Miracle-dz/UniFAR-FTUs huggingface dataset:
A Time-Domain Computing-in-Memory based Processor using Predictable Decomposed Convolution for Arbitrary Quantized DNNs

Time-domain based computing-in-memory (TD-CIM) architectures present superior flexibility, accuracy and scalability for deep neural networks (DNNs). However, there are still three challenges for accelerating multi-bit DNNs due to their inferior bit-split convolution computation: poor network adaptability, massive redundant computations, and high quantization energy and error. This work firstly proposes a unique-weight kernel decomposition based convolution computation method for CIMs to accelerate multi-bit DNNs. Then a TD-CIM based processor is designed with three techniques to further address above challenges: 1) Cross-flipping based fast kernel decomposer to reduce memory accesses for various DNNs, 2) Dual-mode complementary predictor to remove redundant computations, 3) Activation-weight-adaptive pulse quantizer to reduce quantization energy and error. Fabricated in 28nm and evaluated with 1-to-8bit AlexNet and VGG16, the processor achieves a peak energy efficiency of 2.4-to-152.7TOPS/W.
TS-SSPD: A Method for Spatio-temporal Similarity Analysis Between Trajectories

Trajectories representing the mobility of moving objects contain a great deal of spatio-temporal semantic information. Based on a measure of similarity defined between trajectories, trajectory clustering is one of the most simply and efficient ways to gain knowledge from trajectory data. Existing similarity measures do not take full advantage of spatio-temporal characteristics of the trajectory when measuring its distance. If the time dimension is not taken into account, the two trajectories in the opposite direction will be considered as similar. At the same time, in trajectory clustering, hotspot areas must also be determined according to time. In this paper, an improved SSPD distance TS-SSPD is defined, introducing the similarity of time dimension. A trajectory clustering algorithm based on TS-SSPD is also proposed. Compared this improved distance to the others according to their corresponding clustering results, the TS-SSPD improves the accuracy and efficiency of trajectory clustering.
Thinking on the Application of Big Data in Fault Diagnosis of Military Equipment

In recent years, the updating speed of military is accelerating, and the level of technology and complexity is improving, the traditional fault diagnosis technology has been unable to meet the needs of fault diagnosis for large complex military. With the development of big data technology, the application of big data technology to the fault diagnosis of military equipment is of great significance to the maintenance of military equipment. First, the paper introduces the concept and application of big data, then analyzes the problems faced by the fault diagnosis of military equipment are analyzed, and the advantages of fault diagnosis based on big data, as well as the technical methods of big data used in military equipment fault diagnosis. Finally, puts forward the problems that need to be saved in big data application.
Thingsourcing to Enable IoT Collaboration

This paper presents thingsourcing to enable thing collaboration in the context of IoT. Compared to crowdsourcing that refers to a crowd of persons, there is limited research in thingsourcing which deprives things from participating in complex business applications. In this paper, thingsourcing is associated with a platform that acts as an IoT marketplace where things sign-up and sign-off looking for opportunities to complete users’ demands. The platform also has a set of mechanisms that allow to describe, search for, and “glue” things together. For demonstration purposes a car service center is used illustrating how things like service bays and vehicles collaborate in compliance with scripts defined in ComPOS (Composition language for Palcom Oblivious Services).
Real-Valued DOA Estimation for Impulsive Noise

Aiming at DOA estimation under impulsive noise, this paper propose a real-valued sparse Bayesian learning (SBL) method. A unitary transformation is utilized to convert complex-valued direction-of-arrival (DOA) estimation into real ones. The variational Bayesian inference (VBI) technique is then adopted to perform the Bayesian inference with such real-valued prior. Consequently, the computational complexity of this Bayesian inference is significantly reduced. Simulation outcomes demonstrate the great robust performance and low computational load of the new method.
Using Rear Smartphone Cameras as Sensors for Measuring Heart Rate Variability

The measurement of heart rate variability (HRV) is the preferred method for assessing the function of the autonomic nervous system (ANS). Traditional HRV detection requires an electrocardiogram (ECG) or photoelectric sensor. In this paper, we propose a new method for HRV measurement using a rear smartphone camera as a sensor. Video signals from the fingertips of 24 college students were acquired using the rear camera of an HTC M8d smartphone. ECG signals were simultaneously recorded as reference. The video signals were converted into single-frame image sequences over time. Each image frame was transformed into point data through superpositioning of pixel color attribute values and averaging according to space. The point data were sorted by time to obtain a photoplethysmogram (PPG). Finally, the Hilbert transform was used to extract the pulse-to-pulse interval and the R-to-R interval for the PPG and ECG, respectively. Sixteen HRV parameters (mean HR, max HR, min HR, SDNN, RMSSD, NN50, pNN50, VLF, LF, HF, TP, LFnu, HFnu, LF/HF, SD1, and SD2) were analyzed. All 16 HRV parameters were highly correlated (all rs > 0.95, ps < 0.05), and the effect size (ES) differences were small (ES < 0.175) for all indices except for RMSSD, HF, and SD1. Compared with the ECG method, the errors of the 13 HRV parameters measured using this method were within acceptable ranges. The results suggest that this technique can be used as a convenient method to assess and quantify ANS activity and balance.
GaN-FET Class-E Amplifier for 60-MHz Radar

A prototype power amplifier (PA) using GaN FETs is developed for space-based surface-penetrating radar at 60 MHz. LDMOS FETs would typically be used at this frequency, but the need for space-qualified components mandates the use of GaN. A push-pull class-E design delivers 80 W to a matched load with an overall efficiency of 83.3 percent. It delivers 43+ W with 78+ percent efficiency into loads with an SWR of 1.5, and 28+ W with 75+ percent efficiency into loads with an SWR of 1.9. The PA can be operated with either CW or pulsed (FM-CW) signals.
Reconfigurable Substrate Integrated Waveguide Circuits Using Dielectric Fluids

The impact of fluidic pockets with different dimensions and geometries on the quality factor, resonant frequency and phase shift of SIW cavities is reported. A symmetrically tapered fluidic pocket configuration implemented using discrete circular pockets with increasing radius yields a wide resonant frequency range while minimizing quality factor degradation as fluid is added to the pockets. The fluidically reconfigurable cavities are used to synthesize a frequency-reconfigurable third-order Chebyshev BPF on a Rogers 3010 substrate (ϵr = 10.2, tan δ = 0.0035, h = 1.27 mm). Experiments using acetone (ϵr = 20.7, tan δ = 0.05 at 2.4 GHz) as the dielectric fluid show that the filter's center frequency can be shifted over a range of 170 MHz using only 13 mL of liquid per cavity. Additionally, a SIW phase shifter using acetone is presented, demonstrating a maximum phase shift of 44±6° using only 56 mL of fluid.
Bi-directional Flow Relocation For Computation Offloading With Multiple Network Functions

While relocation of bi-directional service flows in mobile services with computation offloading, round trip delay of in-flight service packets should be kept short to provide swift responses in the services. However, when a service flow passes through multiple network functions (NFs), the in-flight service packets incur additional transmission delay every time they arrive at the NFs moving the state for the flow. The additional delay depends on the timing when each NF transmits its state and the packets' direction. Then optimizing the delay extension of the packets headed for a direction does not contribute to suppressing the round trip time extension in the bi-directional service flow. We propose a scheduling method for moving the flow states that ensures that each in-flight packet waits at most once. The method executes the state migration for each NF sequentially and drains all the delayed packets before starting moving the next flow state. Numerical evaluation results show that our method effectively reduces the waiting times of the in-flight packets.
Indirect Adaptive Distributed Fuzzy Control of Semi-linear Parabolic PDE Systems

This paper employs the indirect adaptive fuzzy control technique to solve the stabilization problem for parabolic distributed parameter systems (DPSs) subject to unknown nonlinear dynamics. Initially, a three-dimensional (3-D) fuzzy logic system is constructed on an open subset in a separable Hilbert space to approximate the unknown nonlinear dynamics, and its approximation capability is analyzed via the universal approximation theorem on a real separable Hilbert space. Subsequently, an adaptive distributed fuzzy control scheme is proposed via the 3-D fuzzy logic system. It is shown that given that an LMI-based sufficient condition is fulfilled, all signals in the closed-loop system are practically exponentially stable. Finally, simulation results are presented for a numerical example to show the effectiveness of the proposed adaptive fuzzy control design.
On the Exploitation of Temporal Redundancy to Improve Polyp Detection in Colonoscopy

Colonoscopy is currently the most effective screening method to find precancerous colon polyps and plan their removal. Computer-aided polyp detection can reduce polyp miss detection rates and help doctors find the most critical regions to pay attention to. The challenge in detecting polyps is due to the polyp's morphology and size, and these fall into false-negative. Indeed, polyps may exhibit high variability in shapes (e.g., depressed, flat, pedunculated, etc …). Moreover, the water injected from the endoscope results in artifacts which impede the detection, and the lubricating mucus causes light artifacts due its glossiness. To address this problem, we propose a mask-based attention mechanism to ensure that the employed detector focuses on particular regions of the image in order to reduce misdetection rate. Our contribution takes advantage of information on polyp's position over time within a video sequence. We provide such information through a binary mask which points out the last-known polyp's position. The proposed approach is validated on a dataset that has been labeled by colonoscopy experts. It contains about 200 videos and more than 500 different polyps with high variability in size and textures. Experimental results show that the proposed attention mechanism recover a smaller number of false negatives and achieves an Fl-score of 80.21%.
A2R2: Robust Unsupervised Neural Machine Translation With Adversarial Attack and Regularization on Representations

Unsupervised neural machine translation (UNMT) has recently achieved significant progress without requirement on any parallel data. The models for UNMT are typically the sequence-to-sequence architecture with an encoder to map sentences in different languages to a shared latent space, and a decoder to generate their corresponding translation. Denoising autoencoding and back-translation are called in every iteration for the models to learn the relationship of sentence pairs in languages or between languages. However, sentences generated by the noise model of autoencoding or the reverse model of back-translation are normally different from those written by humans, which may cause inference bias. In this paper, we propose a regularization method for back-translation to explicitly draw representations of sentence pairs closer in the shared space. To enhance the robustness to sentences after autoencoding or back-translation, the adversarial attack on representations is applied. Experiments on unsupervised English<inline-formula> <tex-math notation="LaTeX">$\leftrightarrow $ </tex-math></inline-formula>French, English<inline-formula> <tex-math notation="LaTeX">$\leftrightarrow $ </tex-math></inline-formula>German and English<inline-formula> <tex-math notation="LaTeX">$\leftrightarrow $ </tex-math></inline-formula>Romanian benchmarks show that our approach outperforms the cross-lingual language model (XLM) baseline by <inline-formula> <tex-math notation="LaTeX">$0.4\sim 1.8$ </tex-math></inline-formula> BLEU scores. Additionally, the boost on noisy test sets in most translation directions is over 5 BLEU scores.
QRGAN: Q UANTILE R EGRESSION G ENERATIVE A D - VERSARIAL N ETWORKS

Learning high-dimensional probability distributions by competitively training generative and discriminative neural networks is a prominent approach of Generative Adversarial Networks (GANs) among generative models to model complex real-world data. Nevertheless, training GANs likely suffer from non-convergence problem, mode collapse and gradient explosion or vanishing. Least Squares GAN (LSGANs) and Wasserstein GANs (WGAN) are of representative variants of GANs in literature that diminish the inherent problems of GANs by proposing the modification methodology of loss functions. However, LSGANs often fall into local minima and cause mode collapse. While WGANs unexpectedly encounter with inefficient computation and slow training due to its constraints in Wasserstein distance approximation. In this paper, we propose Quantile Regression GAN (QRGAN) in which quantile regression is adopted to minimize 1-Wasserstein distance between real and generated data distribution as a novel approach in modification of loss functions for improvement of GANs. To study the culprits of mode collapse problem, the output space of discriminator and gradients of fake samples are analyzed to see if the discriminator guides the generator well. And we found that the discriminator should not be bounded to specific numbers. Our proposed QRGAN exposes high robustness against mode collapse problem. Furthermore, QRGAN obtains an apparent improvement in the evaluation and comparison of Frechet Inception Distance (FID) for generation performance assessment compared to existing variants of GANs.
A PV Power Grid-Connected Inverter Current Tracking Control Scheme via Data-Driven Q-Learning Algorithm

In this paper the output current tracking control problem for a PV grid-connected inverter is considered with unknown its dynamics. The performance of the controllers designed with the rated parameters will decrease when the inverter's parameters change for some reasons as the decay of its components. As such an adaptive optimal controller, which does not depend on the prior parameters, is proposed in this paper based on the data-driven off-policy Q-learning algorithm. An augmented system composed of the inverter system and the reference current generator is first established. Then, a discounted game algebraic Riccati equation (GARE) with respect to the augmented system is given assuring the solution to the tracking control problem. Finally, the off-policy Q-learning algorithms are used to solve the discounted GARE without knowing system dynamics which in turn are employed to obtain the optimal state feedback policies. Simulation results are presented to verify the theoretical analysis and demonstrate the controlled inverter's performance.
An Identification Method of Navigation Signal Interference Type based on SqueezeNet Model

With the increasingly complex electromagnetic environment, satellite navigation terminals are often interfered, making it difficult to guarantee service performance. Therefore, the interference monitoring of the navigation signal is urgent. Aiming at the low accuracy of domestic satellite navigation signal interference type recognition, this paper proposes a navigation signal interference type recognition method based on the convolutional neural network: SqueezeNet model. This method first uses the time-frequency analysis of the smooth pseudo Wigner-Ville distribution to transform the timefrequency characteristic domain and generate the timefrequency image. After the time-frequency image is preprocessed, the convolutional neural network of the SqueezeNet model is used to extract the time-frequency image characteristics of the navigation interference signal, so as to realize the automatic, accurate and rapid identification of the common interference signal (Pulse signals, LFM signals, CW signals, BPSK signals) in the satellite navigation field.Finally, through simulation experiments and comparison with other domestic navigation signal interference type identification methods, it is proved that the interference type identification effect of this method is better and the accuracy is higher under the low interference-to-noise ratio.
A Conceptual Model to Identify Vulnerable Undergraduate Learners at Higher-Education Institutions

There is a growing concern around student attrition worldwide, including South African universities. More often than not, the reasons for students not completing their degree in the allocated time frame include academic reasons, socio-pschyo factors, and lack of effective transition from the secondary education system to the tertiary education systems. To overcome these challenges, the tertiary educational institutions endeavor to implement interventions geared toward academic success. One of the challenges, however, is identifying the vulnerable students in a timely manner. This study therefore aims to predict student performance by using a learner attrition model so that the vulnerable students are identified early on in the academic year and are provided support through effective interventions, thereby impacting student success positively. Predictive machine learning methods, such as support vector machines, decision trees, and logistic regression, were trained to deduce the students into four risk-profiles. A random forest outperformed other classifiers in predicting at-risk student profiles with an accuracy of 85%, kappa statistic of 0.7, and an AUC of 0.95. This research argues for a more complex view of predicting vulnerable learners by including the student's background, individual, and schooling attributes.
A Twelve-Element Antenna Array for Tri-Band MIMO Operations in the 5G Smartphone

In this article, a 12-element antenna array covering 2500-2700MHz, 3400-3600MHz, and 4800-5000MHz in mobile smartphone for 5G is presented. The 12-antenna array is constituted by six sets of hybrid antenna pair that are symmetrically printed along two long sides of mobile phone. Each antenna pair is composed of modified U-shaped monopole antenna covering higher and lower band and L-shaped open slot antenna covering middle band. In addition, desired isolation (greater than 12.5dB) is achieved without employing external decoupling structures.
Time-Delay-Based Target Localization in Wireless Sensor Network With Unknown Noise Covariance

This letter addresses the problem of target localization based on the time-delay (TD) measurements in a wireless sensor network (WSN) with a noncooperative illuminator of opportunity (IO), without assuming the prior knowledge of measurement noise covariance (MNC). The joint likelihood of target position and noise variances are built, based on which the target position and MNC can naturally be estimated by maximizing the joint likelihood. However, the resulting maximum likelihood (ML) problem is intractable due to its nonconvexity and nonlinearity. In order to overcome such difficulty, an alternately iterative optimization strategy is proposed in our approach. The approximate weighted least-squares solution and closed-form shrinkage estimator are introduced into the alternative estimation steps for the target location and MNC, respectively. In simulation experiments with 40 TD measurements, the performance of the proposed method, performing without knowledge of MNC, achieves about 8 dB average improvement over the traditional method without the MNC. Its performance is also close to that of the algorithms and Cramér–Rao lower bound (CRLB) working with known MNC, thus verifying the effectiveness of proposed approach.
Implementation of K-band Mushroom meta-material filter for satellite applications

This paper presents a novel metamaterial structure which achieves high performance in terms of loss, power handling and size reduction compared to traditional waveguides. The design procedure and considerations have been elaborated for additive manufacturing of the structure. The filter chosen to implement was a 4th-order filter with four poles; 400 MHz bandwidth and a return loss of better than 15 dB, at the center frequency of 18.5 GHz.
Integration of RF Circuitry and Liquid Cooling Chamber on LTCC Substrate for Tile TR Module

This paper presents a low temperature cofired ceramic (LTCC) package with radio frequency (RF) circuitry and fluidic chamber. The RF power chips can be arranged closely over the cooling chamber, which provides good heat transfer performance. The fabricated fluidic chamber with a size of 20 mm $\times 20$ mm $\times0.4$ mm keeps its shape well, inside which a $3 \times3$ pin-fin array have been added to improve the thermal and mechanical performance of the LTCC substrate. The RF transmission lines such as CPW and quasi-coaxial vertical transition in LTCC show good RF performance up to 12 GHz, while the LTCC substrate has a low pressure loss of 30.4 kPa at the water flow rate of 330 mL/min, which allows a low pump capacity in the cooling system. The proposed LTCC package shows a promising solution for highly integrated X-/Ku-band tile-type transmitter and receiver (TR) module.
Deep Learning for EMG-based Human-Machine Interaction: A Review

Electromyography (EMG) has already been broadly used in human-machine interaction (HMI) applications. Determining how to decode the information inside EMG signals robustly and accurately is a key problem for which we urgently need a solution. Recently, many EMG pattern recognition tasks have been addressed using deep learning methods. In this paper, we analyze recent papers and present a literature review describing the role that deep learning plays in EMG-based HMI. An overview of typical network structures and processing schemes will be provided. Recent progress in typical tasks such as movement classification, joint angle prediction, and force/torque estimation will be introduced. New issues, including multimodal sensing, inter-subject/inter-session, and robustness toward disturbances will be discussed. We attempt to provide a comprehensive analysis of current research by discussing the advantages, challenges, and opportunities brought by deep learning. We hope that deep learning can aid in eliminating factors that hinder the development of EMG-based HMI systems. Furthermore, possible future directions will be presented to pave the way for future research.