Search

In a recent Special Section published in IEEE Transactions on Consumer Electronics we introduced smart electronics. We presented a set of papers which were selected after rigorous review with a scope of artificial intelligence (AI) or machine learning (ML) approaches used for various levels of system abstractions, from device-level to system-level. In one school of thought energy-smart, security-smart, and response-smart are 3 dimensions of smart electronic systems. [ABSTRACT FROM AUTHOR]

Conventional ultrasound probes have inherent limitations in some application scenarios. Because of their fixed shapes and sizes, they can hardly pass through small orifices to examine the target closely. With the recent development of microelectromechanical systems and flexible electronic technologies, collapsible and self-assembling devices are being considered as a potential approach to address this problem. This article presents an innovative transformable ultrasonic transducer (TUT), which can transform between a one-dimensional (1-D) linear array and a 2-D planar array with the ability of switching between different functions including 2-D/3-D imaging and high-intensity focused ultrasound beamforming. The transforming process is accomplished with a specially designed flexible printed circuit board inspired by a paper-folding mechanism. The electrical and acoustic properties of the developed transducer along with the acoustic field distribution in its different forms have been characterized. The 2-D/3-D imaging performances of the TUT have been also evaluated under 1-D and 2-D configurations, respectively. In addition, the performance of a focused concave transforming mode has been also discussed. The evaluation results suggest that TUT can maintain its performance under different structures with precise transforming control. The TUT device has the potential to considerably expand the applications of industrial and clinical ultrasound. [ABSTRACT FROM AUTHOR]

To improve the comfort and user acceptance of the intelligent vehicle braking system, this paper proposes a control strategy for the electronic brake booster (Ebooster) that considers differences in driving behaviors. First, a real-vehicle driving data collection platform is built to collect the driving behavior data of 84 drivers under typical vehicle-following braking conditions. Subsequently, the k-means clustering algorithm optimized by particle swarm optimization (PSO) is used to realize the cluster analysis of driving behaviors, and the support vector machine (SVM) algorithm optimized by the genetic algorithm (GA) is used to complete the identification of driving behaviors. Next, through the analysis of the boost characteristics of the self-designed Ebooster, driving behaviors are matched with boost characteristics, and a personalized basic boosting control strategy based on the driver behavior is designed. Finally, a hardware-in-the-loop (HiL) bench and a real-vehicle test platform are designed and built to verify the effectiveness of the personalized control strategy of the Ebooster. The results show that this method can effectively identify the type of driver and make personalized adjustments to the basic boosting strategy of the Ebooster, which effectively improves the personalization of the braking system, resulting in a higher pedal response and user acceptance. [ABSTRACT FROM AUTHOR]

Packaging radio-frequency microelectromechanical systems (RF-MEMS) is mandatory for their integration in electronic systems. This paper discusses the integration of a Au−Ru/AlCu contact in a MEMS fabrication process with a wafer-level packaging (WLP) step through Al−Ge eutectic bonding. The Au contact is embedded in a movable fixed−fixed beam made of $30 ~ \mu \text{m}$ thick Si while Ru is used as a coating metal on top of the AlCu static electrode. The measured return loss and isolation of the contact, up to 40 GHz, are $\geq -1.7$ and $\leq -12$ dB respectively. The WLP Au−Ru/AlCu contact has an electrical resistance $\leq 1.2 ~ \Omega $ up to 3 million cycles in hot switching at 0.1 W before failing due to high electrical resistance. Au hillocking is identified as the only intrinsic limiting mechanism that can be mitigated by an appropriate design rule. It is demonstrated that Au−Ru/AlCu is a promising contact configuration, compatible with Al−Ge eutectic bonding, and can be integrated in a standard MEMS fabrication process flow for the development of a robust generation of WLP RF-MEMS. [ABSTRACT FROM AUTHOR]

Radar radio source (RRS) recognition plays an important role in the fields of military electronic support systems (ESM) and civilian autonomous driving. The rapid development of machine learning technology, especially deep learning, has effectively and efficiently improved RRS intelligent recognition performances when operating in the increasingly complex electromagnetic environment. However, the data sampling limitation and computation cost are still severe challenges in real RRS recognition scenarios. In this paper, we propose a novel network based on meta-transfer learning, called RRSARNet, to achieve effective adaptive RRS recognition in the context of low signal-to-noise ratio (SNR). First, by using the short-time Fourier transform, a six-type small samples RRS simulation dataset with different SNR levels is constructed. Then, a novel RRSARNet, based on metric learning, is proposed, which consists of a four-layer embedding module and a four-layer relational module. Finally, the RRS dataset is divided into training, supporting and testing subsets, which are used to train and test the RRSARNet in a meta-transfer learning method. Experiments on the RRS dataset show that the proposed RRSARNet can achieve an overall accuracy (OA) above 96% and 99% when the SNR is above −15 dB and −10 dB, respectively. Even when the SNR is -30 dB, OA can reach more than 70%. For 5-way 1-shot and 5-way 5-shot experiments, the inference time of an image is about 0.043 and 0.140 milliseconds, respectively. Besides, experiments on the RRS simulation dataset and the two benchmark datasets, the RRSARNet performs better or more competitive than many existing state-of-the-art technologies in terms of recognition accuracy. [ABSTRACT FROM AUTHOR]

We have been seeing use of word “smart” before almost anything that we can think of in our day-to-day life: smart phone, smart TV, and smart appliance. At a bigger scale smart cities, smart villages, smart healthcare, and smart agriculture, are used to describe new generation of these physical entities. So, an obvious question: What is smart? From dictionary, smart means: “(of a person) clean, tidy, and well dressed” and “(of a device) programmed so as to be capable of some independent action.” Thus, smart and intelligence is the “ability to take decisions based on the data, circumstances, situations.” [ABSTRACT FROM AUTHOR]

Reliability evaluation of electronic systems in space environment is a critical issue. In this work, reliability evaluation of superluminescent diode (SLD) systems, which are sensitive to both displacement damage effects and charge–discharge effects, was investigated by the Bayesian network model. Displacement equivalence model of the SLDs is verified by the irradiation results of 5-MeV protons and 1-MeV electrons. Charge–discharge effects of SLDs are induced by 110-keV electrons. Orbital energy spectra and cumulative distribution functions for the equivalent irradiation fluence are calculated by self-built software. Based on the experimental and calculated results, the Bayesian networks under various conditions are established. The probabilities of the type of faults prone in the system under each condition can then be analyzed and quantified. The proposed method provides a good idea for on-orbit reliability evaluation of complex systems. [ABSTRACT FROM AUTHOR]

This article presents a discrete convolution-based method for reliability evaluation of a grid-connected photovoltaic (PV) system, where special consideration is given to the variable availability of PV array components. Along with the negative impacts of variable solar irradiance and ambient temperature on the PV power generation, they affect the reliability of PV system power electronic components. A variable availability model based on the power loss in the semiconductors of PV system components is needed to account for this effect in the reliability analysis. The article proposes a 3-D capacity outage model of a PV farm that accounts for the variable availability and the redundancies associated with the topologically complex multistring PV arrays. The outage model is used to construct a PV capacity outage probability and frequency table (COPAFT), which along with conventional generation COPAFT, is convolved with the distribution of load to determine system reliability. An integration limit assessment tool is also developed within the proposed framework, ensuring that the PV power integration does not violate the system frequency stability. Simulations on the modified IEEE RTS-79 system demonstrate the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

With the increasing demand for high-performance power electronic systems, the semiconductor device utilization has gained significant importance. This article proposes the Si/SiC devices based active neutral-point-clamped converter comprising SiC-based H-bridge structure. A novel pulsewidth modulation (PWM) scheme is also proposed that results in an effective utilization of the switching devices. In the proposed converter, the Si devices are soft-switched for all power-factor loading conditions. Therefore, high efficiency can be obtained even at high switching frequency operation with wide range of power factor values. Further, the conduction losses of the SiC mosfets are minimized by strategically selecting the switching states in such a way that the SiC mosfets conduct in parallel conduction paths during the null state operation. This leads to further enhanced efficiency. This article presents the detailed operating principle of the proposed topology operated with the presented PWM scheme. Moreover, a device loss analysis is presented to evaluate the conduction and switching losses of the proposed topology. The presented experimental results validate the performance parameters of the proposed topology with the proposed PWM scheme. Finally, the proposed PWM scheme is compared with the existing PWM schemes using efficiency values of the proposed and some of the prominent existing converter configurations. [ABSTRACT FROM AUTHOR]

A fault-tolerant control (FTC) is proposed for enhancing the reliability of power electronic systems. The software-based FTC of parallel-connected three-level T-type converters (3LT2Cs) for suppressing zero-sequence circulating current (ZSCC) induced by a vertical switch open-circuit fault is investigated. A simplified sequential model predictive control (SSMPC)-based FTC technique is developed. Parallel-3LT2Cs and their prediction models are introduced, followed by an SSMPC for the no-fault condition. The model inaccuracies under vertical switch faults are elaborated. For the faulty 3LT2C, two fault-tolerant SSMPC (FT-SSMPC) methods are provided by creating a sequential predictive controller that considers ZSCC suppression and grid current tracking. The control policies for the ZSCC are changed from the standard feedback-free to feedback-based cost function (CF) optimization using the unimproved FT-SSMPC. Furthermore, an improved FT-SSMPC is proposed using a phase-deficient CF, which increases the accuracy of the mathematical relation of the fault condition. After fault diagnosis, the proposed method achieves neutral-point voltage balance and excellent point-of-common-coupling current, effectively suppresses the ZSCC, and increases the control reliability. Finally, experiments demonstrate the effectiveness of the proposed FT-SSMPC under various conditions. [ABSTRACT FROM AUTHOR]

Three-phase four-leg voltage-source inverter has been extensively investigated in recent years for its compactness, small size, and high efficiency, and it has been proved to be the best solution for providing transformer-less neutral connection to three-phase asymmetric loads. The main purpose of this article is to apply 3-D unbalanced coordinate transformation to the four-leg inverter, which supplies stand-alone asymmetric loads, to maintain the balance of the output capacitor voltages, and obtain simpler control structure and better control performance. First, a double closed-loop PI control structure can be designed based on the Clarke and Park transformations. Second, the phases and amplitudes of the capacitor voltages are locked based on the 3-D unbalanced coordinate transformation. Furthermore, the Clarke transformation oriented on the inductor currents is replaced by a 3-D unbalanced coordinate transformation, and the corresponding unbalance indices are continuously adjusted according to the unbalance indices of the capacitor voltages, so as to recover the capacitor voltages to the balanced condition. The simulation and experimental results of the whole control system for the four-leg inverter are provided in the end, so as to verify the feasibility and effectiveness of the 3-D unbalanced coordinate transformation for application to the power electronic systems. [ABSTRACT FROM AUTHOR]

Power electronic systems employing wide-bandgap GaN transistors promise high efficiency operation and power density but require minimized parasitic circuit elements and an effective cooling concept. This article presents a half-bridge module integrating two 600 V/170 mΩ gallium nitride (GaN) high-electron mobility transistors with their gate drive stages and a fraction of the dc-link capacitance on a patterned multilayer polycrystalline AlN-substrate. The high-voltage isolation at a layer distance of 10 μm and a dense chip-by-chip integration on the GaN half-bridge module enable a compact lateral commutation loop design combined with improved cooling capability of the power transistors. Consequently, the GaN half-bridge module allows for higher load currents at lower device temperature while most parasitic circuit elements are reduced compared to a conventional printed circuit board (PCB) design. The parasitic circuit elements of the GaN half-bridge module and a reference four-layer PCB half-bridge are evaluated using 3D-FEM field simulation and in-circuit measurements. Selected finite element method (FEM) simulation results are validated by S-parameter measurements and further used to parametrize a lumped commutation loop model. The thermal characterization of the GaN half-bridge module validates the improved cooling capability of the GaN half-bridge power module. Transient switching characteristics are studied in hard-switching mode. The device temperature and converter efficiency are evaluated in dc/dc buck-converter operation. [ABSTRACT FROM AUTHOR]

Due to the successive switching actions in a power electronic system, considerable conducted electromagnetic interference (EMI) is generated. This has been a major concern in designing power converters, which requires additional EMI filtering efforts. Passive EMI filters are the most common solutions to EMI reduction but usually consume a large portion of the volume as well as weight in power converters. Active EMI filters (AEFs) or hybrid EMI filters (HEFs) are considered best candidates over traditional passive EMI filters to achieve higher power density in power converters. The utilization of current transformer to sense or to compensate the EMI noise can increase the volume of an AEF. In this article, a transformer-less AEF, sprung from the concept of virtual impedance enhancement, is proposed. The proposed design features feedforward control with current-sensing-voltage-compensating. The operation principle of the proposed AEF is thoroughly introduced, and its performance is evaluated through simulations and experiments. Experimental results with an ac–dc power converter and a dc–ac inverter show that the proposed AEF can be generally applied to reduce conducted EMI on either ac or dc power lines. [ABSTRACT FROM AUTHOR]

Active electromagnetic interferences (EMI) cancellation is a promising solution to reduce the size of passive filters. Power electronic systems may generate periodic disturbances if they are controlled by periodic signals in steady-state operation. Periodic disturbances can be represented by a set of sine waves, according to the Fourier theory, and can be suppressed by an appropriate set of cancelling sine waves injected into the system. Since bothersome effects, like delay times or complex transfer functions, can be compensated by individually selecting the appropriate amplitude and phase for each cancelling sine wave, high EMI reductions can be achieved in a wide frequency range. In this contribution, this cancellation method is applied to the common-mode EMI of a three-phase motor inverter in stationary operation with periodic control signals in the frequency range from 150 kHz to 30 MHz. Approximately 2.4 million harmonics are suppressed. The characteristics of the overall system are discussed and the challenges for the cancellation system are elaborated. A method for the synthesis of the cancellation signal is introduced. The design of the cancellation system is described, and its signal processing is presented. The outstanding performance is proven by reference measurements in a laboratory setup. Extensions for practical applications are discussed. [ABSTRACT FROM AUTHOR]

Electrical connectors play an indispensable role in electrical and electronic systems. The frequently occurred intermittent failure events seriously affect the reliability of application systems. In this article, the repetitive mechanical insertion and withdrawal experiment of electrical connector is carried out by using our designed test rig and the intermittent failure phenomena recurs. The typical waveforms of contact resistance and force during the mating operation are presented. The variation laws of static contact resistance and dynamic contact resistance under intermittent failure condition are counted and analyzed. And the dynamic contact resistance is proven to be a suitable sensitive parameter to characterize the intermittent failure event. Based on the mathematical model, variation of surface morphology, and element analysis, the failure mechanism of electrical connectors under repetitive operation is proposed. [ABSTRACT FROM AUTHOR]

The vehicle industry’s primary theme is the increase in automating and integrating more robust intelligent electronic systems to transform towards Industry 4.0, enhance safe functioning with more flexibility, and extend the virtual productive age of the vehicular systems. Further, significant efforts focus on developing monitoring systems for abnormal driving conditions such as wheel spinning and sliding that can increase the operating environment’s hazards and speed up the wearing-off process. Thus, these events play a primary role in influencing the safety of the involved humans and determining the vehicle’s lifespan and the incurred maintenance costs because of the encumbrances by the generated vibrations and shocks. However, the existing measurement systems are not entirely digitized, and their initial development did not concern the concept of functional safety like redundancy to be used in safety-critical environments. Further, the current systems depend on a costly sensory system that requires strict and complex installation procedures. Therefore, the prevention of wheel spinning and sliding events by a safety-related miniaturized digital intelligent monitoring system comes to a corresponding meaning. Accordingly, this research work investigates the development of a novel safety-related platform following the standard IEC 61508 for monitoring and controlling abnormal driving conditions through vibration sensors incorporated with rotation sensors. The research work tests the novel system on a prototype vehicle where the conducted experiments prove the system’s capability to detect the events with reasonable accuracy. Further, this novel approach’s evaluation shows that the system represents a significant enhancement for various similar researches and applications. [ABSTRACT FROM AUTHOR]

The coupling of short wavelength electromagnetic (EM) interference to critical electronic systems in highly reverberant enclosures is a growing concern in the EM interference/compatibility community. In such highly reverberant cavities, prior research has shown that the induced EM fields, voltages, or currents can be modeled using the wave-chaotic random coupling model (RCM). The RCM partitions the interaction within the cavity into a universally fluctuating part, derived from random matrix theory, and a system-specific part, defined by the radiation impedance of the ports of interest, where the voltages or currents are induced. Earlier researchers have treated the radiation impedance as a time-invariant, frequency-dependent complex quantity. This is true for passive structures but is not true for active semiconductor devices, such as microcontrollers, which can exhibit time-varying radiation properties depending on the instruction cycle being executed at a given instant of time. The estimate of such a time-varying radiation impedance and its correlation with instruction cycles in an elemental microcontroller is the focus of this work. Utilizing clustering algorithms, we observe that the measured radiation impedance, as well as radiative emissions, are correlated to the class of instruction cycles being executed. By clustering the radiation impedance and emissions of the general-purpose input/output ports utilizing the class of instructions being executed, predictive models for microcontroller susceptibility can be derived even when detailed knowledge of the specific instruction cycle being executed is unknown a priori. Such a predictive capability can find multiple applications in the EMI/EMC community. [ABSTRACT FROM AUTHOR]

The control of very high switching frequency power electronic converter systems featuring latest generation wide bandgap devices requires current measurements with a very high bandwidth (BW) to achieve high closed-loop control dynamics. One example is an ultrahigh BW $\mathbf {4.8}$ MHz parallel-interleaved multilevel GaN inverter ac power source with a target large-signal output BW of $\mathbf {100}$ kHz. This work investigates the combination of commercially available low-frequency (LF) Hall-effect current sensors (BW $\mathbf {\approx 1}$ MHz) with suitable high-frequency (HF) sensors to extend the BW above $\mathbf {10}$ MHz, i.e., enough to measure the HF inductor current. Based on a conventional PCB-integrated Rogowski coil, for the HF sensor, three improved pickup coil (PUC) geometries based on proposals in the literature are investigated. The LF and HF sensor signals are combined with a precision fully differential combiner circuit. Design guidelines for the HF sensors as well as the combiner circuit are presented and thereby the influence of mismatches and tolerances is examined. The performance of the proposed HF sensors is experimentally verified and compared with previous solutions based on galvanically isolated inductor voltage sensing and a current transformer. The PUCs reach a BW $\mathbf {>50}$ MHz, which is an improvement by more than a factor of $\mathbf {50}$ compared to the fastest available Hall sensor. Furthermore, it is proven that all investigated sensors are capable to accurately measure the triangular inductor current ripple in a hardware prototype of the aforementioned ac power source. In a final step, the influence of common-mode (CM) disturbances originating from fast $\mathbf {{d}v/{d}t}$ switching transients on the sensor performance is analyzed. The presented current sensors achieve a CM rejection ratio of almost $\mathbf {100}$ dB. [ABSTRACT FROM AUTHOR]

Deinterleaving mixtures of radar pulse sequences is the first and the most vital step for modern electronic reconnaissance systems to intercept and analyze the intentions of noncooperative radars. Currently, these systems are facing great challenges due to the emerging complexity of radar modulations. This article proposes a novel method for deinterleaving mixtures of radar pulse sequences based on the time series characteristics of each source (component pulse sequences). First, the mathematical representations are established to describe the structural characteristics of each source. Then, the deinterleaving problem is formulated as a minimization of a maximum-likelihood cost function that can be solved efficiently through a supervised neural machine translation (NMT) network, i.e., to translate each received pulse in the interleaved pulse sequence to the corresponding source label. A sequence-to-sequence NMT model is proposed to capture the structural relationships among the nonadjacent pulses originated from the same source in the mixtures and assign the corresponding label to each pulse. The proposed method does not require the exact knowledge of each component pulse sequence. The experimental results based on the time of arrival sequence of mixed pulse sequences show that the proposed method outperforms the state-of-the-art deinterleaving methods and achieves satisfactory performance under highly nonideal situations with measuring noise and lost pulse conditions. The proposed method can be directly applied to other fields involving deinterleaving problems and multivariate input conditions. [ABSTRACT FROM AUTHOR]

Fault-tolerant control (FTC) strategies have been proposed to improve the reliability of power electronic systems. However, the FTC of the three-level T-type converter based shunt active power filter (SAPF) remains limited by redundant devices. To address this problem, an FTC strategy called sequential model predictive tolerant control (SMPTC) is combined with a nonintegral lexicographic optimization (LO) algorithm for solving multiobjective optimization problems. First, the power circuit, space voltage vector diagram, harmonic extraction, and prediction models of the SAPF system are introduced. Second, based on power-switch fault analysis, an adaptive dc bus voltage regulation method is proposed, which ensures that the candidate voltage vectors are fully utilized in the fault state. Third, an LO-SMPTC strategy is proposed by designing a sequential predictive controller that considers the neutral point (NP) voltage and the tracking of the SAPF output current. Compared with the traditional model predictive control, the proposed method not only achieves NP voltage balance and excellent harmonic compensation but also eliminates the selection of weighting factors, thereby improving the control flexibility and time efficiency. Finally, the effectiveness of the LO-SMPTC on the dc bus voltage, NP voltage oscillations, grid current power quality, and pole voltage under different scenarios is demonstrated through experiments. [ABSTRACT FROM AUTHOR]

The emerging flexible multilayer foils (FMLFs)-based winding design has a great potential to promote the magnetic integration technique in power electronic systems. This article investigates the full integration of a single-phase electromagnetic interference (EMI) choke with well-designed FMLFs. By sharing a UU-type core and configuring the windings reasonably, all the common-mode (CM) inductance and capacitances and differential-mode (DM) inductances and capacitances can be integrated into the same core unit. Moreover, the proper terminal configuration of conductive layers can realize function-decoupling between CM and DM filtering elements, which contributes to simplifying the equivalent CM and DM models, then predigesting the parameters design procedure. Following the modeling and theoretical analysis, prototypes of the proposed and existing EMI chokes have been built for a single-phase 200-kHz SiC-mosfet inverter system. Through experimental measurements, performance comparisons have been presented to demonstrate the validity of this model. [ABSTRACT FROM AUTHOR]

Early fault detection in power electronic systems (PESs) to maintain reliability is one of the most important issues that has been significantly addressed in recent years. In this article, after reviewing various works of literature based on fault detection in PESs, data mining-based techniques including artificial neural network, machine learning, and deep learning algorithms are introduced. Then, the fault detection routine in PESs is expressed by introducing signal measurement sensors and how to extract the feature from them. Finally, based on studies, the performance of various data mining methods in detecting PESs faults is evaluated. The results of evaluations show that the deep learning-based techniques given the ability of feature extraction from measured signals are significantly more effective than other methods and as an ideal tool for future applications in the power electronics industry are introduced. [ABSTRACT FROM AUTHOR]

Recent trends in automotive electronics such as automated driving will increase the number and complexity of electronics used in safety-relevant applications. Applications in logistics or ridesharing will require a specific year of service rather than the conventional mileage usage. Reliable operations of the electronic systems must be assured at all times, regardless of the usage condition. A more dynamic and on-demand way of assuring the system availability will have to be developed. This article proposes a thermomechanical stress-based prognostics method as a potential solution. The goal is achieved by several novel advancements. On the experimental front, a key microelectronics package is developed to directly apply the prognostics and health management concept using a piezoresistive silicon-based stress sensor. Additional hardware for safe and secure data transmission and data processing is also developed, which is critically required for recording in situ and real-time data. On the data management front, proper data-driven approaches have to be identified to handle the unique dataset from the stress sensor employed in this study. The approaches effectively handle the massive amount of data that reveals the important information and automation of the prognostic process and thus to be able to detect, classify, locate, and predict the failure. The statistical techniques for diagnostics and the machine learning algorithms for health assessment and prognostics are also determined to implement the approaches in a simple, fast, but accurate way within the capacity of limited computing power. The proposed prognostics approach is implemented with actual microelectronics packages subjected to harsh accelerated testing conditions. The results corroborate the validity of the proposed prognostics approach. [ABSTRACT FROM AUTHOR]

Consumer electronics hardware is designed and manufactured following a global supply chain which opens doors to their security challenges. Even with the advanced methods of formal verification and coverage analysis, there is still a chance of hiding malicious hardware/software which can degrade performance, leak data, or even stop functionalities. In this work, we present security solution of Network on Chip (NoC) based Multiprocessor System-on-Chip (MPSoC) consumer electronics (CE) systems such as set-top boxes and autonomous vehicles. Most of the existing methods targeted for such systems focus on protection in Network Interfaces (NI) and other software solutions rather than routers against Hardware Trojans (HT) which can be embedded in NoC by a rogue designer. In this work, we propose a 3-tier methodology leading to “Fortified-NoC” to secure the data and resources against different kinds of threats. A Trojan cognizant routing algorithm (TCRA) is proposed which limits the HTs to a particular router that contains them. Data shuffling with Trojan detectability is also used to mislead and identify the HTs. We validated the proposed approach using various experiments. Our proposed method is capable of mitigating the Trojan attacks such as data leakage, performance degradation, denial of service and live locking of data packets at the cost of a little latency and, some extra hardware. It is able to recover more than 80% of lost packets, improve the throughput by $1.3\times $ against performance degrading Trojan attacks. [ABSTRACT FROM AUTHOR]

A distinctive frequency selective surface (FSS) to effectively enhance the electromagnetic shielding performance of high-speed electronic systems while preserving good mechanical ventilation is proposed in this article. The FSS consists of periodic segmented circular patterns in two-dimensional view and hollow vent holes in spatial, which exhibits a wide 2 GHz stopband at the interested frequency of 28 GHz. The new design also demonstrates stability to wave oblique incidence and polarization. The FSS shielding panel is prototyped and measured for validation. A satisfactory agreement between simulation and physical experiment is achieved. [ABSTRACT FROM AUTHOR]

Electronic system components can fall prey to counterfeiting via untrustworthy parties in the semiconductor supply chain, which has established a worldwide span to reduce costs, time to market, and increase productivity. Recently, integrated circuits (ICs) counterfeiting has threatened systems security and reliability that utilize ICs in all domains. This article focuses on the most counterfeited area—recycled and remarked ICs—and aims to develop a technique to distinguish between new and used digital ICs based on an aging sensor mechanism. Aging sensors have been studied based on path-delay fingerprinting and ring oscillators (ROs) frequency degradation, but their resolution requires further development to accurately detect short usage. This study proposes a novel differential aging sensor to measure the discharge time ($\tau {{{dv}}}$) increase that depends on the subthreshold leakage current due to aging with two on-chip designs. Simulations were conducted using the GlobalFoundries (GF) 22 nm for aging with bias temperature instability and hot carrier injection (HCI) combined. The results show that the $\tau {{{dv}}}$ increase is 14.72% after 15 days of usage and increases to 60.49% after three years. This further increases at higher temperatures; the highest simulated temperature ($125~^{\circ }\text{C}$) $\tau {{{dv}}}$ increases by 55.93% after 15 days and 310.17% after three years. The proposed method also outperformed the traditional frequency degradation-based aging estimation method, which at nominal temperature is found to be 5.00% after 15 days and 23.68% after three years. Therefore, discharge time is a sensitive indicator for aging, surpasses frequency in detecting previous usage and is robust against process, voltage, and temperature variations (PVTs). [ABSTRACT FROM AUTHOR]

This article proposes a machine learning (ML)-based emulation of model predictive control (MPC) for modular multilevel converters (MMCs). In particular, the artificial neural network model, trained offline by the data collected from the traditional fast MPC method, is used to control the MMCs with high accuracy. With this offline training, the majority of computational burden is transferred from online to offline. Therefore, the proposed ML MPC can replace the role of the traditional MPC. The experimental results show that the proposed ML-based MPC has the same performance as the conventional MPC but a significantly computationally efficient structure. The finding from the letter provides ground for many other applications for ML-based emulation of complex controllers in power electronic systems. [ABSTRACT FROM AUTHOR]