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.
A dual-polarized symmetrically cross-slotted square patch (SCSSP) antenna with multimode resonance is proposed for 5G millimeter-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 polarizations, 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-polarized 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-polarization 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.
Facial expression has been widely used in clinical practice to assess pain in newborns. However, the inherent visual attention required to make such vital inference is poorly understood. It is also unknown whether this inference occurs differently when comparing health professionals with other adults. To investigate these issues, we have recorded and monitored the pupil size signal of 102 subjects (44 experts, 29 parents, and 29 non-experts) while visually analyzing 20 frontal face images of 10 distinct newborns after a painful procedure and at painless rest. Our experimental results have showed that neonatal pain assessment is more cognitively demanding when analyzing the presence of pain rather than its absence. Moreover, our results disclose that a 2-second exposure to a facial expression is sufficient to make this assessment, regardless whether done by health professionals or non-health ones, suggesting that this highly specific visual task is not driven by clinical experience.
Interrupted sampling repeater jamming (ISRJ) is a novel intra-pulse coherent jamming based on digital radio frequency memory (DRFM). By repeatedly sampling and retransmitting the radar transmitting signal fragments, a series of false targets can be formed after pulse compression (PC), posing a severe threat to modern radar systems. Inspired by the energy function method and histogram analysis in image processing, an adaptive time-frequency (TF) filtering method is proposed in this letter. The ISRJ-contaminated regions can be accurately determined in the TF image after histogram analysis and subsequent energy accumulation. Guided by the energy function, the proposed method can automatically adjust the intensity threshold in TF image histogram analysis and therefore reveals better robustness compared with other competing methods. Simulations have verified the effectiveness and robustness of the proposed method against ISRJ under various circumstances.
A high speed and low power input/output buffer for time interleaving circuit is proposed in this letter. The buffer can be applied to high speed circuits operating at 20GS/s. This novel two-stage buffer is employed with bandwidth expansion and slew-rate enhanced techniques. An improved common-mode feedback circuit stabilizes the output common-mode voltage. This prototype buffer is fabricated in 45nm COMS process, and achieves 7.2bit ENOB at 10GHz input frequency with power consumption of 20.4mW, load of 0.3fF.
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.
Deep learning-based classification algorithms have been used for automatic modulation recognition (AMR). However, most methods only focus on end-to-end mapping and neglect the classic key features. In this paper, signals are enforced with key classification features to propose a novel deep learning model for AMR by learning the shared latent space of the aligned signals and key features (LLAF); this is done to increase the generalizability of the model and to ensure the physical plausibility of the results. To obtain adequate signal representations, an encoder-decoder architecture is proposed to learn the shared latent space, and the architecture is trained to approximate prior label distributions for precise signal classification. Simulation results verify the high recognition accuracy of the proposed LLAF model under different signal-to-noise ratios (SNRs).
We demonstrate a high-speed 8.5 μm quantum cascade laser with room temperature continuous wave operation. The maximum output power of 141 mW is obtained at 20 ℃. The parasitic capacitance of the device is decreased from 36.6 pF to 7.1 pF by monolithic integrating a π-shape metal contact electrode. This results in an increase in the -3 dB RF modulation bandwidth from 870 MHz to 4.5 GHz compared with the conventional electrode configuration.
Identifying cohesive subgraphs is an important topic in graph theory and complex network analysis. The quasi-clique, as a generalization of clique, can be used to identify functional and structural properties of various networks. In this paper, we study the maximum weighted quasi-clique problem, and propose a local search algorithm for solving the problem. In the algorithm, an iterated local search method is used as the search framework. To find the quasi-clique with the maximum total weights, hybrid vertex selection strategies are proposed and incorporated into our algorithm. The hybrid strategies utilize a probability-based mechanism for choosing sub-strategies in each round of the local search. We conduct experiments on synthetic networks and real-world networks to show the effectiveness of our algorithm. The results indicate that hybrid strategies perform better than existing methods, and thus our algorithm has a good ability to tackle various networks.
In this letter, the issue of mitigating strong co-channel interference (CCI) in communication systems is addressed. Unlike conventional model-based methods, a novel data-driven scheme is proposed. A recurrent neural network (RNN) is trained to directly demodulate the desired signal under strong CCI. Instead of inputting the original received signal, in-phase and quadrature interference-robust features (IRF) are extracted through preprocess. The RNN is then trained offline to implement sequence labelling, with the IRF sequences and known code sequences of the desired signal as inputs and ground-truth labels. Meanwhile, a guard zone is introduced when loading the IRF sequences to enable better contextual information exploitation by the RNN demodulator. Online tests validated the low bit error rate (BER) of the RNN demodulator, under strong CCI. Moreover, the proposed scheme outperformed existing model-based and data-driven interference mitigation schemes in terms of the BER, especially in low signal-to-interference ratio region. Inspiringly, the proposed data-driven scheme generalized well to varied unseen test conditions.
Beamforming technique can effectively improve the spectrum utilization of multi-antenna systems, while the dirty-paper coding (DPC) technique can reduce inter-user interference. In this letter, we aim to maximize the weighted sum-rate under power constraint in a multiple-input-single-output (MISO) system with the DPC. However, the existing methods of beamforming optimization mainly rely on customized iterative algorithms, which have high computational complexity. To address this issue, by utilizing the deep learning technique and the uplink-downlink duality, and carefully exploring the optimal solution structure, we devise a beamforming neural network (BFNNet), which includes a deep neural network module and a signal processing module. Besides, we use the modulus of the channel coefficients as the input of deep neural network, which reduces the input size. Simulation results show that a well-trained BFNNet can achieve near-optimal solutions, while significantly reducing computational complexity
The improvement of the performance of a distributed Bragg reflector laser bar emitting near 905 nm through the use of multiple epitaxially stacked active regions and tunnel junctions is reported. The bar consisting of 48 emitters (each having an aperture of 50 μm) emits an optical power of 2.2 kW in 8 ns long pulses at an injection current of 1.1 kA. This corresponds to an almost threefold increase of the pulse power compared to a bar with lasers having only a single active region. Due to the integrated surface Bragg grating, the bar exhibits a narrow spectral bandwidth of about 0.3 nm and a thermal tuning of only 68 pm/K.
In this letter, a novel sext-band bandpass filter (BPF) using two split-type multiple-mode resonators is proposed. The filter is composed of two sets of resonators which are respectively designed to realize Filter 1 with two passbands and Filter 2 with quad passbands. Then the two BPFs are combined by parallel coupling feed lines for sext-band responses, and each of the six centre frequencies in the proposed sext-band BPF is able to be controlled due to the design freedom. To validate the design and analysis, a prototype filter has been fabricated with six passbands centered at 1.88/2.59/3.48/5.26/5.82/6.75 GHz. The measured result of the fabricated filter agrees well with the simulation, which shows that the proposed structure is a good candidate for sext-band BPF designs and validates the proposed design flow well.