Miracle-dz/UniFAR-FTUs huggingface dataset:
Bayesian Learning-Based Linear Decentralized Sparse Parameter Estimation in MIMO Wireless Sensor Networks Relying on Imperfect CSI

Optimal linear minimum mean square error (MMSE) transceiver design techniques are proposed for Bayesian learning (BL)-based sparse parameter vector estimation in a multiple-input multiple-output (MIMO) wireless sensor network (WSN). Our proposed transceiver designs rely on majorization theory and hyperparameter estimates obtained from the BL module for minimizing the mean square error (MSE) of parameter estimation at the fusion center (FC). The linear transceiver design framework is initially proposed for the general scenario with arbitrary SNR sensor observations, followed by a special case with high-SNR sensor observations scenario. Our analysis also incorporates the channel correlation. The MMSE channel estimates are determined for the sensors (SNs), followed by a robust transceiver design procedure that is resilient to the channel state information (CSI) uncertainty arising due to the channel estimation error, an aberration that is unavoidable in practical implementations. Our simulation results demonstrate the improved performance of the proposed BL framework and optimal MMSE transceiver design in sparse parameter estimation relying on realistic imperfect channel estimates over the benchmarks.
Quadcopter UAV Control based on Input-Output Linearization and PID

A dynamical model of the quadcopter UAV is obtained considering the frictional effects in the positional and rotational coordinates. Then, a two-level control strategy is designed using an input-output Linearization in combination with PID controllers. The inner loop stabilizes the pitch, roll and yaw angles, whilst the outer-loop control is related to the position control, avoiding local Linearization. A PID control gain selection is analyzed to harmonize both control loops. The performance of the control approach is shown by numerical simulations.
Scheduling Virtual Network Reconfigurations in Parallel in Hybrid Optical/Electrical Datacenter Networks

A hybrid optical/electrical datacenter network (HOE-DCN) integrates the benefits of electrical packet switching (EPS) and optical circuit switching (OCS) for better architectural scalability. Meanwhile, as each network service usually involves multiple virtual machines (VMs) that form a virtual network (VNT), how to reconfigure the VNTs in an HOE-DCN to adapt to the dynamic network environment becomes an interesting and important problem to tackle. In this work, we optimize the procedure of VNT reconfigurations that remap VNTs to given virtual network embedding (VNE) schemes, i.e., scheduling the required VM migrations and optical cross-connect (OXC) reconfiguration properly such that the overall VNT reconfiguration latency can be minimized. We first assume that all the VM migrations should be scheduled before the OXC reconfiguration to minimize service interruptions, and formulate a mixed integer linear programming (MILP) model to solve the scheduling problem exactly. Then, with the same assumption, we propose a time-efficient heuristic to schedule parallel VM migrations in batches such that the bandwidth competition can be significantly relieved. Finally, we divide the OXC reconfiguration into several progressive steps, and design a joint scheduling algorithm to arrange VM migrations together with the OXC reconfiguration steps.
First-Principles Investigation of CH₄ Adsorption on a Singly and Doubly Decorated (8,0) Single Wall Carbon Nanotube With Palladium/Oxygen Species

We employ dispersion-corrected density functional theory to study the adsorption of CH4 molecule on a singly and doubly decorated (8,0) single wall carbon nanotube (CNT80) with palladium atom and oxygen molecule. The obtained optimized energies indicate that double decoration with palladium atom and oxygen molecules significantly enhances the adsorption of methane on the CNT80, while single decoration with oxygen molecules gives rise to a weak physical bonding for methane molecules. Moreover, we observe that methane adsorption decreases the bandgap and the distance of the Fermi level to the top of the valance band of the doubly decorated CNT80. The calculated charge density plots and energy band diagrams demonstrate a charge transfer from doubly decorated CNT80 to CH4. Regarding the p-type nature of the structure, our results support the increment of the conductance of the doubly decorated CNT80 after methane adsorption, in agreement with a real measurement in the ambient conditions. This observation underlines the importance of ambient oxygen in the real performance of CNT-based gas sensors.
Phosphorus Mobility and Behavior in Soils Treated with Calcium, Ammonium, and Magnesium Phosphates

The fertilizer industry has attempted to increase P mobility and lability after fertilizer application by using nonconventional phosphates or by including additives in the fertilizer formulation. We incubated granular monoammonium phosphate (MAP), sulfur-coated MAP, humic acid-coated MAP, triple superphosphate (TSP), ammonium potassium polyphosphate (AKPP), and ammonium magnesium phosphate (struvite) with soils from the United States and Brazil in Petri dishes for 56 d. We estimated P mobility by measuring P movement away from fertilizer granules and assessed P lability through sequential chemical fractionation of soil collected from the dishes. In addition, we monitored the change in soil pH with distance from fertilizer placed in the Petri dish. Soil pH changed in response to fertilizer additions as a function of initial soil pH. In fertilized soils, the soil pH response followed a quadratic function as the distance from the fertilizer placement site increased. Soil characteristics influenced P mobility, with mobility decreasing from the Hubbard (12% clay; pH 5.3), to Brazil (20% clay; pH 6.5), to Normania (22% clay; pH 5.5), and then Barnes (31% clay; pH 8.0) soil. The use of MAP-based fertilizers resulted in the greatest mobility, while struvite provided the lowest mobility. In contrast, struvite granules dissolved the least resulting in the highest labile P concentrations, due to direct extraction of fertilizer P from undissolved granules (average of 73% of applied P). Comparatively, TSP provided the lowest amount of labile P (average of 52% applied P). Sulfur and humic acid-coated MAP had no effect on P lability or mobility.
Design of Power Monitoring System Based on Internet of Things (IoT) with Calibration Interface

In a power monitoring system, accuracy is subject to change. Therefor, a calibration interface on the system’s dashboard is required. This research aims to propose a design of a power monitoring system with a calibration interface based on Internet of Things technology. For the hardware, this research proposes the use of CT sensors, Arduino, and Raspberry Pi. Node-RED is utilized to process the data and to create the dashboard. The proposed design consists of two dashboard pages. The first page is the monitoring page, which displays the measurement of voltage, current, frequency, true power, apparent power, and power factor. The second page is the setting page, which allows the user to calibrate the measurement by adjusting the parameter value on the dashboard. The proposed power monitoring system in this research offers simplicity to maintain the system’s accuracy.
A Taichi-Bagua-like Metamaterial Covering High Isolation MIMO for 5G Application

A Taichi-Bagua-like metamaterial (MTM) structure is realized using a single-layer substrate and covered on 2-port 5G multiple-input multiple-output (MIMO) antenna to achieve high isolation. The MTM elements in the shape of Taichi-Bagua-like structure are devised on the top layer and bottom layer of FR-4 with a 180° rotating, and they are periodically arranged to form a meta-surface and suspended above the MIMO antenna with a spacing of 3mm. The simulated results shown that the -10dB bandwidth of the proposed antenna covers 4.73-5.20 GHz with mutual coupling reduction better than 20dB in the implementation bandwidth, where the peak mutual is around -43 dB at center resonance frequency. Additionally, the radiation patterns for the devised 5G MIMO antenna with/without MTM are nearly same, providing a potential application for 5G indoor base station.
Multiservice Loss Models for Cloud Radio Access Networks

In this paper, a cloud radio access network (C-RAN) is considered where the remote radio heads (RRHs) are separated from the baseband units which form a common pool of computational resource units. Depending on their capacity, the RRHs may form one or more clusters. Each RRH accommodates multiservice traffic, i.e., calls from different service-classes with different radio and computational resource requirements. Arriving calls follow a Poisson process and simultaneously require radio and computational resource units in order to be accepted in the serving RRH. If their resource requirements cannot be met then calls are blocked and lost. Otherwise, calls remain in the serving RRH for a generally distributed service time. Assuming the single-cluster C-RAN, we model it as a multiservice loss system, prove that a product form solution exists for the steady-state probabilities and determine call blocking probabilities via an efficient convolution algorithm whose accuracy is validated via simulation. Furthermore, we generalize the previous multiservice loss model by considering the more complex multi-cluster case where RRHs of the same capacity are grouped in different clusters.
An Efficient Method to Enhance the Quality of Ultrasound Medical Images

Today, non-invasive imaging has become one of the popular and prevalent methods in medical applications. Ultrasound imaging is a popular imaging modality as it avoids harmless radiation, has low computation cost, can operate in real time and be implemented in low-cost instruments. However, the image quality decreases due to diverse noise sources. In this paper, a new method based on CEEMDAN to Speckle denoise of the ultrasound images is introduced. The results show an improvement in SSIM and PSNR of 14% and 29%, respectively. In addition, this method can enhance the image quality by adding weights to the primary modes, which increases the sharpness and contrast by 56% and 22%, respectively.
A 0.85V Supply, High PSRR CMOS Voltage Reference without Resistor and Amplifier for Ultra-Low Power Applications

This paper presents an ultra-low power CMOS voltage reference (CVR) which is free of amplifier and resistor. A current generator circuit is used to generate a supply independent current to bias the active load in the temperature compensation circuit. Drain-source voltage of two critical MOSFETs is made equal in the current generator circuit by using a feedback arrangement to ensure a high PSRR of -75dB for the reference voltage. Temperature compensation is achieved by using the complementary to absolute temperature (CTAT) nature of gatesource of a MOSFET operating in the subthreshold region and proportional to absolute temperature (PTAT) nature of conventional composite pair architecture. The proposed CVR is designed in TSMC 180nm technology. The circuit works desirably for a supply range of 0.85V to 2.3V while generating a reference voltage of around 0.68V. Maximum temperature coefficient (TC) of 184 ppm/°C and minimum TC of 69 ppm/°C are noted for mismatch and process variations in the Monte-Carlo simulation for 1000 samples. The circuit consumes only 46nW of power, which makes it suitable for ultra-low power applications.
An Efficient im2row-Based Fast Convolution Algorithm for ARM Cortex-M MCUs

With the rise of IoT and edge computing, deploying neural networks (NNs) on low-power edge computing devices is drawing more and more attention. In NNs, convolutional layers take up the majority of the computing cycles, especially when NNs are implemented on ARM processors. Therefore, it is necessary to optimize the convolutional implementation on ARM Cortex-M MCUs. This paper proposes an efficient im2row-based fast convolution algorithm with two innovations. First, a novel im2row method for reusing the data of adjacent convolutional windows is presented. This method utilizes a reusable im2row buffer for data reuse, significantly reducing the amount of data copied during im2row and improving efficiency. Second, in algorithm implementation, a <italic>q7_t</italic> to <italic>q15_t</italic> data type extension technique that avoids data reordering is employed. This technique eliminates data reordering instructions, thus reducing the runtime of the algorithm. We evaluate our algorithm in separate convolutional layers and NNs. The results for convolutional layers show that, compared to baseline, the proposed algorithm speeds up the convolutional layer by an average of <inline-formula> <tex-math notation="LaTeX">$1.42\times $ </tex-math></inline-formula>, and the maximum speedup is up to <inline-formula> <tex-math notation="LaTeX">$2.9\times $ </tex-math></inline-formula>. Experiments on different NNs demonstrate that our algorithm can speed up the overall NN by up to <inline-formula> <tex-math notation="LaTeX">$2.15\times $ </tex-math></inline-formula>.
Developing Integrated Performance Dashboards with Power BI – a Case Study in a Medium-Size Manufacturer

Performance measurement system is essential for any organization to guide them optimizing their activities. For a small and medium size entities, the performance measurement system mostly exists but not integrated. The study is a case study in a medium size metal manufacturer producing metal-based parts and component for various industries. The research captured the performance measurement requirement from each division with interviews, studying company’s records, and participative observation. The result was visualized into inter-connected IDEF0 diagrams to ensure performance integration. The performance measurement system was then constructed from their existing excel worksheets into several visualized dashboards using Power BI.
High-Performance Software Load Balancer for Cloud-Native Architecture

Driven by increasing in the demand for cloud computing, cloud providers are constantly seeking configuration mechanisms designed to simply install a reliable and easy-to-manage cloud architecture—similar to installing an operating system on a computer using a thumb drive. Accordingly, cloud software components can be packaged into lightweight and portable containers, and then be easily deployed and managed through orchestration tools such as Kubernetes. Similarly, load balancers can also be deployed in containerized cloud environments and managed as a container, simplifying the process of scaling in or out according to the network status or amounts of incoming traffic. In this study, we implemented a containerized high-performance load balancer that distributes traffic using eBPF/XDP within the Linux kernel, which can easily be managed via Kubernetes. We compared the performance of the proposed load balancer with iptables DNAT and loopback based on the RFC2544 performance standard, and also performed tests simulating real-world traffic patterns by using IMIX traffic streams. Our experimental results indicate that the throughput performance of the proposed load balancer is considerably better than that of iptables DNAT; the difference in performance increased with decreasing packet size. The difference in performance between the loopback (representing the theoretical maximum performance limit) and the proposed load balancer was minimal.
Quantum Tree-Based Planning

Reinforcement Learning is at the core of a recent revolution in Artificial Intelligence. Simultaneously, we are witnessing the emergence of a new field: Quantum Machine Learning. In the context of these two major developments, this work addresses the interplay between Quantum Computing and Reinforcement Learning. Learning by interaction is possible in the quantum setting using the concept of oraculization of environments. The paper extends previous oracular instances to address more general stochastic environments. In this setting, we developed a novel quantum algorithm for near-optimal decision-making based on the Reinforcement Learning paradigm known as Sparse Sampling. The proposed algorithm exhibits a quadratic speedup compared to its classical counterpart. To the best of the authors’ knowledge, this is the first quantum planning algorithm exhibiting a time complexity independent of the number of states of the environment, which makes it suitable for large state space environments, where planning is otherwise intractable.
FMM Accelaration of 2D Well-Conditioned T-Matrix Method for Multiple Scatterers

The T-Matrix method for a multitude of two-dimensional dielectric targets can be well-conditioned based on the ideas on Analytical Regularization Method. A peculiar implementation of Fast Multipole Acceleration for this method is revisited. The advantages of working with linear algebraic equations of the second kind instead of the first kind are underlined in terms of reduction in the number of iterations for the iterative solver for a specific residual error.
Physico-mechanical properties

SUMMARY The concurrent influence of the processing aid (Paraloid K 120N) and the carbon black ratio, as well as the nature and the ratio of the impact modifier (CPE 3615 and Kane Ace B56 A) on the main physico-mechanical characteristics of the poly(viny1 chloride)-based unplasticized mixtures have been studied. The results obtained, processed mathematically and plotted graphically in the form of response surfaces, evidenced that the improvement of certain physico-mechanical properties becomes possible by the introduction of 2.5 parts and 5.0 parts carbon black into these compounds. This is due to the introduction of impact modifiers and processing aids into the mixtures.
A Lower-body Exoskeleton Platform with Remote Actuation*

Exoskeletons are being designed to rehabilitate the disabled population all over the world. The performance of these devices depends upon the design and placement of various components. Experimentation with human subjects is a key part to understand the effect of these design choices. This article presents the design of a lower-body exoskeleton platform that can be quickly modified for human-in-loop experimentation. The platform has a decoupled remote actuation and a lightweight body interface. Low-level system and control architectures are also presented.
Vegetation phenology detection of deciduous broad-leaf forest using YOLOv3 from PhenoCam

Vegetation phenology identification is significance to the exploration of vegetation growth and is also conducive to the impact of phenology on the ecological environment. Recently, vegetation phenology detection is based on a time series of vegetation phenology to index simulation of vegetation growth time indirectly. In this study, we identify the vegetation phenology of deciduous broad-leaved forest through the deep learning method within a single PhenoCam image. The result of the phenology identification of growing regions, the accuracy MAP of daily identification in daily scales mAP up to 10.2%, which could identify the growing period of most deciduous broad-leaved forests. The identification accuracy mAP in the 8-day scale is up to 69%, and the identification mAP accuracy of vegetation could reach 98.2% when it was divided into four categories. The purpose of this study is to detect the phenological growth period of deciduous broad- leaved forest with rapid development, high precision, and fast deep learning methods. It has a great improvement on the current method of calculating the vegetation phenology period by using the traditional measurement and related mathematical and physical models. While obtaining the phenology period more quickly, it can automatically and accurately obtain the growth area and growth period of the study area, making a certain contribution to the study of vegetation phenology.
Image-Based TF Colorization With CNN for Direct Volume Rendering

In the direct volume rendering (DVR), it often takes a long time for a novice to manipulate the transfer function (TF) and analyze the volume data. To lessen the difficulty in volume rendering, several researchers have developed deep learning techniques. However, the existing techniques are not easy to apply directly to existing DVR pipelines. In this study, we propose an image-based TF colorization with CNN to automatically generate a direct volume rendering image (DVRI) similar to a target image. Our system includes CNN model training, TF labeling, image-based TF generation, and volume rendering by matching the target image. We introduce a technique for training CNN and labeling the TF with images similar to the input volume dataset. Moreover, we extract the primary colors from the target image according to the labels classified with the CNN model. We render the volume data with the TF to produce the DVRI reproducing the prominent colors in the target image.
A Novel Ring Amplifier with Low Common-Mode Voltage Variation and Noise Reduction Using Floating Power Technique

This paper proposed a floating power (FP) technique suitable for the switched-capacitor (SC) circuits. The FP generates an isolated voltage power supply, which reduces the output noise compared to the traditional power supply technique. It is scaling-friendly since it only requires a capacitor and a non-overlapping clock. A self-biased ring amplifier (RAMP) utilizing the FP technique is designed to verify the proposed technique. The simulation result has proved excellent effectiveness in common voltage locking and noise reduction. The common voltage variation reduces to less than 50% compared to the traditional auto-zero (AZ) technique. Besides, the FP technique has less correlation with PVT variation. The proposed RAMP’s output common voltage depends on the preset voltage of the floating capacitor during the charging phase. Therefore, the proposed RAMP removes the dedicated CMFB circuits.
Efficient Two-Party Integer Comparison With Block Vectorization Mechanism

Private integer comparison has been an essential computation function for many applications, including online auction, credential identification, data mining, and joint bidding. In the setting of two-party computation, two parties with private inputs (<inline-formula> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$y$ </tex-math></inline-formula>) want to jointly compare them without revealing the value of those inputs to others (also known as the Millionaires’ problem) while the output should ensure correctness and preserve data privacy. The private inputs only can be revealed if they are equal, i.e., <inline-formula> <tex-math notation="LaTeX">$x=y$ </tex-math></inline-formula>. Many related works have been proposed to solve the integer comparison problem in various settings, focusing on different properties such as round and computation complexity. Most solutions decompose integers into bitwise representation and then securely evaluate the function in a Boolean circuit on encrypted bits. However, this type of solution is costly (especially for large integers) as each bit requires encryption and decryption. In this paper, we transform the private integer comparison into a block comparison problem. In particular, we employ a block vectorization mechanism to encode the private inputs into blocks. We show the security of our two-party protocol in the semi-honest model. Also, we implement the protocol to demonstrate its efficiency using block vectorization mechanism and homomorphic encryption. The experimental result proves that our proposed solution achieves high efficiency, particularly for large integer comparisons.