Showing papers in "Electronics Letters in 2023"
TL;DR: In this article , the authors proposed an active integrated array antenna (AIAA) for dual-beam switchable functionality, which consists of a first-harmonic push-push oscillator, four linearly polarized microstrip antennas, and a switchable feed network.
Abstract: This paper proposes a new active integrated array antenna (AIAA) for dual-beam switchable functionality. The proposed AIAA comprises a first-harmonic push–push oscillator, four linearly polarized microstrip antennas, and a switchable feed network. The first-harmonic push–push oscillator eliminates the impact of load variation and usage of a filter to suppress the second- and higher-harmonic frequency signals. By using the switchable feed network, the main beam direction of the radiation pattern can be switched between two states, i.e. θ = ± 10 ∘ $\mathbf {\theta = \pm }10\mathbf {^{\circ }}$ . The performance of the proposed antenna is experimentally evaluated. The measured results show an effective isotropic radiated power (EIRP) of +27.5 dBm, DC-to-RF efficiency of 20.26%, and figure of merit (FOM) of −155.2 dBc Hz−1 at the fundamental frequency of 5.9 GHz.
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TL;DR: In this paper , a deep vision sensing-based fuzzy control scheme is proposed for smart feeding in industrial recirculating aquaculture systems (IRAS), where a deep learning-based object detection model is introduced to capture two aspects features as the decision factors.
Abstract: In industrial recirculating aquaculture systems (IRAS), the autonomous decision control of feeding strategies remains a practical concern. Conventionally, control schemes were established from data-driven view, which fails to comprehensively perceive activity status of fishes. To deal with this issue, a deep vision sensing-based fuzzy control scheme is proposed for smart feeding in IRAS. In the first stage, a deep learning-based object detection model is introduced to capture two aspects features as the decision factors: residual bait and eating frequency. In the second stage, a fuzzy neural network model is formulated to calculate control decision strategies via fuzzy inference. And experiments on real-world visual scenes are conducted to verify the proposal.
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TL;DR: Wang et al. as mentioned in this paper proposed a dynamic attention-based network (DANet) to mitigate the disturbance of smoke in images, and a decoupled detection head was presented, which can predict category, regression, and object score independently to boost the performance.
Abstract: Recently, flame detection has attracted great attention. However, existing methods have the issues of low detection rates, high false alarm rates, and lack of smoke anti-interference ability. In this letter, a novel dynamic attention-based network (DANet) is proposed for autonomous flame detection in various scenarios. To mitigate the disturbance of smoke in images, a dynamic attention strategy is proposed to discover the potential features among scale-awareness and spatial-awareness. Then, based on dynamic attention module, a decoupled detection head is presented, which can predict category, regression, and object score independently to boost the performance. A self-contained challenging flame dataset, which is multi-scene, multiscale, and multi-interference informative is constructed to evaluate the proposed model and organize the experiments. Extensive ablation and comparison studies on self-labelled dataset reveal the effectiveness of the proposed dynamic attention-based network.
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TL;DR: In this article , the Naive Bayes algorithm with Gaussian distribution of the function in combination with Chi-squared-based attributes selection approach was used to identify cancerous (malignant) and non-malignant (non-cancerous) cells in a breast cancer database.
Abstract: This work aims to identify cancerous (malignant) and non-malignant (non-cancerous) cells in a breast cancer database. Wisconsin breast cancer data (WBC) was utilized and obtained from the University of California, Irvine's machine learning repository. The proposed approach involves the Naive bayes algorithm with Gaussian distribution of the function in combination with Chi-squared-based attributes selection approach. This experimentation has been done after reducing the dimensional space of the used data with extended Kernel Principal Component Analysis (K-PCA). Five different kernels in K-PCA have been tested after the implementation of necessary pre-processing techniques. The performance assessment of the proposed system has been evaluated based on confusion matrix-based accuracy, precision, sensitivity, and specificity. Our proposed methodology with six selected feature and sigmoid K-PCA attained the best accuracy of 99.28%. This result outer performs many state-of-the-art studies recently published on the identical dataset.
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TL;DR: A port-assignment model of an anti-parallel Schottky barrier diode (AP-SBD) and a 0.3-THz sub-harmonic mixer (SHM) is presented in this article .
Abstract: This letter presents a new port-assignment model of an anti-parallel Schottky barrier diode (AP-SBD) and a 0.3-THz sub-harmonic mixer (SHM) as the application design. Using the proposed method, the AP-SBD is accurately modelled compared to conventional modelling methods. Because the characteristics of an AP-SBD have the greatest impact on mixer performance, an accurate modelling method for AP-SBD helps design SHMs with good performance. To verify the proposed modelling in the terahertz region, a 0.3-THz SHM based on the modelled AP-SBD is designed and assembled. The measurement result reveals that the optimal double-sideband (DSB) conversion loss of the mixer is 6.7 to 10.1 dB within 279 to 318 GHz when the fixed local oscillator (LO) signal is 6.4 dBm at 152 GHz. All measurement results confirm that the proposed methodology is concise and feasible for the design of terahertz SHMs.
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TL;DR: In this paper , a novel denoiser based on a residual autoencoder structure is proposed to speed up the training process and boost the performance due to its structure effectively extracting compressed features.
Abstract: A joint neural network decoder and denoiser scheme demonstrated superior performance compared to individual modules. However, there is still a limitation that the existing denoisers cannot effectively learn patterns of encoded signals. To overcome the limitation, a novel denoiser based on a residual autoencoder structure is proposed. The proposed denoiser speeds up the training process and boosts the performance due to its structure effectively extracting compressed features. For the evaluation, a joint system model with a hyper-graph-network decoder that is known for outstanding decoding performance is considered. Simulation results show that this denoiser outperforms the existing denoisers. Furthermore, the proposed joint model shows significant performance improvement compared to the individual hyper-graph-network decoder with only 1% of the number of epochs for the training.
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TL;DR: In this paper , a dual-polarized symmetrically cross-slotted square patch (SCSSP) antenna with multimode resonance is proposed for 5G millimetre-wave broadband applications.
Abstract: A dual-polarised symmetrically cross-slotted square patch (SCSSP) antenna with multimode resonance is proposed for 5G millimetre-wave broadband applications. A symmetrical cross-slot is etched on the square patch surface to change the original E-field distribution where a whole square patch is cut into four disconnected equal parts. For both polarisations, this etching also tunes the resonance frequencies of the two modes to be sufficiently close to each other. Therefore, the antenna can realize good impedance performance within the wide designated bandwidth. Dual-polarised radiation of the SCSSP is vertically excited by three bow-tie-shaped slots and fed by two orthogonal substrate integrated coaxial lines. Simulated and measured results show that the fabricated prototype achieves a broad overlapped impedance bandwidth of 50.0% (24–40 GHz), isolation higher than 30 dB between the two input ports, stable radiation pattern, and low cross-polarisation over the operating band. Moreover, the proposed SCSSP antenna with compact size, planar shape, and simple vertical feeding is very well-suited for two-dimensional array design.
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TL;DR: In this article , a fast beam-steering capability has been demonstrated using an array of high-gain corrugated substrate integrated waveguide (CSIW) slot antennas coupled with a bespoke dielectric lens.
Abstract: A fast beam-steering capability has been demonstrated using an array of high-gain corrugated substrate integrated waveguide (CSIW) slot antennas coupled with a bespoke dielectric lens. The proposed system prototype has been simulated, fabricated, and measured. The proposed system is shown to have a full 360° beam-steer in azimuth plane with an angular sensitivity of 9°, half power beamwidth (HPBW) of 18° and a gain of < 20.5 dBi at n257 frequency band of 26 to 29 GHz. Further, the antenna is shown to have a fixed frequency beam steer of ±15° along with a frequency-controlled beam-scanning of ±45° in the elevation plane. The antenna setup is shown to be suitable for applications for 5G base-stations.
TL;DR: Naseer et al. as discussed by the authors integrated a recurrent neural network (RNN) based on the Long Short-Term Memory (LSTM) architecture with the reinforcement learning-based Deep Deterministic Policy Gradient (DDPG) algorithm.
Abstract: Robots with telepresence capabilities are typically employed for tasks where human presence is not feasible due to geography, safety risks like fire or radiation exposure, or other factors like any epidemic disease. Time delay is a significant consideration in controlling a telepresence robot. This study proposes a deep learning-based approach to compensate for the delay by predicting the behaviour of the teleoperator. We integrate a recurrent neural network (RNN) based on the Long Short-Term Memory (LSTM) architecture with the reinforcement learning-based Deep Deterministic Policy Gradient (DDPG) algorithm. The proposed method predicts the teleoperator’s angular and linear controlling commands by using data gathered by embedded sensors on the specially designed and built telepresence robot. Simulations and experiments assess the operation of the proposed technique in Gazebo simulation and MATLAB with ROS integration, which shows 2.3% better response in the presence of static and dynamic obstacles. A novel approach to compensate delay in communication by predicting teleoperator behaviour using deep learning and reinforcement learning to control telepresence robot Fawad Naseer, Muhammad Nasir Khan, Akhtar Rasool, and Nafees Ayub 1 Electrical Engineering Department, The University of Lahore, Lahore, Pakistan 2 Mechanical Engineering Department, Beijing Institute of Technology, Beijing, China 3 Computer Science Department, Government College University Faisalabad, Faisalabad, Pakistan Email: fawadn.84@gmail.com Robots with telepresence capabilities are typically employed for tasks where human presence is not feasible due to geography, safety risks like fire or radiation exposure, or other factors like any epidemic disease. Time delay is a significant consideration in controlling a telepresence robot. This study proposes a deep learningbased approach to compensate for the delay by predicting the behaviour of the teleoperator. We integrate a recurrent neural network (RNN) based on the Long Short-Term Memory (LSTM) architecture with the reinforcement learning-based Deep Deterministic Policy Gradient (DDPG) algorithm. The proposed method predicts the teleoperator’s angular and linear controlling commands by using data gathered by embedded sensors on the specially designed and built telepresence robot. Simulations and experiments assess the operation of the proposed technique in Gazebo simulation and MATLAB with ROS integration, which shows 2.3% better response in the presence of static and dynamic obstacles.
TL;DR: In this paper , a special coprime array structure is proposed to improve the accuracy of direction-of-arrival estimation, which has lower root mean square error and higher estimation accuracy than other methods.
Abstract: Coprime array can provide high degrees of freedom by using difference–sum co-array under the same physical sensors, and can estimate the source under uncertain conditions. A special coprime array structure is proposed in this paper. Its difference–sum co-array can improve the degrees of freedom. Finally, the proposed array structure estimation is used for direction-of-arrival. Simulation results and analysis show that the proposed array structure can effectively improve the accuracy of direction-of-arrival estimation. Under the same signal-to-noise ratio and snapshot number, it has lower root mean square error and higher estimation accuracy than other methods.
TL;DR: In this paper , the authors proposed a new fully passive noise shaping (NS) successive approximation register (SAR) ADC, which can realize 2 × gain with a simple structure, leading to the reduced comparator power and less parasitics.
Abstract: The fully passive noise shaping (NS) successive approximation register (SAR) analog-to-digital converters (ADCs) are simple, operational transconductance amplifier (OTA) free and scaling friendly. Previous passive NS-SAR ADCs rely on the multi-path-input comparator or capacitors stacking to realize the passive gain for compensating the signal attenuation during passive integration. However, the former causes high comparator power consumption, and the latter suffers from additional signal attenuation due to the parasitics and is hard to extend to high-order systems. This work proposes a new fully passive NS-SAR technique, it can realize 2 × gain with a simple structure, leading to the reduced comparator power, and less parasitics. This technique is also easy to extend to high-order NS-SAR ADCs.
TL;DR: In this paper , a more straightforward and efficient model-based value iteration algorithm is proposed, which leverages prior knowledge obtained through empirical channel models to develop a sampled coverage map that can be used in value iteration, and a deep neural network is subsequently trained with supervised learning to approximate the optimal Q function in continuous state space.
Abstract: This letter presents an efficient coverage map-based unmanned aerial vehicle (UAV) navigation framework in cellular communication systems. Unlike previous research that focused on viewing UAV navigation as a Markov decision process in unknown continuous state space and leveraged various model-free and deep neural network-based reinforcement learning algorithms, a more straightforward and efficient model-based value iteration algorithm is proposed. The algorithm leverages prior knowledge obtained through empirical channel models to develop a sampled coverage map that can be used in value iteration. A deep neural network is subsequently trained with supervised learning to approximate the optimal Q function in continuous state space. Finally, the trained neural network is applied to obtain a UAV trajectory that optimizes the objective function.
TL;DR: In this paper , a classification-prediction joint framework is proposed to accelerate inter coding of VVC, which combines classification and prediction to process different CTUs through different networks with appropriate capacities.
Abstract: Aiming at accelerating the inter coding of versatile video coding (VVC), the existing deep-learning-based methods utilize a single convolutional neural network (CNN) to directly predict the quadtree plus multi-type tree (QTMT)-based partition of the whole coding tree unit (CTU). However, these methods adopt one prediction network for unevenly distributed CTUs and ignore that the different CTUs have different partition prediction difficulties, leading to performance degradation and computation waste. To overcome these limitations, a classification-prediction joint framework is proposed to accelerate inter coding of VVC in this letter, which combines classification and prediction to process different CTUs through different networks with appropriate capacities. To achieve effective partition prediction of the whole CTU, the QTMT-based partition is first modeled as the partition homogeneity map (PHM), which is a value map reflecting the partition of each 8× 8 unit. Second, the classification module classifies the CTUs into different classes according to their partition prediction difficulty, and then different prediction sub-networks with appropriate capacities are utilized to predict the PHM for the corresponding CTU class. Finally, the decision tree (DT) is adopted to determine the optimal split modes based on the predicted PHM. Experimental results show that the approach achieves 44.5% time saving with 1.94% BD-BR increase, outperforming state-of-the-art approaches.
TL;DR: In this article , a real-time inference machine for A-mode ultrasonic echo pattern recognition is proposed, which is based on a combination of a support vector machine and a finite state machine.
Abstract: This brief proposes a field programmable gate array (FPGA) based real-time inference machine for A-mode ultrasonic echo pattern recognition. The proposed scheme can aid a probe positioning of single-transducer based ultrasound devices by detecting specific echo patterns on a scanline. The inference machine is based on a combination of a support vector machine and a finite state machine. The proposed inference machine utilizes a minimal pre-processing for a feature extraction, considering a nature of distinctive echo patterns. In addition, a primary computation of linear support vector machine is performed sequentially, and the finite state machine for robust binary classification is relatively compact. As a result, the overall structure of the inference machine can be concisely implemented on FPGA. The proposed inference machine was assembled with a customized A-mode ultrasound scanner. The maximum utilization percentage of look-up-tables in FPGA was less than 1.5%. In evaluation of inferences, the resultant sensitivity and the specificity were 96.2% and 99.87%, respectively.
TL;DR: In this article , a novel active integrated array antenna (AIAA) is introduced for the purpose of enabling pattern switchable functionality, which includes a Gunn oscillator, four microstrip antennas, and two switched-line phase shifters.
Abstract: In this paper, a novel active integrated array antenna (AIAA) is introduced for the purpose of enabling pattern switchable functionality. The AIAA includes a Gunn oscillator, four microstrip antennas, and two switched-line phase shifters. The Gunn oscillator provides high-output RF power to excite the antenna elements through the switched-line phase shifters. The switched-line phase shifter allows for switching the main beam direction of the radiation pattern among three states, specifically θ = − 10 ∘ , 0 ∘ ${\theta =-10^{\circ }, 0^{\circ }}$ , and + 10 ∘ ${+10^{\circ }}$ in the ϕ = 90 ∘ $\phi \,=\,90^{\circ }$ -plane without shifting its high resonance frequency. Besides, the AIAA requires fewer RF switching diodes in a compact, cheap, and simple layout when compared to previously reported AIAAs. An experimental evaluation of the proposed antenna's performance is conducted and the results indicate an effective isotropic radiated power (EIRP) of +16.09 dBm at the frequency of 9.41 GHz. In all three states, the cross-polarization suppression is better than 15 dB.
TL;DR: In this article , the authors proposed criteria for recess etching to fabricate T-gate used in InGaAs HEMTs, where the ratio of before and after etching for each R ds and I ds can be used as criteria to determine the point in time to stop etching.
Abstract: We propose criteria for recess etching to fabricate T-gate used in InGaAs HEMTs. By patterning additional rectangular pads on the source and drain metals in the e-beam lithography step, it is possible to measure the drain-to-source resistance ( R ds ) and current ( I ds ). the ratio ( Γ ) of before and after etching for each R ds and I ds can be used as criteria to determine the point in time to stop etching. By performing recess etching with Γ = 1.97 for R ds and Γ = 0.38 for I ds on an epiwafer having cap doping concentration of 2= 10 19 cm -3 and channel indium content of 0.7, we have fabricated InGaAs mHEMT device showing g m,max = 1603 mS/mm and f t = 290 GHz at L g = 124 nm. The criteria presented can be applied to InGaAs HEMTs with various epitaxial structures.
TL;DR: In this paper , a planar cavity-backed multiplexing antenna (SMA) is proposed and validated experimentally, where the individual feeds are created in such a way, that antenna produces five distinct resonant frequencies in the dominant mode while maintaining sufficient mutual port isolation.
Abstract: In this letter, a planar cavity-backed multiplexing antenna (SMA) is proposed and validated experimentally. A planar cavity is realized by using a novel transmission line known as substrate-integrated waveguide (SIW) technology. The design comprises five SIW cavities and each one consisting of a slot for launching the energy in free space. Three different types of slots are employed for lower mutual coupling and compact circuit integration. The individual feeds are created in such a way, that antenna produces five distinct resonant frequencies in the dominant mode while maintaining sufficient mutual port isolation. Finally, the proposed concept is validated with experiments and results show acceptable agreement with the simulated counterparts. The design shows five operating frequency channels with center frequencies of 5.2, 5.5, 5.75, 6.2, and 6.8 GHz. The mutual isolation between any two radiating elements retains below −21 dB and the average gain of the antenna is better than 3.5 dBi at each resonant frequency. The proposed SMA element can be used to cover a wide range of wireless access points and seems suitable for multiple wireless system integration.
TL;DR: In this article , a novel microwave limiter with non-reciprocal limiting threshold is proposed to protect the transceiver switch or the transmitter from high-power signals input in both directions.
Abstract: A novel microwave limiter with non-reciprocal limiting threshold is proposed in this paper to protect the transceiver switch or the transmitter. The directivity of the directional coupler is utilized to make the power of the received signal input to the detection circuit larger than that of the transmitted signal, thereby the detection circuit provides different DC bias voltage to the limiter circuit and changes the threshold level of the limiter diode. The test results show that this limiter has a threshold level of 35 dBm for the transmitted signal and 17 dBm for the received signal, which has a non-reciprocal limiting threshold for high-power signals input in both directions.
TL;DR: In this article , a tunnel junction cascaded semiconductor laser with thin waveguide and narrowed spacing between adjacent emitters is designed in order to reduce the vertical beam parameter product (BPP) of the multi-emitter cascaded laser.
Abstract: A tunnel junction cascaded semiconductor laser with thin waveguide and narrowed spacing between adjacent emitters is designed in this work. It can be concluded theoretically that the spatial size of the spots arrangement in fast axis has a greater impact on the vertical beam parameter product (BPP) of the multi-emitter cascaded laser than the divergence angle. The measured vertical BPP of the designed narrow emitter spacing (NES) laser has been reduced 42.5%, which implies that the beam quality performance of the laser has been improved and proves the feasibility of this method. Meanwhile, the slope efficiency of the thin waveguide NES laser can reach up to 3.55 W/A under 10 ns duration pulse current.
TL;DR: In this paper , rainfall rate statistics for radio propagation applications collected during 1-year measurement with a 1-min integration time in three locations of Venezuela are presented and analyzed for the first time in the country.
Abstract: In the present study, rainfall rate statistics for radio propagation applications collected during 1-year measurement with a 1-min integration time in three locations of Venezuela are presented and analyzed for the first time in the country. Specifically, the statistics of the worst-month (WM) are assessed and compared with the Recommendation ITU-R P.841-7 prediction model. The results indicate that the model does not accurately predict the statistics of the WM in the country. Also, the performance of Recommendation ITU-R P.837-7 (Annex 1 and Annex 2) in estimating the rain rate for a 1-min integration time at the three locations is examined, with the ITU-R P.837-7 Annex 1 exhibiting the best performance and providing results that encourage its use in the prediction of complementary cumulative distributions of rain rate in the country.
TL;DR: In this article , a method based on machine learning techniques is presented which extracts reliable performance parameters from transfer characteristics of 4H-SiC MOSFETs including a quantitative estimate of the density of interface traps.
Abstract: The performance of 4H silicon carbide (SiC) MOSFETs critically depends on the quality of the SiC/silicon oxide interface, which typically contains a high density of interface traps. To solve this problem, fast and reliable characterization methods are required. The commonly used evaluation schemes for 3-terminal transfer characteristics, however, neglect the presence of interface traps. Here, a method based on machine-learning techniques is presented which extracts reliable performance parameters from transfer characteristics of 4H-SiC MOSFETs including a quantitative estimate of the density of interface traps. This method is successfully validated by comparison with Hall-effect measurements and applied to various MOSFET types.
TL;DR: In this paper , a two-phase synchronous buck converter with a low swing pulse width-modulated (PWM) signal is presented. But the converter requires additional buffers or preamplifiers, which poses a challenge in the field of power conversion and dynamically power supply applications.
Abstract: The need for a high-frequency and high-efficiency gallium nitride (GaN)-based buck converter that can be controlled directly by a low swing pulse width-modulated (PWM) signal, without the need for additional buffers or preamplifiers, poses a significant challenge in the field of power conversion and dynamically power supply applications. To solve this problem, this letter presents a monolithic two-phase synchronous buck converter, fabricated using a 0.25-um GaN-on-Si process, integrated both the drivers and power stage transistors, without requiring additional buffers or preamplifiers. So, the proposed converter can be directly controlled by a PWM signal with a swing of only 1 V (−0.5 to 0.5 V). At a 100-MHz switching frequency, the converter achieves a maximum output power of 12 W with a power stage efficiency of 82%. The converter includes two identical half-bridge Monolithic Microwave Integrated Circuit (MMICs), and with the frequency multiplication property of multi-phase topology, the equivalent switching frequency can reach 200 MHz in the open-loop operating mode. The experimental results show that the converter can track a 40-MHz bandwidth envelope signal (256 QAM, 6 dB PAPR) with 80.1% efficiency.
TL;DR: In this article , a portable exoskeleton for human arm motions is designed to address the problem of high manufacturing costs and patients' inability to purchase one due to their high equipment costs.
Abstract: Over the past two decades, the number of people with arm disabilities has dramatically increased. Many researchers have begun to concentrate on developing technologies to address this challenge. Exoskeleton is one of the most successful technologies out of these since it does not require medical staff to accompany the patients and can continually rehabilitate the patients until they regain the ability to move voluntarily. However, the majority of exoskeletons are anchored to the ground and individuals are unable to purchase one due to its high manufacturing costs. Furthermore, since this type of exoskeleton is anchored to the ground, patients must go to hospitals to get rehabilitation services. In this study, a portable assistive exoskeleton for human arm motions is designed to address this problem. This device can carry patients' arms to the desired position by adhering to a prescribed trajectory by medical specialists. The main parts of this exoskeleton are made of polylactic acid and produced using 3D printing technology. Therefore, the total manufacturing costs of the exoskeleton are not excessive and the weight of it is not high as well.
TL;DR: In this article , a novel bandgap reference circuit that utilizes both curvature and folding compensation to achieve a temperature coefficient (TC) of 2.23 ppm/°C was proposed.
Abstract: This letter proposes a novel bandgap reference circuit that utilizes both curvature and folding compensation to achieve a temperature coefficient (TC) of 2.23 ppm/°C. Unlike traditional BGRs, the unique folding compensation method of this circuit improves the performance at low temperature and can also be applied within a specific temperature range.
TL;DR: In this paper , the authors used a nano-pixel structure to realize a power splitter with a desired splitting ratio, which exhibited precise asymmetric splitting of 1.7:1 successfully at 1550 nm.
Abstract: Power-splitter is one of the fundamental elements for photonic integrated circuits. Y-junction, multi-mode-interference (MMI), and directional coupler may have been widely used as power splitter; however, the design theory to realize precise asymmetric splitting ratio is not well established. In addition, the device length is relatively long and less robustness in general. To realize a power splitter with a desired splitting ratio, the authors used a nano-pixel structure. Nano-pixel structures consist of an array of pixels in which the rectangular waveguide region is divided into many nano-scale regions. The implemented devices were realized in an area of 3.2 μm × 3.4 μm. The device exhibited precise asymmetric splitting of 1.7:1 successfully at 1550 nm.