Data Communication Research Articles

Bandwidth-voltage trade-off paradigm in low-loss LNOI electro-optic modulators, using equalizer configuration

The ever-increasing demand for high-speed data communication has fueled the development of ultra-fast electro-optic modulators. Our proposed equalizer configuration in lithium niobate on insulator electro-optic (LNOI-EO) modulators offers a novel approach to the bandwidth-voltage trade-off. Using 3D simulations, we achieved an ultra-high bandwidth of 300 GHz, delivering more than three times enhancement compared to the conventional modulators with the same base modulator length and half-wave voltage of 4.4 V. The device design incorporates a crossing segment to ensure distortion-free modulated signals and a dual-layered electrode design to minimize interference. These modulators have excellent potential for future photonic integrated circuits, marking a new era of superior bandwidth-voltage trade-offs.

Design and Implementation of Encryption/ Decryption Architectures for BFV Homomorphic Encryption Scheme

Abstract: Now a days security is the prime part for both, the satellites communication of the electronics data and the stored data, hence encryption is important for information processing system and communication network. The proposed approach is easy to learn due the use of speed efficient Vedic multiplier. Since it minimizes the execution time and area, so the delay and power consumption is further decrease by the compact and flexible approach in the Mix column transform which takes different approach rather than conventional multiplication previously. The structure style of modeling helps to easy understandable the proposed design of algorithm. BFV is the symmetrical has designed and verified in the Verilog HDL in Xilinx tool. In this project we present using kogge-stone adder and Vedic multiplier.

A review of federated learning algorithms in image classification

Image classification is one of the most popular applications of machine learning. It has shown its potential in fields like healthcare, auto-driving and face recognition. Federated learning (FL) emerged in 2017, creating a major innovation to the field. The new structure brings new possibilities, but create new challenges such as data heterogeneity, privacy leakage and communication burdens in parameter updating. The paper solves the problem that there are relatively few papers providing a complete analysis relating to the application of FL in image classification. The paper contributes to describe the new challenges of FL in image classification and show the related reasons behind respectively, then analyze the current state-of-the-art algorithms designed for solving the challenges and improving image model performance by discussing the basic ideas and steps of algorithms and showing their pros and cons. The paper further more contributes to compare the performance of each model in accuracy and communication speed, and outline several possible directions for future advancement.

Automated debt recovery systems: Harnessing AI for enhanced performance

Amidst an upsurge in the quantity of delinquent loans, the financial industry is experiencing a fundamental transformation in the approaches utilised for debt recovery. The debt collection process is presently undergoing automation and improvement through the utilisation of Artificial Intelligence (AI), an emergent technology that holds the potential to revolutionise this sector. By leveraging machine learning, natural language processing, and predictive analytics, automated debt recovery systems analyse vast quantities of data, generate forecasts regarding the likelihood of recovery, and streamline operational processes. Debt collection systems powered by AI are anticipated to be compliant, precise, and effective. On the other hand, conventional approaches are linked to increasing expenditures and inefficiencies in operations. These solutions facilitate efficient resource allocation, customised communication, and rapid data analysis, all while minimising the need for human intervention. Significant progress has been made in data analytics, predictive modelling, and decision-making through the application of artificial intelligence (AI) in debt recovery; this has the potential to revolutionize the financial sector’s approach to debt management. The findings of the research underscore the criticality of artificial intelligence (AI) in attaining efficacy and precision, in addition to the imperative of a data-centric framework to fundamentally reshape approaches to debt collection. In conclusion, artificial intelligence possesses the capacity to profoundly transform the existing approaches utilized in debt management, thereby guaranteeing financial institutions’ sustained profitability and efficacy. The application of machine learning methodologies, including predictive modelling and logistic regression, signifies the potential of the system.

Implementasi Teknologi LoRa untuk Monitoring Real-Time Lampu PJU Berbasis Solar Panel

In the digital age, public road lighting (PJU) monitoring efficiency is becoming essential for effective infrastructure management. The research developed a PJU monitoring system based on LoRa technology and an Arduino microcontroller to monitor the operating conditions of PJU solar panels in real time. The system uses the LoRa 433 MHz module for remote data communication and is equipped with a voltage sensor to monitor batteries and solar panels as well as an ACS712 current sensor to measure current consumption on LED lights. The data is displayed on an I2C 16x2 LCD screen, making monitoring easy. LoRa technology offers the advantages of broad communication range and low power consumption. The development method used is prototyping, including needs analysis, system design, implementation, testing, and maintenance. Test results show that the system works well, with sensors providing adequate accuracy and LoRa communication enabling remote data access. The system improves the efficiency and accuracy of PJU monitoring, as well as reduces time and effort in the monitoring process. Overall, the system is an effective solution for PJU management in the digital age.

Enhancing Smart City Infrastructure: An Iot-Integrated System Approach for Real-Time Urban Management

Objectives: The primary objective of this study is to evaluate the effects of new information technologies on the workability, scalability, and reliability of urban installations. This study also aims to enhance smart city infrastructure by integrating various Internet of Things (IoT) systems, particularly for real-time smart city management. Methods : A complete experimental setup was established in a controlled urban environment encompassing numerous IoT technologies, such as environmental sensors, traffic management units, and public security systems, as well as data communication units. The Tensorflow framework, along with Python, was used to perform simulations and experiments. The system design was categorised into four key levels: data reception and transmission, computation, network and data analytics, and application. The proposed systems were subsequently tested in real-world environments, including zones frequently hosting urban events, to analyse key performance indicators. Findings : The results demonstrate significant improvements in urban management through IoT initiatives, including a 25% reduction in fine particulate matter (PM2.5) and nitrogen dioxide (NO2) levels, leading to decreased respiratory hospital admissions and enhanced air quality and health benefits. Additionally, notable advancements were observed in noise control, energy efficiency, and emergency response. Specifically, the findings recorded a 10 dB decrease in nighttime noise, a 30% reduction in energy consumption for public lighting, and a 40% faster emergency response time. These outcomes highlight the broad impact of IoT on urban sustainability and safety. Novelty : This paper introduces a novel concept focused on the design perspective of IoT-connected systems to boost smart city infrastructure, with particular emphasis on the real-time management of urban environments. Keywords: Smart Cities, IoT Technologies, Urban Infrastructure, Real ­Time Urban Management, System Architecture

Online Signals of Extremist Mobilization.

Psychological theories of mobilization tend to focus on explaining people's motivations for action, rather than mobilization ("activation") processes. To investigate the online behaviors associated with mobilization, we compared the online communications data of 26 people who subsequently mobilized to right-wing extremist action and 48 people who held similar extremist views but did not mobilize (N = 119,473 social media posts). In a three-part analysis, involving content analysis (Part 1), topic modeling (Part 2), and machine learning (Part 3), we showed that communicating ideological or hateful content was not related to mobilization, but rather mobilization was positively related to talking about violent action, operational planning, and logistics. Our findings imply that to explain mobilization to extremist action, rather than the motivations for action, theories of collective action should extend beyond how individuals express grievances and anger, to how they equip themselves with the "know-how" and capability to act.

A compression-based routing strategy for energy saving in wireless sensor networks

Data compression and optimal path discovery for data communication are vital for enhancing energy efficiency in wireless sensor networks (WSNs), crucial for their sustainability due to limited power resources. This study proposes a strategy that combines data compression and routing to optimize energy in WSNs. It is based on a Bio-Inspired Ant-Cuckoo optimized Relay-based Energy Efficient Data Aggregation (BACREED) algorithm which leverages both Ant Colony Optimization (ACO) and Cuckoo Search (CS) algorithms to enhance communication efficiency between cluster leads (CLs) and the destination node through a forwarding node. Comparative evaluation against Low Energy Adaptive Clustering Hierarchy (LEACH) and its variants, Genetic Algorithm Data Aggregation, Ant optimized using Energy Efficient Data aggregation, and Bio-inspired Ant Cuckoo Energy Efficient Data aggregation demonstrate superior performance in energy efficiency, throughput, and network longevity. This work integrates ‘Fast and Efficient Lossless Adaptive Compression Scheme with Outlier Detection and Replacement (FELACS-ODR) data compression algorithm with CS to improve network performance in terms of energy efficiency and optimal path discovery. Simulation results using MATLAB exhibit a path length reduction of 84 % for the proposed algorithm compared to a 73 % rate for the baseline algorithm with an optimal cluster-lead count between 40 to 80. Energy consumption increases slowly with data compression, significantly outperforming rapid increase scenarios without compression, particularly evident as the node count increases from 1100 and 200 nodes respectively. This research underscores the potential of leveraging FELACS-ODR and CS techniques for substantial enhancements in WSN energy efficiency.

Ensuring Cross-Chain Transmission Technique Utilizing TPM and Establishing Cross-Trusted Root Security via SM Algorithm

As blockchain technology advances, cross-chain interoperability has become a crucial requirement for achieving seamless interaction among multiple chains. To solve this problem, this paper proposes a scholarly approach to the development of a cross-chain-secure transmission technique, incorporating the Trusted Platform Module (TPM) and the SM algorithm, which is based on trusted roots. This research paper elaborates on the design and implementation of the cross-chain-secure transmission technique based on TPM and the SM algorithm, emphasizing the academic rigor necessary for addressing the security requirements of cross-chain interactions. The research shows that compared with the previous method, the throughput of production capacity is not significantly different from that before and after adding the network delay, and the success rate of preventing attacks is increased by 46.9% by simulating related attacks. After multiple rounds of experiments, the effectiveness and feasibility of this technology in protecting cross-chain communication data are verified.

Stochastic switching and analog-state programmable memristor and its utilization for homomorphic encryption hardware

Homomorphic encryption performs computations on encrypted data without decrypting, thereby eliminating security issues during the data communication between clouds and edges. As a result, there is a growing need for homomorphic encryption hardware (HE-HW) for the edges, where low power consumption and a compact form factor are desired. Here, a Pt/Ta2O5/Mo metallic cluster-type memristors (Mo-MCM) characterized by the Mo as a mobile species, and its utilization for the HE-HW via a 1-trasistor-1-memristor (1T1M) array as a prototype HE-HW is proposed. The Mo-MCM exhibits inherent stochastic set-switching behavior, which can be utilized for generating the random numbers required for encryption key generation. Furthermore, the device can accurately store analog conductance states after set-switching, which can be used as an analog non-volatile memristor. By simultaneously leveraging these two characteristics, encryption key generation, data encryption, and decryption are possible within a single device through an in-memory computing manner.

Maturity Assessment of District Health Information System Version 2 Implementation in Ethiopia: Current Status and Improvement Pathways.

Although Ethiopia has made remarkable progress in the uptake of the District Health Information System version 2 (DHIS2) for national aggregate data reporting, there has been no comprehensive assessment of the maturity level of the system. This study aims to assess the maturity level of DHIS2 implementation in Ethiopia and propose a road map that could guide the progress toward a higher level of maturity. We also aim to assess the current maturity status, implementation gaps, and future directions of DHIS2 implementation in Ethiopia. The assessment focused on digital health system governance, skilled human resources, information and communication technology (ICT) infrastructure, interoperability, and data quality and use. A collaborative assessment was conducted with the engagement of key stakeholders through consultative workshops using the Stages of Continuous Improvement tool to measure maturity levels in 5 core domains, 13 components, and 39 subcomponents. A 5-point scale (1=emerging, 2=repeatable, 3=defined, 4=managed, and 5=optimized) was used to measure the DHIS2 implementation maturity level. The national DHIS2 implementation's maturity level is currently at the defined stage (score=2.81) and planned to move to the manageable stage (score=4.09) by 2025. The domain-wise maturity score indicated that except for ICT infrastructure, which is at the repeatable stage (score=2.14), the remaining 4 domains are at the defined stage (score=3). The development of a standardized and basic DHIS2 process at the national level, the development of a 10-year strategic plan to guide the implementation of digital health systems including DHIS2, and the presence of the required competencies at the facility level to accomplish specific DHIS2-related tasks are the major strength of the Ministry of Health of Ethiopia so far. The lack of workforce competency guidelines to support the implementation of DHIS2; the unavailability of core competencies (knowledge, skills, and abilities) required to accomplish DHIS2 tasks at all levels of the health system; and ICT infrastructures such as communication network and internet connectivity at the district, zonal, and regional levels are the major hindrances to effective DHIS2 implementation in the country. On the basis of the Stages of Continuous Improvement maturity model toolkit, the implementation status of DHIS2 in Ethiopia is at the defined stage, with the ICT infrastructure domain being at the lowest stage as compared to the other 4 domains. By 2025, the maturity status is planned to move from the defined stage to the managed stage by improving the identified gaps. Various action points are suggested to address the identified gaps and reach the stated maturity level. The responsible body, necessary resources, and methods of verification required to reach the specified maturity level are also listed.

IoT-WMSN based data communication for wild animal detection using optimized deep learning approach

IoT-WMSN based data communication for wild animal detection using optimized deep learning approach

PIM GPT a hybrid process in memory accelerator for autoregressive transformers

Decoder-only Transformer models such as Generative Pre-trained Transformers (GPT) have demonstrated exceptional performance in text generation by autoregressively predicting the next token. However, the efficiency of running GPT on current hardware systems is bounded by low compute-to-memory-ratio and high memory access. In this work, we propose a Process-in-memory (PIM) GPT accelerator, PIM-GPT, which achieves end-to-end acceleration of GPT inference with high performance and high energy efficiency. PIM-GPT leverages DRAM-based PIM designs for executing multiply-accumulate (MAC) operations directly in the DRAM chips, eliminating the need to move matrix data off-chip. Non-linear functions and data communication are supported by an application specific integrated chip (ASIC). At the software level, mapping schemes are designed to maximize data locality and computation parallelism. Overall, PIM-GPT achieves 41 − 137 × , 631 − 1074 × speedup and 123 − 383 × , 320 − 602 × energy efficiency over GPU and CPU baseline on 8 GPT models with up to 1.4 billion parameters.

Research on Optimization of Communication Network Security and Intelligent Supervision Model in Urban Community Aging Adaptation Renovation

In the process of increasing the number of elderly people, their care and attention have received greater attention, which has made traditional community environments face insufficient data encryption performance and data mining services in the communication network security of aging communities. This has made communication security optimization and intelligent monitoring models in urban community aging adaptation and transformation a focus of research in the field of data communication security. This study delves into the improvement and enhancement of intelligent monitoring models for communication network security in urban communities. Its goal is to improve the security and intelligent monitoring mode of data communication networks during the transformation process through technological innovation and progress in management practices, and further improve the security performance of data communication. This research has solved the communication network security problems existing in intelligent devices and Internet services in the community aging transformation. Through the analysis of improvement strategies, this article compares the basic performance indicators of data encryption, firewall performance, data analysis and mining, and intelligent recommendation in communication network security and intelligent recommendation between different models. The optimization of communication network security in urban community aging adaptation can ensure the data security of terminal devices and further improve and enhance intelligent recommendation services for aging, providing more references and suggestions for communication network security and intelligent monitoring.

5G as Communication Platform for Solar Tower Plants

Wiring of heliostat fields for solar tower plants is a cost factor that becomes more important as the overall cost target is decreasing. Wireless heliostats with radio communication and autarchic energy supply have therefore been proposed in the past. But none of the communication solutions investigated so far could realistically scale to commercial size plants with tens of thousands of heliostats. Moreover, the digitalization of CSP plants with numerous mobile and stationary sensor systems requires a suitable data communication, too. The new generation of mobile radio communication (5G) is capable of handling the heterogenous communication profile portfolio comprising large numbers of units with low data rates – like heliostats - and few units with very large data volume – like e.g. drone-based camera systems. The communication requirements of a typical solar tower installation are assessed in this work and a data traffic model is created for the most relevant communication channels. The various existing 5G implementations are assessed to find the most suitable solution. Different operator models for 5G are considered and their applicability in CSP target countries is discussed. A simulation test case is presented that models the radio communication traffic of a heliostat field control during a cloud passage. Finally, the experimental 5G campus network is introduced that is currently installed at the Solar Tower Jülich research plant and will be operated in the upcoming months to demonstrate the technical feasibility of 5G radio for communication in solar fields.

Smartphone-based markers of social connectivity in schizophrenia and bipolar disorder

Social isolation and social impairment are hallmarks of progression as well as predictors of relapse in psychiatric disorders. We conducted a pilot study to assess the feasibility of sensing the social activity phenotype and loneliness using active and passive markers collected using a smartphone application. The study included 9 schizophrenia and bipolar disorder patients followed in the Bipolar Longitudinal study for at least 1 month and for whom mobile communication data was collected using the Beiwe smartphone application. Subjects completed daily surveys on digital and in-person social activity, and feelings of being outgoing or lonely. We described the level and variability of social activity features. We employed k-means clustering to identify “important contacts”. Further, we investigated whether social network-derived features of mobile communication are independent predictors of weekly counts of outgoing calls and text, weekly average self-reported digital social activity, and loneliness using mixed effect models and clustering with dynamic time warping distance. Subjects were followed between 5 and 208 weeks (number of days of observation = 2538). The k-means cluster analysis approach identified the number of “important contacts” among close friends and family members as reported in clinical interviews. The cluster analysis and longitudinal regression analysis indicate that the number of individuals a person communicates with on their phone is an independent predictor of perceived loneliness, with stronger evidence when “important contacts” only are included. This study provides preliminary evidence that the number of “important contacts” a person communicates with on their phone is a promising marker to capture subjects’ engagement in mobile communication activity and perceived loneliness.

On Some Distance Spectral Characteristics of Trees

Graham and Pollack in 1971 presented applications of eigenvalues of the distance matrix in addressing problems in data communication systems. Spectral graph theory employs tools from linear algebra to retrieve the properties of a graph from the spectrum of graph-theoretic matrices. The study of graphs with “few eigenvalues” is a contemporary problem in spectral graph theory. This paper studies graphs with few distinct distance eigenvalues. After mentioning the classification of graphs with one and two distinct distance eigenvalues, we mainly focus on graphs with three distinct distance eigenvalues. Characterizing graphs with three distinct distance eigenvalues is “highly” non-trivial. In this paper, we classify all trees whose distance matrix has precisely three distinct eigenvalues. Our proof is different from earlier existing proof of the result as our proof is extendable to other similar families such as unicyclic and bicyclic graphs. The main tools which we employ include interlacing and equitable partitions. We also list all the connected graphs on ν ≤ 6 vertices and compute their distance spectra. Importantly, all these graphs on ν ≤ 6 vertices are determined from their distance spectra. We deliver a distance cospectral pair of order 7, thus making it a distance cospectral pair of the smallest order. This paper is concluded with some future directions.

Dual-band hybrid fiber-wireless delivery system for high-speed data communications in CRAN based on polarization multiplexing with switching capabilities

This work presents a Radio-over-Fiber architecture for Cloud Radio Access Networks (CRAN) based on polarization division multiplexing for delivering up-converted high-speed data signals using a single laser-photodetector pair and free of polarization tracking devices. This system enables the allocation of data signals in two different bands defined as low-frequency (5–25 GHz) and high-frequency (55–65 GHz) millimeter waves (mmWave). Polarization multiplexing ensures minimal crosstalk when data signals are photonically up-converted while providing all-optical inter-band switching capabilities. Proof-of-concept experiments have been performed by simultaneously transmitting NRZ and multi-carrier modulated (Discrete Multitone - DMT) data signals over a radio-over-fiber link consisting of 1 km of standard optical fiber and radio transmission in the 7.5 GHz (wired) and 60 GHz (wireless) bands, respectively, with an estimated potential total throughput of at least 7.5 Gbps when considering the transmission of NRZ signals in these mmWave carriers. Results exhibit a BER below the FEC limit of 3.8 × 10−3 in all cases analyzed when the received optical power at the remote radio head (RRH) is above −4 dBm. The reduced number of components and versatility of this implementation provide a cost-effective solution for delivering and switching high-speed data signals in RF fronthaul for future 5G/6G communications.

[formula omitted] : An adaptive data aggregation algorithm for IoT infrastructure

In IoT infrastructure, high-frequency sensing and subsequent transmission of sensed data to computational facilities can lead to redundant data storage and processing, consuming significant storage and processing capacity. As a result, the IoT infrastructure needs more data transmission cycles, leading to data redundancy and low network up-time due to the drainage of limited battery capacity. Conversely, if the data is communicated at a lower rate, it may cause absolute data delivery to the processing unit, which is useless. As a result, a well-designed data aggregation algorithm is required. This paper proposes the ADA2−IoT, an Adaptive Data Aggregation Algorithm for IoT Infrastructure tailored to optimize parameters such as low data redundancy, limited data communication cycles, and high IoT infrastructure up times. The proposed algorithm consists of two key components: the Route Data Aggregator (RDA) performs aggregation during data transit towards the Edge node or gateway, and the Node Data Aggregator (NDA) performs data aggregation during capturing or sensing data. The algorithm employs metrics like Age of Information (AoI) and data freshness factor during the node and route data aggregation phase to capture and timely deliver data to the Edge node, where this data is processed for informed decision-making. The proposed algorithm was efficiently tested on a simulation and IoT hardware deployment environment. Both simulation and hardware results demonstrate a substantial improvement in QoS parameters, such as a decrease in data redundancy and packet exchanges, leading to considerable energy savings and prolonging the lifespan of IoT infrastructure.

Using Civic Service Design Methods to Redevelop a Data Communication Website With a Health Literacy Lens.

Public health agencies routinely publish data in hopes that data influence public health policy and practice. However, data websites can often be difficult to use, posing barriers to people trying to access, understand, and use data. Working to make data websites easier to use can add value to public health data communication work. The New York City Department of Health and Mental Hygiene (DOHMH) redesigned its Environment and Health Data Portal, a website used to communicate environmental health data, with the goal of making data more accessible and understandable to a broader audience. The DOHMH used Civic Service Design methods to establish priorities and strategies for the redesign work, to build a data communication website that emphasizes a high level of usability, and content that explains data. By following a Civic Service Design process, the DOHMH synthesized findings from health communications, data visualization and communication, and web usability to create an easy-to-use website with explanations of data and findings alongside datasets. On the new site, automated dataset visualizations are supplemented with narrative content, explanatory content, and custom interactive applications designed to explain data and findings. Web analytics showed that, in its first year of operation, the site's web traffic grew substantially, with the last 12 weeks recording weekly page views 150% higher than the first 12 weeks of operation (7185 average weekly page views compared with 2866 average weekly page views). Two-thirds (66.3%) of page views include recorded user engagement. Additional evaluations to measure specific aspects of usability compared with the previous version of the site are planned. By following a Civic Service Design process, the DOHMH redesigned a vital data communication platform to increase its usability and saw significant increase in engagement in its first year of operations. By designing data material with usability in mind, public health departments have the potential to improve public health data communication work.