Miracle-dz/UniFAR-FTUs huggingface dataset:
PMDS-SLAM: Probability Mesh Enhanced Semantic SLAM in Dynamic Environments

Visual Simultaneous Localization and Mapping (vSLAM) is one of the key technologies in the field of robotics. The assumption of scene static is typical in SLAM algorithms. Such a strong assumption limits the use of most vSLAM systems in populated real-world environments. Recently, the semantic vSLAM systems towards dynamic scenes have gradually attracted more and more attentions. Existing semantic vSLAM system usually solely simply combines semantic information and motion check to obtain dynamic target contours, and delete all feature points on the contour. This article propose a new framework to exclude feature points using a mask produced by probabilistic mesh. It proposes to use superpoint segmentation to divide the picture into probabilistic meshs, and use the feature point matching relationship of historical frames to propagating probability, Only use the feature points in the low probability mesh to stably estimate camera motion. Experiments conducted on TUM’s RGBD dataset [15] show that the average accuracy of the camera trajectory estimated by this method is 90% higher than that of the original ORB-SLAM2 [1], and it is also compared with other SLAM systems that can cope with dynamic environments.
Application of the Quasi-Static Memdiode Model in Cross-Point Arrays for Large Dataset Pattern Recognition

We investigate the use and performance of the quasi-static memdiode model (QMM) when incorporated into large cross-point arrays intended for pattern classification tasks. Following Chua’s memristive devices theory, the QMM comprises two equations, one equation for the electron transport based on the double-diode circuit with single series resistance and a second equation for the internal memory state of the device based on the so-called logistic hysteron or memory map. Ex-situ trained memdiodes with different MNIST-like databases are used to establish the synaptic weights linking the top and bottom wire networks. The role played by the memdiode electrical parameters, wire resistance and capacitance values, image pixelation, connection schemes, signal-to-noise ratio and device-to-device variability in the classification effectiveness are investigated. The confusion matrix is used to benchmark the system performance metrics. We show that the simplicity, accuracy and robustness of the memdiode model makes it a suitable candidate for the realistic simulation of large-scale neural networks with non-idealities.
Cluster Formation in Scalable Cell-free Massive MIMO Networks

Inter-cell interference remains to be a bottleneck for conventional cellular networks as cell-edge users continue to suffer from poor performance. Cell-free massive MIMO is a novel network architecture that suppresses inter-cell interference by eliminating cell boundaries. It promises uniform performance throughout the coverage area, enabled by the coherent joint transmission from multiple distributed antennas. To make the network scalable, a user-centric approach is adopted where each user is served by a cluster of nearby access points (APs). In this work, we study the impact of cluster formation on the total fronthaul requirement, which is a limiting factor in practical coordinated distributed systems. We also investigate its effect on the guaranteed quality of service (QoS) of the network. Using our proposed algorithms, we look into the optimal cluster sizes for a given scenario and show that good performance can be achieved even with relatively small user-centric clusters, which then translates to fronthaul savings.
Distributed Cooperative and Noncooperative Joint Power and Beamforming Adaptation Game for MIMO Sensor Network

Distributed Joint beamforming and power adaptation algorithms are of interest for MIMO ad hoc networks. Cooperative and non-cooperative games have been presented to decrease interference (mutual) at each sensor node, under receiver signal-to-interference and noise (SINR) constraints. In reduced feedback algorithms, the optimum transmitter node beamformer is selected from a predefined codebook. This paper introduces a cooperative optimal beamformer selection algorithm to minimize the total power consumption for cluster-based network topology under different minimum SINR constraints. The algorithm reduces overhead incurred by 48% and 10% while increasing the convergence rate to the steady-state allocation by 17% and 9% for the cooperative and non-cooperative beamformer selection games, respectively. Simulation results verify the proposed theoretical analysis, and demonstrate the performance of the Enhanced Co-Operative Power Minimization Algorithm (ECOPMA), Reinforcement Learning based Power allocation and Beamformer Algorithm (RLPBA) for the non-cooperative game [1], with state of the art methods and centralized (optimal) solutions as a fair benchmark.
Broadcast-Multicast Single Frequency Network versus Unicast in Cellular Systems

Video is an important factor of the load in cellular networks due to the growing popularity of streaming and linear services. In unicast transmission mode, the same data is transmitted as many times as the number of receivers demanding the same video content. Conversely, in broadcast transmissions using the Single Frequency Network (SFN) technique, a set of base stations perform synchronized transmission of the same waveform to a potentially infinite number of users. The objective of this study is to compare the performance of unicast and broadcast. More precisely, we determine the minimum number of users downloading the same data from which a broadcast transmission is more efficient than multiple unicast transmissions. In this paper, a model to calculate the Signal-to-Interference-plus-Noise Ratio (SINR) in unicast and broadcast modes is presented, considering Poisson distributed base stations, path loss, fading, shadowing, trisectored antennas, SFN with a different number of base stations and beamforming in unicast mode. Results show that even when an SFN is formed by just 2 base stations and unicast transmissions are performed using beamforming with 8 antennas per sector, broadcast outperforms unicast when there are at least 8 users per cell demanding the same content.
Algorithmic Steps for SAR Backprojection on Radar Based Motion Estimation

When adapting SAR (synthetic aperture radar) techniques to vehicles it becomes obvious, that there are some dramatic differences to air-born SAR systems. The main difference in automotive applications is, that vehicles does not drive with constant speed in straight direction only as planes do. Therefore in this paper a novel algorithm is developed what enables to estimate vehicles ego motion very precisely and at exactly those time steps when it is needed. This algorithm enables both, generating SAR maps as well as autarkic ego motion estimation. Tests in real traffic scenarios show promising results.
Modeling optical properties of plant epicuticular wax

Wax is found on the leaves almost of all plants. Its physicochemical and protective properties are widely discussed in the scientific literature. In the presented work, the influence of the structure of wax on the optical properties in wheat leaves and needles of blue spruce has been revealed for the first time. Long-period ordering was established. The structural elements are nanotubes up to several microns in length and about 150 nm in diameter. The paper presents the influence of a long-period structure on the optical properties and local characteristics of light waves, including the transmission spectrum, the density of photon states, as well as fluorescence spectra and polarization microscopy. Using modern mathematical tools, the main spectral and optical characteristics were calculated using the example of the wax cover of blue spruce and glaucas wheat. Based on calculations that establish the relationship between the structural, spectral and optical properties, the influence of wax structures on the process of photosynthesis was revealed.
And QUIC meets IoT: performance assessment of MQTT over QUIC

We study the performance of the Message Queuing Telemetry Transport Protocol (MQTT) over QUIC. QUIC has been recently proposed as a new transport protocol, and it is gaining relevance at a very fast pace, favored by the support of key players, such as Google. It overcomes some of the limitations of the more widespread alternative, TCP, especially regarding the overhead of connection establishment. However, its use for Internet of Things (IoT) scenarios is still under consideration. In this paper we integrate a GO-based implementation of the QUIC protocol with MQTT, and we compare the performance of this combination with that exhibited by the more traditional MQTT/TLS/TCP approach. We use Linux Containers and we emulate various wireless network technologies by means of the ns3 simulator. The results of an extensive measurement campaign, show that QUIC protocol can indeed yield good performances for typical IoT use cases.
Synthesis And Structural Properties Of CU2ZnSnSe4 Nanocrystals For Nanoinks To Print Flexible Electronic Devices

Semiconductor CU2ZnSnSe4 nanocrystals were synthesized by the polyol approach. With the help of X-ray diffraction methods and transmission electron microscopy, its chemical composition, some morphological and structural properties were studied. Nanoparticles synthesized under optimal conditions were applied to create nano inks to print absorber layers of solar cells using 2D and 3D printers.
Simulation and optimization of Lattice Mismatched InGaAs Thermophotovoltaic Cell

Thermophotovoltaic (TPV) system harvests heat from thermal radiation where the photons are absorbed by a photovoltaic (PV) cell device and generates electrical energy. InGaAs is one of the popular III-V semiconductors material and has a great potential to be an efficient TPV cell if further optimization and improvements are made. In this paper, In0.68 Ga0.32 As with bandgap energy of 0.6 eV and cut-off wavelength at 2.1 $\mu \mathrm{m}$ is modeled and optimized using TCAD simulation software. InAsP buffer layers were incorporated to reduce 1.1% lattice-matched effect between the device layer and lnP substrate. The cell’s base and emitter layers were optimized by varying the thickness and the doping concentration of the cell layer individually under 1400 K blackbody spectrum. The optimization of emitter thickness and base doping concentration significantly contribute to a higher cell performance. An emitter thickness of 0.06 $\mu \mathrm{m}$ contributes to an efficiency ($\eta$) of 25.55% while a base doping concentration of 1 × 1016 cm−3 recorded 23.08% of $\eta$.
Smart Control of an Electric Vehicle for Ancillary Service in DC Microgrid

This article presents a two-stage framework for optimal Electric Vehicle (EV) charging/discharging strategy for DC Microgrid (MG) with Distributed Generators (DGs). A multi-objective optimisation task aimed at minimising system losses and EV battery degradation with Vehicle-to-Grid (V2G) peak shaving service has been realised. This coordinated EV integration into the DCMG was formulated as a directed weighted single source shortest path problem that was solved using a modified Dijkstra’s algorithm. The weights of the edges were obtained using primal-dual interior point method. The proposed framework has been experimentally verified using simulations with a test DCMG system with practical IEEE European low voltage test feeder load profiles. Results show realisation of peak demand shaving leveraging on EV discharge with minimal on-board battery degradation as well as reduced system losses. It is also shown that the proposed two-stage framework reduces the battery state of charge (SOC) sample space requirements in the analysis, thus, reducing the computational burden.
Multimodal Genetic Algorithm with Phase Analysis to Solve Complex Equations of Electromagnetic Analysis

In this contribution, a new genetic-algorithm-based method of finding roots and poles of a complex function of a complex variable is presented. The algorithm employs the phase analysis of the function to explore the complex plane with the use of the genetic algorithm. Hence, the candidate regions of root and pole occurrences are selected and verified with the use of discrete Cauchy’s argument principle. The algorithm is evaluated in an electromagnetic benchmark that successfully solves the eigenvalue problem determining the propagation of surface waves along a spatially dispersive graphene sheet. The numerical results show that the possibility to find all roots and poles of the function may be limited by the initial population size, especially when the search region is large and roots and poles are located close to each other.
Downlink Interference in Multi-Beam 5G Macro-Cell

Multi-beam antenna systems are the basic technology that is used in developed fifth-generation systems. This article is devoted to assessing the impact of a multi-beam antenna system on the interference level in the downlink. These interference are generated by neighboring base station antenna beams. The presented analysis is based on simulation studies in which the multi-elliptic propagation model is used. Transmission characteristics of propagation environments such as power delay profile and antenna beam patterns that define the geometric structure of the model were adopted on the basis of the 3GPP standard. The obtained results show the possibility of using the presented method to assess the separation angle between co-channel beams. It is the basis for minimizing spectral resources in the system.
Synthesis and biological evaluation of phosphatidylcholines with cinnamic and 3-methoxycinnamic acids with potent antiproliferative activity

A series of eight novel phosphatidylcholines containing cinnamic or 3-methoxycinnamic acids (3a-b, 5a-b, 9a-b, 10a-b) at sn-1 and/or sn-2 positions were synthesized and tested for their antiproliferative activity in an in vitro model against representative six human cancer cell lines (MV4-11, A549, MCF-7, LoVo, LoVo/DX, HepG2) and a normal cell line BALB/3T3. The structures of the new compounds were confirmed by spectral analysis. Biological evaluation revealed that all the tested conjugates exhibited higher antitumor activity than the corresponding free aromatic acids. Compounds 3b and 9b turned out to be the most active, with IC50 values of 32.1 and 30.5 μM against the LoVo/DX and MV4-11 cell lines, respectively. Studies of the mechanism of the antitumor action were carried out for 1-palmitoyl-2-cinnamoyl-sn-glycero-3-phosphocholine (5a), and it was shown to be active toward almost all the tested types of cancer cells, showing that this compound could effectively arrest the cell cycle in G2/M and decrease the mitochondrial membrane potential of leukemia MV4-11 cells. The obtained results proved that the strategy of the incorporation of cinnamic and 3-methoxycinnamic acids into phospholipids could expand their potential application in industry, as well as could improve their antiproliferative activity and selectivity toward cancer cell lines.
An End-to-End Deep Learning Framework for Recognizing Human-to-Human Interactions Using Wi-Fi Signals

Channel state information (CSI)-based human activity recognition plays an essential role in various application domains, such as security, healthcare, and Internet of Things. Most existing CSI-based activity recognition approaches rely on manually designed features that are classified using traditional classification methods. Furthermore, the use of deep learning methods for CSI-based activity recognition is still at its infancy with most of the existing approaches focus on recognizing single-human activities. The current study explores the feasibility of utilizing deep learning methods to recognize human-to-human interactions (HHIs) using CSI signals. Particularly, we introduce an end-to-end deep learning framework that comprises three phases, which are the input, feature extraction, and recognition phases. The input phase converts the raw CSI signals into CSI images that comprise time, frequency, and spatial information. In the feature extraction phase, a novel convolutional neural network (CNN) is designed to automatically extract deep features from the CSI images. Finally, the extracted features are fed to the recognition phase to identify the class of the HHI associated with each CSI image. The performance of our proposed framework is assessed using a publicly available CSI dataset that was acquired from 40 different pairs of subjects while performing 13 HHIs. Our proposed framework achieved an average recognition accuracy of 86.3% across all HHIs. Moreover, the experiments indicate that our proposed framework enabled significant improvements over the results achieved using three state-of-the-art pre-trained CNNs as well as the results obtained using four different conventional classifiers that employs traditional handcrafted features.
Federated Multi-View Spectral Clustering

Multi-view spectral clustering (MVSC) has become a popular approach to harvest knowledge about group information from multiple views of data, owned by different parties. A high quality MVSC approach usually requires collecting massive amount of data from each view party, in order to perform MVSC algorithm in a centralized manner. However, such centralized MVSC approach raises serious privacy concerns, not only in terms of the sensitivity property of many real-world data such as medical or financial records, but also in terms of the regulations from authorities to preclude centralized operations. Hence it is crucial to design new paradigm for training spectral clustering model on multi-view data in industrial scenarios. In this article, we propose a distributed and secure framework named Federated Multi-view Spectral Clustering (FMSC), in which a group of view parties collaboratively perform a MVSC model, but couldn’t learn the data of other participants. FMSC is inspired by the concept of federated learning, and utilize Homomorphic Encryption (HE) and Differential Privacy (DP) to achieve secure and private clustering. We conduct a series of extensive experiments to verify the effectiveness of FMSC on both synthetic and real-world datasets. Evaluations show that FMSC achieves respectable clustering results over conventional centralized approaches.
Single-Frequency Time-Step Ionospheric Delay Gradient Estimation at Low-Latitude Stations

The irregularity of the local-area ionospheric delay is a primary impediment for Ground-Based Augmentation System (GBAS) services. Excessive ionospheric delay gradients may degrade aircraft positioning for high precision landing systems. Therefore, the spatial gradients of the nominal background ionosphere must be studied as their statistics will be sent to the approaching aircraft. For the well-known station-pair method, ionospheric delay gradient estimation requires at least 2 Global Navigation Satellite System (GNSS) reference stations. This method can be applied to both single or dual-frequency GNSS receivers. However, when the GNSS stations are far apart, it is not suitable for estimating the ionospheric delay gradients at short baselines, and the time-step method is an attractive alternative. In this work, we propose a single-frequency time-step method for ionospheric delay gradient estimation. Careful baseline length selection is needed, due to ionospheric piercing point movements. We applied our method to GNSS data in 2014, at the peak of the 24th solar cycle, and showed that the standard deviations of the vertical ionospheric delay gradients were comparable to those derived from the dual-frequency time-step method. The standard deviations of vertical ionospheric gradients, $\sigma _{\mathrm {VIG}}$ , ranged between 4 and 6 mm/km. The $\sigma _{\mathrm {VIG}}$ values around the equinoxes were ~1.5 mm/km greater than at other times.
Spatial Pyramid Pooling with Atrous Convolutional for MobileNet

Disease screening through the fundus image is one of the hottest research topics in biomedical engineering. There are various diseases that can be screened through human retinal information, which include glaucoma, myopia, macular degeneration, diabetic retinopathy, and cataracts. Hence, an automated system to screen all these diseases will be beneficial to health practitioners. Previously, each of the disease features needs to be designed by hand if the traditional machine learning approach is used. It is hard to process various diseases as a single system through this approach, especially if a new disease that needs to be added to the system does not fit well with the handcrafted features. Thus, a deep learning approach that utilizes learned features is the better alternative as the model can be updated easily if a new disease wants to be added to the system. This paper proposes a modified MobileNet architecture by replacing the top layers with a spatial pyramid pooling module. Three parallel flows of max-pooling operation through kernel sizes of $\times$$,\times$, and $\times$ are implemented to improve the algorithm robustness towards multi-scale input. Atrous convolution is also employed by adding the dilation rate to each of the pointwise convolution operators. The results show that a dilation rate of 4 produces the best mean accuracy of 0.7433 for the 5-fold cross-validation test. The algorithm retains its lightweight nature where the total number of parameters used is around 3 million. The model can be trained better if the number of data among the classes is more or less the same, which will reduce the training bias.
Human Related Challenges in Agile Software Development of Government Outsourcing Project

In 2019, a government organization in Indonesia has developed several systems that will run in parallel using Agile by utilizing vendor services. Based on internal project reports, there are indications of human-related issues or challenges during the development process of these systems. The case study is one of the critical systems of failed projects in this government organization. In this study, a Systematic Literature Review (SLR) was used to identify human-related challenges or issues that could lead to failure in an ASD project. These issues or challenges were qualitatively validated based on expert judgment from external and internal organizations by interview and questionnaire. The final results of this study were 20 human-related challenges grouped into 5 categories, which were identified as human-related challenges that led to the failure of the ASD project in this case study. Proposed solutions based on best practices are also provided for each challenge or issue by conducting business research methods with open and axial coding. Besides, the comparison of views between vendors and organizations on human-related challenges as well as the implications of this study are also presented at the end, so that readers can get insight into these challenges.
InGaAs/GaAs Multi-Quantum Well Nano-Ridge Waveguide Photodetector Epitaxially Grown on a 300-mm Si Wafer

We demonstrate p-i-n InGaAs/GaAs quantum well nano-ridge waveguide photodetectors epitaxially grown and fully processed on a 300mm Si wafer. The devices exhibit low dark currents of 10pA, current density of 1. 43 × 10−5 A/cm2, at −1V bias and internal responsivities of 0.48A/W at 1020nm wavelength.
Multi-Contrast OCTA for Small Animal Imaging

Vision-robbing disorders are frequently characterized by their effects on vasculature and the retinal pigment epithelium (RPE). Polarization diversity detection (PDD) selectively visualizes the RPE to evaluate its structure and integrity. We present multi-contrast OCTA on small animal models for simultaneous RPE and vasculature display.