Miracle-dz/UniFAR-FTUs huggingface dataset:
Coarse-to-Fine Tranformer for articular disc of the temporomandibular joint Segmentation

The most important subtypes of joint abnormalities in patients with temporomandibular disorders are different forms of disc displacement and deformation. An effective segmentation model for jaw joint detection to support the diagnosis of TMJ disease on magnetic resonance imaging is very crucial. Data for this study were obtained from 204 MRI images of patients with articular discs and the corresponding MRI segmentation labels of the temporomandibular joints. These images were used to evaluate four deep learning-based semantic segmentation methods. Using a multi-scale structured C2Ftrans segmentation model transformed from coarse to fine, it describes medical image segmentation as a coarse to fine process. It is able to perform accurate target boundary segmentation with lower computational complexity. Tested on this dataset, comparing U-Net, Unet ++ and Attention-U net models for data segmentation results show the C2Ftrans model performs best with the highest dice of 73.5% and the lowest computational complexity.
A Deep Proximal-Unfolding Method for Monaural Speech Dereverberation

Speech is often distorted by reverberation in an enclosure when the microphone is placed far away from the speech source, reducing speech quality and intelligibility. Recent years have witnessed the development of deep neural networks, and many deep learning-based methods have been proposed for dereverberation. Most deep learning-based methods remove the reverberation by directly mapping the reverberant speech to target speech, which often lacks adequate interpretability, limiting the performance upper bound. This paper proposes a deep un-folding method with an interpretable network structure. First, the dereverberation problem was reformulated based on maximum posterior criterion, and an iterative optimization algorithm was then devised by using proximal operators. Second, we unfolded the iterative optimization algorithm into multi-stage deep neural network, where each stage corresponded to a specific operation of the iterative procedure. Experiments were conducted on the WSJO-SI84 corpus, and the results on both simulated and real RIRs showed that the proposed model outperformed previous models and achieved state-of-the-art performance in terms of PESQ, ESTOI and frequency-weighted segmental SNR.
A Multi-agent Approach to Improve Visual SLAM Performance using Miniature Robots

Miniature UGVs provide a low-cost and scalable solution in the field of robotics. With a multi-agent formation, visual SLAM systems can provide a redundant and robust solution to failures caused by track loss or mechanical faults. In this paper, we have proposed a multi-agent approach to improve the visual SLAM performance using miniature robots. By allowing multiple miniature robots to perform the same SLAM task, we decrease the probability of SLAM failure caused in the mapping of an unknown environment. We have performed multi-agent SLAM with the help of indigenously developed miniature UGVs as small as the palm of our hand. Each of the UGVs are operating and sending data with the help of Robot Operating System (ROS) installed over a Linux based edge device. We have also demonstrated the integration of OAK-D camera, Nano Pi computer and an ATMega328 based miniature computer by using socket programming and ROS. The experimentation results show that the SLAM failure is minimized due to the redundancy demonstrated by multiple SLAM agents. A set of vision based obstacle avoidance techniques are also applied for smooth navigation of the multiple robots.
A Two-Level Cascade Detection Algorithm for X-Band Marine Radar

X-Band marine radar plays an important role in maritime detection. The targets are the extended targets due to the high resolution and the strong reflection targets have strong sidelobe. Besides, there are much clutter and noises in the radar echo. In this letter, we propose a two-level cascade detection algorithm for X-Band marine radar. The first-level detector, which consists of CFAR detector, Ostu algorithm and opening operation, aims to detect the suspicious extended targets as many as possible. Firstly, the CFAR detector is utilized to suppress the clutter and sidelobe. Then the Ostu algorithm and opening operation are utilized to segment the extended targets. Furthermore, we impose different ratios for the targets to handle different targets scales. The designed lightweight convolutional neural network (CNN), which combines the AlexNet design idea and convolutional block attention module (CBAM), acts as the second-level detector to distinguish between the true targets and false alarms. Experiments show that the proposed two-level cascade detection algorithm has satisfying detection performance
Evaluation of Multi-objective Evolutionary Algorithms for the Design of Resilient OTN over DWDM Networks

Optical networks are the most appropriate technology to meet the growing demand for services through the internet. Optical Transport Network (OTN) brings virtualization’s economic and operational benefits. However, depending on the size of the network, the network planning process can present a high computational cost. Thus, the use of metaheuristics became interesting in this context. In this work, we compared the performance of modern multi-objective algorithms for the planning of resilient OTN networks over DWDM. The evaluation was performed in two different topologies, one with four nodes and seven linkbundles and the other one with 16 nodes and 32 linkbundles. We observed that OMOPSO and SMPSO achieved better results for the first Topology and obtained the best solutions using about 70% fewer iterations than the other algorithms. In contrast, HYPE proved to be more efficient for the second Topology. Despite not being the fastest algorithm, HYPE obtains the smallest number of interfaces when the Rate of Unsuccessful Recovery Fault (RURF) equals zero.
Taxonomy of Aging-related Bugs in Deep Learning Libraries

Deep learning libraries are the cornerstone of deep learning systems, and millions of deep learning applications are built on top of deep learning libraries. Due to long-term continuous running, many numerical operations and heavy dependence on resources, deep learning libraries are prone to the effects of software aging. Aging in deep learning libraries can threaten the reliability of deep learning systems and make training and application of deep learning more time-consuming and expensive, causing users to lose confidence in it. In this work, we manually screened 138 bug reports containing aging-related bugs from a total of 13,694 bug reports in four popular deep learning libraries (i.e., TensorFlow, MXNET, PaddlePaddle and MindSpore). We analyzed the information in these 138 bug reports to answer three questions: What categories of aging-related bugs exist in deep learning libraries? What is the distribution of different categories of aging-related bugs in deep learning libraries? Which deep learning phases are most susceptible to software aging? Finally, we conducted a fine-grained taxonomy of aging-related bugs, including four levels and seventeen categories, and obtained eight important findings with corresponding practical implications.
Successive Interference Cancellation for Signal Demodulation of Multiple LPWA Systems

With the development of the Internet of Things, the number of low power wide area (LPWA) terminals and LPWA systems has been increasing in unlicensed Sub-GHz bands. Accordingly, it is expected that radio frequency resources will be depleted remarkably, which may cause frequent intra-system and inter-system interferences at gateway (GW) of each system. Thus, we propose power-domain successive interference cancellation (SIC) among multiple LPWA systems. In our method, it is assumed that the GW has modulators and demodulators of multiple systems and can accumulate superposed and received signals. In the SIC algorithm, first, the GW modulates demodulated binary sequence as a replica signal with the modulator of the same system as the demodulator. Then, the GW subtracts the replica signal from the accumulated signals so that the GW can attempt to demodulate the subtracted signals iteratively. Because of the aforementioned assumptions, the subtracted signals can be shared among the demodulators of multiple systems. Simulation results show that our proposed method can significantly reduce packet error rate for the whole system compared to the conventional demodulation without the SIC at the GW of each system.
A Horizontal Gradual Ultra-Low-Reflection Stripline to Grounded Coplanar Waveguide Transition

In this letter, a horizontal gradual low-reflection stripline (SL) to ground coplanar waveguide (GCPW) transition is proposed, which consists of a gradient SL transition with a non-linear gradient slot and a GCPW transition with two extra ground plates. To measure performance of the proposed SL-to-GCPW transition, a back-to-back GCPW-SL-GCPW structure is designed and fabricated under low temperature co-fired ceramic (LTCC) process. Simulation and measurement results agree well. The results show the bandwidth of the proposed transition up to 110GHz. From DC to 67 GHz, the return loss and insertion loss are larger than 15 dB and less than 0.8 dB, respectively.
Observer-based fuzzy adaptive control for non-linear time-varying delay systems with unknown control direction

A novel fuzzy state observer-based adaptive control is proposed for a class of strict-feedback non-linear time-varying delay systems with unknown control direction. A state observer with fuzzy approximators is established to estimate the system states. Based on the Nussbaum-type function in detecting the control direction and backstepping method, adaptive output feedback controller is constructed without requiring a priori knowledge of the signs of the unknown control directions, which can achieve the output tracking. It is rigourously proven that the proposed Lyapunov-Krasovskii functionals and the adaptive backstepping method are able to guarantee semi-globally uniform ultimate boundedness of all the signals in the closed-loop systems, whereas the tracking error converges to a small neighbourhood of the origin. A main advantage of the proposed controller is that it contains only one novel adaptive parameter that needs to be updated online, which can improve the transient and steady-state performances, and the other one is that the proposed scheme can be applied to the systems that do not satisfy the matching conditions. Finally, the simulations had been provided to verify the effectiveness of the proposed scheme.
Design and Construction of Japanese Multimodal Utterance Corpus with Improved Emotion Balance and Naturalness

This paper describes the development of a corpus of multimodal emotional behaviors. So far, many databases of multimodal affective behaviors have been developed. These databases are divided into spontaneous and acted behavior databases. Acted behavior databases can easily collect words with a balanced number of emotions; however, it has been pointed out that acted speech differs from spontaneous speech. In this work, we aim to collect acted multimodal emotional utterances that sound as natural as possible. To this end, we first collected scenes from tweets in which emotional balance was considered. Then, we performed an initial corpus collection, demonstrating that we could collect various emotional utterances. Next, we collected the corpus using a crowdsourcing platform. Then, we evaluated the naturalness of the collected speech by comparing it with the naturalness of the read speech database (JTES) and the spontaneous speech database (SMOC). As a result, the collected corpus was more natural than JTES, which indicates that the recording program effectively collected naturally-sounding emotional behavior corpus.
Improving the Detection Capability of Imaging MIMO Radars by TX Beamforming

In autonomous driving, the detection of all relevant road users is a key requirement for the sensors. In order to improve the detection capability and to increase the signal to noise ratio, transmit (Tx) beamforming can be applied. In this paper, the possibility of adaptive Tx beamforming in a multiple-input multiple-output radar imaging system using state-of-the-art radar transceivers is analyzed both in theory and by measurements. The imaging performance is shown for an exemplary intersection scenario, using an antenna system with a high virtual channel count exceeding the state-of-the-art. For the first time, high-resolution radar images are shown both with and without Tx beamforming. To enable the detection of a pedestrian in the proximity of a strong target, such as a car, the target response and side lobes of the strong target can be weakened by more than 11dB, even at a large incident angle.
Meta Phase-Correcting-Surfaces and Feed Arrays for High-Gain Non-Resonant Partially Reflector Antennas (Invited paper)

Resonant Partially reflective surface (PRS) antennas have limited high-gain performance. This paper investigates and develops non-resonant PRS antennas loaded with a phase-correcting surface (PCS) and fed by a large inter-element spacing feed array. The PCS is used to provide the required compensation phases so that the waves above the PCS are in-phase and the main radiation beam can form. Hence, the high gain bandwidth can be obtained. Our simulation and analysis show that the gain performance improves with the increase of the element spacing of the feed array.
Investigation of Geomagnetically Induced Currents (GICs) Susceptibility of Different Three-Phase Power Transformer Cores

This paper investigates the susceptibility of different three-phase power transformer cores against Geomagnetically Induced Current (GICs). GIC, which is a quasi-DC current induced in the grid due to extreme space weather, will saturate power transformers due to half-cycle saturation caused by zero sequence fluxes. 15kVA small-scale three-phase three-limb, three-phase five-limb, and three single-phase transformers were designed and built. Then, the transformers were simulated using PSCAD/EMTDC to obtain the current and voltage waveforms under GIC. After that, the transformers underwent normal AC load testing at 25%, 50%, 75% and 100% load. This provides a temperature curve and waveforms comparison with the results under GIC conditions, which is modelled as DC injection on the secondary neutral point of the transformer. Lastly, the DC injection test was conducted. The temperature of the transformers increases during DC injection as the total circulating current in the transformers increases. Even harmonics enhancement and increase in reactive power demand are also observed due to transformers saturation under GIC. The results align with the zero-sequence flux theory and simulation waveforms, which suggest that the three single-phase transformers are the most susceptible to GIC, followed by the three-phase five-limb and the three-phase three-limb transformers.
DaTOS: Data Transmission Optimization Scheme in Tactile Internet-based Fog Computing Applications

In the Tactile Internet-based fog computing architecture, the sensor devices represent the basic elements for sensing the surrounding environment. They gather a large amount of data due to their use in various real-world Tactile Internet applications. The huge amount of transmitted data from sensor devices to the fog gateway then to the cloud would lead to high data traffic over the network, increased consumed energy, and increased delay to provide the decision at the Fog gateway. These challenges represent a hurdle in the Tactile Internet-based fog system. This paper suggests a Data Transmission Optimization Scheme (DaTOS) in Tactile Internet-based Fog Computing Applications. The protocol works on two-level devices in the Tactile Internet-based fog computing architecture: sensor devices and fog gateway. The DaTOS implements a Lightweight Redundant Data Removing (LiReDaR) Algorithm at the sensor devices level to lower the gathered data before sending them to the fog gateway. In fog gateway, it executes a Data Set Redundancy Elimination (DaSeRE) approach to discard the repetitive data set resulting from the spatial correlation among the data readings sets of sensor nodes. To evaluate the performance of the DaTOS, it was compared to its counterpart methods in the literature like ATP, PFF and Harb. Simulation results indicate that DaTOS outperforms these methods in terms of transmitted data, energy consumption, and data accuracy.
TCUDA: A QoS-based GPU Sharing Framework for Autonomous Navigation Systems

Autonomous navigation systems (ANS) consist of several software modules, such as sensing, perception, and planning to achieve traffic perception and fast decision making. These modules are required to process large amounts of data, such as images, in real-time. GPUs are commonly exploited on ANS to speed up data processing. As GPUs provides tremendous computation resources, it is common that one software module uses only partial GPU resources, leading to low GPU utilization and energy inefficiency. Traditionally, GPU sharing is a method to address this problem. However, GPU sharing is not supported on current embedded GPU platforms, which are widely used by ANS. Furthermore, GPU sharing challenges task Quality of Service (QoS) that requires task execution in a fixed latency. To achieve QoS requirement, we propose a progress bar scheduling policy to provide GPU tasks with QoS guarantee in GPU sharing environments. Based on this policy, a GPU sharing framework named TCUDA, is proposed to endow existing GPU tasks with QoS guarantee and improve GPU utilization. Finally, results show TCUDA reduces GPU task latency by 16.8% with QoS guarantee on an embedded GPU platform Xavier.
Irrelevance of bending angle in simple Y-branch power splitter design

Early development work in the design of optical power splitters, likely influenced by similar construction in the microwave regime, placed heavy emphasis on Y-branch designs with the output waveguides immediately branching from the input waveguide at non-zero angle. This design approach, which is still prevalent, is fundamentally flawed from the perspective of both optical power flow and fabrication, as it leads to significant excess loss and/or a large statistical variance. If inherent broadband response is not a critical requirement, directional-coupler or multimode-interference splitters are usually chosen instead. We demonstrate, choosing a minimal function perspective where the optical design is sensitive to the smallest possible set of critical fabrication parameters, that robust and low-loss Y-branch designs are indeed possible. The minimum gap width between waveguides being the critical parameter, we reveal the dependence of the irreducibly simplest design on all elements of the parameter space, as they relate to the critical one. In so doing, we show that the concept of bending angle is irrelevant.
Multi-band Shared Aperture Circularly Polarized Antenna With Metal Isolation Columns

A multi-band circularly polarized shared-aperture antenna is designed in this paper. The antenna covers five Bei-Dou Navigation Satellite System (BDS) bands and uses a stacked antenna structure. The coaxial lines run through the stack antenna structure to feed the antenna. The influence of the coaxial lines on the resonance of the bottom two-layer patch antenna is eliminated by introducing the isolation column structure. The simulation results of impedance bandwidth $(\vert \mathrm{S}11\vert \leq 15\mathbf{dB})$ and axial beamwidth meet the design criteria. This kind of antenna could be widely used in satellite communication.
Slip Frequency Type Vector Control for Cup Rotor Permanent Magnet Doubly Fed Machine

Considering the problem of complexity and cost of the powertrain in traditional hybrid electric vehicles (HEVs), the cup rotor permanent-magnet doubly fed machine (CRPMDFM) was applied to HEVs. For CRPM-DFM, a slip frequency type vector control strategy is proposed to improve dynamic performance and robustness of the system. Firstly, the state space model of CRPM-DFM is established. Then, a slip frequency type vector control strategy is presented according to the rotor field orientation control. This strategy is simple and easy to implement. Finally, the simulation results verify the effectiveness of the proposed control method.
A Novel Switching Sequence Design for Integrated Modulation of Parallel NPC Inverters with Reduced Circulating Current

Integrated modulation of parallel three level (3L) inverters is beneficial to the output harmonics reduction compared with conventional interleaved paralleling, and its switching sequences are well-designed to maintain averaged zero common-mode circulating current. However, the instantaneous circulating current still needs to be suppressed. A novel switching sequence design is proposed in this paper for the integrated modulation process of parallel operated neutral-point-clamped (NPC) inverters, which can suppress the circulating current between parallel inverters while still maintaining the same output current harmonics. Detailed analysis and implementation process are presented, and the simulation and experimental results are provided to verify the effectiveness and advantages of the proposed method.
Metamaterial-Loaded Pattern Reconfigurable Dielectric Resonator Antenna for 5G Multi-Beam Base Station Applications

This work proposes a compact mushroom-loaded pattern steerable antenna using the Huygens theory for 5G-NR antenna applications. The compact vertically polarized (VP) dielectric resonator (DR) antenna with a low profile of 0.09 λ0 is achieved by integrating the periodic metamaterial and the DR radiator. Using the Huygens radiation principle, a novel miniaturized pattern steerable DR antenna/radiator with eight switchable end-fire patterns is achieved by only adopting four switch diodes. The proposed beam steerable radiator as the array element is used in a 4 × 4 inexpensive large-angle beam-steering array. Owing to the tilted beam, the beam steering region of the presented 2D phased array can extend from [−35°, 35°] to [−75°, 75°]. The half-power beam-width (HPBW) can cover [−102°, 102°] in each plane. With the merits of a compact, broadband, inexpensive, and large-angle scanning/coverage capacity, this design is a promising candidate for smart antenna systems and 5G-NR multi-beam applications.
Modified Scalable Model Considering Kink Effect for High-Power GaN HEMTs

This paper proposes a modified scalable model considering kink effect for high-power Gallium Nitride (GaN) high-electron-mobility-transistors (HEMTs). Fixtures that can withstand high voltage and high current are designed to complete current-voltage (I-V) measurements of different sizes of high-power GaN HEMTs. Based on the measured I-V data, a scalable output current model including the kink effect is proposed. The scaling rule of the kink effect in GaN HEMTs with different sizes is studied and analyzed. The accuracy of the proposed scalable model is verified by applying the model to different sizes of GaN HEMTs.
Performance Improvement of a Micro Permanent Magnet Motor

Considering the crucial role of electric motors in the drive system of unmanned aerial vehicle (UAV) and the demand for endurance of UAV, this paper studies and optimizes the performance of a micro permanent magnet motor for UAV. This paper firstly designs and analyzes the effect of different poles-slots matching on the efficiency and power density of the micro permanent magnet motor, then uses different silicon steel (SS) materials for the selected poles-slots matching in the motor, and analyzes the effect of different SS materials on the motor and its application characteristics in depth with the simulation results.