Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Spreading Factor Assignment in LoRa Wireless Networks

It is vital and challenging to devise new efficient transmission techniques for next generation wireless networks that support internet of things (IoT) systems. Long range (LoRa) wireless networks are based on the deployment of connected devices with limited energy and where end-devices need higher data rates. This system is a key technology that enables smart city applications. The chirp spread spectrum is used as the modulation technique for LoRa networks which consists of assigning various orthogonal spreading factors (SF) among the connected devices in the network. In particular, each device uses a fixed SF for data transmission, which is assigned based on its distance from the gateway. This paper proposes a new SF assignment scheme aiming at enhancing the overall performance. In particular, the proposed scheme no longer assigns SFs based on the distance; but instead, it assigns them depending on the instantaneous channel realizations. Such a dynamic assignment of the SFs among LoRa users significantly enhances the overall performance compared to conventional SF assignment schemes. The proposed system is evaluated in terms of symbol error rate (SER) via numerical simulations.
The Rate of Peat Growth in the Erie Basin

The rate at which peat accumulates has been a matter of interest to geologists ever since the relation between peat deposits and coal beds has been appreciated. More recently the problem has assumed fresh importance because of the increasing use of peat microfossils as a key to post-Pleistocene climate. In particular the relative percentages of different kinds of fossil pollen preserved in peat have been much studied in Europe. The purpose of such studies has been to interpret the changes in vegetation adjacent to the peat deposit during the course of its formation. Since most of the pollen preserved is brought in by the wind, the picture of changes appears to be somewhat generalized for the locality, and, therefore, it is often of value in interpreting climatic history. In a general way such evidence in Europe has supported the earlier findings of Blytt, Sernander and others regarding the shifts in moisture and temperature during postglacial times (Biilow, '29). In North America a number of pollen analyses have been made, some of which are not regarded by their authors as shedding light on climatic change. The senior writer, however, has now examined material from Pennsylvania, Ohio, Michigan, Indiana, Iowa and Arkansas. In every case he has obtained what seems like reasonable evidence of considerable climatic changes during postglacial times (Sears, '32). The most certain of these changes is a period of recent relatively dry climate followed by a gradual change to the somewhat more humid climate of the present. In the case of deep columns of peat from Ohio, Michigan, Indiana and Iowa there appears to be good evidence of an earlier dry period separated from the one just described byA a period of marked humidity. If these indications may l)e trusted the postglacial climate of North America has exhibited fluctuations similar to those of Europe. In Europe the postglacial climates have been accurately dated by de Geer's measurement of clay layers (Billow, '29). Since this means of measurement has not been generally available in this country we have endeavored to find other means. Of these the most hopeful has seemed to be a determination of the mean rate of peat accumulation particularly in the Ohio-Michigan area with which we are most familiar. The following paper describes the results obtained in this connection. Before proceeding with the paper acknowledgments should be made of
Koopman operator-based model identification and control of hydraulic fracture propagation

The moving boundary nature of hydraulic fracturing process makes it extremely difficult to approximate using local models (i.e., approximate models whose validity is limited by the training data). In this work, we implement the Koopman operator methodology for closed-loop control of fracture propagation. Koopman theory models nonlinear dynamical systems as linear systems by lifting the states to an infinite-dimensional space of functions called observables. It is particularly attractive because of its ability to provide (nearly) global linearization valid in a larger domain (in some cases the entire basin of attraction) compared to local models. Additionally, due to its linear structure, it allows convex predictive control formulations that avoid any issues associated with nonlinear optimization. The numerical results show that the Koopman linear model shows excellent agreement with the real system and successfully achieves the desired fracture geometry.
Cooperative Avoidance Control with Relative Velocity Information and Collision Sector Functions for Car-like Robots*

This paper presents a closed-form feedback control design to guarantee collision-free maneuvering for four-wheel car-like robots. Relative velocity information and collision sector functions between the host robot and its neighbors are introduced to adjust the gain of avoidance controllers in real-time, such that controllers create smoother maneuvers and thus further improving the dynamic performance of the system. Meanwhile, the velocity-based switching functions are designed to make the avoidance control terms only active when robots and/or obstacles move towards each other, which effectively reduces unnecessary dispersal and energy consumption in collision conflicts.
A Comparative Study for Broken Rotor Bars Fault Detection in Induction Machine using DWT and MUSIC techniques

In Squirrel Cage Induction Machines (SCIMs), the diagnosis of Broken Rotor Bars (BRBs) fault is the most difficult. Special attention has been given to the Discrete Wavelet Transform (DWT) to detect the BRBs. Also, high-resolution techniques such as MUltiple SIgnal Classification (MUSIC) have been proposed to improve the BRBs detection. Therefore, this paper examines the effectiveness of DWT and MUSIC techniques to detect the BRBs in a three-phase SCIM. In both techniques, the stator current signal is analyzed in steady-state to extract the fault frequencies of BRBs.Experimental results obtained using a SCIM of 1.1 kW confirm the high performance of the MUSIC technique to extract the related-faults frequencies of BRBs compared to the DWT.
Site‐specific contribution of Toll‐like receptor 4 to intestinal homeostasis and inflammatory disease

Toll‐like receptor 4 (TLR4) is a highly conserved protein of innate immunity, responsible for the regulation and maintenance of homeostasis, as well as immune recognition of external and internal ligands. TLR4 is expressed on a variety of cell types throughout the gastrointestinal tract, including on epithelial and immune cell populations. In a healthy state, epithelial cell expression of TLR4 greatly assists in homeostasis by shaping the host microbiome, promoting immunoglobulin A production, and regulating follicle‐associated epithelium permeability. In contrast, immune cell expression of TLR4 in healthy states is primarily centred on the maturation of dendritic cells in response to stimuli, as well as adequately priming the adaptive immune system to fight infection and promote immune memory. Hence, in a healthy state, there is a clear distinction in the site‐specific roles of TLR4 expression. Similarly, recent research has indicated the importance of site‐specific TLR4 expression in inflammation and disease, particularly the impact of epithelial‐specific TLR4 on disease progression. However, the majority of evidence still remains ambiguous for cell‐specific observations, with many studies failing to provide the distinction of epithelial versus immune cell expression of TLR4, preventing specific mechanistic insight and greatly impacting the translation of results. The following review provides a critical overview of the current understanding of site‐specific TLR4 activity and its contribution to intestinal/immune homeostasis and inflammatory diseases.
Confirmation of ancient polyploidy in Dahlia (Asteraceae) species using genomic in situ hybridization

The successful production of interspecific hybrids between members of the dysploid chromosome series inDahlia offers a unique opportunity to investigate chromosome evolution. Analysis of meiotic metaphase I in these hybrids using genomic in situ hybridization (GISH) has shown that pairing occurs both between and within parental genomes. These results have provided clear evidence for the allotetraploid origin of Dahlia species with 2n=32 and suggests that species with 2n=34 and 2n=36 have also arisen via allopolyploidy. A bivalent promoting mechanism proposed for species with 2n=32 also appears to be present in Dahlia species with 2n=34 and 2n=36 .
Deep Random Forest for Facial Age Estimation Based on Face Images

The face image of an individual is important for most biometrics systems. The face picture gives loads of helpful informations, including the individual’s personal identity, gender, ethnicity, age, emotional expression, and so forth. As of late, a few applications that endeavor age estimation have risen. This paper was aimed to address the problem of image-based human age estimation. It has the following main contributions. We used the advantages of the recent method and algorithms named Gcforest, which proved in several classification tasks, this novel approach includes the power of the decision trees and the advantages of a Cascades structure which allows the interaction between trees. We provide a comparison between two feature types handcrafted and deep feature, we used three databases FG NET, PAL and MORPH II.
Control Plane Optimisation for an SDN-Based WBAN Framework to Support Healthcare Applications

Software-Defined Networking (SDN) offers an abstract view of the network and assists network operators to control the network traffic and the associated network resources more effectively. For the past few years, SDN has shown a lot of merits in diverse fields of applications, an important one being the Wireless Body Area Network (WBAN) for healthcare services. With the amalgamation of SDN with WBAN (SDWBAN), the patient monitoring and management system has gained much more flexibility and scalability compared to the conventional WBAN. However, the performance of the SDWBAN framework largely depends on the controller which is a core element of the control plane. The reason is that an optimal number of controllers assures the satisfactory level of performance and control of the network traffic originating from the underlying data plane devices. This paper proposes a mathematical model to determine the optimal number of controllers for the SDWBAN framework in healthcare applications. To achieve this goal, the proposed mathematical model adopts the convex optimization method and incorporates three critical SDWBAN factors in the design process: number of controllers, latency and number of SDN-enabled switches (SDESW). The proposed analytical model is validated by means of simulations in Castalia 3.2 and the outcomes indicate that the network achieves high level of Packet Delivery Ratio (PDR) and low latency for optimal number of controllers as derived in the mathematical model.
High Mobility Channel Parameter Acquisition over Massive MIMO System

In this paper, we examine the acquisition of uplink and downlink channel parameters for the massive multiple-input multiple-output (MIMO) networks in the high mobility scene. We firstly formulate the time domain massive MIMO signal model along the uplink and adopt the expectation maximization based variational Bayesian (EM-VB) framework to recover the uplink channel parameters including angle, delay, Doppler frequency, and channel gain for each physical scattering path. Then, we fully exploit the angle, delay and Doppler reciprocity between uplink and downlink and reconstruct angles, delays, and Doppler frequencies for the downlink massive channels at the base station. Various numerical examples are presented to confirm the validity and robustness of the proposed scheme.
Semi-Decentralized Interference Aware Scheduling in D2D-Enabled Cellular Networks

We investigate the problem of semi-decentralized, interference aware scheduling in a cellular network with Device-to-Device (D2D) links. Our goal is an algorithm that allows optimal resource allocation and power control in spite of mutual interference from and to D2D links, as well as the creation of a reporting structure that is independent of the exact optimization goal and D2D modes. To achieve this, we formulate an interference aware scheduling problem with general rate-utility and generalized reuse constraints. By applying a Generalized Benders Decomposition, the problem is decomposed into two coupled sub-problems, a “primal” power control problem and a “master” scheduling problem, that can be dealt with independently. By this, we decouple the tedious power optimization from the scheduling part. We evaluate performance of the proposed structure analytically and find it to allow optimal scheduling independent of the used utility and D2D modes, provided that the master scheduling problem is solved optimally. Finally, we propose a solution for the master problem of weighted sum-rate maximization. Our simulations indicate that using the proposed scheme, D2D reuse can increase by an order of magnitude compared to the often targeted one-fold reuse, reaching a maximum of 31-fold frequency reuse in our set-ups. It is further capable of improving sum-rate performance by around 35% over existing works, while keeping signaling overhead and optimization delay at the same order of magnitude.
Challenges in Scaling Down of Free-Floating Implantable Neural Interfaces to Millimeter Scale

Implantable neural interface devices are an emerging technology for continuous brain monitoring and brain-computer interface (BCI). Recording of electrical signals from neurons of interest has led to more efficient and personalized diagnosis, treatment, and prognosis of neurological disorders. It facilitates the dynamic mapping of the whole brain, improving our understanding of the links between brain functions and behaviors. Stimulation of targeted nerves and neurons has been utilized as an effective therapy for Parkinson’s disease, essential tremor, dystonia, etc. It has also been developed as motor neuroprosthetic devices, improving the quality of life of many. Although initial designs were bulky, recent advances in semiconductor and nano-, micro-technology has enabled the miniaturization of such devices to the millimeter scale. Free-floating neural implants of miniature size are highly scalable to be distributed around the targeted region of interest more extensively. They are more clinically viable as they cause less disturbance to the body and induce less tissue immune response. However, these research efforts for scaling down have faced challenges resulted from decreasing the size of such implants, including issues pertaining to wireless powering, data communication, neural signal recording, and neural stimulation. Through extensive literature research and simulations, the limits and trade-offs regarding neural implant miniaturization are investigated and analyzed for each aspect of the challenges. State-of-the-art development and future trends for advanced neural interfaces are also explored.
Control of Wafer Scanners: Methods and Developments

In this tutorial paper, control design aspects of wafer scanners used in the semiconductor industry will be highlighted. At the same time, challenges for control design development as to meet the ever increasing demands on accuracy and speed are presented. Mechatronic systems that will be discussed are: (a) the light source needed to generate the ultraviolet light that is used for wafer exposure, (b) the optical and metrology systems needed for accurate measurement and imaging, and (c) the reticle and wafer stage systems needed for accurate and fast positioning. The control challenges associated with these systems mainly involve dealing with: (a) rejection of high frequency aliased disturbances, (b) large-scale or fast-updated (state) reconstruction, (c) vibration control and isolation in view of structural vibrations and disturbances, (d) inherent design tradeoffs like Bode’s sensitivity integral and gain-phase relationships, (e) multivariable plant identification of (quasi-static) deformations and structural dynamics for point-of-interest control, and (f) thermal modelling, model reduction, and the control of (local) time-varying deformation. Results will be discussed using representative examples.
Magnetic Vector Calibration for Real-Time Indoor Positioning

Geomagnetic signals are attractive media for indoor localization since they have less noise than RF and don’t require additional equipment installation for signal generation. The fingerprinting technique used in geomagnetic field based indoor positioning systems (IPS) estimates the position by matching the magnetic vector sampled from the current location with the magnetic vectors recorded in the magnetic field map. However, since the magnetic field is represented by a 3-dimensional vector, the values of a magnetic vector may change depending on the user’s orientation or the grip position. Thus, the sampled magnetic vector may have different values from the vector values stored in the magnetic field map depending on the sensor’s orientation. This may substantially lower the positioning accuracy. To avoid this problem, the existing studies use only the magnitude of a magnetic vector, but this reduces the uniqueness of the fingerprint, which may also degrade the positioning accuracy. In this paper we propose a vector calibration algorithm which can adjust the sampled magnetic vector to the vector of the magnetic field map by using the parametric equation of a circle. This can minimize mismatching with the magnetic field map. To implement this, we need to compute the relative rotation angle from the moving direction of the current user to the moving direction during the field map collection. Since we can measure the moving direction by using the gyroscope and accelerometer, we can compute this relative rotation angle dynamically. To evaluate our vector calibration algorithm, we compare the value mismatches with and without vector calibration for 6 random-walk paths in our campus testbed of 2470 square meters. Our results show that with the calibration, we can decrease the difference between the sampled magnetic vector and the magnetic field map vectors from 17.61$\mu$T to 2.38 $\mu$T in x dimension, from 17.24$\mu$T to 2.59 $\mu$T in y dimension, and from 6.86 $\mu$T to 2.16 $\mu$T in z dimension on average. This translates to 83% reduction in the map mismatch compared to the numbers without calibration. In addition, we also demonstrate the effectiveness of the calibration by applying the algorithm to our long short-term memory (LSTM)-based IPS.
Scam Detection for Online Shopping using Deep Learning

Internet transactions have recently raised big concerns. The problem with making business through the internet lies in the fact that neither the card nor the cardholder needs to be present during sales. Credit card fraud event cause loses is a fact of life for all merchants, who accept credit card payment as a part of their business operations. It is impossible for the retailers to check whether the customer is the genuine cardholder or not. Fraud is an adaptive crime, so it needs special methods of intelligent data analysis to detect and prevent it. Fraud detection is one of the earliest industrial applications of data mining and machine learning. Typical machine learning tasks are concept learning or predictive modeling, finding predictive patterns. This paper shows how advanced Machine Learning algorithm and neural network algorithm combined successfully to obtain a high fraud coverage and also with a low false alarm rate. Artificial neural network when trained properly can work as a human brain, depend for their working on the neuron, which is the small functional unit in brain as well as ANN. Deep learning, advanced machine learning algorithm is used for fraudulent detection based on the user’s behavior from their transactional
Low-Complexity Detection of Uplink NOMA by Exploiting Properties of the Propagation Channel

Uplink non-orthogonal multiple access (NOMA) has been proposed as an efficient technique to support massive connectivity and reduce access-latency. However, due to the inherent multiuser interference within such a system, iterative joint detection is required, which is of high-complexity. In this paper, we exploit the propagation properties of wireless channels to reduce the detection complexity. In particular, when neighboring spreading-blocks on the time-frequency grid experience similar channel conditions, then it is possible to reuse the calculated filter weights between them. We propose four detection strategies and compare them across a wide range of time- and frequency-selectively. Then, assuming the base station is equipped with a sufficient number of antennas, we replace the MMSE filter with a lower-complexity approximation using Neumann series expansion. The results show that our strategies incur only a small performance loss, while substantially cutting down complexity.
Inferential Methods for Additive Manufacturing Feedback

Adaptive manufacturing has revolutionized desktop prototyping and the production of physical models for non-load bearing or stress inducing applications. Many extrusion-based printers are available for purchase by entrepreneurial enthusiasts or businesses with manufacturing space limitations. These low-cost printers allow for quick prototyping but are not designed or intended for high quality production or high-cycle production, requiring extensive user tuning and upkeep to maintain the printer in usable condition. In a quest to apply modern deep learning and reinforcement learning based models, this work focuses on the development of control systems and infrastructure needed to resolve many of these intrinsic limitations of desktop 3D printers. A series of real-time agents were designed and deployed to actively monitor the printing of every layer and make continuous corrections in the printing parameters and G-code commands to reduce the variance in the tensile strength of homogeneous parts printed in a large batch.
Multi-Pitch Estimation of Polyphonic Music Based on Pseudo Two-Dimensional Spectrum

Multi-pitch estimation is a fundamental and key problem in music information retrieval, but still remains challenging due to the intrinsic complexity of polyphonic music. To address this problem, a pseudo 2-D spectrum-based method is proposed in this article. The pseudo 2-D spectrum is first constructed to map the time domain signal into the 2-D frequency space, where the harmonic signal exhibits a typical 2-D pattern. Then, pitch estimation is carried out by cross-correlation between the pseudo 2-D spectrum and the fixed 2-D harmonic template. Finally, the pitches of adjacent frames are grouped into pitch contours, where the contours whose lengths are shorter than the minimum note length limitation are discarded. And the remained pitches are refined using the estimates of neighboring frames by removing probable errors and reconstructing estimates. The proposed method exploits the harmonic structure of pitched sounds in a two-dimensional frequency plane, can work in the case where some notes contain few harmonics, and the harmonic overlap proportions are reduced greatly in the harmony cases. The experimental results show that the proposed method achieves promising performance comparing with the state-of-the-art methods on the evaluation datasets, and outperforms the bispectrum-based method on both evaluation datasets.