustering strategies, which are suboptimal, to satisfy both sides. The bordering IoT devices of the adjacent clusters are then migrated heuristically toward each other to obtain the optimal system capacity maximization. Finally, the transmission throughput is optimized using the Nash equilibrium. The simulation results demonstrate that the algorithms proposed in this study exhibit rapid convergence, within 10 iterations, even in a large environment. The performance evaluation demonstrates that the proposed scheme improves the system capacity of the existing schemes by approximately 28%. Full article

In this paper, two light-load efficiency improvement methods are presented and applied to the ultrahigh step-down converter. The two methods are both based on skip mode control. Skip Mode 1 only needs one half-bridge driver integrated circuit (IC) to drive three switches, so it has the advantages of easy signal control and lower cost, whereas Skip Mode 2 requires one half-bridge driver integrated circuit IC, one common ground driver IC, and three independent timing pulse-width-modulated (PWM) signals to control three switches, so the cost is higher and the control signals are more complicated, but Skip Mode 2 can obtain slightly higher light-load efficiency than Skip Mode 1. Although the switching frequency used in these methods are reduced, the transferred energy is unchanged, but the output voltage ripple is influenced to some extent. Full article

Currently, most of the transportation systems require changes to intelligent transportation systems, but most of them focus on efficient transportation rather than on improvement in human life. Sometimes, traffic systems are designed for economic value, and safety-related issues are neglected. A traffic information system that reflects various kinds of environmental information related to people’s safety must be able to reflect not only the existing economic goals but also a safe traffic environment. The traffic environment can be thought of as safety and direct information such as rainfall, including information on specific days when many people are scheduled to be gathered for certain events nearby. Intelligent transportation systems using this information can provide safety-related information for traveling to a specific area or for business trips. In addition, traffic congestion is a social problem and is directly related to a comfort life for individuals. Therefore, addressing various social and environmental factors could make human life more stable and reduce stress as a result. To do that, we need to estimate the impact on traffic based on environmental Big Data. The data can generally be divided into structured data and unstructured data. In inference, structured data analysis is relatively easy due to the precise meaning of the data. Nonetheless, it can be very difficult to predict environmentally sensitive data, such as traffic volume in intelligent transportation systems. To cope with this problem, there are a few systems for handling unstructured data to find out specific events that affect the traffic volume and improve its reliability. This paper shows that it is possible to estimate the exact volume of traffic using correlation analysis with various predicted data. Thus, we may apply this technique to the existing intelligent transportation system to predict the exact volume of traffic with environmentally sensitive data including various unstructured data. Full article

Internet telephony permit callers to manage self-asserted profiles without any subscription contract nor identification proof. These cost-free services have attracted many telemarketers and spammers who generate unsolicited nuisance calls. Upon detection, they simply rejoin the network with a new identity to continue their malicious activities. Nuisance calls are highly disruptive when compared to email and social spam. They not only include annoying telemarketing calls but also contain scam and voice phishing which involves security risk for subscribers. Therefore, it remains a major challenge for Internet telephony providers to detect and avoid nuisance calls efficiently. In this paper, we present a new approach that uses caller reputation to detect different kinds of nuisance calls generated in the network. The reputation is computed in a hybrid manner by extracting information from call data records and using recommendations from reliable communicating participants. The behavior of the caller is assessed by extracting call features such as call-rate, call duration, and call density. Long term and short term reputations are computed to quickly detect the changing behavior of callers. Furthermore, our approach involves an efficient mechanism to combat whitewashing attacks performed by malicious callers to continue generating nuisance calls in the network. We conduct simulations to compute the performance of our proposed model. The experiments conclude that the proposed reputation model is an effective method to detect different types of nuisance calls while avoiding false detection of legitimate calls. Full article

In this paper, the application of the supervised descent method (SDM) for 2-D microwave thorax imaging is studied. The forward modeling problem is solved by the finite element-boundary integral (FE-BI) method. According to the prior information of human thorax, a 3-ellipse training set is generated offline. Then, the average descent direction between an initial background model and the training models is calculated. Finally, the reconstruction of the testing thorax model is achieved based on the average descent directions online. The feasibility using One-Step SDM for thorax imaging is studied. Numerical results indicate that the structural information of thorax can be reconstructed. It has potential for real-time imaging in future clinical diagnosis. Full article

Using Spatial Domain Correlation Pattern Recognition (CPR) in Internet-of-Things (IoT)-based applications often faces constraints, like inadequate computational resources and limited memory. To reduce the computation workload of inference due to large spatial-domain CPR filters and convert filter weights into hardware-friendly data-types, this paper introduces the power-of-two (Po2) and dynamic-fixed-point (DFP) quantization techniques for weight compression and the sparsity induction in filters. Weight quantization re-training (WQR), the log-polar, and the inverse log-polar geometric transformations are introduced to reduce quantization error. WQR is a method of retraining the CPR filter, which is presented to recover the accuracy loss. It forces the given quantization scheme by adding the quantization error in the training sample and then re-quantizes the filter to the desired quantization levels which reduce quantization noise. Further, Particle Swarm Optimization (PSO) is used to fine-tune parameters during WQR. Both geometric transforms are applied as pre-processing steps. The Po2 quantization scheme showed better performance close to the performance of full precision, while the DFP quantization showed further closeness to the Receiver Operator Characteristic of full precision for the same bit-length. Overall, spatial-trained filters showed a better compression ratio for Po2 quantization after retraining of the CPR filter. The direct quantization approach achieved a compression ratio of 8 at 4.37× speedup with no accuracy degradation. In contrast, quantization with a log-polar transform is accomplished at a compression ratio of 4 at 1.12× speedup, but, in this case, 16% accuracy of degradation is noticed. Inverse log-polar transform showed a compression ratio of 16 at 8.90× speedup and 6% accuracy degradation. All the mentioned accuracies are reported for a common database. Full article

This paper proposes Hermes, a container-based preemptive GPU scheduling framework for accelerating hyper-parameter optimization in deep learning (DL) clusters. Hermes accelerates hyper-parameter optimization by time-sharing between DL jobs and prioritizing jobs with more promising hyper-parameter combinations. Hermes’s scheduling policy is grounded on the observation that good hyper-parameter combinations converge quickly in the early phases of training. By giving higher priority to fast-converging containers, Hermes’s GPU preemption mechanism can accelerate training. This enables users to find optimal hyper-parameters faster without losing the progress of a container. We have implemented Hermes over Kubernetes and compared its performance against existing scheduling frameworks. Experiments show that Hermes reduces the time for hyper-parameter optimization up to 4.04 times against previously proposed scheduling policies such as FIFO, round-robin (RR), and SLAQ, with minimal time-sharing overhead. Full article

In this paper, we present a new method designed to recognize single parametric faults in analog circuits. The technique follows a rigorous approach constituted by three sequential steps: calculating the testability and extracting the ambiguity groups of the circuit under test (CUT); localizing the failure and putting it in the correct fault class (FC) via multi-frequency measurements or simulations; and (optional) estimating the value of the faulty component. The fabrication tolerances of the healthy components are taken into account in every step of the procedure. The work combines machine learning techniques, used for classification and approximation, with testability analysis procedures for analog circuits. Full article

Semantic image segmentation has a wide range of applications. When it comes to medical image segmentation, its accuracy is even more important than those of other areas because the performance gives useful information directly applicable to disease diagnosis, surgical planning, and history monitoring. The state-of-the-art models in medical image segmentation are variants of encoder-decoder architecture, which is called U-Net. To effectively reflect the spatial features in feature maps in encoder-decoder architecture, we propose a spatially adaptive weighting scheme for medical image segmentation. Specifically, the spatial feature is estimated from the feature maps, and the learned weighting parameters are obtained from the computed map, since segmentation results are predicted from the feature map through a convolutional layer. Especially in the proposed networks, the convolutional block for extracting the feature map is replaced with the widely used convolutional frameworks: VGG, ResNet, and Bottleneck Resent structures. In addition, a bilinear up-sampling method replaces the up-convolutional layer to increase the resolution of the feature map. For the performance evaluation of the proposed architecture, we used three data sets covering different medical imaging modalities. Experimental results show that the network with the proposed self-spatial adaptive weighting block based on the ResNet framework gave the highest IoU and DICE scores in the three tasks compared to other methods. In particular, the segmentation network combining the proposed self-spatially adaptive block and ResNet framework recorded the highest 3.01% and 2.89% improvements in IoU and DICE scores, respectively, in the Nerve data set. Therefore, we believe that the proposed scheme can be a useful tool for image segmentation tasks based on the encoder-decoder architecture. Full article

Studying interactions of children with humanoid robots in familiar spaces in natural contexts has become a key issue for social robotics. To fill this need, we conducted several Child–Robot Interaction (CRI) events with the Pepper robot in Polish and Japanese kindergartens. In this paper, we explore the role of trust and expectations towards the robot in determining the success of CRI. We present several observations from the video recordings of our CRI events and the transcripts of free-format question-answering sessions with the robot using the Wizard-of-Oz (WOZ) methodology. From these observations, we identify children’s behaviors that indicate trust (or lack thereof) towards the robot, e.g., challenging behavior of a robot or physical interactions with it. We also gather insights into children’s expectations, e.g., verifying expectations as a causal process and an agency or expectations concerning the robot’s relationships, preferences and physical and behavioral capabilities. Based on our experiences, we suggest some guidelines for designing more effective CRI scenarios. Finally, we argue for the effectiveness of in-the-wild methodologies for planning and executing qualitative CRI studies. Full article

A comprehensive analysis of two types of artificial neural networks (ANN) is performed to assess the influence of quantization on the synaptic weights. Conventional multilayer-perceptron (MLP) and convolutional neural networks (CNN) have been considered by changing their features in the training and inference contexts, such as number of levels in the quantization process, the number of hidden layers on the network topology, the number of neurons per hidden layer, the image databases, the number of convolutional layers, etc. A reference technology based on 1T1R structures with bipolar memristors including $Hf{O}_2$ dielectrics was employed, accounting for different multilevel schemes and the corresponding conductance quantization algorithms. The accuracy of the image recognition processes was studied in depth. This type of studies are essential prior to hardware implementation of neural networks. The obtained results support the use of CNNs for image domains. This is linked to the role played by convolutional layers at extracting image features and reducing the data complexity. In this case, the number of synaptic weights can be reduced in comparison to MLPs. Full article

Radio-over-fiber (RoF) links successfully provide high data rates and bandwidth capacity with a low complexity system architecture, as compared to its counterpart digital-RoF. In addition, the compound of quadrature amplitude modulation (QAM) and orthogonal frequency division multiplexed (OFDM) modulation schemes further enhance the process of these achievements. However, high data rates and bandwidth-capacity-supported RoF links face nonlinearities (NLs), linear distortions (LDs), and phase noise challenges that degrade the reliability of communication networks (CNs). Therefore, in this paper, to suppress NLs, LDs, and phase noise, next generation cloud radio access networks (CRANs) are investigated using RoF links and wavelength division multiplexing (WDM) methodology based on 16, 32, and 64 QAM-OFDM modulation schemes. The receiver of the proposed framework is designed, applying an improved digital signal processing (DSP) system that includes overlap frequency domain equalization (OFDE), a synchronization process, and time domain equalization (TDE). Theoretical and simulation models are organized for estimating the proposed RoF link with the aid of different values of transmission ranges, input power, output power, bit rate, bits per symbol, channel spacing, and the number of users. The fitness of the model matches that of existing approaches. Full article

In the case of level 3 automated vehicles, in order to safely and quickly transfer control authority rights to manual driving, it is necessary that a study be conducted on the characteristics of human factors affecting the transition of manual driving. In this study, we conducted three experiments to compare the characteristics of human factors that influence the driver’s quality of response when re-engaging and stabilizing manual driving. The three experiments were conducted sequentially by dividing them into a normal driving situation, an obstacle occurrence situation in front, and an obstacle and congestion on surrounding roads. We performed a statistical analysis and classification and regression tree (CART) analysis using experimental data. We found that as the number of trials increased, there was a learning effect that shortened re-engagement times and increased the proportion of drivers with good response times. We found that the stabilization time increased as the experiment progressed, as obstacles appeared in front and traffic density increased in the surrounding lanes. The results of the analysis are useful for vehicle developers designing safer human–machine interfaces and for governments developing guidelines for automated driving systems. Full article

New smartphones made by small companies enter the technology market everyday. These new devices introduce new challenges for mobile forensic investigators as these devices end up becoming pertinent evidence during an investigation. One such device is the PinePhone from Pine Microsystems (Pine64). These new devices are sometimes also shipped with OSes that are developed by open source communities and are otherwise never seen by investigators. Ubuntu Touch is one of these OSes and is currently being developed for deployment on the PinePhone. There is little research behind both the device and OS on what methodology an investigator should follow to reliably and accurately extract data. This results in potentially flawed methodologies being used before any testing can occur and contributes to the backlog of devices that need to be processed. Therefore, in this paper, the first forensic analysis of the PinePhone device with Ubuntu Touch OS is performed using Autopsy, an open source tool, to establish a framework that can be used to examine and analyze devices running the Ubuntu Touch OS. The findings include analysis of artifacts that could impact user privacy and data security, organization structure of file storage, app storage, OS, etc. Moreover, locations within the device that stores call logs, SMS messages, images, and videos are reported. Interesting findings include forensic artifacts, which could be useful to investigators in understanding user activity and attribution. This research will provide a roadmap to the digital forensic investigators to efficiently and effectively conduct their investigations where they have Ubuntu Touch OS and/or PinePhone as the evidence source. Full article

The proliferation of info-entertainment systems in nowadays vehicles has provided a really cheap and easy-to-deploy platform with the ability to gather information about the vehicle under analysis. With the purpose to provide an architecture to increase safety and security in automotive context, in this paper we propose a fully connected neural network architecture considering position-based features aimed to detect in real-time: (i) the driver, (ii) the driving style and (iii) the path. The experimental analysis performed on real-world data shows that the proposed method obtains encouraging results. Full article

Postural instability owing to poor proprioception is considered a main cause of low back pain and falls. However, the effect of local vibratory stimulation on a poor proprioceptor on proprioceptive control strategy has yet to be evaluated. Therefore, in this study, we proposed an evaluation method of the immediate effect on proprioceptive control strategies by applying local vibratory stimulation to the poor proprioceptor. First, using our device, we determined the poor proprioceptors in each of six elderly patients with non-specific low back pain. Furthermore, we applied local vibratory stimulation to the poor proprioceptor. Finally, we compared the proprioceptive control strategy before and after applying local vibratory stimulation. As a result, the proprioceptive control strategy improved for three patients with impaired muscle spindles that responded to a higher frequency (p < 0.05). Thus, the impaired proprioceptive control strategy caused by a decline in the muscle spindle responding to a higher frequency might be improved by local vibratory stimulation. Furthermore, it was shown that our developed device and protocol might be used to evaluate proprioceptive control strategies within multiple frequency ranges, as well as activate a poor proprioceptor based on diagnosis and improve the proprioceptive control strategies. Full article

The paper presents some investigation results on the properties of forced cooling systems dedicated to electronic devices. Different structures of such systems, including Peltier modules, heat sinks, fans, and thermal interfaces, are considered. Compact thermal models of such systems are formulated. These models take into account a multipath heat transfer and make it possible to compute waveforms of the device’s internal temperature at selected values of the power dissipated in the device. The analytical formulas describing the dependences of the thermal resistance of electronic devices co-operating with the considered cooling systems on the power dissipated in the cooled electronic device and the power feeding the Peltier module and the speed of airflow caused by a fan are proposed. The correctness of the proposed models is verified experimentally in a wide range of powers dissipated in electronic devices operating in different configurations of the used cooling system. Full article

This paper focuses on the calibration problem using stereo camera images. Currently, advanced vehicle systems such as smart cars and mobile robots require accurate and reliable vision in order to detect obstacles and special marks around. Such modern vehicles can be equipped with sensors and cameras together or separately. In this study, we propose new methodologies of stereo camera calibration based on the correction of distortion and image rectification. Once the calibration is complete, the validation of the corrections is presented followed by an evaluation of the calibration process. Usually, the validation section is not jointly considered with the calibration in other studies. However, the mass production of cameras widely uses the validation techniques in calibrations owned by manufacturing businesses. Here, we aim to present a single process for the calibration and validation of stereo cameras. The experiment results showed the disparity maps in comparison with another study and proved that the proposed calibration methods can be efficient. Full article

A microstrip-fed air-substrate-integrated waveguide (ASIW) slot array with high efficiency and low cost is presented. The design cuts out the substrate material within SIW, replaces the vias with metallic sidewalls, and uses a simple microstrip line-waveguide transition to feed the slot array. Radiating slots are cut on a 5-mil brass-plate, which covers the top of the substrate cutout to resemble a hollow waveguide structure. This implementation provides a simple and efficient antenna array solution for millimeter-wave (mm-wave) applications. Meanwhile, the fabrication is compatible with the standard printed circuit board (PCB) manufacturing process. To demonstrate the concept, a 4-element ASIW slot array working at the n257 band for 5G communications was designed using low-cost Rogers 4350B and FR4 substrate materials. Our simulation result shows 18% more efficiency than a conventional SIW slot array using the same substrate. The fabricated prototype shows |S11| < −15 dB over 27–29 GHz and a peak realized gain of 10.1 dBi at 28.6 GHz. The design procedure, prototyping process, and design analysis are discussed in the paper. Full article

A theoretical study of the influence of word line and common source electrodes on the program operation in shrank SuperFlash memory is proposed. Numerical simulations demonstrate that the literature model defined for previous nodes is not always suitable, due to the continuous cell physical size reduction and to the consequent increment of capacitive coupling between the floating gate and adjacent electrodes. To get a deeper insight, an analytical model of the electric field in the region of source side injection is proposed. This model describes the impact of the cell physical and electrical parameters on the vertical and horizontal field components and highlights the strong dependence of the carrier injection on the technology node. Furthermore, the numerical and analytical models estimate the influence of the word line and common source electrodes on the time-to-program, the floating gate potential and the source side injection efficiency, taking into consideration, at the same time, their possible impact on the cell reliability. Full article

We demonstrate spectroscopic imaging using a compact ultra-broadband terahertz semiconductor source with a high-power, mid-infrared quantum cascade laser. The electrically pumped monolithic source is based on intra-cavity difference-frequency generation and can be designed to achieve an ultra-broadband multi-mode terahertz emission spectrum extending from 1–4 THz without any external optical setup. Spectroscopic imaging was performed with three frequency bands, 2.0 THz, 2.5 THz and 3.0 THz, and as a result, this imaging technique clearly identified three different tablet components (polyethylene, D-histidine and DL-histidine). This method may be highly suitable for quality monitoring of pharmaceutical materials. Full article

After a disaster happens, effective communication and information sharing between emergency response team members play a crucial role in a successful disaster response phase. With dedicated roles and missions are assigned to responders, role-based communication is a pivotal feature that an emergency communication network needs to support. Previous works have shown that Named Data Networking (NDN) has many advantages over traditional IP-based networks in providing this feature. However, these studies are only simulation-based. To apply NDN in disaster scenarios, real implementation of a deployment architecture over existing infrastructure during the disaster should be considered. Not only should it ensure efficient emergency communication, but the architecture should deal with other disaster-related challenges such as responder mobility, intermittent network, and replacement possibility due to disaster damage. In this paper, we designed and implemented an NDN-based disaster response support system over Edge Computing infrastructure with KubeEdge as the chosen edge platform to solve the above issues. Our proof-of-concept system performance shows that the architecture achieved efficient role-based communication support, fast mobility handover duration, quick network convergence time in case of node replacement, and loss-free information exchange between responders and the management center on the cloud. Full article

A 2.4 GHz flexible monopole antenna fed by a coplanar waveguide (CPW) was presented on polyimide (PI) as the dielectric substrate, which was fabricated by in situ self-metallization. The technology does not depend on expensive equipment or complex experimental environments, including hydrolysis, ion exchange, and reduction reaction. The measurement results show that the resonance frequency of the proposed antenna is 2.28 GHz, the bandwidth is 2.06–2.74 GHz, and the relative bandwidth is 28.33% under the flat state. The bending and folding test was also carried out. Whether it was flat, bent, or folded, the measured results met the requirements of the antenna. A fatigue test was carried out to illustrate that the prepared film has high mechanical flexibility, which expands the application field of antenna. Full article