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With the continued proliferation of mobile Internet and geo-locating technologies, carpooling as a green transport mode is widely accepted and becoming tremendously popular worldwide. In this paper, we focus on a popular carpooling service called ride hitching, which is typically implemented using a publish/subscribe approach. In a ride hitching service, drivers subscribe ride orders published by riders and continuously receive matching ride orders until one is picked. The current systems (e.g., Didi Hitch) adopt a threshold-based approach to filter out ride orders. That is, a new ride order will be sent to all subscribing drivers whose planned trips can match the ride order within a pre-defined detour threshold. A limitation of this approach is that it is difficult for drivers to specify a reasonable detour threshold in practice. In addressing this problem, we propose a novel top-k subscription query called Top-k Ride Subscription (TkRS) query, which continuously returns the best k ride orders that match drivers' trip plans to them. We propose two efficient algorithms to enable the top-k results maintenance. We also design a novel hybrid index and a two-level buffer to efficiently track the top- $k$ results. Finally, extensive experiments on real-life datasets suggest that our algorithms can achieve desirable performance in practical settings.

A 50-year-old man with atrial fibrillation without anticoagulation presented to the hospital after tonic–clonic seizures, followed by pulseless cardiopulmonary arrest with return of spontaneous circulation after 2 min. Imaging demonstrated a left superior cerebellar thromboembolic ischaemic

Mission profile emulation for submodules (SMs) in the modular multilevel converter (MMC) is a crucial step before a full-scale system is put into field operation. Existing methods suffer from limitations of the testing accuracy due to the staircase-like arm voltages which result in distorted loading current and require large filter inductance. This is especially the case when limited number of SMs are under test. In this paper, an efficient MPE method is proposed to emulate the loading behaviors of multiple SMs in MMC. The testing circuit allows two arms of MMC with flexible SM number, operating in inverting and rectifying mode under nearest level modulation (NLM), to be tested simultaneously with reduced DC supply voltage. A common H-bridge circuit is introduced in the testing circuit, with each arm of the H-bridge circuit functioning as the voltage compensator by using feed-forward control. In this way, the current distortion can be significantly suppressed and testing accuracy can be guaranteed in a cost-efficient manner even though no large inductor is included in the testing circuit. The proposed method allows SMs under test to be loaded as in the actual back-to-back MMC system. Simulations and experimental measurements are given to validate the proposed method.

One of the main application scenario that the upcoming 5G systems are expected to support is Internet of Things (IoT) traffic backhauling. With the exponential increase of devices, it follows the critical challenge of handling the huge data volume that they produce. Terrestrial Networks may not be sufficient for that, or not always available. Satellites then come into play, and they can provide the ubiquitous coverage targeted by 5G. On the other side, aggregation can be an efficient strategy to reduce the amount of traffic, and use network resources in an efficient manner. Building on that, in this work we consider an hybrid IoT-satellite network architecture for collecting MQTT traffic. We design an advanced filter namely MQTT-MFA that performs MQTT topics aggregation in close proximity of MQTT Publishers before data is sent over the satellite link. The developed MQTT-MFA implementation leverages on MQTT bridge, and it is compliant with Mosquitto, an open source MQTT broker. Extensive simulation results have demonstrated that MQTT-MFA requires only 10% of the TCP/IP overhead generated in a baseline architecture (where the bridge is not set up) and 1/4 number of bytes to transmit the same amount of information, when compression of the data payload is also enabled.

Object tracking is a fundamental task for the visual perception system on the intelligent unmanned aerial vehicle (UAV). The high efficiency of correlation filter (CF) based trackers has advanced the widespread development of online UAV object tracking. This kind of method can effectively train a filter to discriminate the target from the background. However, most CF-based methods require a fixed label function over all the previous samples, leading to over-fitting and filter degradation, especially in complex drone scenarios. To address this problem, a novel adaptive response reasoning approach is proposed for CF learning. It can leverage temporal information in filter training and significantly promote the robustness of the tracker. Specifically, the proposed response reasoning method goes beyond the standard response consistency requirement and constructs an auxiliary label of the current sample. Besides, it helps learn a generic relationship between the previous and current filters, thereby realizing self-regulated filter updating and enhancing the discriminability of the filter. Extensive experiments on four well-known challenging UAV tracking benchmarks with 278 videos sequences show that the presented method yields superior results to 40 state-of-the-art trackers with real-time performance on a single CPU, which is suitable for UAV online tracking missions.

Different from typical circuit breakers, breakers used to switch AC filters in convertor stations have to operate frequently according to the real-time operation mode and daily load changes. Therefore, the performance of filter breakers decreases very quickly, which leads to frequent accidents. Both preinsertion resistors and controlled switching devices can suppress switching transients. In this paper, a comparative analysis of whether a controlled switching device and preinsertion resistor should be adopted is carried out using PSCAD. The transient is calculated as the combined result of the switching angle and preinsertion resistor value, and the optimal configurations are proposed. After determining the resistor value and whether a controlled switching device should be included, the reference voltage of the arrester should be reevaluated.

The three-phase dual active bridge (3p-DAB) is a popular topology suitable for high power DC-DC isolated conversion. This converter features bi-directional power flow, zero-voltage switching capability, and reduced transformer and filter volume. These features meet the requirements of several applications, such as solid-state transformers. But when operating under light load the resulting converter efficiency is highly degraded by the loss of soft-switching capability. This paper introduces the use of a Wye-extended-Delta (Y-e) transformer winding in the 3p-DAB converter, which increases the efficiency under light load, keeping the simplicity of phase-shift control. The framework developed allows a complete analysis of the converter, including the determination of the power and rms current, zero-voltage switching boundaries and the impact of the selected phase-shifting angle in the transformer design. Additionally, this paper presents a negative sequence modulation scheme, allowing maximum efficiency under light loads to be achieved for both direct and reverse power flows. The performance of the 3p-DAB with Y-e transformers is compared with previously reported winding options for 3p-DAB. The experimental results show a converter efficiency increase, up to 10\% for direct power flow and up to 9\% for reverse power flow, when operating at light load (10\% of nominal power).

Modern industrial plants generally consist of multiple manufacturing units, and the local correlation within each unit can be used to effectively alleviate the effect of spurious correlation and meticulously reflect the operation status of the process system. Therefore, the local correlation, which is called spatial information here, should also be taken into consideration when developing the monitoring model. In this study, a cascaded monitoring network (MoniNet) method is proposed to develop the monitoring model with concurrent analytics of temporal and spatial information. By implementing convolutional operation to each variable, the temporal information that reveals dynamic correlation of process data and spatial information that reflects local characteristics within individual operation unit can be extracted simultaneously. For each convolutional feature, a submodel is developed and then all the submodels are integrated to generate a final monitoring model. Based on the developed model, the operation status of the newly collected sample can be identified by comparing the calculated statistics with their corresponding control limits. Similar to the convolutional neural network (CNN), the MoniNet can also expand its receptive field and capture deeper information by adding more convolutional layers. Besides, the filter selection and submodel development in MoniNet can be replaced to generalize the proposed network to many existing monitoring strategies. The performance of the proposed method is validated using two real industrial processes. The illustration results show that the proposed method can effectively detect process anomalies by concurrent analytics of temporal and spatial information.

Federated learning enables collaborative deep learning over multiple clients without sharing their local data, and it becomes increasingly popular due to the good balance between data privacy and model usability. Generally, it faces the heavy communication overhead when a large number of clients are involved and the low convergence rate incurred by non-IID data. However, few existing solutions cannot simultaneously address the communication and statistic challenges. In this paper, we propose a computation and communication efficient federated learning via adaptive sampling. By capturing different data distribution among clients, we utilize the concept of self-paced learning to adaptively adjust thresholds to filter training data for each client and also to select suitable clients to be involved in each global learning round. We prove its correctness through theoretical analysis and also evaluate its performance through experimental evaluations on real-world datasets. Detailed experimental results show that it can effectively reduce the communication cost while achieving the good trade-off between accuracy and efficiency.

The design of electromagnetic interference (EMI) filter with smaller size and lightweight for a high efficiency and high power density 100 kW SiC five-level T-type (5LT2) photovoltaic (PV) inverter becomes more challenging due to the increased EMI noise resulted by high switching frequency, fast switching transients, and the removal of LCL filter. This paper proposes an unbalanced Wheatstone bridge and a two-stage notch filter to attenuate the increased common mode (CM) and differential mode (DM) noise respectively. The conventional Wheatstone bridge will suffer size and weight penalty for 100 kW PV converter due to the high parasitic capacitance of the 100 kW PV panel. An unbalanced Wheatstone bridge with a one-stage LC filter is therefore developed to address this issue and reduce the total CM inductance. The proposed two-stage notch filter is designed to achieve high attenuation, solve its parameter variation issue and help reduce the size and weight of DM filter. The total weight of the designed EMI filter for this 100 kW PV inverter is 3.54 kg. The measured conducted EMI spectrum of the 100 kW SiC PV inverter with and without proposed EMI filter are provided to validate the effectiveness of the EMI filter design.

Accurate and real-time prediction of network traffic can not only help system operators allocate resources rationally according to their actual business needs but also help them assess the performance of a network and analyze its health status. In recent years, neural networks have been proved suitable to predict time series data, represented by the model of a long short-term memory (LSTM) neural network and a temporal convolutional network (TCN). This article proposes a novel hybrid prediction method named SG and TCN-based LSTM (ST-LSTM) for such network traffic prediction, which synergistically combines the power of the Savitzky-Golay (SG) filter, the TCN, as well as the LSTM. ST-LSTM employs a three-phase end-to-end methodology serving time series prediction. It first eliminates noise in raw data using the SG filter, then extracts short-term features from sequences applying the TCN, and then captures the long-term dependence in the data exploiting the LSTM. Experimental results over real-world datasets demonstrate that the proposed ST-LSTM outperforms state-of-the-art algorithms in terms of prediction accuracy.

In this paper, a novel zero-voltage switching (ZVS) full-bridge converter is proposed to overcome the disadvantages of traditional phase-shifted full-bridge (PSFB) converter. In the proposed converter, a half-bridge (HB) converter with center-tap rectifier (CTR) is integrated into the traditional PSFB converter by sharing the lagging-leg switches and output filter. The HB converter operates with full duty cycle to ensure continuous energy transmission and the output voltage is regulated by adjusting the phase-shifted time of FB converter. The proposed converter has many advantages such as reduced filter requirement, wide ZVS range and low primary circulating current. In addition, the rectifier in the proposed converter is composed of a full-bridge rectifier (FBR) section and a CTR section. This feature makes the proposed converter well appropriate for the medium output voltage applications. Key operation principle and characteristics are fully discussed in this paper. The effectiveness of proposed converter is verified using a 1.2-kW prototype circuit with 280 V input voltage and 60-120 V output voltage.

In recent years, the number of traffic accident deaths due to distracted driving has been increasing dramatically. Fortunately, distracted driving can be detected by the rapidly developing deep learning technology. Nevertheless, considering that real-time detection is necessary, three contradictory requirements for an optimized network must be addressed: a small number of parameters, high accuracy, and high speed. We propose a new D-HCNN model based on a decreasing filter size with only 0.76M parameters, a much smaller number of parameters than that used by models in many other studies. D-HCNN uses HOG feature images, L2 weight regularization, dropout and batch normalization to improve the performance. We discuss the advantages and principles of D-HCNN in detail and conduct experimental evaluations on two public datasets, AUC Distracted Driver (AUCD2) and State Farm Distracted Driver Detection (SFD3). The accuracy on AUCD2 and SFD3 is 95.59% and 99.87%, respectively, higher than the accuracy achieved by many other state-of-the-art methods.

As a sort of model-free tracking approach, discriminative correlation filter (DCF)-based trackers have shown prominent performance in unmanned aerial vehicle (UAV) tracking. Nevertheless, typical DCFs acquire all samples oriented to filter training merely from the current frame by cyclic shift operation in the spatial domain but ignore the consistency between samples across the timeline. The lack of temporal cues restricts the performance of DCFs under object appearance variations arising from object/UAV motion, scale variations, and viewpoint changes. Besides, many existing methods commonly neglect the channel discrepancy in object position estimation and generally treat all channels equally, thus limiting the further promotion of the tracking discriminability. This work proposes a novel tracking approach based on a multi-regularized correlation filter, i.e., MRCF tracker. By regularizing the deviation of responses and the reliability of channels, the tracker enables smooth response variations and adaptive channel weight distributions simultaneously, leading to favorable adaption to object appearance variations and enhancement of discriminability. Exhaustive experiments on five authoritative UAV-specific benchmarks validate the competitiveness and efficiency of MRCF against top-ranked trackers. Furthermore, we apply our proposed tracker to monocular UAV self-localization under air-ground robot coordination. Evaluations indicate the practicability of the presented method in UAV localization applications.

This work presents three single-phase isolated ac-ac converters based on the dual active bridge (DAB) converter and interleaved transformers. The structures allow obtaining filter elements with reduced dimensions, as well as bidirectional power flow and high efficiency over a wide load range. Multilevel voltage waveforms exist in the input and output sides of the symmetrical converters, resulting in reduced harmonic content. The possible arrangements include a symmetrical half-bridge converter (SHBC), a symmetrical full-bridge converter (SFBC), and a symmetrical interleaved full-bridge converter (SIFBC). The introduced topologies can be regarded as prominent solutions for applications involving solid-state transformers (SSTs), dynamic voltage restorers (DVRs), and uninterruptible power supplies (UPSs). A thorough mathematical analysis of the SHBC is carried out and experimental results are presented to investigate the converter performance in steady-state and transient conditions.

Epipolar geometry is a fundamental constraint used in computer vision systems to estimate parameters using correspondence. The most common way to describe epipolar geometry is by means of a 3x3 matrix called the fundamental matrix, and such matrices are used to store the precise geometric information relating a pair of stereo images. Its efficient estimation substantially improves initialization in visual simultaneous localization and mapping (vSLAM), which uses correspondence-based epipolar geometry to determine the trajectory of the camera and a three-dimensional scene. Conventional robust methods for epipolar geometry estimation can become computationally inefficient and inaccurate when there are low-quality correspondences. Because semantic information can be more stable than pixel intensities/descriptors, a novel Faster Region-based Convolutional Network (R-CNN) learning-based approach called the semantic filter is proposed in this paper to address these problems. The semantic filter is first trained on different semantic patches, which are described in terms of their different outlier distributions, providing different semantic labels for image contexts. Then, the patches with low-level semantic labels are filtered out. Finally, precise and robust correspondences can be determined by matches using the high-level semantic contexts, making the correspondence-based calculation more accurate. For dynamic outdoor scenes, the results of extensive experiments show that our semantic filter can help vSLAM localize accurately and robustly on a map from different viewpoints. In a completely static scenario, our semantic filter can remove the low-quality correspondences, enabling the mobile robot to operate well.

Motor drive systems supplied by a fuel-cell/battery are especially demanding when it comes to the design of the inverter. Besides a high performance (high efficiency η and power density ρ), the inverter has to cope with the wide DC voltage variation of the fuel-cell/battery that supplies the motor drive. A promising three-phase inverter topology, denoted as Y-VSI, is presented in this paper. The Y-VSI is a modular three-phase inverter, and comprises three identical phase-modules connected to a common star “Y” point. Each phase-module is equivalent to a buck-boost DC/DC converter, which allows the AC output voltages to be higher or lower than the DC input voltage. Thereby, the Y-VSI effectively copes with the wide variation of the fuel-cell/battery voltage. Each phase-module can be operated in a similar fashion to a conventional DC/DC converter, independently of the remaining two phases. Accordingly, a straightforward and simple operation/control of the Y-VSI is possible. In addition, the Y-VSI features an integrated output filter. This allows for continuous/sinusoidal motor voltage waveforms, eliminating the need of an additional filter between the inverter and the motor. This paper details the operating principle of the Y-VSI, and comparatively evaluates two modulation strategies. In order to validate the proposed concepts, a Y-VSI hardware prototype is assembled within the context of a high-speed motor drive. In the investigated drive system, a fuel-cell supplies the Y-VSI, which in return controls a 280 krpm 1 kW electric compressor. The Y-VSI hardware prototype is compared against a state-of-the-art hardware prototype, which features two energy conversion stages. It is shown that the Y-VSI is ∆η = +2.3% more efficient and at the same time ∆ρ = +10% more power dense compared to the conventional inverter solution.

We show theoretically and experimentally how a flat-top second-order response can be achieved with a self-coupled single add-drop ring resonator based on two couplers with different splitting ratios. The resulting device is a 1x1 filter, reflecting light back in the input waveguide at resonating wavelengths in the passbands, and transmitting light in the output waveguide at all other non-resonating wavelengths. Different implementations of the filter have been designed and fabricated on a micron-scale silicon photonics platform. They are based on compact Euler bends - either U-bends or L-bends - and Multi-Mode Interferometers as splitters for the ring resonators. Different finesse values have been achieved by using either 50:50 MMIs in conjunction with 85:15 MMIs or 85:15 MMIs in conjunction with 95:05 double MMIs. Unlike ordinary lowest order directional couplers, the MMIs couple most of the power in the cross-port which make them particularly suitable for the topology of the self-coupled ring, which would otherwise require a waveguide crossing. Experimental results are presented, showing good agreement with simulations. The proposed devices can find applications as wavelength-selective reflectors for relatively broad-band lasers or used as 2x2 add-drop filters when two exact replicas of the device are placed on the arms of a Mach-Zehnder interferometer., 9 pages, 17 references, 12 figures

High-performance multicast-enabled packet forwarding engines (PFEs), as an essential component of high-end switches, use a polynomial-time membership query algorithm to determine which port(s) the data packet should be forwarded. The currently widely used query algorithm is Bloom Filter (BF), which has been proven to have many fatal flaws. Another error-free membership query algorithm includes Scalar-pair Vectors Routing Forwarding (SVRF), Fractional- N Scalar-pair Vectors Routing Forwarding (Frac- N SVRF), and the Per-Port Prime Filter Array (P3FA) also have some shortcomings in space and time efficiencies. In this paper, we proposed a hybrid strategy: Divaricate Heterogeneous SVRF (DH-SVRF) scheme, which based on the P3FA and Frac- N SVRF, which randomly divides all member ships into N groups, and each group has the same structure and is independent of each other to obtain higher time efficiency and space utilization. Finally, we also discussed the selection of the optimal egress-diversity threshold. Through mathematical modeling and simulation, we validate that the proposed DH-SVRF scheme is superior to the SVRF/Frac -N SVRF and traditional BF in terms of scalability, space utilization, and time efficiency in specific conditions such as appropriate egress-diversity thresholds.

In this article, a common-mode noise filter with a wideband suppressing and dual-band high absorbing common mode filter, which is called absorptive common-mode filter, is proposed. The filter is constructed by a reflective-type common-mode filter, a phase matching, and an absorber. It is composed of three layers with 14.98 mm × 9.585 mm. The broadband common-mode noise suppression can be achieved by positioning multiple transmission zeros. By using dual-band phase matching and an absorber, the dual-band absorption can be realized. The frequencies are in the range of 2.4 to 2.5 GHz and 5.17 to 5.33 GHz, respectively. The positioning of transmission zeros and absorption points at those frequencies leads to a high absorption efficiency. The simulation result shows that the common-mode suppression, which is below −10 dB, is from 2 to 11 GHz. The fractional bandwidth is 138%. The differential-mode signal keeps integrity in the range from DC to 12 GHz. The absorption efficiencies in the two frequency sections are 97% and 96%, respectively. The experiment results show only slight differences from the simulation results.

In this paper, a new lumped-element Class-F3/E and transmission-line (TL) Class-F3/E2 power amplifiers are presented. It is shown that employing the series output filter (inverse Class-E) combined with a third harmonic control circuit (Class-F) leads to the new Class-F/E power amplifier. Compared with other switch-mode Class-E, Class-F/E, and Class-E/F power amplifiers, the proposed circuit has a higher maximum operating frequency (fmax), higher output power capability (Cp), and lower peak drain voltage (Vmax). The capability of the Class-F/E mode with a series filter has been demonstrated by two design examples, one with lumped elements at 100 MHz and the other with transmission lines at 2.21 GHz. Two test boards of power amplifiers were measured, and high-performance results have been obtained. The simulated and measured drain efficiency (DE) of 86.4/85%, output power of 41/40.6 dBm, and gain of 15.9/15.6 dB for the lumped-element Class-F3/E power amplifier have been obtained. On the other hand, the simulated/measured DE of 83/81%, power added efficiency of 79/78%, and output power of about 40/39.8 dBm for Class-F3/E2 power amplifier at an input power of 27 dBm were obtained.

In this paper, a new 5-level inverter is developed by inserting a switched-capacitor (SC) unit into the traditional 3-level neutral-point-clamped (NPC) inverter phase-leg. The SC unit consists of two capacitors and one bidirectional switch, all of which withstand a quarter of the dc input voltage. Whilst increasing the output levels, the performance is also improved in terms of power loss, common-mode voltage, switching stress dv/dt and output filter. Compared to other 5-level inverters like the conventional NPC and active NPC 5-level topologies, the new solution not only reduces the number of components and simplifies the design, but also has the advantage of self-balanced capacitor voltages. The analysis, simulation and experiment indicate that the proposed inverter is suitable for a wide range of applications like renewable source grid-connected interfaces and motor drivers. Simulation and experimental results of grid-connected operation verify that the new inverter is capable of providing both active and reactive power to the grid. Its excellent performance is also experimentally evaluated by a 1.2 kW prototype and the measured efficiency is above 97% for a wide range of load.

In this paper, by combining substrate integrated waveguide (SIW) and microstrip technologies, a novel dual-band filter is proposed to achieve a quasi-elliptic response for both passbands. The dual-mode characteristics of the SIW cavities, namely TE101 and TE201 modes, are used to create two passbands. Different with the conventional inductive windows, two pairs of uniform impedance resonators (UIRs) with electric mutual coupling, which are etched between the two rectangular SIW cavities, are used to couple the TE101 and TE201 modes, respectively. Good merits are achieved in this way, including: 1) Independent coupling paths are created for TE101 and TE201 modes, which would reduce the complexity of numerical optimization; 2) Since the electric couplings are generated between each pair of the UIRs, it would be easy to realize a cross coupling by only constructing inductive windows between the SIW cavities. A filter prototype with central frequencies operated at 8 GHz and 10 GHz is then designed and fabricated for validation, which shows a compact size and high selectivity with controllable transmission zeros.

In this paper, an active electromagnetic interference (EMI) filter is designed to reduce the DC side common mode (CM) EMI in motor drive system. Different from the traditional passive EMI filter which is added in the main circuit, the designed AEF in this paper is added in the grounding circuit which is connected between motor frame and the ground and it can be decoupled from the main circuit. By properly setting the components value, the DC side CM EMI can be suppressed while maintaining a low motor frame voltage. The structure of the AEF is simple and the volume of the AEF can be kept small. The operation principle of the AEF is introduced first and the performance of this AEF is evaluated through simulation. Experiment results verified the effect of this AEF that it can suppress the DC side CM EMI and the motor frame voltage can be kept low.

In this paper, a novel on-chip dual-band bandpass filter (D-BPF) with two independently controllable transmission zeros (TZs) is proposed. Based on resonators transformation, the schematic and design procedure of the filter are presented. Using even-/odd-mode analysis, analytical equations are used for demonstration of the theory. As an example, a 3.32/5.17-GHz D-BPF with 3-dB fractional bandwidth (3-dB FBW) of 25.45%/14.20% for 5G applications is designed, fabricated, and measured. The size of the filter in low-temperature cofired ceramic (LTCC) is only 11.4 mm * 6 mm * 0.94 mm. The filter has been successfully measured with the minimum insertion loss of 1.38/1.53-dB and high return loss of 28/23-dB at the center frequency.

In this paper, a novel battery-less wireless sensor design is proposed and demonstrated. The proposed passive wireless sensor node integrates a UHF RFID chip, a filter, and a reactive sensor to enable the reading of environmental conditions wirelessly, without increasing the complexity of a typical RFID chip and substantial loss of read-range. The sensor changes the phase of the reflected RFID signal based on the sensed parameter, whereas, the filter helps in resolving any phase ambiguity that may arise due to the placement of the node at different distances. The phase change is detected at the reader using a non-coherent IQ demodulator. The design can easily integrate any type of sensors such as temperature, humidity, and water level sensor. A flood sensor and a temperature sensor were used to demonstrate the performance of the proposed design in a home and office environment.

A predistorted folded substrate integrated waveguide (FSIW) filter is developed for a flat passband. The unloaded Q-factor of FSIW cavity is predicted numerically and then applied to the coupling matrix synthesis. A filter prototype is fabricated with the low temperature cofired ceramic technology (LTCC), where the direct and cross couplings are realized in a 3D configuration. The synthesized, simulated and measured results agree well with each other. The insertion loss is 3.48 dB at 5 GHz and the 0.2-dB bandwidth is 338 MHz which is equivalent to a conventional filter with the uniform unloaded Q-factor of 870, much higher than the one of 180 provided by the applied FSIW resonator. Two transmission zeros locate at 4.50 and 5.46 GHz to improve the frequency selectivity. The filter size is only 18.04×9.2×1.4 mm3 and the occupied area is 0.72×0.37 λd2.

This brief reports on the electromagnetic (EM) design and the practical development of frequency-reconfigurable quasi-reflectionless bandpass filters (BPFs) using substrate integrated coaxial (SIC) resonators. The filter concept is based on in-series-cascaded quasi-reflectionless stages that are shaped by a first-order bandpass section and two resistively-terminated first-order bandstop sections. For the first time, we explore the realization of these filters using tunable SIC resonators that exhibit high quality factor (Q) and can be widely tuned with commercially-available linear piezoelectric actuators. Synthesized examples alongside various EM design and practical integration aspects are discussed in detail. The concept is experimentally validated at S-band through the manufacturing and testing of two reconfigurable (one-stage and two-stage) prototypes.

This paper presents the design of tunable CMOS pseudo-resistors aimed at achieving large and predictable resistance values. Instrumental to the proposed pseudo-resistor is the design of current sources of extremely low currents and transistors operating in subthreshold. The effects of the leakage currents of reverse-biased pn-junctions on the pseudo-resistor resistance are also evaluated. The proposed pseudo-resistor circuit was validated while in use as part of a filter integrated in a 180 nm CMOS process. Pseudo-resistors with resistances between 180 GΩ and 700 GΩ were employed to obtain low-frequency poles of a band-pass filter adjustable from 0.6 Hz to 2 Hz.

This article presents an ultracompact bandpass filter (BPF) realized by slow wave substrate integrated groove gap waveguide (SW-SIGGW). The SW-SIGGW is composed of two layers of the dielectric substrate, in which periodic mushroom-type electromagnetic band-gap (EBG) unit cells are employed as packaging structures. The gap layer is realized by the upper substrate, and the via-patch mushrooms are designed on the lower substrate. Due to the slow wave structure in the form of periodical metallic via-holes designed on the lower substrate, the guided wavelength is reduced by about 52.8% compared with normal SIGGW. A dual-band resonator and a cascaded second-order BPF are designed based on SW-SIGGW. Compared with the conventional second-order SIGGW BPF, the proposed SW-SIGGW BPF achieves a 62.6% reduction in the area. By adopting vertical stacking technology, the area of the SW-SIGGW BPF is further miniaturized. The final area of the stacked SW-SIGGW BPF is only 18.7% of the conventional filter based on normal SIGGW without slow wave and stacked structure, which is very competitive for the room-limited applications. A prototype of the stacked SW-SIGGW BPF with 5.4% and 7.4% fractional bandwidths at 11.8 and 18 GHz is fabricated and measured. A good agreement between the measured and simulated results verifies the validity of the miniaturized design of SW-SIGGW BPF.

Conditional privacy preservation and message authentication serve as the primary research issues in terms of security in vehicular networks. With the arrival of 5G era, the downloading speed of network services and the message transmission speed have significantly improved. Consequently, the content exchanged by users in vehicular networks is not limited to traffic information, and vehicles moving at high speeds can share a wide variety of contents. However, sharing content reliably and efficiently remains challenging owing to the fast-moving character of vehicles. To solve this problem, we propose a reliable and efficient content sharing scheme in 5G-enabled vehicular networks. The vehicles with content downloading requests quickly filter the adjacent vehicles to choose capable and suitable proxy vehicles and request them for content services. Thus, the purpose of obtaining a good hit ratio, saving network traffic, reducing time delay, and easing congestion during peak hours can be achieved. The security analysis indicates that the proposed scheme meets the security requirements of vehicular networks. Our cryptographic operations are based on the elliptic curve, and finally, the proposed scheme also displays favorable performance compared to other related schemes.

A five-wavelength-switchable thulium-doped fiber laser with a fiber Bragg grating (FBG)-based five-channel transmitting Fabry-Pérot (F-P) filter and a multi-coupler based triple-ring cavity (CTRC) filter, is experimentally proposed. For the first time, a multi-channel F-P filter and specially-designed coupler-based compound cavity filter were combined to realize multi-wavelength-switchable operation, with single-longitudinal-mode (SLM) lasing for each wavelength channel in a 2 µm band fiber laser. The theoretical analysis of the CTRC filter and the principle for achieving SLM operation by corporately using the F-P filter and CTRC filter were presented in detail. The experimental results demonstrated the good performance of the proposed filters. Five lasing wavelengths (1941.13 nm, 1941.22 nm, 1941.30 nm, 1941.39 nm, and 1941.47 nm) were obtained with good switchability, and each operated in a stable SLM state. The OSNR of each lasing wavelength was higher than 50 dB, and the linewidths of all obtained wavelengths were less than 8 kHz.

Super spreader identification has a lot of applications in network management and security monitoring. It is a more difficult problem than heavy hitter identification because flow spread is harder to measure than flow size due to the requirement of duplicate removal. The prior work either incurs heavy memory overhead or requires heavy computations. This paper designs a new super-spreader monitor capable of identifying all flows whose spreads are greater than a user-specified threshold with a probability that can be arbitrarily set. It introduces a generalized geometric hash function, a generalized geometric counter, and a novel geometric-min filter that blocks out the vast majority of small/medium flows from being tracked, allowing us to focus on a small number of flows in which super spreaders are identified. We provide an analytical way of properly setting the system threshold to meet probabilistically guaranteed identification of super spreaders, and implement it on both hardware (FPGA) and software platforms. We perform extensive experiments based on real Internet traffic traces from CAIDA. The results show that with proper parameter settings, the new monitor can identify more than 99% super spreaders with a low memory requirement, better than the prior art.

Feature selection plays a key role in target recognition especially for multiple combined sensors’ applications. This study presents an improved relief feature selection (Relief‐F) model to rank features for multi‐target recognition. Besides, the evaluation of the effectiveness of the method is conducted based on the following three measuring indices: feature redundancy, class separability and overall accuracy. The simulated data acquired by radar infrared combined sensors are used to verify the metrics of the proposed method. The experimental results demonstrate that the Relief‐F method reveals the best results compared to other feature selection methods, including principal component analysis and Fisher linear discriminant analysis. It indicates that Relief‐F can be an effective option for feature selection with high‐class separability and a low feature redundancy rate. Furthermore, high overall accuracy can be obtained using a relatively small amount of features selected by the Relief‐F method.

In this article, high-performance compact defective ground technique based microstrip low-pass filter with and without dumble -shaped structure is proposed. By etching of dumble geometry on to ground plane, the presented filter gives a wideband operating frequency and sharp cut at stop band with high attenuation. The presented geometry is designed on software. The simulated outcomes shows that the presented low pass filter has very significant advantages in term of wide band stop band from 2.5 GHz to 6.6 GHz with the S21 much better than −35 dB and the presented LPF can meet the requirements of wireless communication systems and microwave applications with its stated efficiency.

This brief presents a low-noise, low-power bio-signal acquisition analog front-end (Bio-AFE). It mainly includes a capacitively coupled chopper-stabilized instrumentation amplifier (CCIA), a programmable gain amplifier (PGA), a low-pass filter (LPF), and a successive approximation analog to digital converter (SAR ADC). A chopper-capacitor-chopper integrator based DC servo loop (CIB-DSL) to suppress the electrode DC offset (EDO) with low noise, low process-voltage–temperature (PVT) sensitivity is proposed. Besides, a low power GM-C filter of which the input linear range is extended via inverse hyperbolic tangent pre-distortion circuit is proposed to filter out the spikes, ripples, and high frequency noise in front end. A capacitive feedback PGA is employed to realize a tunable gain of the front end. And a low power SAR ADC is applied to quantify the acquired bio-signal. The proposed analog front end has been fabricated in a 0.18m CMOS process with an area of 0.57mm, consuming 3.5 A from a 1V supply voltage. It achieves an integral noise from 0.5 to 100Hz of 1.16Vrms, ±90mV EDO cancellation range, an ADC effective number of 9.06 bit, and the -59.6 dB total harmonic distortion (THD) of total AFE.

Two-dimensional (2D) crystals of conjugated polymers (2DCCPs) have attracted significant attention in the past few decades due to their superior and intriguing optoelectronic properties. With the increase in brain-inspired neuromorphic computing, 2DCCPs have also been employed in the construction of synaptic devices because they offer clear advantages of easy integration, high stability, long–range order and no biological rejection. However, pursuing controllable strategies to obtain high-quality and large-scale 2DCCPs remains a challenge on account of their intrinsic complex structures. Herein, we present a universal methodology to grow centimeter-scale 2DCCPs in a controlled manner within minutes by selecting an appropriate growth-assisting solvent and surfactant. The thickness of the as-produced 2DCCPs was down to two molecular layers. The photonic synaptic transistors based on the as-grown 2DCCPs could perform typical synaptic functions, including short-term plasticity, paired-pulse facilitation, long-term plasticity and spike intensity-dependent plasticity. The transistor was also proven to have potential application as a high-pass filter. This methodology shows great potential to be expanded to many other polymer systems and opens new pathways for large-area integrated wireless communication systems and artificial visual systems.

In this article, we focus on addressing the nonlinear filtering problem with unknown measurement noise covariance and measurement outliers, which may be encountered in the application in strapdown inertial navigation system/global positioning system integrated navigation system. Although the existing methods, such as the adaptive Kalman filter, are widely used in the integrated navigation system, their estimation accuracy is poor, this paper proposes a variational Bayesian-based robust cubature Kalman filter to address this problem, which not only retains the adaptivity when the measurement noise is unknown but also exhibits robustness in the presence of measurement outliers, First, the variational Bayesian method is applied to the estimation of measurement noise, then the maximum correntropy criterion is introduced to replace the minimum mean square error criterion, which improves the robust performance of the filter. The numerical simulation demonstrates that the proposed filter outperforms the existing filters both in estimation accuracy and robustness, and the effectiveness of the proposed filter is verified on the integrated navigation system.

With the performance increase of convolutional neural network (CNN), the disadvantages of CNN's high storage and high power consumption are followed. Among the methods mentioned in various literature, filter pruning is a crucial method for constructing lightweight networks. However, the current filter pruning method is still challenged by complicated processes and training inefficiency. This paper proposes an effective filter pruning method, which uses the saliency of the feature map (SFM), i.e. information entropy, as a theoretical guide for whether the filter is essential. The pruning principle use here is that the filter with a weak saliency feature map in the early stage will not significantly improve the final accuracy. Thus, one can efficiently prune the non‐salient feature map with a smaller information entropy and the corresponding filter. Besides, an over‐parameterized convolution method is employed to improve the pruned model's accuracy without increasing parameter at inference time. Experimental results show that without introducing any additional constraints, the effectiveness of this method in FLOPs and parameters reduction with similar accuracy has advanced the state‐of‐the‐art. For example, on CIFAR‐10, the pruned VGG‐16 achieves only a small loss of 0.39% in Top‐1 accuracy with a factor of 83.3% parameters, and 66.7% FLOPs reductions. On ImageNet‐100, the pruned ResNet‐50 achieves only a small accuracy degradation of 0.76% in Top‐1 accuracy with a factor of 61.19% parameters, and 62.98% FLOPs reductions.

In digital communication, a filter is any device that selectively attenuates any unwanted component from a received signal. It essentially changes or reshapes the waveform of a signal in the desired manner. In this work, we have designed and studied low pass filter using Rectangular, Bartlett, Hamming, Hanning, Tukey and Kaiser Window algorithms and compared them with each other for further analysis. The parameters we used for designing the filter are sampling frequency, cut off frequency, filter order and a variable parameter, α. MATLAB toolbox for DSP, namely FDA Tool and FV Tool were employed for the realization of the design.

The application of graph convolutional networks (GCNs) to hyperspectral image (HSI) classification is a heavily researched topic. However, GCNs are based on spectral filters, which are computationally costly and fail to suppress noise effectively. In addition, the current GCN-based methods are prone to oversmoothing (the representation of each node tends to be congruent) problems. To circumvent these problems, a novel semi-supervised locality-preserving dense graph neural network (GNN) with autoregressive moving average (ARMA) filters and context-aware learning (DARMA-CAL) is proposed for HSI classification. In this work, we introduce the ARMA filter instead of a spectral filter to apply to GNNs. The ARMA filter can better capture the global graph structure and is more robust to noise. More importantly, the ARMA filter can simplify calculations compared with the spectral filter. In addition, we show that the ARMA filter can be approximated by a recursive method. Furthermore, we propose a dense structure, which not only implements the ARMA filter in the structure, but is also locality-preserving. Finally, we design a layerwise context-aware learning mechanism to extract the useful local information generated by each layer of the dense ARMA network. The experimental results on three real HSI datasets show that DARMA-CAL outperforms the compared state-of-the-art methods.

By using integrated lumped elements based on through-silicon via (TSV) technology, a 3-D low-pass filter (LPF) with ultracompact size and ultrawide stopband is proposed. The 3-D magnetic coupling is enhanced between microsize spiral inductors to increase the mutual inductance and improve the high-frequency performance. A coaxial TSV with enhanced capacitance in high resistivity silicon (HRS) is utilized both as capacitor and as a vertical interconnection without any overhead in area and routing. The 3-D nature of the proposed filter yields a miniaturized size of 0.2 x 0.10 mm². The LPF is analyzed with equivalent circuit model and validated with the well-matched S-parameters obtained by finite element method simulation and measurement. With -3-dB cutoff frequency at 2.6 GHz, the LPF exhibits an insertion loss below 1 dB, a reflection loss over 17 dB in the passband, and a suppression level over 20 dB from 5.28 up to 40 GHz in the stopband.

For the single-phase high-power railway traction system, the inevitable second-order resonating power has become a critical issue to the traction system. In this article, a power-decoupling solution is proposed to replace the conventional passive LC resonance filter by utilizing the buck-type bidirectional dc/dc converter (BBDC) of hybrid electric multiple units, which is designed to power the train in nonelectrified routes using the on-board battery. By considering both the current ripples in the battery mode and the power decoupling in the catenary mode, the corresponding parameter design process of the BBDC is presented. Based on the power coupling phenomenon and the spectrum characteristic of the BBDC, a direct resonance control method is proposed to decouple the low-order resonating power. Simulations and experiments are carried out to validate the effectiveness of the new proposal. The results demonstrate that the proposal is almost the same as using the conventional passive LC resonance filter for power decoupling in both steady and dynamic operation scenarios.

We consider weakly-supervised video anomaly detection in this work. This task aims to learn to localize video frames containing anomaly events with only binary video-level annotation, i.e., anomaly vs. normal. Traditional approaches usually formulate it as a multiple instance learning problem, which ignore the intrinsic data imbalance issue that positive samples are very scarce compared to negative ones. In this paper, we focus on addressing this issue to boost detection performance further. We develop a new light-weight anomaly detection model that fully utilizes enough normal videos to train a classifier with a good discriminative ability for normal videos, and we employ it to improve the selectivity for anomalous segments and filter out normal segments. Specifically, in addition to boosting anomalous prediction, a novel contrastive attention module additionally produces a converted normal feature from anomalous video to refined anomalous predictions by maximizing the classifier making a mistake. Moreover, to remove the stubborn normal segments selected by the attention module, we also design an attention consistency loss to employ the classifier with high confidence for normal features to guide the attention module. Extensive experiments on two large-scale datasets, UCF-Crime, ShanghaiTech and XD-Violence, clearly demonstrate that our model largely improves frame-level AUC over the state-of-the-art. Code is released at \url{https://github.com/changsn/Contrastive-Attention-for-Video-Anomaly-Detection