Miracle-dz/UniFAR-FTUs huggingface dataset:
Full-Duplex Two-Tier Heterogeneous Network With Decoupled Access: Cell Association, Coverage, and Spectral Efficiency Analysis

The ever increasing thirst for the higher capacity demands radical changes in the design of cellular networks, such as a leap from single-tier homogeneous networks to multi-tier heterogeneous networks and the use of millimeter wave frequency band. A typical point-to-point full-duplex transmission link can double the link rate by simultaneously using the same spectrum for bidirectional traffic. However, the characterization of full-duplex two-tier heterogeneous networks is not as straightforward as that of point-to-point full-duplex systems, specially when the different tiers of the heterogeneous network use different frequency bands (millimeter wave and microwave) for their transmissions. This paper characterizes a full-duplex two-tier heterogeneous network with decoupled access, where both tiers operate on different frequency bands (millimeter wave and microwave). To evaluate the achievable spectral efficiency and association behavior of users and base stations, a two-tier heterogeneous network model is proposed in which all users and base stations are modeled using Poisson point processes. First a signal-to-interference-plus-noise-ratio optimal user association scheme is characterized. Based on the user association scheme, the spectral efficiencies of the uplink and downlink transmission links are derived. In addition, a thorough analysis of the signal-to-interference-plus-noise-ratio coverage is also provided. Moreover to render the analytical model more comprehensive and robust, i.e., different from the convention of noise-limited millimeter wave network, the interference in millimeter wave networks is also accounted for in the analytical model. Lastly, the pragmatic value of full-duplex heterogeneous networks and decoupled access is discussed in detail through numerous numerical and simulation results.
An FPGA-Based Simple RGB-HSI Space Conversion Algorithm for Hardware Image Processing

In this paper, a new, low-complexity, easy-to-implement hardware method for color space conversion between the red-green-blue (RGB) and the hue-saturation-intensity (HSI) color spaces called the simple RGB-HSI space conversion (S-SC) algorithm is proposed, which aims to provide more rapid computing due to the need for fewer operations. In the S-SC algorithm, we reconstruct the model of space conversion between the RGB color space and the HSI color space (RGB-HSI) by inverting the conversion from the HSI color space to the RGB color space of the traditional geometric derivation algorithm. As a result, the nonlinear model-realized RGB-HSI color space conversion by the geometric derivation algorithm is transformed into a linear conversion model, which can avoid complicated calculations such as trigonometric and inverse trigonometric functions in the color space conversion process. The model can effectively reduce the computational complexity of the algorithm and facilitate hardware implementation at the same time. To evaluate the performance of the S-SC algorithm, we first compare the S-SC algorithm with the geometric derivation algorithm from the computational complexity perspective. On this basis, we compare the S-SC algorithm with five other RGB-HSI color space conversion algorithms from the perspectives of error and conversion effect. Finally, we use the field programmable gate array (FPGA) hardware platform to analyze and verify the timing sequence and logical resource consumption and verify the effectiveness of the proposed algorithm with experimental results. We show that the S-SC algorithm achieves good performance in terms of conversion accuracy, logical unit resource occupancy, and output timing.
Location Privacy-Preserving Truth Discovery in Mobile Crowd Sensing

Truth discovery techniques are commonly used in mobile crowd sensing (MCS) applications to infer accurate aggregated results based on quality-aware data aggregation. However, the location information of participants may be exposed when they upload their sensitive geo-tagged sensory data to relative platforms. While there are considerable existing privacy preserving truth discovery schemes for MCS, they mainly focus on protecting the privacy of sensory data, neglecting the tagged location information which is of equal if not higher importance for the privacy of participants. In this paper, we propose a novel and efficient location privacy preserving truth discovery (LoPPTD) mechanism, which can achieve data aggregation with high accuracy, while protecting both location privacy and data privacy of users. By structuring multi-dimensional sensory data obtained at different locations and exploiting homomorphic Paillier encryption, our approach can prevent leakage of both sensory data and tagged locations effectively. Also, super-increasing sequence techniques are employed in Lo-PPTD to ensure efficiency and feasibility. Theoretical analysis and thorough experiments performed on real-world datasets demonstrate that the proposed scheme can achieve high aggregation accuracy while providing complete privacy protection for users.
A Current Sensorless Delay–Based Control Scheme for MPPT–Boost Converters in Photovoltaic Systems

The results presented in this paper deal with the design of a current sensorless delay–based controller for the closed–loop stabilization of a photovoltaic system under an MPPT scheme using a boost dc/dc converter. Some applications of such topology are dc microgrids, solar vehicles, or stand-alone systems, to mention a few. The basis of this control scheme relies on the feedback linearization control technique coupled with a delay–based low-order controller. In order to study the stability, the proposed approach uses a geometric point of view which allows the partitioning of the controller parameters space into regions with similar stability characteristics (same number of unstable characteristic roots). The most important contribution of the paper relies on providing practical guidelines to tune the gains of the proposed delay–based controller, ensuring asymptotic stability of the closed–loop system and fulfilling the requirements for photovoltaic applications. In addition, the proposed approach allows the design a non-fragile controller with respect to the controller gains. Furthermore, in order to test the effectiveness of the control scheme presented, experimental results evaluating the closed–loop system performance under set-point changes and abrupt irradiance disturbances are addressed using a solar array simulator and a battery bank as load.
Automatic stimuli classification from ERP data for augmented communication via Brain-Computer Interfaces

Brain-computer interfaces (BCIs) are systems initially designed to compensate for motor disabilities affecting people whose control of the muscular system is compromised. However, recent developments open the BCIs market to a wide range of medical and non-medical applications. This raises the need for systems capable of interpreting more and more stimuli, even from different sensory domains. In this work, we design a machine-learning system able to fit both application domains accurately recognizing visual and auditory stimuli starting from the event-related potentials (ERPs) they generate. The obtained results are promising and some practical and realization aspects are discussed.
Cost Minimization for Remote Health Monitoring Under Delay and Reliability Constraints

Recently, the pandemic of Coronavirus disease 2019 (COVID-19) highlights the great potential of remote health for resisting infectious diseases. The remote healthcare methodologies have achieved significant development based on reliable and low-latency transmissions of 5G. In remote health monitoring, the Internet of Things (IoT)-based physical monitoring devices need to transmit collected physical data in a real time and highly reliable manner to ensure accurate monitoring of patients. However, due to the unreliability of wireless link and the latency of data queue, it is a challenging issue to achieve high reliability communication and low delay transmission at the same time. Meanwhile, the available bandwidth is limited and the cost of renting spectrum is not negligible. Thus, it is essential for the devices to reduce communication costs while ensuring the efficient data transmission. Toward this end, our goal is to minimize the total costs of leasing bandwidth by jointly optimizing the access link and backhaul transmission, subject to the delay and reliability constraints. Considering the requirements of access link and queue together, a cost minimization problem is formulated, which is non-linear and non-convex and is hard to solve straightforwardly. Through problem equivalent transforming, a gradient descent-based algorithm is proposed to find a suboptimal solution. Simulation results validate the performance through cost, reliability, delay and those under harsh conditions.
Research on reconstruction algorithm of lensless microimaging

The reconstruction algorithm of single-frame lensless microscopic imaging was investigated. The experimental setup of the lensless microscopic imaging system and the reconstruction algorithms of angle spectrum method was introduced. Comparing the resolution of the USAF resolution plate image, the amplitude image resolution acquired by the angle spectrum method is up to 3.1 $\mu \mathrm{m}$ and proves this method is good reconstruction algorithm of single-frame lensless microscope imaging. Results show that the lensless imaging field of view is 4.4 times than that of a 5X microscope, and its resolution is between 5X and 10X optical microscopes.
Enhanced 6P Transaction Methods for Industrial 6TiSCH Wireless Networks

IEEE802.15. 4 time-slotted channel hopping (TSCH) is designed to provide reliability even in harsh wireless networks compromised by metal structures and external noise. TSCH provides reliability through coordinated communication based on link schedule however, no policies regarding this are defined by the IEEE802.15. 4 standard. 6TiSCH (IPv6 over the TSCH mode of IEEE802.15.4e) provides management policies and link schedule negotiation mechanism that are not defined in the standard, and many studies are based on it. However, in most link-scheduling studies, 6top protocol (6P) for link schedule negotiation remains a premise, and the effect of harsh networks on 6P is rarely considered. Experimental results demonstrate that the performance of 6P is degraded due to the loss of 6P packets and transaction errors. These problems can threaten the fundamentals of existing link-scheduling studies. The main disadvantage of 6P in heavy traffic low-power and lossy networks (LLN)s is that nodes indiscriminately generate 6P requests according to their own demand. In this study, we analyze the performance and problems of 6P in harsh networks and propose parent-initiated 6P transaction and transaction revert methods to overcome such problems. The proposed methods enable stable cell negotiation even in LLNs under heavy traffic load through coordinated transactions of a parent based on the cell requirement of a child. The proposed methods are implemented using OpenWSN, an open-source project that implements the 6TiSCH IoT network stack, and its performance is evaluated through extensive experimentation. The results reveal that the proposed methods improve the success rate of 6P transactions and PDR of data packets by over 100% and 22%, respectively.
Software Architecture Degradation in Open Source Software: A Systematic Literature Review

Software architecture (SA) has a prominent role in all stages of system development. Given the persistent evolution of software systems over time, SA tends to be eroded or degraded. Such phenomenon is called architectural degradation. In light of this phenomenon, the current study focuses on problems of architectural erosion in the open-source software (OSS). There has been a significant research activity on the OSS over the last decade. Nonetheless, the architectural degradation problems in the OSS are still scattered and disorganized. In addition, there has been no systematic attempt made on existing studies to provide evidence, insight and better understanding for researchers and practitioners. The main objective of the present study is to provide a profound understanding and to review the existing studies on the architectural erosion of the OSS. In this study, we conduct a systematic literature review (SLR) to gather, organize, classify, and analyze the architectural degradation of previous papers published until the year 2020. The data for this study were collected from eight major online databases (ACM, Springer, ScienceDirect, Taylor, IEEE Explorer, Scopus, Web of Science, and Wiley). A total of 74 primary studies were identified as the final samples of this research. The results indicated that rapid software evolution, frequent changes, and the lack of developers’ awareness are the most common causes occurred in architecture degradation. Meanwhile, the prominent key indicators of architectural erosion symptoms are code smells and architectural smells. Additionally, the results indicated the most commonly used of the proposed solution for addressing architectural erosion is the metrics-based strategy. Acknowledging the limitations of the current study, more studies are needed that focus on determining other causes that are still ambiguous and improving the other solutions to provide better results in the precision and effectiveness of addressing architectural erosion.
Perturbed Amplitude Flow for Phase Retrieval

In this paper, we propose a new non-convex algorithm for solving the phase retrieval problem, i.e., the reconstruction of a signal <inline-formula><tex-math notation="LaTeX">$ {\mathbf x}\in {\mathbb {H}}^n$</tex-math></inline-formula> (<inline-formula><tex-math notation="LaTeX">${\mathbb {H}}={\mathbb {R}}$</tex-math></inline-formula> or <inline-formula><tex-math notation="LaTeX">${\mathbb {C}}$</tex-math></inline-formula>) from phaseless samples <inline-formula><tex-math notation="LaTeX">$ b_j=\vert \langle {\mathbf a}_j, {\mathbf x}\rangle \vert $</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$ j=1,\ldots,m$</tex-math></inline-formula>. The proposed algorithm solves a new proposed model, perturbed amplitude-based model, for phase retrieval, and is correspondingly named as <italic>Perturbed Amplitude Flow</italic> (PAF). We prove that PAF can recover <inline-formula><tex-math notation="LaTeX">$c{\mathbf x}$</tex-math></inline-formula> (<inline-formula><tex-math notation="LaTeX">$\vert c\vert = 1$</tex-math></inline-formula>) under <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(n)$</tex-math></inline-formula> Gaussian random measurements (optimal order of measurements). Starting with a designed initial point, our PAF algorithm iteratively converges to the true solution at a linear rate for both real, and complex signals. Besides, PAF algorithm needn’t any truncation or re-weighted procedure, so it enjoys simplicity for implementation. The effectiveness, and benefit of the proposed method are validated by both the simulation studies, and the experiment of recovering natural images.
Advanced Passive Operating System Fingerprinting Using Machine Learning and Deep Learning

Securing and managing large, complex enterprise network infrastructure requires capturing and analyzing network traffic traces in real-time. An accurate passive Operating System (OS) fingerprinting plays a critical role in effective network management and cybersecurity protection. Passive fingerprinting doesn’t send probes that introduce extra load to the network and hence it has a clear advantage over active fingerprinting since it also reduces the risk of triggering false alarms. This paper proposes and evaluates an advanced classification approach to passive OS fingerprinting by leveraging state-of-the-art classical machine learning and deep learning techniques. Our controlled experiments on benchmark data, emulated and realistic traffic is performed using two approaches. Through an Oracle-based machine learning approach, we found that the underlying TCP variant is an important feature for predicting the remote OS. Based on this observation, we develop a sophisticated tool for OS fingerprinting that first predicts the TCP flavor using passive traffic traces and then uses this prediction as an input feature for another machine learning algorithm for predicting the remote OS from passive measurements. This paper takes the passive fingerprinting problem one step further by introducing the underlying predicted TCP variant as a distinguishing feature. In terms of accuracy, we empirically demonstrate that accurately predicting the TCP variant has the potential to boost the evaluation performance from 84% to 94% on average across all our validation scenarios and across different types of traffic sources. We also demonstrate a practical example of this potential, by increasing the performance to 91.3% on average using a tool for TCP variant prediction in an emulated setting. To the best of our knowledge, this is the first study that explores the potential for using the knowledge of the TCP variant to significantly boost the accuracy of passive OS fingerprinting.
Wordoids: Boid Based Personalized Word Clustering System in Dark Side Ternary Stars

Personalized systems are required in many domains. However, gathering training data for personalization from individuals, as is necessary with deep learning, is a difficult and timeconsuming task. With our proposed method, less or no training data is required to adapt to individuals’ preferences, even when they shift over time. We introduce a potential field based method “Dark Side Ternary Stars” which has three components, GAGPL, Wordoids, and EGO. In this paper, we focus on two of them, ”Wordoids”, which adopt extends Boids algorithms to perform individualized classification of keywords by topic and improved our previous work ”GAGPL”, which calculates the individualized semantic orientation of sentences by using learned words per topic. As experimental results, we applied this method to news articles about Japanese professional baseball and we show that our method can obtain individualized semantic orientations and summaries of the article per individual.
Structure and Functioning of Information Systems of Background Monitoring of Landscape Elements of Gorgany Nature Reserve

This paper discusses the structural organization of the background monitoring system (BMS) of the transboundary UNESCO World Heritage Site “Beech forests of the Carpathians and ancient beech forests of Germany” using actual data and information from Gorgany Nature Reserve in Ivano-Frankivsk, Ukraine. The features of the structure and functions of background monitoring of the protected natural resources, forests and wildlife of Gorgany Nature Reserve is based on modern methods of collecting and processing of digital data, implementation of the principles of Internet of Things and cloud technologies, combined with GPS-systems, sensor networks with the use of optical and fiber-optic telecommunication channels. The information technology of the background monitoring system is based on figurative cluster models of monitoring objects according to theoretical principles of statistical, correlatory, spectral, cluster, logic-statistical and entropy analysis.
Computational Edge for Multiple Autonomous Objects

This research explores possibilities of creating software architectures for managing multiple autonomous objects in computational environments, which move away from clouds and use computational power of objects, at the edge of computing and communication networks. The emphasis is on shaping the behaviour of autonomous objects through human involvement in order to manipulate functions and change the purpose and levels of autonomy of these objects. The proposed computational model, generated from the software architectures, which gives rise to serverless and edge computing, should work across problem domain. By collecting relevant data and allowing a variable level of human input, the solution will enable us to choose, merge and combine multiple objects for a variety of tasks and according to environments in which autonomous objects reside.
CPchain: A Copyright-Preserving Crowdsourcing Data Trading Framework Based on Blockchain

Crowdsourcing data trading is a novel paradigm in which the crowdsourcing technology is adopted to collect big data for trading. At present, existing crowdsourcing data trading systems usually depend on a trusted broker and haven’t considered the truthfulness and the quality of data (QoD) simultaneously. Besides, copyright protection is the another issue that has not been properly addressed. To tackle these problems, we propose a Copyright-Preserving crowdsourcing data trading framework based on Blockchain, named CPchain, which mainly includes a smart contract. We design an auction algorithm based on semantic similarity to guarantee the truthfulness and individual rationality while ensuring QoD. Moreover, we combine digital fingerprint technology with blockchain to protect data copyright without a third-party certification authority. Furthermore, we develop a simple prototype of our proposed trading framework on the Ethereum test network. We have carried out a lot of experiments to demonstrate the significant performances of our framework.
Design and Development of Cascade Heating Control for a «Smart» Residential Housing

The article discusses the issues of modeling and control of heating systems in a residential building. The results of the analysis, selection of parameters of the cascade control system are presented. An algorithm is presented that implements the operation of a virtual object. The modeling of typical life support structures in the CX-Programmer environment is carried out. A virtual object control program has been developed in the language of ladder diagrams (relay-contact circuits). The multi-circuit management of residential building infrastructure facilities in the light of the requirements of Smart City has been proposed. The selection and justification of the method of controlling the heating system with cascade temperature control is considered. A heating system stand has been created to test the proposed automation systems. The description of the hydraulic, electrical and software parts of the stand. To control the operation of the stand, a controller is proposed, implemented on the Arduino platform. The results confirming the attainability of the proposed structural changes. The results of experimental studies are presented.
A Family of Single-Channel Spectrally-Null-Constrained Sequences With Low Correlation

Spectrally-constrained sequences (SCSs) with good correlation properties, and low spectral power under the spectral constraint have found many practical applications in communication, and radar systems operating over non-contiguous carriers or frequency slots. In this letter, a construction of single-channel spectrally-null-constrained sequence (SNCS) families with good correlation is proposed. Following the proposed construction, a single-channel polyphase SNCS family with period <inline-formula><tex-math notation="LaTeX">$N(N$</tex-math></inline-formula> + 1), and family size <inline-formula><tex-math notation="LaTeX">$(p-1)$</tex-math></inline-formula> can be obtained, which has zero power leakage under the spectral constraint <inline-formula><tex-math notation="LaTeX">$\lbrace 1+a(N+1):a\in \mathbb {Z}_N\rbrace$</tex-math></inline-formula>, where <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> is an odd integer, and <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula> is the minimum prime factor of <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula>. Note that the maximum periodic correlation magnitude of the derived single-channel SNCS family is <inline-formula><tex-math notation="LaTeX">$(N+1)$</tex-math></inline-formula>, which asymptotically achieves the correlation lower bound as <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula> increasing.