The development of mega constellations inevitably brings various problems for the development of routing techniques. Most of the existing work considers end-to-end delay and load balancing problems, while the analysis of routing strategies in case of link performance degradation is neglected, and an optimization approach applicable to mega satellite networks is not developed. In this letter, we propose a robust routing strategy based on deep reinforcement learning (RRS-DRL) that regards the Age of Information (AoI) of packets as an optimization target, and ensures the effectiveness of message transmission throughout the network. Extensive simulation results show that our proposed RRS-DRL algorithm obtains a lower average AoI across the network and better utilization of the resources than the traditional shortest path algorithm, significantly increasing the robustness of the constellation.

The tracking and data relay satellite (TDRS) in the geosynchronous orbit (GEO) is capable of providing ranging and communication services for user spacecraft in low Earth orbit (LEO) through the inter-satellite link (ISL). The time delay of TDRS’ ISL transponder in orbit differs from that before launch and changes due to complex space environment, which contributes non-negligible errors to user spacecraft’s orbit determination based on the four-way ranging data. We construct a calibration system and propose an efficient on-orbit calibration method for time delay of ISL transponder in TDRS. Experimental results verify the effectiveness of the proposed method and the RMS of calibration residuals is less than 0.5 meters.

In this paper, a 2-bit digital reflection-type phase shifter working at 120 GHz is presented. It uses a compact coupled-lines coupler with low insertion loss and high isolation over a wide bandwidth. The loads are made by a microstrip-line loaded by three PIN diodes whose states are tuned ON/OFF to obtain 90°, 180° and 270° phase shift relative to the reference (0°). Measurement results show RMS phase and amplitude error equal to 10.3° and 1.2 dB, respectively. The maximum insertion loss is equal to 8.6 dB, leading to a figure of merit of 31°/dB. As shown by simulation, by flipping PIN diodes and use negative voltage for biasing, the maximum insertion loss could be reduced to 3.6 dB (figure of merit of 75°/dB) along with a great improvement in RMS phase and amplitude errors, thus showing the potential of the proposed architecture.

A low-power NPN-based bandgap voltage reference (BGR) over an ultra-wide temperature range is presented. The conventional NPN-based BGRs cannot maintain a low-temperature coefficient (TC) over an ultra-wide temperature range due to the inherent substrate leakage current of the NPN bipolar junction transistors (BJT) in the high-temperature range. This work introduces a new NPN-based BGR unaffected by substrate leakage current and receives low TC over the range of -40℃ to 150℃. The proposed circuit was fabricated in a 180 nm CMOS process. It consumes 2uA from a 4V power supply, and its average TC is 14.89ppm/℃. Also, the average line sensitivity is 0.039%/V.

With the increasing attention on remote monitoring of human heart rate by radar, there is a need to develop a method that can estimate heart rate quickly and reliably. In this study, a new estimation method using a periodic average magnitude difference function (PAMDF) is proposed to estimate the heart rate from the radar signal. PAMDF advances the classical average magnitude difference function (AMDF) with the help of maximum likelihood (ML) theory. It operates in the time domain and estimates the heart rate by calculating the signal magnitude difference between all heartbeat periods. The proposed technique is more accurate than AMDF and allows rounding interpolation to improve resolution, while maintaining the low complexity advantage of AMDF. The algorithm was validated using radar data from a publicly available dataset.

In the process of identifying non-line-of-sight (NLOS), acoustics-based indoor positioning needs to collect audio recordings of sound fields in multiple rooms and upload them to the central server for training. Once the transmission process and server-side suffer malicious attacks, private data will also be leaked. To solve the training difficulty and privacy issues at the same time, we propose a novel Personalized Federated Learning (PFL) model combined with user frequency and room data capacity, taking into account the significant differences in positioning data with room layout. The proposed model can accurately identify the differences between different room data when aggregating on the server-side. By collecting data in the actual indoor environment and comparing the existing algorithms, the accuracy of the proposed method in the data verification of unfamiliar rooms is 90%.

In this paper, a quasi-optical resonator with new coupling method and parasitic-mode-suppressing in 110~170GHz is proposed. The coupling of THz quasi-optical resonator in this paper is realized by PCB substrate processing and pressurized flange. At the same time, the parasitic mode is effectively suppressed by slitting the plane mirror to reduce the influence on the main mode during the test. And fused quartz sample was tested to verify the feasibility of the system for complex dielectric testing of materials in 110~170GHz.

The fully passive noise shaping (NS) successive approximation register (SAR) analog-to-digital converters (ADCs) are simple, 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.

This paper focuses on the compressed sensing $\ell_1-\ell_2-$minimization model and develops new bounds on cumulative coherence $\mu_1(s)$. We point out that if cumulative coherence $\mu_1(s)$ satisfies (2) or (11), then the sparse signal can stably recover in noise model and exactly recover in free noise by $\ell_1-\ell_2$-minimization model.

Low power IoT communication signals (e.g., Bluetooth and ZigBee) may seriously suffer from the presence of high-power co-channel interference like wireless local area network (WLAN) signal. They may effectively avoid WLAN interference by exploiting dynamic characteristics of WLAN traffic. Representing the arrival/departure of WLAN users using an M/M/m/m queueing structure, we consider the characterization of large-scale dynamics of WLAN channel occupancy. We also consider the characterization of small-scale dynamics of WLAN channel occupancy by generating WLAN signal using a two-state semi-Markovian process. Simulation results show that the proposed model generates WLAN signal having similar statistical characteristics to those of real WLAN signal.

NiOx thin films were prepared on an Au substrate using the electrodeposition (ED), spin-coating (SP), and ED after SP (SP/ED) techniques to realize their application as an electrochemical sensor for the selective detection of trace substances. Results indicated that the electrodeposited films had nanoparticles formed as coarse-grain morphology, and the spin-coated films had a uniform layer with ~60 nm thickness. The thin film prepared by the SP/ED technique showed the highest electrochemical activity, and it was used to record a linear sweep voltammogram to measure the target substance, MSG and glucose, from low concentrations (2 nM) to high concentrations (200 μM). Within the range of concentrations, high R2 values of ≥0.99 were observed for both target substances, confirming that the SP/ED thin films can be used as an electrochemical sensor with high reliability.

This letter reports a 28-GHz multi-port magneto-electric (ME) dipole array for 5G applications. The proposed antenna array enlarges the system polarization diversity with the capability of being utilized as a balanced antenna, a dual-polarized antenna and a circularly polarized antenna. Co-planar waveguide (CPW) lines are used to connect ME dipole radiators to achieve a high gain with simple feeding structure. The proposed antenna array exhibits a -10dB impedance bandwidth of 12.8% and a maximal peak realized gain of 13.52 dBi as a balanced antenna, and exhibits a -10dB impedance bandwidth of 28.57%, a 3-dB axial ratio bandwidth of 16% and a maximal peak realized gain of 12.15 dBi as a circularly polarized antenna.

It is well known that long time coherent integration (LTCI) can effectively improve the radar detection ability of manoeuvring weak targets, since a considerable signal-to-noise ratio (SNR) improvement can be achieved [1]. However, for most existing LTCI algorithms [2-5], there is a common assumption that the observed target is of the single motion stage (i.e., the motion parameters of targets are uniform) during the coherent processing interval (CPI). However, with the advancement of manoeuvrability and the increasement of CPI, the observed target might be of multiple motion stages. In this case, the above-mentioned LTCI algorithms will not be effective any more. The specific LTCI algorithms developed for manoeuvring weak target with multiple motion stages are relatively few. In [6], a short-time generalized radon-Fourier transform (STGRFT) based LTCI algorithm is proposed to remove range migration (RM) and Doppler frequency migration (DFM) effects and estimate the stage-changing point. Similar as GRFT[5], STGRFT can be able to obtain an excellent SNR gain through multi-dimension parametric searching. In [7], a reference signal is introduced to compensate the motion parameters change (MPC) effect between different motion stages, and then GRFT is utilized to achieve the coherent integration during the CPI. However, the computational load of these algorithms is quite high, since the key procedure is based on the multi-dimension parametric searching. This may deteriorate the engineering practicability of these algorithms.

Low-cost air pollutant sensors suffer several interferences due to the variation of climatic elements. Recent studies look for calibration solutions based on different regression and classification machine learning algorithms. The present work brings together the implementation and extraction of performance metrics from these algorithms in a single open-source tool. Both the input data and parameters for each algorithm are automatically configured. This feature makes the tool compatible with any input dataset and removes the need to interact with complex codes.

The dynamic on-state resistance instability of a high-current cascode multi-GaN-chip power module under high frequency and voltage switching conditions is demonstrated in this paper. The presented double pulse test (DPT) topology is utilized to evaluate switching dependencies on voltage, current, and frequency, showing its versatility in investigating the switching instability of the device. The extended defects in the buffer layer resulted in a decrease in dynamic on-state resistance (RDS-ON) under hard switching conditions. Despite this, no noticeable RDS-ON degradation occurs under harsh switching conditions due to electron de-trapping. This study comprehensively analyzes the dynamic stability of a multi-GaN-chip cascode module with devices.

This letter considers the problem of beamforming in multiple-input multiple-output (MIMO) radar. The mismatch phenomenon of MIMO radar virtual array steering vector is addressed and a new robust beamforming method for MIMO radar is proposed. The object function of this robust MIMO radar beamformer is constructed from an infinite norm of the output data, which is solved by linear programming. The performance of the proposed beamformer is verified by simulation results. Numerical results illustrate that proposed beamformer exhibits good performance improvement in virtual array steering vector mismatch compared to conventional methods.

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.

Synthetic aperture sonar image reconstruction relies on the coherence of overlapping phase centers to provide accurate micronavigation for a sensed scene. It is shown that phase centers lose coherence for near-range scattering from large SAS arrays due to the fundamentally bistatic nature of these sensors. This effect is modeled using the van Cittert-Zernike theorem and a point-based sonar scattering model. Reduction of the window length used in the delay estimation process can partially mitigate the loss of coherence at the expense of increased variance in the resulting delay estimates.