Miracle-dz/UniFAR-FTUs huggingface dataset:
The Study of Denitration and Mercury Removal from Flue Gas by Biomass Semi-Coke Absorbents

The biomass of rice straw was chosen to prepare the biomass semi-cokes which are characterized by nitrogen adsorption/desorption and Fourier transformation infrared spectroscopy (FT-IR). The influence of biomass semi-cokes activity on pore structure and surface properties of the semi-cokes was investigated. Denitration and mercury removal experiments were carried out in a fixed-bed reactor. The results show that the active biomass semi-cokes have more specific surface area and micro-pore volume than the biomass semi-cokes. It has direct relationship between the NO and mercury adsorption capacity. It is supposed that oxygen-containing functional and basic groups may have dominant effect on the mercury removal from the glue gas.
Deep Learning Based Low-Resolution Hybrid Precoding Design for mmWave MISO Systems

Millimeter wave (mmWave) communication has been considered as a key enabling technology for next generation cellular systems. In order to combat severe path-loss of mmWave channel, hardware-efficient hybrid beamforming schemes are widely considered in mmWave massive multi-input multi-output (MIMO) systems. However, the high computational complexity for obtaining accurate channel estimation and designing hybrid beamformer is a major challenge, which hurdles the deployment of mmWave massive MIMO systems. In this paper, we consider a multi-user mmWave communication system with low-resolution phase shifters (PSs) and aim to design a novel deep learning based low-complexity and robust hybrid precoding design algorithm. A convolutional neural network (CNN) is utilized to predict/design analog precoders with roughly estimated channels using a small amount of pilot signals. Analog precoder can be efficiently predicted from the imperfect channel convolutional neural network (IC-CNN). Once the analog precoder is obtained, the digital precoder is designed by minimum mean square error method. Simulation results demonstrate that the proposed algorithm is efficient and can achieve satisfactory performance with a significant reduction in computing time.
10.7 A 64GS/s 4×-Interpolated 1b Semi-Digital FIR DAC for Wideband Calibration and BIST of RF-Sampling A/D Converters

The integration of complex DSP functions together with the entire RF-frontend enables software-defined base-station radio and mandates the availability of power-efficient and compact data converters in advanced digital CMOS processes. In a self-calibrated RF-sampling ADC-system, a co-integrated digital non-linear post-equalizer (NLEQ) can substantially increase the receiver performance [1], but wideband ADC-calibration also requires a wideband, calibration-free and ultra-linear reference signal source to train the NLEQ-coefficients during a dedicated calibration period, for example at startup, or during maintenance intervals. Another function with renewed importance in complex transceiver systems is the analog built-in-self-test (aBIST) capability [2] to spot-check the performance of data converters and RF-frontend building blocks, for example during scheduled test intervals in the field. In this work, a single-bit calibration-DAC (CALDAC), capable of wideband reference signal and linear sinewave generation at fclk/4 for these applications is presented. With fclk $=16$ GHz and $4 \times-$ interpolation, the CALDAC effectively samples at 64GS/s and implements a semi-digital FIR-architecture [3] –[5].
The Untapped Potential of Feature Selection for Emotion Recognition: Literature Review

Emotional state identification has many applications in human computer interaction, intelligent machine interface, smart classrooms and medical application. Many approaches are introduced to recognize emotions from face expression, speech, gesture, body poses and skin conductance. There is a high correlation between emotions and physiological changes and since they are unconscious responses, physiological signals have added benefits. Electrocardiogram (ECG) is one of the physiological signals, which results from activity of autonomous nervous system (ANS) reflects the underlying true emotional state. The aim of this work is to give an overview of methods to select features from ECG contributing to emotion recognition. The reviewed studies reveal that the best combination of features leads to improved accuracy.
Optimal Active and Reactive Power Coordinated Dispatch in Unbalanced Distribution Networks

The increasing penetration of distributed generations (DGs) has brought huge challenges to distribution networks operation, such as reverse power flow and overvoltage. Under this background, an optimal active and reactive power dispatch (OARPD) method is proposed in this paper. Considering the three-phase unbalance property, the bus injected model (BIM) of power flow is established while a novel high precision linearized method is proposed. Based on the linearized power flow model, the multi-objective OARPD model which aims to minimize networks losses while maximize the absorption of renewable energy is constructed. Moreover, to ensure the system safe operation and ameliorate the adverse effects caused by single-phase integration of DG, a novel voltage unbalance constraint is established. Finally, examples have been used to demonstrate the validity and effectiveness of the proposed method.
The Structural Phase Transformation and Elastic Instability in F.C.C. Metals

A simple analytical embedded-atom potential model with four adjustable parameters is presented. This potential and several other potential models (including a pair potential) are used to study structural phase transformation and theoretical strength of f.c. c. metals Al, Ag, Au, Cu, Ni, Pd, Pt under [100] uniaxial loading. It is found that due to the dependence on the elastic constants c 11 , c 12 , c 22 , and c 23 the theoretical strengths calculated are independent of the potential models used for each one of these metals and the result for the metal Cu is in agreement with the experimental value. However, due to c 23 = c 44 the predictions of the phase transformations by the pair potential are different from those by embedded-atom potentials. In the pair potential calculations, like the previous ones, the unstressed b.c.c. phase is found to be stable. In embedded-atom potential calculations the stress-free b.c.c. phase is found to be unstable by our present potential model or that of Voter and Chen, though the potential of Rosato et al. gives a b.c.c. phase favourable over the f.c. c. phase for the metals Ag, Au, Cu and Ni. The energy differences of f.c. c. -b.c.c. phases predicted by our present potential are comparable with those by the first-principles calculations for all these metals considered. Also, an embedded-atom potential, where its embedding energy as a function of electron density becomes negatively defined, may give an erroneous prediction for the stability of the b.c.c. phase in these f.c. c. metals.
Designing a Symbol Classifier for Inaudible Sound Communication Systems Using a Neural Network

This study has developed a system that performs data communications using high frequency inaudible band of sound signals. Unlike radio communication systems using specified wireless devices, it only requires microphones and speakers employed in ordinary telephony communication systems. In this study, we investigate the possibility of a machine learning approach to improve the recognition accuracy identifying binary symbols transmitted through sound signals. This paper describes some experiments evaluating the performance of our proposed technique employing a neural network as its classifier. The experimental results indicate that the proposed technique may have certain appropriateness for designing an optimal classifier for the symbol identification.
Survey of Deep Learning Neural Networks Implementation on FPGAs

Deep learning has recently indicated that FPGAs (Field-Programmable Gate Arrays) play a significant role in accelerating DLNNs (Deep Learning Neural Networks). The initial specification of DLNN is usually done using a high-level language such as python, followed by a manual transformation to HDL (Hardware Description Language) for synthesis using a vendor tool. This transformation is tedious and needs HDL expertise, which limits the relevance of FPGAs. This paper presents an updated survey of the existing frameworks for mapping DLNNs onto FPGAs, comparing their characteristics, architectural choices, and achieved performance. Besides, we provide a comprehensive evaluation of different tools and their effectiveness for mapping DLNNs onto FPGAs. Finally, we present the future works.
UHF Compact Dual-Band Circularly Polarized Antenna Loaded With Split-Ring Resonators for Satellite Communications

A novel UHF compact dual-band circularly polarized (CP) antenna loaded with split-ring resonators (SRRs) for satellite communications is presented. The main body of the proposed antenna consists of three sequentially rotationally arranged bent PIFA elements which can effectively reduce the size of the antenna. Additionally, three integrated SRRs are loaded in the middle of the main body for the dual-band operating and further miniaturization of the antenna. The three PIFA elements and SRRs are closely spaced, resulting in a very compact structure. A prototype working at 405/495 MHz with the size of 0.15×0.15×0. 07λ03 is designed, which merits low cost, compact size, easy fabrication and good performance.
PlaneCalib: Automatic Camera Calibration by Multiple Observations of Rigid Objects on Plane

In this work, we propose a novel method for automatic camera calibration, mainly for surveillance cameras. The calibration consists in observing objects on the ground plane of the scene; in our experiments, vehicles were used. However, any arbitrary rigid objects can be used instead, as verified by experiments with synthetic data. The calibration process uses convolutional neural network localisation of landmarks on the observed objects in the scene and the corresponding 3D positions of the localised landmarks � thus fine-grained classification of the detected vehicles in the image plane is done. The observation of the objects (detection, classification and landmark detection) enables to determine all typically used camera calibration parameters (focal length, rotation matrix, and translation vector). The experiments with real data show slightly better results in comparison with state-of-the-art work, however with an extreme speed-up. The calibration error decreased from 3.01% to 2.72% and 1223 � faster computation was achieved.
CFD Modeling of Particulate Matter PM2.5 Inside BRT Public Transport Buses

BRT (Bus Rapid Transit) public transport systems, are highly frequented micro environments by the population of a city. In this research a TSI DustTrak II was used to determine the PM2.5 concentration inside the buses of the BRT system in Bogotá, and a GPS app, MyCarTracks®, to record the speed and location data. Measurements were performed in three different segments of the system, and in different seats inside the buses. PM2.5 concentration levels above 120 µg/m3 were measured for all the segments. Time travels of the BRT passengers was determined to be around 20 to 40 minutes average, where they are exposed directly to these high levels of pollution. Thereafter, a CFD model was implemented to simulate and assess the behavior of the tailpipe emissions. Contours of concentration showed the self-pollution phenomenon as a fraction around 18% of the total concentration of PM2.5 measured inside the cabin of the BRT.
Optimal Droop Offset Adjustments for Accurate Energy Trading in Rural DC Mini-Grid Clusters

Off-grid micro-grids and solar home systems have become an enabler for the electrification of rural areas in developing countries. Their future integration into full-scale grids poses multiple technical challenges. This paper presents a concept of local DC mini-grid formed by the interconnection of nearby micro-grids. This is envisioned as a step towards the bottom-up development of a larger grid. The paper places special emphasis on the analysis of the controller structure, which is based on a variation of the common droop controller. Generally, decentralised droop schemes present inherent limitations to achieve, simultaneously, DC voltage regulation and accurate energy tracking. The decentralised controller proposed here improves conventional approaches by optimally adjusting the droop controller setpoints based on an Optimal Power Flow (OPF) algorithm that takes into account the characteristics of individual converters and the energy trading commands. This approach can benefit from the communication layer required by the energy trading system. The control design of the mini-grid is customised for its deployment on a variation of the dual active bridge (DAB) power electronic converter. Despite the use of a centralised OPF, the system stability is guaranteed by the decentralised low-level controller. The performance of the control approach and its sensitivity to unknown system parameters is measured and validated through detailed simulation studies.
Improved Soft-k-Means Clustering Algorithm for Balancing Energy Consumption in Wireless Sensor Networks

Energy load balancing is an essential issue in designing wireless sensor networks (WSNs). Clustering techniques are utilized as energy-efficient methods to balance the network energy and prolong its lifetime. In this article, we propose an improved soft-<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-means (IS-<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-means) clustering algorithm to balance the energy consumption of nodes in WSNs. First, we use the idea of clustering by fast search and find of density peaks (CFSFDPs) and kernel density estimation (KDE) to improve the selection of the initial cluster centers of the soft <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-means clustering algorithm. Then, we utilize the flexibility of the soft-<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-means and reassign member nodes considering their membership probabilities at the boundary of clusters to balance the number of nodes per cluster. Furthermore, the concept of multicluster heads is employed to balance the energy consumption within clusters. Extensive simulation results under different network scenarios demonstrate that for small-scale WSNs with single-hop transmission, the proposed algorithm can postpone the first node death, the half of nodes death, and the last node death on average when compared to various clustering algorithms from the literature.
17.3 A 1.25GHz Fully Integrated DC-DC Converter Using Electromagnetically Coupled Class-D LC Oscillators

Over the past years, the constant reduction in the size of consumer electronics has strengthened the demand for fully integrated power management circuits. Buck converters offer high efficiency, but they cannot satisfy the stringent size requirements because bulky off-chip inductors are required [1]. Switched-capacitor (SC) approaches provide fully integrated power management solutions; however, their power density is limited by the on-chip capacitance density [2]. Resonant switched capacitor (ReSC) converters need 3D die-stacked inductors or PCB-integrated inductors to achieve appropriate power density values, posing challenges for monolithic integration [3]. A fully integrated ReSC has been presented [4], which implements an on-chip resonator, avoiding any external or 3D stacked passive components. However, the switching losses associated with the four transistors driving the resonator limit the switching frequency to 10s of MHz, bounding the power density scaling to 0.097W/mm 2.
A Self-Adaptive Deep Learning-Based Algorithm for Predictive Analysis of Bitcoin Price

Bitcoin generates a massive amount of data every day due to its innate transparency and capacity of operating completely decentralised. In this paper, we introduce on-chain metrics derived from data on the bitcoin network that enable us to describe the state and usage of the underlying network. Based on their characteristics, we classify them into user, miner, exchange activities and run a correlation analysis with the price to understand the dynamics of bitcoin’s price and its underlying mechanics. Using the correlated data, we develop a deep learning model. However, determining the best values of parameters in a deep learning model can be a very challenging and time-consuming task. Hence, we propose a self-adaptive technique using a jSO optimization algorithm to find the best values of these parameters to accurately predict the price of bitcoin. Compared to traditional LSTM model, our approach is highly accurate and optimised with a minimum error rate.
Understanding the Behavior of Data-Driven Inertial Odometry With Kinematics-Mimicking Deep Neural Network

In navigation, deep learning for inertial odometry (IO) has recently been investigated using data from a low-cost IMU only. The measurement of noise, bias, and some errors from which IO suffers is estimated with a deep neural network (DNN) to achieve more accurate pose estimation. While numerous studies on the subject highlighted the performances of their approach, the behavior of data-driven IO with DNN has not been clarified. Therefore, this paper presents a quantitative analysis of kinematics-mimicking DNN-based IO from various aspects. First, the new network architecture is designed to mimic the kinematics and ensure comprehensive analyses. Next, the hyper-parameters of neural networks that are highly correlated to IO are identified. Besides, their role in the performances is investigated. In the evaluation, the analyses were conducted with publicly-available IO datasets for vehicles and drones. The results are introduced to highlight the remaining problems in IO and are considered a guideline to promote further research.
Microscopical Observation of the Marine Bacterium Vibrio Natriegeus Growth on Metallic Corrosion

Biofilm formation and the adsorption of nonsulfate-reducing marine bacterium Vibrio natriegens (V. natriegens) to different metallic surfaces have been studied by atomic force microscopy (AFM). The nature of extracellular polymeric substance (EPS) has been monitored through the force-distance curve. EPS may act as adhesive binding adjacent cells and thus further enhance the aggregation of microbes during cluster formation due to its adhesiveness in nature. The AFM images supplied the change of the morphology of microbial cells and colonies, the distribution of microbial colonies, and the presence of EPS on the surfaces with immersion. Results showed that the biofilms formed on the surfaces increased in thickness and heterogeneity with time and that 3–7 days was the proliferation period of the bacteria on metallic surfaces. The corrosion type of the metals by V. natriegens could be confirmed as typical pitting attack through scanning electron microscopy (SEM) observation.
Deep Deterministic Policy Gradient Based Computation Offloading in Wireless-Powered MEC Networks

With the upsurge of Internet of Things (IoT), mobile edge computing (MEC) and wireless power transfer (WPT) are becoming promising methods to relieve IoT devices from the limited computation capacity and battery capacity. In this paper, we study the joint optimization of the local computing central processing unit (CPU) frequency and the offloading ratio in a dynamic WPTMEC network. In the proposed network, all the IoT devices follow a harvest-then-compute protocol so as to be charged by the MEC server and then compute the tasks. Our goal is to minimize the long-term averaged maximal computing delay with stochastic task arrivals and time-varying wireless channels. It is challenging to find the optimal solution due to the uncertain network information and the non-convex objective function. To tackle this problem, we propose a deep reinforcement learning-based deep deterministic policy gradient (DDPG) framework, which can learn a centralized policy to coordinate the local computing CPU frequencies and offloading ratios of IoT devices. Simulation results show that the proposed DDPG algorithm can achieve up to 73.2% and 16.8% gain in terms of the averaged maximal computing delay compared to the local-computing-only method and the deep Q-network algorithm, respectively.
Compact SIW 2-D Butler Matrix and Its Multibeam Application

This letter presents a compact 2-D Butler matrix (BM) realized by multilayer substrate integrated waveguide (SIW) technology. The 2-D BMs in the open literature suffer from either bulky size or poor integration with the radiation portion. Facing this issue, a new SIW 2-D BM is developed and realized in planar configuration easily integrated with the slot antenna array. The key point of this letter is to design planar 2-D components, including 2-D couplers, 2-D crossovers, and 2-D phase shifters, of the proposed SIW 2-D BM, which can perform coupling, intersection, or phase shifting in both E- and H-planes. The proposed SIW 2-D BM also has the merit of ease integration with radiation portion and it is used to build a compact 2-D multibeam antenna in this letter. Simulation, fabrication, and experiments are conducted to verify the performance of the realized 2-D multibeam antenna.
SAT-Based Automata Construction for LTL over Finite Traces

In this paper, we consider the automata construction problem for Linear Temporal Logic over finite traces, i.e., $\mathbf{LTL}_{f}$. We propose a SAT-based approach to translate an $\mathbf{LTL}_{f}$ formula to both of its equivalent Nondeterministic and Deterministic Finite Automata (NFA and DFA). Notably, the generated automata are transition-based instead of state-based, which may potentially be a better fit for the applications that can be achieved on the fly, e.g. $\mathbf{LTL}_{f}$ satisfiability checking and synthesis. Unlike extant approaches to translate $\mathbf{LTL}_{f}$ formulas to the equivalent finite automata, which are indirect and have to introduce intermediate procedures, our methodology enables the direct construction from $\mathbf{LTL}_{f}$ formulas to the finite automata. We evaluated our NFA construction together with other two $\mathbf{LTL}_{f}$ -to-automata approaches implemented in the MONA and SPOT tools, which shows that the performance of our construction is comparable to the other two. We leave the comparison on the DFA construction in the future work.
Recent Progress in SISL Circuits and Systems: Review of Passive and Active Circuits Demonstrating SISL's Low Loss and Self-Packaging and Showcasing the Merits of Metallic, Shielded, Suspended Lines

With the rapid development of modern communication and radar technology, there is a need for the miniaturization, planarization, and modularization of microwave and millimeter-wave (mm-wave) systems, without losing excellent performance and reliability. The transmission line is the most basic component of microwave and mm-wave circuits and systems, and its characteristics-including loss, size, integration, cost, and weight-directly or indirectly affect the performance of the entire circuit and system. Commonly used transmission lines [1] include nonplanar transmission lines, such as a metal waveguide and coaxial line, and planar transmission lines, such as a microstrip, strip line, coplanar waveguide, and slot line.