Table of Contents

A switching losses reduction technique for the model predictive control (MPC) algorithm, which uses double-vector in the three-phase rectifier, is presented. The proposed method controls the output voltage of the rectifier by using reference rectifier input voltages with the offset voltage injection to reduce the switching losses. One leg with the largest source current among the three legs in the rectifier is clamped to either the positive or negative output voltage in the proposed method. The proposed method calculates the offset voltage on the basis of the future rectifier input voltages obtained by the reference rectifier input voltage, output voltage, and the source currents in every sampling period, so the clamping region in the leg conducting the largest input current is optimally varied depending on the reference rectifier input voltages and the source currents. Therefore, the proposed method can reduce the switching losses of the rectifier regardless of the different source power factor angle. Due to the effects of clamped legs, the quality of the input current waveform inevitably deteriorated. Thus, in the proposed method, double vectors were utilized to avoid degradation of current qualities and achieved compromised performance by reducing switching losses and keeping the current waveform quality. A performance comparison between the conventional method and the proposed method was made to show performance differences. Additionally, the simulation and experiment were conducted to verify the effectiveness of the proposed method. Full article

In this paper, a novel model predictive control strategy, with a 24-h prediction horizon, is proposed to reduce the operational cost of microgrids. To overcome the complexity of the optimization problems arising from the operation of the microgrid at each step, an adaptive evolutionary strategy with a satisfactory trade-off between exploration and exploitation capabilities was added to the model predictive control. The proposed strategy was evaluated using a representative microgrid that includes a wind turbine, a photovoltaic plant, a microturbine, a diesel engine, and an energy storage system. The achieved results demonstrate the validity of the proposed approach, outperforming a global scheduling planner-based on a genetic algorithm by 14.2% in terms of operational cost. In addition, the proposed approach also better manages the use of the energy storage system. Full article

Although image inpainting based on the generated adversarial network (GAN) has made great breakthroughs in accuracy and speed in recent years, they can only process low-resolution images because of memory limitations and difficulty in training. For high-resolution images, the inpainted regions become blurred and the unpleasant boundaries become visible. Based on the current advanced image generation network, we proposed a novel high-resolution image inpainting method based on multi-scale neural network. This method is a two-stage network including content reconstruction and texture detail restoration. After holding the visually believable fuzzy texture, we further restore the finer details to produce a smoother, clearer, and more coherent inpainting result. Then we propose a special application scene of image inpainting, that is, to delete the redundant pedestrians in the image and ensure the reality of background restoration. It involves pedestrian detection, identifying redundant pedestrians and filling in them with the seemingly correct content. To improve the accuracy of image inpainting in the application scene, we proposed a new mask dataset, which collected the characters in COCO dataset as a mask. Finally, we evaluated our method on COCO and VOC dataset. the experimental results show that our method can produce clearer and more coherent inpainting results, especially for high-resolution images, and the proposed mask dataset can produce better inpainting results in the special application scene. Full article

This paper presents an extremely cost-effective radio-frequency integrated circuit (RFIC) implementation technique by employing a digital logic CMOS process and reducing area occupation for voltage-controlled oscillators (VCOs) using all miniature inductors for wireless sensor network (WSN) applications. The designed VCO operates in the 4.0 GHz band with a power consumption of 1.4 mW and a phase noise of −113.6 dBc/Hz at 1 MHz, occupying a Si area of 0.283 × 0.682 mm². In addition, we confirmed that the figure of merit (FOM) of 183.8 in our design is competitive with that of other LC-VCOs that were fabricated using the RF option and designed with conventional inductors. Full article

Generally, ultrasound receive beamformers calculate the focusing time delays of fixed sound speeds in human tissue (e.g., 1540 m/s). However, phase distortions occur due to variations of sound speeds in soft tissues, resulting in degradation of image quality. Thus, an optimal estimation of sound speed is required in order to improve image quality. Implementation of real-time sound speed estimation is challenging due to high computational and hardware complexities. In this paper, an optimal sound speed estimation method with a low-cost hardware resource is presented. In the proposed method, the optimal mean sound speed is determined by measuring the amplitude variance of pre-beamformed radio-frequency (RF) data. The proposed method was evaluated with phantom and in vivo experiments, and implemented on Virtex-4 with Xilinx ISE 12.4 using VHDL. Experiment results indicate that the proposed method could estimate the mean optimal sound speed and enhance spatial resolution with a negligible increase in the hardware resource usage. Full article

This paper reports the development of an efficient evolutionary learning algorithm designed specifically for real-time embedded visual inspection applications. The proposed evolutionary learning algorithm constructs image features as a series of image transforms for image classification and is suitable for resource-limited systems. This algorithm requires only a small number of images and time for training. It does not depend on handcrafted features or manual tuning of parameters and is generalized to be versatile for visual inspection applications. This allows the system to be configured on the fly for different applications and by an operator without extensive experience. An embedded vision system, equipped with an ARM processor running Linux, is capable of performing at roughly one hundred 640 × 480 frames per second which is more than adequate for real-time visual inspection applications. As example applications, three image datasets were created to test the performance of this algorithm. The first dataset was used to demonstrate the suitability of the algorithm for visual inspection automation applications. This experiment combined two applications to make it a more challenging test. One application was for separating fertilized and unfertilized eggs. The other one was for detecting two common defects on the eggshell. Two other datasets were created for road condition classification and pavement quality evaluation. The proposed algorithm was 100% for fertilized egg detection and 98.6% for eggshell quality inspection for a combined 99.1% accuracy. It had an accuracy of 92% for the road condition classification and 100% for pavement quality evaluation. Full article

The active and reactive powers, P and Q, are crucial variables in the parallel operation of single-phase inverters using the droop method, introducing proportional droops in the inverter output frequency and voltage amplitude references. P and Q, or P-Q, are calculated as the product of the inverter output voltage and its orthogonal version with the output current, respectively. However, when sharing nonlinear loads these powers, Pav and Qav, should be averaged by low-pass filters (LPFs) with a very low cut-off frequency to avoid the high distortion induced by these loads. This forces the droop method to operate at a very low dynamic velocity and degrades the system stability. Then, different solutions have been proposed in literature to increase the system velocity, but only considering linear loads. Therefore, this work presents a method to calculate Pav and Qav using second-order generalized integrators (SOGI) to face this problem with nonlinear loads. A double SOGI (DSOGI) approach is applied to filter the nonlinear load current and provide its fundamental component to the inverter, leading to a faster dynamic velocity of the droop-based load sharing capability and improving the stability. The proposed method is shown to be faster than others in the literature when considering nonlinear loads, while smoothly driving the system with low distortion levels. Simulations, hardware-in-loop (HIL) and experimental results are provided to validate this proposal. Full article

This contribution deals with the microwave linear characterization and noise figure measurement of four on-wafer GaAs pseudomorphic high-electron mobility transistors having scaled gate widths. The proposed measurement campaign has been fulfilled in two different laboratories: The University of Messina, Italy and US Naval Research Laboratory, Washington, DC, USA. Two equivalent approaches have been straightforwardly employed: a standard tuner-based technique and a novel tuner-less technique. The effectiveness of the novel technique has been confirmed as carried out independently by the two laboratories, evidencing the benefits of both techniques. The proposed experimental activity highlights the applicability of the tunerless technique for the noise characterization of advanced on-wafer devices without the constraint imposed by the addition of a source tuner to the standard measurement setup. Full article

Optical communication networks (OCNs) provide promising and cost-effective support for the ultra-fast broadband solutions, thus enabling them to address the ever growing demands of telecommunication industry such as high capacity and end users’ data rate. OCNs are used in both wired and wireless access networks as they offer many advantages over conventional copper wire transmission such as low power consumption, low cost, ultra-high bandwidth, and high transmission rates. Channel effects caused by light propagation through the fiber limits the performance, hence the data rate of the overall transmission. To achieve the maximum performance gain in terms of transmission rate through the OCN, an optical downlink system is investigated in this paper using feed forward equalizer (FFE) along with decision feedback equalizer (DFE). The simulation results show that the proposed technique plays a key role in dispersion mitigation in multi-channel optical transmission to uphold multi-Gb/s transmission. Moreover, bit error rate (BER) and quality factor (Q-factor) below ${10}^{-5}$ and above 5, respectively, are achieved with electrical domain equalizers for the OCN in the presence of multiple distortion effects showing the effectiveness of the proposed adaptive equalization techniques. Full article

In current Chip Multi-Processor (CMP) systems, data sharing existing in cache hierarchy acts as a critical issue which costs plenty of clock cycles for maintaining data coherence. Along with the integrated core number increasing, the only shared cache serves too many processing threads to maintain sharing data efficiently. In this work, an enhanced router network is integrated within the private cache level for fast interconnecting sharing data accesses existing in different threads. All sharing data in private cache level can be classified into seven access types by experimental pattern analysis. Then, both shared accesses and thread-crossed accesses can be rapidly detected and dealt with in the proposed router network. As a result, the access latency of private cache is decreased, and a conventional coherence traffic problem is alleviated. The process in the proposed path is composed of three steps. Firstly, the target accesses can be detected by exploring in the router network. Then, the proposed replacement logic can handle those accesses for maintaining data coherence. Finally, those accesses are delivered in the proposed data deliverer. Thus, the harmful data sharing accesses are solved within the first chip layer in 3D-IC structure. The proposed system is also implemented into a cycle-precise simulation platform, and experimental results illustrate that our model can improve the Instructions Per Cycle (IPC) of on-chip execution by maximum 31.85 percent, while energy consumption can be saved by about 17.61 percent compared to the base system. Full article

The first enactment of a single national e-government act took place in Korea in 2001. Subsequently, the United States enacted its electronic government act in November 2002. Unified e-government acts in Korea and the United States have since been established and enforced for nearly two decades, and provide interesting case studies for examining the long-term influences of the e-government act on national e-government and digital government policies. The e-government act of the United States is much more comprehensive than the e-government act of Korea. The US e-government act focuses on strengthening the federal government’s ability to regulate the Office of Management and Budget (OMB)’s role in e-government implementation. The OMB has overall jurisdiction over the e-government promotion process and will continue to consult with ministries on appropriate budget support for each project. In contrast, the e-government law in Korea is based on electronic document processing as the basic viewpoint and has been downgraded to a level that supports document reduction and electronic processing of documents, rather than a comprehensive law that can support e-government projects. The comparative case study of e-government acts in Korea and the United States revealed that, from the standpoint of digital government transformation using information technology, it is most important to promote digital government policy directly from the ministry that manages the budget, or to establish a dedicated organization under the ministry to secure strong coordination while linking it with the budget. Full article

Recently, device-to-device (D2D) communications have been attracting substantial attention because they can greatly improve coverage, spectral efficiency, and energy efficiency, compared to conventional cellular communications. They are also indispensable for the mobile caching network, which is an emerging technology for next-generation mobile networks. We investigate a cellular overlay D2D network where a dedicated radio resource is allocated for D2D communications to remove cross-interference with cellular communications and all D2D devices share the dedicated radio resource to improve the spectral efficiency. More specifically, we study a problem of radio resource management for D2D networks, which is one of the most challenging problems in D2D networks, and we also propose a new transmission algorithm for D2D networks based on deep learning with a convolutional neural network (CNN). A CNN is formulated to yield a binary vector indicating whether to allow each D2D pair to transmit data. In order to train the CNN and verify the trained CNN, we obtain data samples from a suboptimal algorithm. Our numerical results show that the accuracies of the proposed deep learning based transmission algorithm reach about 85%∼95% in spite of its simple structure due to the limitation in computing power. Full article

Device-to-device (D2D) communication and non-orthogonal multiple access (NOMA) have been considered promising techniques to improve system throughput. In the NOMA-enhanced D2D scenario, a joint channel and power allocation algorithm based on the Stackelberg game is proposed in this paper. The social relationship between the cellular and D2D users is utilized to define their utility functions. In the two-stage Stackelberg game, the cellular user is the leader and the D2D group is the follower. Cellular users and D2D groups are matched via the Kuhn–Munkres (KM) algorithm to allocate channels for D2D groups in the first stage. The power allocation of D2D users is optimized through a penalty-function-based particle swarm optimization algorithm (PSO) in the second stage. The simulation results show that the proposed algorithm can effectively strengthen the cooperation between cellular and D2D users and improve their utility. Full article

To defend against insider attacks in wireless sensor networks (WSNs), trust mechanisms (TMs) using the notion of trust in human society have been proposed and are still actively researched. In the WSN with a trust mechanism (TM), each sensor node evaluates the trustworthiness of its neighbor sensors based on their behaviors, for example packet forwarding, and collaborates only with trustworthy neighbors while removing untrustworthy neighbor from its neighbor list. The reputation system (RS) is an advanced type of trust mechanism that evaluates the trustworthiness of a node by additionally considering neighbor nodes’ observations or evaluations about it. However, intelligent inside attackers in WSNs can discover the security vulnerabilities of trust mechanisms by examining the operations of TM (or RS), because the software modules of the TM (or RS) are installed and operating in their local storage and memory, and thus, they can avoid detection by the trust mechanisms. Bad-mouthing attacks and false-praise attacks are well-known examples of such intelligent insider attacks. We observed that existing trust mechanisms do not have effective countermeasures to defend against such attacks. In this paper, we propose an enhanced trust mechanism with a consensus-based false information filtering algorithm (TM-CFIFA) that can effectively defend against bad-mouthing attacks and false-praise attacks. According to our experiment results, compared with an existing representative RS model, our TM-CFIFA shortened the detection time of a packet drop attacker, which is supported by a false-praise attacker by at least 83%, and also extended the lifetime of a victim sensor node that is under bad-mouthing attacks by at least 15.8%. Full article

Future intelligent transportation systems (ITS) hold the promise of supporting the operation of safety-critical applications, such as cooperative self-driving cars. For that purpose, the communications among vehicles and with the road-side infrastructure will need to fulfil the strict real-time guarantees and challenging dependability requirements. These safety requisites are particularly important in wireless vehicular networks, where road traffic presents several threats to human life. This paper presents a systematic survey on fault tolerance techniques in the area of vehicular communications. The work provides a literature review of publications in journals and conferences proceedings, available through a set of different search databases (IEEE Xplore, Web of Science, Scopus and ScienceDirect). A systematic method, based on the preferred reporting items for systematic reviews and meta-analyses (PRISMA) Statement was conducted in order to identify the relevant papers for this survey. After that, the selected articles were analysed and categorised according to the type of redundancy, corresponding to three main groups (temporal, spatial and information redundancy). Finally, a comparison of the core features among the different solutions is presented, together with a brief discussion regarding the main drawbacks of the existing solutions, as well as the necessary steps to provide an integrated fault-tolerant approach to the future vehicular communications systems. Full article

In recent years, NAND Flash-based solid-state drives (SSDs) have become more widely used in data centers and consumer markets. Data centers generally choose to provide high-quality storage services by deploying a large number of SSDs, but there are no effective preventive measures to reduce the impact of SSD failures currently. Some existing studies have analyzed the relevant factors related to SSD failures from different angles, but the characteristics of reliability changes exhibited by SSD throughout the life cycle have not been explored in depth. On the other hand, although the 3D manufacturing process has increased the storage density of the SSD, the mutual influence between the flash units has also increased, resulting in severe degradation of the performance and lifetime of the SSD. Therefore, in order to fully understand the reliability varying process of SSD throughout the life cycle, we first designed an SSD lifetime endurance test method, then conducted the endurance test and collected the reliability data for the entire life cycle of the 3D TLC SSD in the laboratory environment with reference to the JEDEC standard. Through the analysis of experimental data and its statistical correlation, it is found that SSD will produce a large number of uncorrectable errors before reaching the endurance limit, and there will be a phenomenon of continuous high operating temperature, as well as showing some intrinsic relationships about SSD reliability data. The findings in this paper are valuable for identifying whether an SSD is going to fail. Full article

This article proposes a 2 × 2 circularly polarized array with enhanced isolation that could be used in the adaptive anti-jamming system for China’s Beidou Navigation Satellite System (BDS), and a compact dual-band microstrip antenna for GPS and GLONASS is designed at the center part of the array for multiple modes operation. The substrate integrated split ring resonators with negative effective permeability are used as the magnetic metamaterials. Four groups of the magnetic metamaterials are placed between the BDS antenna elements, which could constrain the EM filed in the area of the active element, and then significantly reduce the mutual coupling among the elements. The results tell that the isolation among the BDS elements are improved by more than 10 dB, and the performance of radiation patterns and the axial ratio of each BDS elements are also apparently improved compared to the array without magnetic metamaterials. Full article

The fifth generation (5G) wireless systems are anticipated to meet unprecedented capacity and latency requirements. In order to resolve these challenges in 5G, non-orthogonal multiple access (NOMA) is considered as a promising technique due to its ability to enhance spectrum efficiency and user access. As opposed to conventional orthogonal multiple access (OMA) which relies on orthogonal resource sharing, NOMA has a potential of supporting a higher number of users by multiplexing different users in the same resource in a non-orthogonal manner. With advanced receiver techniques, such as successive interference cancellation (SIC), the intra-user interference can be minimized at the NOMA receiver. To date, there are comprehensive surveys on NOMA, which describe the integration of NOMA with different communication technologies and discuss different NOMA classifications. However, the existing literature is scarce in reviewing state-of-the-art applications of NOMA from the perspective of its application to cellular networks (CNs), device-to-device (D2D) communications, and wireless sensor networks (WSNs). Therefore, the purpose of this survey is to fill this gap in knowledge. Specifically, NOMA with its underlying concepts are elaborated in detail. In addition, detailed system model of different NOMA-based wireless networks is presented. Furthermore, irrespective of the underlying spatial topology of the considered NOMA-based wireless network, general analytical expressions are presented to characterize the network performance. Finally, some challenges related to NOMA design are highlighted and potential research directions are pointed out to address these issues. Full article

Falls have been one of the main threats to people’s health, especially for the elderly. Detecting falls in time can prevent the long lying time, which is extremely fatal. This paper intends to show the efficacy of detecting falls using a wearable accelerometer. In the past decade, the fall detection problem has been extensively studied. However, since the hardware resources of wearable devices are limited, designing highly accurate embeddable models with feasible computational cost remains an open research problem. In this paper, different types of shallow and lightweight neural networks, including supervised and unsupervised models are explored to improve the fall detection results. Experiment results on a large open dataset show that the lightweight neural networks proposed have obtained much better results than machine learning methods used in previous work. Moreover, the storage and computation requirements of these lightweight models are only a few hundredths of deep neural networks in literature. In tested lightweight neural networks, the best one is proved to be the supervised convolutional neural network (CNN) that can achieve an accuracy beyond 99.9% with only 441 parameters. Its storage and computation requirements are only 1.2 KB and 0.008 MFLOPs, which make it more suitable to be implemented in wearable devices with restricted memory size and computation power. Full article

An inverter-based on-chip resistor capacitor (RC) oscillator with logic transition voltage (LTV) tracking feedback for circuit delay compensation is presented. In order to achieve good frequency stability, the proposed technique considers the entire inverter chain as a comparator block and changes the LTV to control the oscillation frequency. Furthermore, the negative feedback structure also reduces low-frequency offset phase noise. With a 1.8 V supply and at room temperature, the suggested oscillator operates at 18.13 MHz, consuming 245.7 $\mathsf{\mu }$ W. Compared to the free-running case, the proposed technique reduces phase noise by 7.7 dB and 5.45 dB at 100 Hz and 1 kHz, respectively. The measured phase noise values are −60.09 dBc/Hz at 1 kHz with a figure of merit (FOM) of 151.35 dB/Hz, and −106.27 dBc/Hz at 100 KHz with an FOM of 157.53 dBc/Hz. The proposed oscillator occupies 0.056 mm² in a standard 0.18 $\mathsf{\mu }$ m CMOS process. Full article

The powerful support vector regression framework is proposed in a novel method for near-field focusing using antenna arrays. By using this machine-learning method, the set of weights required in the elements of an array can be calculated to achieve an assigned near-field distribution focused on one or more positions. The computational cost is concentrated in an initial training process so that the trained system is fast enough for applications where moving devices are involved. The increased learning capabilities of support vector machines allow using a reduced number of training samples. Thus, these training samples may be generated with a prototype or a convenient electromagnetic analysis tool, and hence realistic effects, such as coupling or the individual radiation patterns of the elements of the arrays, are accounted for. Illustrative examples are presented. Full article

Over the years, several Ultrawideband (UWB) localization systems have been proposed and evaluated for accurate estimation of the position for pedestrians. However, most of them are evaluated for a particular wearable sensor position; hence, the accuracy obtained is subject to a given wearable sensor position. This paper is focused on studying the effects of body wearable sensor positions i.e., chest, arm, ankle, wrist, thigh, forehead, and hand, on the localization accuracy. According to our results, the forehead and the chest provide the best and worst body sensor location for tracking a pedestrian, respectively. With the wearable sensor at the forehead and chest position, errors lower than 0.35 m (90th percentile) and 4 m can be obtained, respectively. The reason for such a contrast in the performance lies in the fact that, in non-line-of-sight (NLOS) situations, the chest generates the highest multipath of any part of the human body. Thus, the large errors obtained arise due to the signal arriving at the target wearable sensor by multiple reflections from interacting objects in the environment rather than by direct line-of-sight (LOS) or creeping wave propagation mechanism. Full article

The unmanned aerial vehicle (UAV), which is a typical multi-sensor closed-loop flight control system, has the properties of multivariable, time-varying, strong coupling, and nonlinearity. Therefore, it is very difficult to obtain an accurate mathematical diagnostic model based on the traditional model-based method; this paper proposes a UAV sensor diagnostic method based on data-driven methods, which greatly improves the reliability of the rotor UAV nonlinear flight control system and achieves early warning. In order to realize the rapid on-line fault detection of the rotor UAV flight system and solve the problems of over-fitting, limited generalization, and long training time in the traditional shallow neural network for sensor fault diagnosis, a comprehensive fault diagnosis method based on deep belief network (DBN) is proposed. Using the DBN to replace the shallow neural network, a large amount of off-line historical sample data obtained from the rotor UAV are trained to obtain the optimal DBN network parameters and complete the on-line intelligent diagnosis to achieve the goal of early warning as possible as quickly. In the end, the two common faults of the UAV sensor, namely the stuck fault and the constant deviation fault, are simulated and compared with the back propagation (BP) neural network model represented by the shallow neural network to verify the effectiveness of the proposed method in the paper. Full article

The physical layer signaling of the 5G new radio still utilizes cyclic prefix orthogonal frequency division multiplexing (CP-OFDM) and discrete Fourier transform-spread-OFDM (DFT-s-OFDM) to support 5G services for the sake of system-backward compatibility. However, the transmission requirements among these services differ, and this poses a challenge to the adaptability of the waveforms with regard to the peak-to-average power ratio (PAPR) issue. In particular, DFT-s-OFDM serving as a low-PAPR option for uplink signaling still has room for PAPR improvement in cases such as machine-type and device-to-device communications. We propose polynomial cancellation coded (PCC)-DFT-s-OFDM to flexibly reduce the PAPR of conventional DFT-s-OFDM. The principle of the proposed method, including its transform, is analyzed in the time domain. The results show that it can also be regarded as a novel spectral shaping scheme for PAPR reduction. Through a parameter designed for adjusting the cost of spectral efficiency, the proposed method can regulate the extent of improvement compared with the conventional DFT-s-OFDM, not only in the PAPR, but also for the spectral radiation and bit error rate when considering the nonlinearity of the power amplifier. The increase in computational complexity is neglectable owing to the simplicity of generalized PCC, making it apt to be deployed in existing systems. Full article

In this short paper, we discuss the problem of resource allocation for cloud computing. The cloud provides a variety of resources for users based on their requirements. Thus, one of the main issues in cloud computing is to design an efficient resource allocation scheme. Each job generated by a user in the cloud has some resource requirements. In this work, we propose a resource allocation method which aims at maximizing the resource utilization and distributing the system’s resources in a fast and fair way, by controlling the flow according to the resources available and by analyzing the dominant demands of each job. Moreover, by parallelizing the computations required, the runtime of the proposed strategy increases linearly as the number of jobs N increases. Here, we present some initial experimental results for small sets of users, that have shown that our strategy allocates the available resources among user jobs in a fair manner, while increasing the overall utilization of each resource. Full article

A push–push transformer-based oscillator (TBO) adopting a power leakage suppression technique has been proposed. The proposed technique reduces the power loss due to unwanted leakage path without additional DC power consumption, hence improving the output power and DC-to-RF efficiency. The measured output power of the proposed single core oscillator is −4.5 dBm at 270 GHz with 2.1% DC-to-RF efficiency. Full article

Finite control set model predictive control (FCS-MPC) is able to handle multiple control objectives and constraints simultaneously with good dynamic performance. However, its industrial application is limited by its high dependence on system model and the huge computational effort. In this paper, a novel robust two-layer MPC (RM-MPC) with strong robustness is proposed for the full-bridge neutral-point clamped (NPC) voltage mode Class-D amplifier (CDA) aiming at this problem. The errors caused by the parameter mismatches or uncertainties of the LC filter and the load current are regarded as lumped disturbance and estimated by the designed Luenberger observer. The robust control can be achieved by compensating the estimated disturbance to the used predictive model. In order to reduce computation of the controller, a two-layer MPC is proposed for the full-bridge NPC inverter with an LC filter. The first layer is used to calculate the optimal output level which minimizes the tracking error of the output voltage. The second layer is used to determine the switching state for the purpose of capacitor voltage balancing. The experimental results show that the lumped model error is observed centrally through only one observer with low complexity. The two-layer MPC further reduced the computation without affecting the dynamic performance. Full article

Transducer electronic data sheets (TEDS) are a key element of smart transducers because they support core features such as plug and play, self-calibration, and self-diagnostics. The ISO/IEC/IEEE 21451-4 standard defines templates to describe the most common types of transducers and suggests the use of one-wire memories to store the corresponding data. In this paper we explore new ways to store and access TEDS tables, including near field communication (NFC) tags and QR codes. We also present a mobile TEDS parser, compatible with Android, that is capable of reading TEDS data from all supported mediums (one-wire memories, NFC tags, and QR codes) and decoding them as human-readable text. The idea is to make TEDS available in the easiest way possible. We also underline the need to extend the 21451-4 standard by adding support for frequency–time sensors. A new TEDS template is proposed, and filling examples are presented. The main novelties of the paper are (i) the proposal of new ways to store 21451-4 TEDS tables using NFC tags and QR codes; (ii) the release of new tools to access TEDS tables including a mobile parser; and (iii) the definition of a new TEDS template for frequency–time sensors. Full article

Electrical stimulation technology has advanced from cardiac pacemakers towards cochlear implants, physical rehabilitation, and treatment for Parkinson’s and other diseases. Electrical stimulation parameters, such as waveform, amplitude, and stimulation frequency, have been shown to affect neurostimulation properties. However, the effect of signal time rate of change on electrical stimulation applications has not been studied, and there are no stimulators that specifically allow the study of these effects. A novel pulse generator circuit is presented and characterized. An analytical expression is derived from the circuit, that allows for the design of pulse parameters, such as maximum amplitude voltage, time for peak amplitude, and stimulation duration. The analytical expression is verified to match design values and measurements in an electrical model and phosphate-buffered saline (PBS) solution. The circuit is shown to operate at variable output stimulation frequencies, and an expression is derived in order to determine the limit of the output stimulation frequency. The pulse generation circuit is thought to be the first dedicated output pulse generator for research of the effect of signal time rate of change on electrical stimulation applications such as neurostimulation. Full article

DARPA, the Defense Advanced Research Projects Agency from the United States, has started the Spectrum Collaboration Challenge with the aim to encourage research and development of coexistence and collaboration techniques of heterogeneous networks in the same wireless spectrum bands. Team SCATTER has been participating in the challenge since its beginning, back in 2016. SCATTER’s open-source software defined physical layer (SCATTER PHY) has been developed as a standalone application, with the ability to communicate with higher layers through a set of well defined messages (created with Google’s Protocol buffers) and that exchanged over a ZeroMQ bus. This approach allows upper layers to access it remotely or locally and change all parameters in real time through the control messages. SCATTER PHY runs on top of USRP based software defined radio devices (i.e., devices from Ettus or National Instruments) to send and receive wireless signals. It is a highly optimized and real-time configurable SDR based PHY layer that can be used for the research and development of novel intelligent spectrum sharing schemes and algorithms. The main objective of making SCATTER PHY available to the research and development community is to provide a solution that can be used out of the box to devise disruptive algorithms and techniques to optimize the sub-optimal use of the radio spectrum that exists today. This way, researchers and developers can mainly focus their attention on the development of smarter (i.e., intelligent algorithms and techniques) spectrum sharing approaches. Therefore, in this paper, we describe the design and main features of SCATTER PHY and showcase several experiments performed to assess the effectiveness and performance of the proposed PHY layer. Full article