Fast Communication Channels Research Articles

Healico—a wound care assistant in your pocket

Non-optimal wound management and late referral to specialised units negatively impacts patient prognosis and quality of life, as well as healthcare costs. Healico is a new mobile application (app), created in the wound care field, in response to the challenges and difficulties encountered by health professionals (HPs) who deal with patients with wounds on a daily basis. This article aims to describe how this new app was developed, how it works, as well as the real-life clinical benefits and evidence supporting its use. The Healico App assists nurses, physicians and other HPs by: supporting a holistic approach to patient management; facilitating wound assessment and documentation, irrespective of where care is provided (primary, specialised or hospital services, in either public or private institutions); and supporting consistent and safe clinical practice, as well as reducing variation in care. It also provides a fast, fluid and secure communication channel, and effective coordination between HPs, supporting early interventions. The app has also been shown to improve therapeutic adherence of patients by promoting inclusive dialogue with them.

What drives selection decisions of private universities in Jakarta?

In many circumstances, the issues of competitiveness and selection decisions are becoming more important as a strategic focal point of the institutional function in higher education (HE). With the implementation of a social market economy in Indonesia, as an intermediary stage between socialism and capitalism, the need to incorporate competitiveness within an institutional role becomes more pressing. Despite the fact that private higher education in Indonesia grew steadily, there has been no published research on the competitiveness of private higher education. The purpose of this study is to examine the assessment of the competitiveness and selection decision of private universities (PTS) and the implications for the decision to attend a private university. The sample size is 300 students who choose to continue their education at PTS as part of the Higher Education Service Institute (LLDIKTI) Region III Jakarta. We employed the Analytical Hierarchy Process (AHP), the SWOT analysis, and the Business Model Canvas. The AHP results showed that a variety of variables could be utilized to choose private universities in LLDIKTI Region III Jakarta. Meanwhile, the SWOT analysis findings were located in the third quadrant (defense). In this situation, private universities have a big market opportunity, yet there are hurdles inside the private universities themselves. As a result, the PTS strategy should focus on minimizing internal PTS issues in order to capture the greater possibilities to compete with other PTS in LLDIKTI Region III Jakarta. Furthermore, the results of the business model canvas analysis proved that optimizing the role of websites, social media, alumni, scholarships, and competitiveness was expected to be able to make private universities superior and of high quality through the provision of accurate, fast, and easy information and communication channels that followed the technological developments and needs, as well as customer segments.

Fuzzy Logic Based Handover Authentication in 5g Telecommunication Heterogeneous Networks

Under various deployment circumstances, fifth-generation (5G) telecommunications delivers improved network compound management with fast communication channels. Due to the introduction of the Internet of Things (IoT) in data management, the majority of the ultra-dense network models in 5G networks frequently have decreased spectral efficiency, weak handover management, and vulnerabilities. The majority of traditional handover authentication models are seriously threatened, making them vulnerable to a variety of security attacks. The authentication of networked devices is the most important issue. Therefore, a model that incorporates the handover mechanism and authentication model must be created. This article uses a fuzzy logic model to create a handover and key management system that focuses on cloud handover management and authentication performance. In order to decrease delays in 5G networks, the fuzzy logic is built with multiple criteria that aim to reduce the number of executed handovers and target cell selection. The simulation is run to evaluate the model’s performance in terms of latency, spatial complexity, and other metrics related to authentication attack validation.

Switching Strategy for Connected Vehicles Under Variant Harsh Weather Conditions

With the development of 5G networks and advanced communication technologies, connected vehicles (CV) are becoming an increasingly important aspect of the future of transportation. The connected vehicles will usually generate a large amount of data that require fast and reliable communication channels with low latency. 5G millimeter-wave (mmWave) is crucial for the next generation of vehicle-to-vehicle (V2V) communications in CV scenarios. However, harsh weather conditions such as rain, snow, dust, and sand can significantly impact the performance of 5G mmWave channels for V2V communications. Maintaining seamless connections for connected vehicles during harsh weather conditions is a significant challenge that researchers must address. In this paper, we propose a two-stage strategy enabling connected vehicles to operate effectively under moderate and severe weather conditions. Our proposed approach involves a prediction step, which uses machine learning techniques to forecast weather patterns and determine the optimal communication strategy, followed by a switching step, which seamlessly chooses between frequency or channel switch based on the prediction. By incorporating these two steps, we aim to provide a robust and efficient communication system that can adapt to different weather conditions. The NS3 simulation results show that our switching strategy is effective and can benefit the field of connected vehicle technology.

THE ROLE OF ENGLISH AS GLOBAL LANGUAGE

English is now used as a lingua franca, most of the native and non-native speakers of English are using English as their mode of communication in their business matters or business organizations. English language is spread all over the world into almost all the fields such as science, engineering and technology, medicine, trade and commerce, scientific research, education, tourism, internet, banking, business, advertising, film industry, transportation, pharmacy and to name a few. The international expansion of English has been predicted and promoted regularly by leaders on both sides of the Atlantic over the past 200 years. In October 1934 the Carnegie Foundation sponsored a conference in New York on ‘The use of English as a world language’. ELF is used in a number of fields in which English is used as the mutual language of choice, such as science, politics, business, and education. The materials of higher education in English is to make it available for the learners and the teachers wherever they are located on this earth, most of the students, teachers and research scholars make use of this opportunity to enhance their qualification as well as knowledge. The widespread of English as an international language and with the advent of the internet as a mode of fast communication channel which has no boundaries, both are mutually enforcing novel trends and bringing tremendous changes in this age of globalization. English is the only language that is internationally spoken and accepted. Furthermore, the impact of the English language on the internet has been disclosed above and it also has been proved that there are many websites using English as their medium of communication. Even though there are certain fields that are not mentioned, it seems that English plays a dominant in those areas also.

Cascaded Smart Gate Drivers for Modular Multilevel Converters Control: A Decentralized Voltage Balancing Algorithm

Recent Modular Multilevel Converter (MMC) topology allows for drastic improvements in power electronic conversion such as higher energy quality, lower power semiconductors electrical stress, decreased Electro-Magnetic Interferences (EMI), and reduced switching losses. MMC is widely used in High Voltage Direct-Current (HVDC) transmissions as it offers, theoretically, no voltage limit. However, its control electronic structure is not modular itself. Especially, the insulation voltage between the submodule gate drivers’ primaries and secondaries depends on the number of submodules. The converter voltage levels cannot be increased without designing all gate driver isolations again. To solve that issue, the novel concept of distributed galvanic insulation is introduced for multilevel converters. The submodule’s gate drivers are daisy-chained, which naturally reduces the insulation voltage to the submodule capacitor voltage, regardless of the number of submodules. The MMC becomes truly modular as the number of submodules can be increased without impacting on the previous control electronic circuit. Such an innovative control structure weakens the link between the main control unit and the gate drivers. This inherent structural problem can be solved through the use of Smart-Gate Drivers (SGD), as they are often equipped with fast and bidirectional communication channels, while highly increasing the converter reliability. The innovation proposed in that work is the involvement of smart gate drivers in the distributed galvanic insulation-based MMC control and monitoring. First, the numerous benefits of smart gate drivers are discussed. Then, an innovative Voltage Balancing Algorithm directly integrated on the chained gate drivers is proposed and detailed. It features a tunable parameter, offering a trade-off between accurate voltage balancing and execution time. The proposed embedded algorithm features a low execution time due to simultaneous voltage comparisons. Such an algorithm is executed by the gate drivers themselves, relieving the main control unit in an original decentralized control scheme. A simulation model of a multi-megawatts three-phase grid-tied MMC inverter is realized, allowing validation of the proposed algorithm. Matlab/Simulink logic blocs allow us to simulate a typical CPLD/FPGA component, often embedded on smart gate drivers. The converter with the proposed embedded algorithm is simulated in steady-state and during load impact. The controlled delay and slew rate inferred by the algorithm do not disturb the converter behavior, allowing its conceptual validation.

Twitter Event Summarization by Exploiting Semantic Terms and Graph Network

Twitter is a fast communication channel for gathering and spreading breaking news, and it generates a large volume of tweets for most events. Automatically creating a summary for an event is necessary and important. In this study, we explored two extractive approaches for summarizing events on Twitter. The first one exploits the semantic types of event related terms, and ranks the tweets based on the score computed from these semantic terms. The second one utilizes a graph convolutional network built from a tweet relation graph to generate tweet hidden features for tweet salience estimation. And the most salient tweets are selected as the summary of the event. Our experiments show that these two approaches outperform the compared methods.

COVID-19: cross-border contact tracing in Germany, February to April 2020.

IntroductionThe Robert Koch Institute (RKI) managed the exchange of cross-border contact tracing data between public health authorities (PHA) in Germany and abroad during the early COVID-19 pandemic.AimWe describe the extent of cross-border contact tracing and its challenges.MethodsWe analysed cross-border COVID-19 contact tracing events from 3 February to 5 April 2020 using information exchanged through the European Early Warning Response System and communication with International Health Regulation national focal points. We described events by PHA, number of contacts and exposure context.ResultsThe RKI processed 467 events, initiating contact to PHA 1,099 times (median = 1; interquartile range (IQR): 1–2) and sharing data on 5,099 contact persons. Of 327 (70%) events with known exposure context, the most commonly reported exposures were aircraft (n = 64; 20%), cruise ships (n = 24; 7%) and non-transport contexts (n = 210; 64%). Cruise ship and aircraft exposures generated more contacts with authorities (median = 10; IQR: 2–16, median = 4; IQR: 2–11) and more contact persons (median = 60; IQR: 9–269, median = 2; IQR: 1–3) than non-transport exposures (median = 1; IQR: 1–6 and median = 1; IQR: 1–2). The median time spent on contact tracing was highest for cruise ships: 5 days (IQR: 3–9).ConclusionIn the COVID-19 pandemic, cross-border contact tracing is considered a critical component of the outbreak response. While only a minority of international contact tracing activities were related to exposure events in transport, they contributed substantially to the workload. The numerous communications highlight the need for fast and efficient global outbreak communication channels between PHA.

Identification of Fast Time-varying Communication Channels Using the Preestimation Technique

Abstract Accurate identification of stochastic systems with fast-varying parameters is a challenging task which cannot be accomplished using model-free estimation methods, such as weighted least squares, which assume only that system coefficients can be regarded as locally constant. The current state-of-the-art solutions are based on the assumption that system parameters can be locally approximated by a linear combination of appropriately chosen basis functions. The paper shows that tracking performance of the resulting local basis function estimation algorithms can be further improved by means of regularization. The method is illustrated by an important recent application - identification of fast time-varying acoustic channels used in underwater communication.

Hardware-In-the-Loop Simulation to Test Advanced Automation Devices in Power Distribution Networks

The sustainable development of power distribution systems must evolve into smart grids, where advanced automation with fast communication channels is essential. The analysis of their behavior uses the Hardware-In-the-Loop simulation for studying normal and critical operating conditions. In this work, we propose a hybrid technique for transient simulation in distribution systems by combining the high sample rate of the time domain models for voltage profile and electrical current monitoring with the processing speed of algorithms that operate the quasi-stationary, or permanent, phasor models. The proposed simulation platform is also based on the state of the art of standardized communication protocols of the power system. Its evaluation is performed using the comparison with specialized commercial software to assess the transient simulation. The time overcurrent protection function and the verification of messages exchanged between the simulator and the tested device highlights the applicability of the proposed methodology.

Feasibility of reinforcement learning for UAV-based target searching in a simulated communication denied environment

Target searching is crucial in real-world scenarios such as search and rescue in disaster sites and battlefield target reconnaissance. Unmanned aerial vehicles (UAVs) are an ideal technical solution for target searching in large-scale and high-risk areas because they are agile, low cost, and able to collaborate and carry different sensors. In complex scenarios like battlefields, due to the lack of communication infrastructures and the intensive interference, UAVs often operate in communication denied environments. As a result, fast and reliable communication channels between UAVs and ground operators are difficult to establish. Thus, in such conditions, UAVs must be able to complete tasks autonomously and intelligently, without receiving real-time commands from the operators. With the rapid advances in artificial intelligence, reinforcement learning has shown potentiality for solving continuous decision problems. The target searching problem studied in this paper falls into this category and is suitable for adopting reinforcement learning technologies. However, the feasibility of reinforcement learning in UAV-based target searching in communication denied environments is not clear and, thus, requires in-depth investigations. As a pilot study in this direction, this paper models the target searching problem in communication denied and confrontation situations and proposes a simulation environment based on this model. Extensive experiments are conducted to answer the following questions. (1) Can reinforcement learning be applied in target searching by multi-UAVs in communication denied environments? (2) What are the advantages and disadvantages of different reinforcement learning algorithms in solving this problem? (3) How the degree of communication denial influences the performance of these algorithms? The current mainstream reinforcement learning technologies are adopted to perform simulations, whose results are analyzed quantitatively, leading to the following observations. (1) Reinforcement learning can effectively solve target searching problems for multi-UAVs in communication denied environments. (2) Compared with other algorithms, an autonomous decision-making UAV cluster based on a deep Q-network (DQN)exhibits the best problem-solving ability. (3) The algorithm performance changes with the degree of communication denial but remains largely stable when the communication condition varies.

Deep Learning-Based Channel Estimation

In this letter, we present a deep learning algorithm for channel estimation in communication systems. We consider the time–frequency response of a fast fading communication channel as a 2D image. The aim is to find the unknown values of the channel response using some known values at the pilot locations. To this end, a general pipeline using deep image processing techniques, image super-resolution (SR), and image restoration (IR) is proposed. This scheme considers the pilot values, altogether, as a low-resolution image and uses an SR network cascaded with a denoising IR network to estimate the channel. Moreover, the implementation of the proposed pipeline is presented. The estimation error shows that the presented algorithm is comparable to the minimum mean square error (MMSE) with full knowledge of the channel statistics, and it is better than an approximation to linear MMSE. The results confirm that this pipeline can be used efficiently in channel estimation.

Analisis business model canvas pada perpustakaan Institut Bisnis dan Informatika Stikom Surabaya dalam meningkatkan kualitas perguruan tinggi

Introduction. This study aims to analyze the business model canvas by the Library of Stikom Surabaya by using nine elements. They are customer segments, value propositions, channels, customer relationships, revenue streams, key resources, key activities, key partnerships, dan cost structure.Data Collection Method. The study used qualitative descriptive perspective to examine factual information and emerging practices in the field.Results and Discussions. Students, staff and community should be able to leverage the library services. The value offered was technology-based services and visitor convenience. Channels provided were the library website, email, social media. Customer relationships were built through personal assistance and self service. The key resources were human, technology and facilities. The key activities were learning commons. The key partnerships were internal and external partners. Conclusions. The Library is committed to realising its vision as a superior information center by providing a complete and fast information and communication channel in accordance with technological developments and customer needs.

A Novel Reclosing Scheme for Mitigation of Distributed Generation Effects on Overcurrent Protection

This paper proposes a novel scheme to mitigate distributed generation (DG) effects on existing fuse-recloser protection infrastructure in radial distribution networks. The proposed scheme employs a control unit, variable load bank, and dedicated recloser at the point of common coupling (PCC). It detects the increase in the DG terminal current producing a tipping signal when it exceeds a preset value. The proposed scheme also receives a fault detection signal from the head-end recloser via a fast communication channel. Upon verifying both signals, the scheme disconnects the DG unit from the system. Simultaneously, it connects a transfer impedance at the PCC to operate the DG unit at its prefault load-sharing condition. This allows the DG unit to continue supply to the transfer impedance at the prefault load-sharing condition, that is, maintain operation at the prefault level with no need for immediate shut down. Furthermore, it also allows the DG to maintain its speed and frequency at the prefault levels which, in turn, allows faster reconnection of the DG unit to the system after successful reclosing. For the case of unsuccessful reclosing or the case where the fault is not cleared within the set utility time set frame, the proposed scheme shuts the DG source down. The transfer impedance is selected from a variable load bank and is a function of the prefault load-sharing condition; therefore, a sensitivity survey was performed during the investigations of this paper to cover all possible system operating conditions and its corresponding transfer impedances. In the context of this paper, multiple in-depth time-domain simulations are conducted to ascertain the efficiency of the proposed scheme in mitigating the impact of DG sources on existing overcurrent protection infrastructure. Time-domain simulations have been conducted using a typical distribution network in the EMTP-RV software environment for validation purposes.

Formal security analysis of near field communication using model checking

Near field communication (NFC) is a short-range wireless communication technology envisioned to support a large gamut of smart-device applications, such as payment and ticketing. Although two NFC devices need to be in close proximity to communicate (up to 10 cm), adversaries can use a fast and transparent communication channel to relay data and, thus, force an NFC link between two distant victims. Since relay attacks can bypass the NFC requirement for short-range communication cheaply and easily, it is important to evaluate the security of NFC applications. In this work, we present a general framework that exploits formal analysis and especially model checking as a means of verifying the resiliency of NFC protocol against relay attacks. Toward this goal, we built a continuous-time Markov chain (CTMC) model using the PRISM model checker. Firstly, we took into account NFC protocol parameters and, then, we enhanced our model with networking parameters, which include both mobile environment and security-aware characteristics. Combining NFC specifications with an adversary's characteristics, we produced the relay attack model, which is used for extracting our security analysis results. Through these results, we can explain how a relay attack could be prevented and discuss potential countermeasures.

Statistical Analysis of Network Based Issues and Their Impact on Social Computing Practices in Pakistan

Social computing and online groups have accompanied in a new age of the network, where information, networking and communication technologies are enabling systematized human efforts in primarily innovative ways. The social network communities working on various social network domains face different hurdles, including various new research studies and challenges in social computing. The researcher should try to expand the scope and establish new ideas and methods even from other disciplines to address the various challenges. This idea has diverse academic association, social links and technical characteristics. Thus it offers an ultimate opportunity for researchers to find out the issues in social computing and provide innovative solutions for conveying the information between social online groups on network computing. In this research paper we investigate the different issues in social media like users’ privacy and security, network reliabilities, and desire data availability on these social media, users’ awareness about the social networks and problems faced by academic domains. A huge number of users operated the social networks for retrieving and disseminating their real time and offline information to various places. The information may be transmitted on local networks or may be on global networks. The main concerns of users on social media are secure and fast communication channels. Facebook and YouTube both claimed for efficient security mechanism and fast communication channels for multimedia data. In this research a survey has been conducted in the most populated cities where a large number of Facebook and YouTube users have been found. During the survey several regular users indicate the certain potential issues continuously occurred on these social web sites interfaces, for example unwanted advertisement, fake IDS, uncensored videos and unknown friend request which cause the poor speed of channel communication, poor uploading and downloading data speed, channel interferences, security of data, privacy of users, integrity and reliability of user communication on these social sites. The major issues faced by active users of Facebook and YouTube have been highlighted in this research.

Online Questionnaire for Survey Research: Comparative Study of the Features Available with Free Account of Some Selected Tools

The use of Internet is rapidly increasing as a fast and effective communication channel all over the world. The online survey tools available on Internet are very quick, time and cost saving way for creating surveys, collecting responses and to reach large numbers of geographically distributed people from all over the world compared to paper, in-person, or telephone surveys. This paper presents the analysis of features available with free account of five online survey tools. This paper also discusses the advantages and disadvantages of online survey tools.

Exploring combinations of auditory and visual stimuli for gaze-independent brain-computer interfaces.

For Brain-Computer Interface (BCI) systems that are designed for users with severe impairments of the oculomotor system, an appropriate mode of presenting stimuli to the user is crucial. To investigate whether multi-sensory integration can be exploited in the gaze-independent event-related potentials (ERP) speller and to enhance BCI performance, we designed a visual-auditory speller. We investigate the possibility to enhance stimulus presentation by combining visual and auditory stimuli within gaze-independent spellers. In this study with N = 15 healthy users, two different ways of combining the two sensory modalities are proposed: simultaneous redundant streams (Combined-Speller) and interleaved independent streams (Parallel-Speller). Unimodal stimuli were applied as control conditions. The workload, ERP components, classification accuracy and resulting spelling speed were analyzed for each condition. The Combined-speller showed a lower workload than uni-modal paradigms, without the sacrifice of spelling performance. Besides, shorter latencies, lower amplitudes, as well as a shift of the temporal and spatial distribution of discriminative information were observed for Combined-speller. These results are important and are inspirations for future studies to search the reason for these differences. For the more innovative and demanding Parallel-Speller, where the auditory and visual domains are independent from each other, a proof of concept was obtained: fifteen users could spell online with a mean accuracy of 87.7% (chance level <3%) showing a competitive average speed of 1.65 symbols per minute. The fact that it requires only one selection period per symbol makes it a good candidate for a fast communication channel. It brings a new insight into the true multisensory stimuli paradigms. Novel approaches for combining two sensory modalities were designed here, which are valuable for the development of ERP-based BCI paradigms.

Enhancing unsupervised canonical correlation analysis-based frequency detection of SSVEPs by incorporating background EEG.

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have potential to provide a fast communication channel between human brain and external devices. In SSVEP-based BCIs, Canonical Correlation Analysis (CCA) has been widely used to detect frequency-coded SSVEPs due to its high efficiency and robustness. However, the detectability of SSVEPs differs among frequencies due to a power-law distribution of the power spectra of spontaneous electroencephalogram (EEG) signals. This study proposed a new method based on the fact that changes of canonical correlation coefficients for SSVEPs and background EEG signals follow the same trend along frequency. The proposed method defined a normalized canonical correlation coefficient, the ratio of the canonical correlation coefficient for SSVEPs to the mean of the canonical correlation coefficients for background EEG signals, to enhance the frequency detection of SSVEPs. An SSVEP dataset from 13 subjects was used for comparing classification performance between the proposed method and the standard CCA method. Classification accuracy and simulated information transfer rates (ITR) suggest that, in an unsupervised way, the proposed method could considerably improve the frequency detection accuracy of SSVEPs with little computational effort.

Asymmetrical Round Trip Based Synchronization-Free Localization in Large-Scale Underwater Sensor Networks

Underwater sensor networks (UWSNs) have been proposed for many location-dependent applications such as oceanographic data collection, pollution monitoring, mine reconnaissance, etc. Accurate node localization plays an important role in realizing the potential gains of these applications. Although many localization algorithms have been proposed for terrestrial sensor networks in recent years, it is not feasible to directly use these algorithms in UWSNs since UWSNs lack a fast and reliable communication channel. Further, due to their slow convergence speeds and high communication overhead, distributed localization algorithms designed for small-scale UWSNs are not practical for large-scale underwater sensor systems. To achieve accurate and energy efficient node localization in large-scale UWSNs, an asymmetrical round trip based localization (ARTL) algorithm is proposed in this paper. This algorithm has low computational complexity and excellent scalability. Without time synchronization, this algorithm can achieve highly accurate ranging in large-scale UWSNs. Simulation results demonstrate the effectiveness of our design in terms of both localization accuracy and energy consumption.