Miracle-dz/UniFAR-FTUs huggingface dataset:
Dimension-Deficient Channel Estimation of Hybrid Beamforming Based on Compressive Sensing

Due to high hardware costs for digital beamforming, hybrid beamforming (HBF) is widely employed in millimeter-wave (mmWave) communications systems. However, the number of radio frequency chains in the analog part of HBF is far less than that of antennas, which causes a serious dimension-deficient problem. In order to overcome this problem, this paper proposes a compressive sensing algorithm using an adaptive overcomplete dictionary to estimate the sparse channel in the HBF-based mmWave system. The algorithm adaptively generates the dictionary by using the received signal to accurately reconstruct the mmWave channel. The simulation results are presented to demonstrate that the proposed algorithm outperforms its traditional counterparts in terms of the normalized mean square error and the spectral efficiency.
Sediment Characterization at the Equatorial Mid‐Atlantic Ridge From P‐to‐S Teleseismic Phase Conversions Recorded on the PI‐LAB Experiment

Accurate marine sediment characteristics, for example, thickness and seismic velocity, are important for constraining sedimentation rates with implications for climate variations and for seismic imaging of deeper structures using ocean bottom seismic deployments. We analyze P‐to‐S seismic phase conversions from the sediment‐crust boundary recorded by the Passive Imaging of the Lithosphere‐Asthenosphere Boundary (PI‐LAB) experiment to infer the sediment thickness across the Mid‐Atlantic Ridge covering 0‐ to 80‐Myr‐old seafloor. We find Pds‐P delay times of 0.04–0.37 s, or 5‐ to 82‐m thickness. Sediment thickness increases with age. Thickness agrees with global estimates for young (<15–20 Myr) seafloor but is thinner on older lithosphere. Our result may represent a lower limit on sediment thickness, given that several of our stations are on topographic highs. The sedimentation rate decrease observed from 5 to 1.2 mm/kyr at ∼10 Myr suggests a recent increase in productivity related to climate change, eolian dust fluxes, and/or biogenic marine activity.
A Low-Cost Welding Status Monitoring Framework for High-Power Disk Laser Welding (December 2018)

Welding status determines the post-weld quality and is crucial to high-power disk laser welding. A low-cost monitoring system based on two photodiodes is developed to monitor the real-time welding statuses in this paper. A deep learning architecture based on stacked autoencoder (SAE) is proposed to automatically learn more representative features of welding statuses from the raw signal features captured by the visible light photodiode and the reflected laser light photodiode without any manual operations. The maximum correntropy loss function is applied to improve the learning ability of the proposed SAE method. Furthermore, a genetic algorithm is applied to optimize the key parameters of the proposed SAE method. The proposed SAE is applied to the high-power disk laser welding experiments and shows better performance in welding status monitoring than the standard AE framework and the conventional SVM and BP method. Additional experiments with different welding parameters validate the effectiveness and robustness of our proposed SAE method.
Information-Driven Software-Defined Vehicular Networks: Adapting Flexible Architecture to Various Scenarios

Vehicular ad hoc network (VANET) applications have emerged as new opportunities for the automobile industry to provide advanced services for connecting vehicles and their users. Most of these services, such as safety, information, and entertainment systems, require a variety of digital content to be delivered to and from vehicles. In this article, we propose a novel, information-driven, software-defined architecture designed to make content available to vehicular networks. In contrast to the existing solutions, our proposed architecture is flexible and covers important aspects of content delivery in VANETs. To assess the benefits of our proposal, we have implemented a case study to compare it with two baseline solutions.
Pattern Reconfigurable Antenna Applying Spoof Surface Plasmon Polaritons for Wide Angle Beam Steering

A pattern reconfigurable antenna based on switchable spoof surface plasmon polaritons (SSPPs) transmission line is experimentally presented. There are two fundamental modes on traditional SSPPs transmission line, the odd mode and the even mode. In this paper, we propose the switching of fundamental modes transmitted on the SSPPs transmission line by controlling the status of three PIN diodes. As a consequence, the proposed antenna can provide a broadside radiation beam (in the even mode) and an endfire radiation beam (in the odd mode). The measured results show that for broadside radiation, the main lobe direction ranges from 56° to 84° within the operating band (<inline-formula> <tex-math notation="LaTeX">$\vert {S} _{11}\vert < -10$ </tex-math></inline-formula> dB) of 4.85–5.25 GHz, while for the endfire radiation, the main lobe points almost constantly to <inline-formula> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula>-direction within 4.85–5.25 GHz. By switching the antenna working state, the proposed antenna can realize almost 84° beam switching at 4.85 GHz. The pattern reconfigurability of this antenna with favorable performance characteristics, such as high gain, frequency scanning, low profile, and wide-angle beam switching, makes it an ideal candidate for many WLAN and satellite communication applications.
Stability and Control in Complex Networks of Dynamical Systems

Stability analysis of networked dynamical systems has been of interest in many disciplines such as biology and physics and chemistry with applications such as LASER cooling and plasma stability. These large networks are often modeled to have a completely random (Erdös-Rényi) or semi-random (Small-World) topologies. The former model is often used due to mathematical tractability while the latter has been shown to be a better model for most real life networks. The recent emergence of cyber physical systems, and in particular the smart grid, has given rise to a number of engineering questions regarding the control and optimization of such networks. Some of the these questions are: How can the stability of a random network be characterized in probabilistic terms? Can the effects of network topology and system dynamics be separated? What does it take to control a large random network? Can decentralized (pinning) control be effective? If not, how large does the control network needs to be? How can decentralized or distributed controllers be designed? How the size of control network would scale with the size of networked system? Motivated by these questions, we began by studying the probability of stability of synchronization in random networks of oscillators. We developed a stability condition separating the effects of topology and node dynamics and evaluated bounds on the probability of stability for both Erdös-Rényi (ER) and Small-World (SW) network topology models. We then turned our attention to the more realistic scenario where the dynamics of the nodes and couplings are mismatched. Utilizing the concept of ε-synchronization, we have studied the probability of synchronization and showed that the synchronization error, ε, can be arbitrarily reduced using linear controllers.
Electrically Tunable Metal–Semiconductor–Metal Terahertz Metasurface Modulators

We propose metal–semiconductor–metal cavity arrays as active elements of electrically tunable metasurfaces operating in the terahertz spectrum. Their function is based on reverse biasing the Schottky junction formed between top metal strips and the n-type semiconductor buried beneath. A gate bias between the strips and a back metal reflector controls the electron depletion layer thickness thus tuning the Drude permittivity of the cavity array. Using a rigorous multiphysics framework which combines Maxwell equations for terahertz waves and the drift-diffusion model for describing the carrier behavior in the semiconductor, we find a theoretically infinite extinction ratio, insertion loss of around 10%, and picosecond intrinsic switching times at 1 THz, for a structure designed to enter the critical coupling regime once the depletion layer reaches the bottom metal contact. We also show that the proposed modulation concept can be used for devices operating at the higher end of the terahertz spectrum, discussing the limitations on their performance.
Magnetic-Field Measurement Based on Multicore Fiber Taper and Magnetic Fluid

An all-fiber magnetic-field sensor combining a multicore fiber taper and magnetic fluid is proposed and experimentally demonstrated. The magnetic-field sensing mechanism is investigated theoretically and experimentally. Experimental results demonstrate that the dip-wavelength of the proposed sensor regularly varies with the changes of the applied magnetic field. The measuring sensitivity is achieved to be ~34 pm/Gs in the magnetic induction intensity range of 50–300 Gs. Moreover, the sensor responding to the 50-Hz alternating magnetic field is also experimentally investigated, and a minimal detectable magnetic induction intensity of ~23.7 Gs is successfully achieved. The proposed magnetic field sensor is attractive due to its compact size, low cost, and immunity to electromagnetic interference.
A hybrid machine learning approach for the quality optimization of a 3D printed sensor

Sensors play a crucial role in train condition monitoring as it can offer real-time data of the train for health status estimation, fault diagnosis and decision-making of maintenance. In order to improve the performance of the sensors for data acquisition, an aerosoljet 3D printing technology is adopted to print customized sensors in this research. Compared with conventional bulk sensors, the customized sensors have a smaller size, higher accuracy, faster response time and could be printed onto the surface directly. However, as the line morphology of printed patterns has significant influence on the electrical properties, we need to investigate the influence of the process parameters on the line morphology and optimize the printed line quality. In this paper, we consider sheath gas flow rate and carrier gas flow rate as the key process parameters. The line roughness and line overspray are considered as the line quality indices. Latin hypercube sampling is adopted to fully explore the entire design space. And, a hybrid machine learning approach is proposed to analyze the relationship between line morphology and the process parameters, and finally an optimal operating window is identified based on the proposed approach.
A Literature Review of Domain Adaptation with Unlabeled Data

In supervised learning, it is typically assumed that the labeled training data comes from the same distribution as the test data to which the system will be applied. In recent years, machine-learning researchers have investigated methods to handle mismatch between the training and test domains, with the goal of building a classifier using the labeled data in the old domain that will perform well on the test data in the new domain. This problem is called domain adaptation or transfer learning, and it is a common scenario in speech processing applications. Labeled training data are often produced by an expensive hand-annotation process, and may consist of only one or two annotated corpora which are used to train virtually all systems regardless of the target domain. Often little or no labeled data is available for the new domain. In this work, we review the statistical machine learning literature dealing with the problem of “domain adaptation” or “transfer learning”. We focus on unsupervised domain adaptation methods, as opposed to model adaptation or supervised adaptation in which some labeled data is available from the test distribution. We consider four main classes of approaches in the literature: instance weighting for covariate shift; selflabeling methods; changes in feature representation; and cluster-based learning. Covariate shift methods re-weight training samples in the old domain to try to match the new domain, putting more weight on samples in populous regions in the new domain. Self-labeling methods incorporate unlabeled target domain examples into the training algorithm by making an initial guess about their labels and then iteratively refining the guesses or labeling more examples. Feature representation approaches try to find a new feature representation of the data, either to make the new and old distributions look similar, or to find an abstracted representation for domain-specific features. Cluster-based methods rely on the assumption that samples connected by high-density paths are likely to have the same label. Domain adaptation is a large area of research, with related work under several frameworks (and several names). A limited review from March 2008 can be found in [1], and one from Oct 2010 can be found in [2]. A recent book [3] investigates train/test distribution mismatch in machine learning (particularly focused on covariate shift.) Some of the organization here roughly follows that in [1].
Reference Layer Artefact Subtraction (RLAS): Electromagnetic Simulations

The utility of EEG-fMRI is limited by the large artefact voltages produced in EEG recordings made during concurrent fMRI. Novel approaches for reducing the magnitude and variability of the artefacts are, therefore, required. One such approach involves using an EEG cap incorporating a reference layer (RL), which has similar conductivity to biological tissue and is electrically isolated from the scalp. The RL carries a secondary set of electrodes and leads that precisely overlay on the scalp electrodes so that similar voltages are anticipated to induce at the RL and scalp electrodes in the presence of static and time-varying magnetic fields. RL artefact subtraction (RLAS), which involves taking the difference of the voltages at the two electrodes, should, therefore, attenuate artefacts while leaving neuronal voltages unaffected. Previous experimental work has demonstrated the potential of the RLAS system in removing different types of EEG artefact. However, to get the best performance from the RLAS system, it is important to verify the underlying assumptions of RLAS and to optimize the RLAS system based on electromagnetic simulations. In this paper, electromagnetic modeling was used to simulate the voltages induced in a hemispherical RL and a spherical volume conductor (VC) under the influence of a static magnetic field and time-varying magnetic field gradient. By evaluating the differences in the voltages produced in the RL and VC, as the RL geometry is varied, the efficacy of the RLAS approach is tested and an optimal RL design is identified. The simulations performed accounted for realistic rotations, shifts, nodding, and shaking of the RLAS system with respect to the gradient isocentre, thus giving insight into the optimum RLAS set-up and the potential for improved EEG cap design for an RLAS system.
Anomaly Detection in Car-Booking Graphs

The use of car-booking services has gained massive popularity in the recent years - which led to an increasing number of fraudsters that try to game these systems. In this paper we describe a framework for fraud detection in car-booking systems. Our core idea lies in casting this problem as an instance of anomaly detection in temporal graphs. Specifically, we use unsupervised techniques, such as dense subblock discovery, to detect suspicious activity. The proposed framework is able to adapt to the variations in the data inherent to the car-booking setting, and detects fraud with high precision. This work is performed in collaboration with Careem, where the described framework is currently being deployed in production.
Managing Knowledge Provenance using Blockchain

The emergence of a "post-fact" world has seen knowledge being misinterpreted and manipulated to suit diverse purposes. Information from credible sources is often drowned out by ‘facts’ with dubious provenance. It is not enough to simply establish knowledge provenance if this meta-data can be changed afterwards. This paper presents a possible solution using blockchains to manage knowledge provenance. The solution is modelled using a concrete example of knowledge institutions. Variations and extensions to suit other domains are suggested. Particularly, a graph based variation of the blockchain is outlined. We also discuss the impact of our model beyond technology.
Growing Deep Forests Efficiently with Soft Routing and Learned Connectivity

Despite the latest prevailing success of deep neural networks (DNNs), several concerns have been raised against their usage, including the lack of intepretability the gap between DNNs and other well-established machine learning models, and the growingly expensive computational costs. A number of recent works [1],[2],[3] explored the alternative to sequentially stacking decision tree/random forest building blocks in a purely feed-forward way, with no need of back propagation. Since decision trees enjoy inherent reasoning transparency, such deep forest models can also facilitate the understanding of the internal decision making process. This paper further extends the deep forest idea in several important aspects. Firstly, we employ a probabilistic tree whose nodes make probabilistic routing decisions, a.k. a. , "soft routing", rather than hard binary decisions. Besides enhancing the flexibility, it also enables non-greedy optimization for each tree. Second, we propose an innovative topology learning strategy: every node in the ree now maintains a new learnable hyperparameter indicating the probability that it will be a leaf node. In that way, the tree will jointly optimize both its parameters and the tree topology during training. Experiments on the MNIST dataset demonstrate that our empowered deep forests can achieve better or comparable performance than [1],[3], with dramatically reduced model complexity. For example, our model with only 1 layer of 15 trees can perform comparably with the model in [3] with 2 layers of 2000 trees each.
Communication Theoretic Prediction on Time Series Data

With the explosive growth of data, data analytics has emerged as a critical technology and it has attracted considerable research interests. Effective and powerful data analytics are need to deal with big data problems. Network information theory and communication theory have provided some novel methodologies for data analytics in some related works, which presents their feasibility and potential for data analytics in several fresh views. In this paper, an approach based on communication technology is developed for time series data analysis. First, a information channel is established to describe the information flow between two time series data with correlated relationship. Then the structure of communication equalizer is adapted for time series data prediction by inputting with correlated data and history target data. The prediction result shows that the adapted equalizer approach has effectiveness for time series data prediction. For more insights, we modify the update method of tap coefficients inspired by the adaptive filter algorithms. The final experiment results suggest that our proposed approach and its modification have better root-mean-square error performance compared with other approaches in Taiwan Semiconductor Manufacturing Corp stock price time series data prediction, which further demonstrates that network information theory and communication theory have effectiveness and potential and utility for data analytics.
Liveness of Disjunctive and Strict Single-Type Automated Manufacturing System: An ROPN Approach

The resource-oriented Petri net (ROPN) modeling method is applied in this paper to model a class of automated manufacturing systems (AMS) named disjunctive and strict single-type AMSs (DS-AMS), where multiple units of a single resource type are required to support a processing stage. This modeling method is more compact and intuitive than the existing ones in the contemporary literature, named weighted systems of simple sequential processes with resources. Based on the ROPN model, a significant result is re-proved in a different way, which is more succinct and understandable. Such a result presents a sufficient and necessary condition on the liveness of an ROPN. Furthermore, based on this result, we develop efficient strategies to ensure the liveness of an ROPN modeling a DS-AMS. To enforce the liveness of such an ROPN, only some specific transitions need to be controlled by taking advantage of its structural properties. Also, an efficient approach is developed to implement the proposed policy by identifying and controlling such transitions. Two examples are employed to demonstrate the efficiency of the developed method. Some limitations of the method are pointed out in the end, which will be studied in the future.
Optimized Handling Stability Control Strategy for a Four In-Wheel Motor Independent-Drive Electric Vehicle

An optimized handling stability control strategy is put forward aiming at enhancing a four in-wheel independent-drive motors electric vehicle (4WIDEV) performance capable of handling and stability. The optimized control strategy is designed by a hierarchical control structure, which mainly includes vehicle motion controller and vehicle torque distribution controller. Lateral stability controller, in-vehicle motion controller, which yields the generalized force and generalized yaw moment required of the vehicle, is designed into two modes: an instability control mode and a continuous control mode, which mode is activated determining by stability judgment controller based on phase portraits of sideslip angle and yaw rate. When the vehicle state exceeds the stability envelope region, instability control mode based on envelope control is utilized by sliding mode control method, simultaneously controlling the two-vehicle states variables, whereas continuous control mode is performed with adaptive adjustment of the weight parameter according to pre-defined stability degree. The lateral stability controller with adaptive switching two modes exhibits excellent handling stability performance under normal driving conditions, especially under critical conditions, such as on low tire-road adhesion coefficient. The vehicle torque distribution controller assigns driving or regenerative braking torque to four wheels reasonably and efficiently to satisfy the generalized force and the generalize yaw moment acquired by the vehicle motion controller, which synchronously considers the motor output capability and tire friction ellipse constraints while maintains each wheel slip ratio within the stable range. The results in co-simulation experiments based on Carsim and MATLAB/Simulink verify the proposed control strategy, compared with other control strategies, and demonstrate the effective improvement 4WIDEV’s performance in terms of handling stability especially under critical conditions.