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The residential sector contributes a large part of the energy to the global energy balance. To date, housing demand has mostly been uncontrollable and inelastic to grid conditions. Analyzing the performance of a home energy management system requires the creation of various profiles of real-world residential demand, as residential demand is complex and includes multiple factors such as occupancy, climate, user preferences, and appliance types. Average Peak Ratio (A2P) is one of the most important parameters when managing an efficient and cost-effective energy system. At the household level, the larger relative magnitudes of certain energy devices make managing this ratio critical, albeit difficult. Various Demand Response (DR) and Demand Side Management (DSM) systems have been proposed to reduce this ratio to 1. The main ways to achieve this are economic incentives, user comfort modeling and control, or preference-based. In this study, we propose a unique opportunistic social time approach called the Time Utility Based Control Feature (TUBCF), which uses the concept of a utility function from economics to model and control consumer devices. We propose a DR model for residential customers to reduce Peak-to-Average Ratio (PAR) and improve customer satisfaction by eliminating Appliance Wait Time (WTA) during peak periods. For PAR reduction and WTA, we propose a system architecture and mathematical formulation. Our proposed model automatically schedules devices based on their temporal preferences and considers six households with different device types and operational characteristics. Simulation results show that using this strategy can reduce A2P by 80% and improve user comfort during peak hours. [ABSTRACT FROM AUTHOR]

Continuous improvements in very-large-scale integration (VLSI) technology and design software have significantly broadened the scope of digital signal processing (DSP) applications. The use of application-specific integrated circuits (ASICs) and programmable digital signal processors for many DSP applications have changed, even though new system implementations based on reconfigurable computing are becoming more complex. Adaptable platforms that combine hardware and software programmability efficiency are rapidly maturing with discrete wavelet transformation (DWT) and sophisticated computerized design techniques, which are much needed in today’s modern world. New research and commercial efforts to sustain power optimization, cost savings, and improved runtime effectiveness have been initiated as initial reconfigurable technologies have emerged. Hence, in this paper, it is proposed that the DWT method can be implemented on a field-programmable gate array in a digital architecture (FPGA-DA). We examined the effects of quantization on DWT performance in classification problems to demonstrate its reliability concerning fixed-point math implementations. The Advanced Encryption Standard (AES) algorithm for DWT learning used in this architecture is less responsive to resampling errors than the previously proposed solution in the literature using the artificial neural networks (ANN) method. By reducing hardware area by 57%, the proposed system has a higher throughput rate of 88.72%, reliability analysis of 95.5% compared to the other standard methods. [ABSTRACT FROM AUTHOR]

Daily newspapers publish a tremendous amount of information disseminated through the Internet. Freely available and easily accessible large online repositories are not indexed and are in an un-processable format. The major hindrance in developing and evaluating existing/new monolingual text in an image is that it is not linked and indexed. There is no method to reuse the online news images because of the unavailability of standardized benchmark corpora, especially for South Asian languages. The corpus is a vital resource for developing and evaluating text in an image to reuse local news systems in general and specifically for the Urdu language. Lack of indexing, primarily semantic indexing of the daily news items, makes news items impracticable for any querying. Moreover, the most straightforward search facility does not support these unindexed news resources. Our study addresses this gap by associating and marking the newspaper images with one of the widely spoken but under-resourced languages, i.e., Urdu. The present work proposed a method to build a benchmark corpus of news in image form by introducing a web crawler. The corpus is then semantically linked and annotated with daily news items. Two techniques are proposed for image annotation, free annotation and fixed cross examination annotation. The second technique got higher accuracy. Build news ontology in protégé using OntologyWeb Language (OWL) language and indexed the annotations under it. The application is also built and linked with protégé so that the readers and journalists have an interface to query the news items directly. Similarly, news items linked together will provide complete coverage and bring together different opinions at a single location for readers to do the analysis themselves. [ABSTRACT FROM AUTHOR]

English to Urdu machine translation is still in its beginning and lacks simple translation methods to provide motivating and adequate English to Urdu translation. In order tomake knowledge available to the masses, there should be mechanisms and tools in place to make things understandable by translating from source language to target language in an automated fashion. Machine translation has achieved this goal with encouraging results. When decoding the source text into the target language, the translator checks all the characteristics of the text. To achieve machine translation, rule-based, computational, hybrid and neural machine translation approaches have been proposed to automate the work. In this research work, a neural machine translation approach is employed to translate English text into Urdu. Long Short Term Short Model (LSTM) Encoder Decoder is used to translate English to Urdu. The various steps required to perform translation tasks include preprocessing, tokenization, grammar and sentence structure analysis, word embeddings, training data preparation, encoder-decoder models, and output text generation. The results show that the model used in the research work shows better performance in translation. The results were evaluated using bilingual research metrics and showed that the test and training data yielded the highest score sequences with an effective length of ten (10). [ABSTRACT FROM AUTHOR]

Today, road safety remains a serious concern for governments around the world. In fact, approximately 1.35 million people die and 2-50 million are injured on public roads worldwide each year. Straight bends in road traffic are the main cause of many road accidents, and excessive and inappropriate speed in this very critical area can cause drivers to lose their vehicle stability. For these reasons, new solutions must be considered to stop this disaster and save lives. Therefore, it is necessary to study this topic very carefully and use new technologies such as Vehicle Ad Hoc Networks (VANET), Internet of Things (IoT), Multi-Agent Systems (MAS) and Embedded Systems to create a new system to serve the purpose. Therefore, the efficient and intelligent operation of the VANET network can avoid such problems as it provides drivers with the necessary real-time traffic data. Thus, drivers are able to drive their vehicles under correct and realistic conditions. In this document, we propose a speed adaptation scheme for winding road situations. Our proposed scheme is based on MAS technology, the main goal of which is to provide drivers with the information they need to calculate the speed limit they must not exceed in order to maintain balance in dangerous areas, especially in curves. The proposed scheme provides flexibility, adaptability, and maintainability for traffic information, taking into account the state of infrastructure and metering conditions of the road, as well as the characteristics and behavior of vehicles. [ABSTRACT FROM AUTHOR]

A facile approach for developing an interfacial solar evaporator by heat localization of solar-thermal energy conversion at water-air liquid composed by in-situ polymerization of Fe2O3 nanoparticles (Fe2O3@PPy) deposited over a facial sponge is proposed. The demonstrated system consists of a floating solar receiver having a vertically cross-linked microchannel for wicking up saline water. The in situ polymerized Fe2O3@PPy interfacial layer promotes diffuse reflection and its rough black surface allows Omni-directional solar absorption (94%) and facilitates efficient thermal localization at the water/air interface and offers a defect-rich surface to promote heat localization (41.9 °C) and excellent thermal management due to cellulosic content. The self-floating composite foam reveals continuous vapors generation at a rate of 1.52 kg m−2 h−1 under one 1 kW m−2 and profound evaporating efficiency (95%) without heat losses that dissipates in its surroundings. Indeed, long-term evaporation experiments reveal the negligible disparity in continuous evaporation rate (33.84 kg m−2/8.3 h) receiving two sun solar intensity, and ensures the stability of the device under intense seawater conditions synchronized with excellent salt rejection potential. More importantly, Raman spectroscopy investigation validates the orange dye rejection via Fe2O3@PPy solar evaporator. The combined advantages of high efficiency, self-floating capability, multimedia rejection, low cost, and this configuration are promising for producing large-scale solar steam generating systems appropriate for commercial clean water yield due to their scalable fabrication. [ABSTRACT FROM AUTHOR]