Comparison between side-hole optical fiber and a new design of elliptical core fiber has been performed regarding the effect of thermal stress on different polarization. The new design comprises an elliptical core with central airhole and side air-holes. Simulation of these structures was carried out on the basis of finite element method. The analysis shows that both the fibers maintain high birefringence with ultra-flattened dispersion and a wide single polarization bandwidth. In addition, the new design exhibits a higher birefringence and effective area compared to a side-hole fiber and also it is affected less by Raman scattering and produces less loss than that of a side-hole fiber. These findings might serve as a ground for justification of the new design and also for its potential applications.
This paper describes a novel mechanism for joint decoding of the network coded symbols in a multi-way relay node. The mechanism, based on belief propagation algorithm, utilizes the correlation between adjacent network coded symbols to minimize the error propagation problem significantly, compared with previous methods. In case of increasing degree of asynchrony, disjoint decoding exhibits poorer error performance, whereas joint decoding helps to maintain the performance level close to that in the synchronous case both in additive white Gaussian noise and fading channels. Thus, this method adds robustness to the multi-way relay channel against channel imperfections like asynchronism and fading in practical propagation environments.
Abstract This paper has developed an approach to optimise energy sell and price bids at the sellers along with optimising energy purchase decisions at the buyers in a peer-to-peer (P2P) energy trading market. The optimum price and energy sell bids are designed to maximise the profit at the sellers, while buyers make energy purchase decisions to minimise their energy deficit. The proposed approach relies on a day-ahead optimisation mechanism that can utilise the daily generation and demand patterns as well as a rolling horizon based real-time update strategy when there are variations in generation or demand forecasts. The aforementioned approach is evaluated for a real-life generation and demand dataset under different scenarios. The numerical results demonstrate that when the forecasting error is not very high, the proposed optimisation approach can allow sellers to obtain some profit in most of the time intervals during the day.
Anonymity in social media platforms keeps users hidden behind a keyboard. This absolves users of responsibility, allowing them to engage in online rage, hate speech, and other text-based toxicity that harms online well-being. Recent research in the field of Digital Emotion Regulation (DER) has revealed that indulgence in online toxicity can be a result of ineffective emotional regulation (ER). This, we believe, can be reduced by educating users about the consequences of their actions. Prior DER research has primarily focused on exploring digital emotion regulation practises, identifying emotion regulation using multimodal sensors, and encouraging users to act responsibly in online conversations. While these studies provide valuable insights into how users consciously utilise digital media for emotion regulation, they do not capture the contextual dynamics of emotion regulation online. Through interaction design, this work provides an intervention for the delivery of ER support. It introduces a novel technique for identifying the need for emotional regulation in online conversations and delivering information to users in a way that integrates didactic learning into their daily life. By fostering self-reflection in periods of intensified emotional expression, we present a graph-based framework for on-the-spot emotion regulation support in online conversations. Our findings suggest that using this model in a conversation can help identify its influential threads/nodes to locate where toxicity is concentrated and help reduce it by up to 12\%. This is the first study in the field of DER that focuses on learning transfer by inducing self-reflection and implicit emotion regulation.
In this paper, we consider a functional decode and forward (FDF) multi-way relay network (MWRN) where a common user facilitates each user in the network to obtain messages from all other users. We propose a novel user pairing scheme, which is based on the principle of selecting a common user with the best average channel gain. This allows the user with the best channel conditions to contribute to the overall system performance. Assuming lattice code based transmissions, we derive upper bounds on the average common rate and the average sum rate with the proposed pairing scheme. Considering M-ary quadrature amplitude modulation with square constellation as a special case of lattice code transmission, we derive asymptotic average symbol error rate (SER) of the MWRN. We show that in terms of the achievable rates, the proposed pairing scheme outperforms the existing pairing schemes under a wide range of channel scenarios. The proposed pairing scheme also has lower average SER compared to existing schemes. We show that overall, the MWRN performance with the proposed pairing scheme is more robust, compared to existing pairing schemes, especially under worst case channel conditions when majority of users have poor average channel gains.
This paper studies the error propagation phenomenon for a multi-way relay channel based on binary phase shift keying (BPSK) modulation and network coding in the physical layer. Though physical layer network coding enhances the throughput of a multi-way relay system by reducing number of time slots, its error performance can be highly degraded since decision about a user's message depends on previous decisions. In this paper, the error probability of a multi-way relay system is examined through both analytical and numerical methods. We show that the system performance is significantly controlled by signal power and number of users accessing the single relay. Based on numerical analysis, the critical number of users to limit the error rate within an acceptable upper bound is found.
Recently, numerous forecasting models have been reported in the wind power forecasting field, aiming for reliable integration of renewable energy into the electric grid. Decomposition-based hybrid models have gained significant popularity in recent years. These methods generally disaggregate the original time series data into sub-time-series with better stationarity, and then the target data is predicted based on the sub-series. However, existing studies usually utilize future data during the decomposition process and therefore cannot be appropriately employed for real-world applications, due to the inaccessibility of future data. This problem is usually known as the boundary issue. By ignoring the boundary issue during decomposition, the developed decomposition-based forecasting models will inevitably lead to unrealistically high performance than what is practically achievable. These impractical predictions would compromise the scheduling and control decisions made based on them. In light of this, this study provides an in-depth review of decomposition-based models for wind power forecasting, as well as the existing solutions for resolving the boundary issue. We first categorize decomposition-based models with the consideration of the boundary issue, wherein the treatment of the boundary issue varies over different hybrid model architectures (i.e., direct approach and multi-component approach) and decomposition techniques (i.e., empirical mode decomposition, variational mode decomposition, wavelet transform, singular spectrum analysis and hybrid decomposition). Then, we systematically summarize commonly available boundary issue solutions into three categories, namely algorithm-based solutions, sampling-strategy-based solutions and iteration-based solutions. We also evaluate the strengths and limitations of the existing boundary issue solutions and discuss their applicability to different classification of decomposition-based models for wind power forecasting. This study will provide useful references for a wide range of future studies for developing accurate and practical wind power forecasting models.
In this paper, an energy optimisation approach for residential scenario has been investigated with an aim to maximise the utilisation of solar generation to meet the electricity demand of the household appliances. Given that users without energy storage do not have an option to consume the solar generation at a later period, it is important to develop a solution that schedules the deferrable appliances during high solar generation periods. The proposed solution utilises this approach to optimise the operating time of the deferrable appliances in a household, while considering other influential parameters such as weather, solar generation and demand forecasts. The developed approach is implemented for a number of participating users in Geelong, Australia during 2020-2022. The evaluation results demonstrate up to 38.8% savings in energy consumption and up to 48% savings in peak demand over the evaluation period. It can also be observed that the appliances are scheduled during mid-morning to early-afternoon for most of the participants.
Multi-way relay networks (MWRNs) are a growing research area in the field of relay based wireless networks. Such networks provide a pathway for solving the ever increasing demand for higher data rate and spectral efficiency in a general multi-user scenario. MWRNs have potential applications in video conferencing, file sharing in a social network, as well as satellite networks and sensor networks. Recent research on MWRNs focuses on efficient transmission protocol design by harnessing different network coding schemes, higher dimensional structured codes and advanced relaying protocols. However, the existing research misses out the characterization and analysis of practical issues that influence the performance of MWRNs. Moreover, the existing transmission schemes suffer some significant limitations, that need to be solved for maximizing the benefits of MWRNs. In this thesis, we investigate the practical issues that critically influence the performance of a MWRN and propose solutions that can outperform existing schemes. To be specific, we characterize error propagation phenomenon for additive white Gaussian noise (AWGN) and fading channels with functional decode and forward (FDF) and amplify and forward (AF) relaying protocols, propose a new pairing scheme that outperforms the existing schemes for lattice coded FDF MWRNs in terms of the achievable rate and error performance and finally, analyze the impact of imperfect channel state information (CSI) and optimum power allocation on MWRNs. At first, we analyze the error performance of FDF and AF MWRNs with pairwise transmission using binary phase shift keying (BPSK) modulation in AWGN and Rayleigh fading channels. We quantify the possible error events in an L-user FDF or AF MWRN and derive accurate asymptotic bounds on the probability for the general case that a user incorrectly decodes the messages of exactly k (k ∈ [1, L− 1]) other users. We show that at high signal-to-noise ratio (SNR), the higher order error events (k ≥ 3) are vii
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