Search results for: floating architectures

Authors: Abbas Ebrahimi, Mohammad Zandsalimy

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The main purpose of this study is to investigate the feasibility of using FPGA (Field Programmable Gate Arrays) chips as alternatives for the conventional CPUs to accelerate the numerical solution of the Laplace equation. FPGA is an integrated circuit that contains an array of logic blocks, and its architecture can be reprogrammed and reconfigured after manufacturing. Complex circuits for various applications can be designed and implemented using FPGA hardware. The reconfigurable hardware used in this paper is an SoC (System on a Chip) FPGA type that integrates both microprocessor and FPGA architectures into a single device. In the present study the Laplace equation is implemented and solved numerically on both reconfigurable hardware and CPU. The precision of results and speedups of the calculations are compared together. The computational process on FPGA, is up to 20 times faster than a conventional CPU, with the same data precision. An analytical solution is used to validate the results.

Keywords: accelerating numerical solutions, CFD, FPGA, hardware definition language, numerical solutions, reconfigurable hardware

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Authors: Saeedeh Barzegar Khalilsaraei, Manoocheher Kelarestaghi, Farshad Eshghi

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The vehicle color, as a prominent and stable feature, helps to identify a vehicle more accurately. As a result, vehicle color recognition is of great importance in intelligent transportation systems. Unlike conventional methods which use only a single Convolutional Neural Network (CNN) for feature extraction or classification, in this paper, four CNNs, with different architectures well-performing in different classes, are trained to extract various features from the input image. To take advantage of the distinct capability of each network, the multiple outputs are combined using a stack generalization algorithm as an ensemble technique. As a result, the final model performs better than each CNN individually in vehicle color identification. The evaluation results in terms of overall average accuracy and accuracy variance show the proposed method’s outperformance compared to the state-of-the-art rivals.

Keywords: Vehicle Color Recognition, Ensemble Algorithm, Stack Generalization, Convolutional Neural Network

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Authors: Zine-Eddine Bouras, Abdelouaheb Talai

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During the last two decades, the software evolution community has intensively tackled the software merging issue whose main objective is to merge in a consistent way different versions of software in order to obtain a new version. Well-established approaches, mainly based on the dependence analysis techniques, have been used to bring suitable solutions. These approaches concern the source code or software architectures. However, these solutions are more expensive due to the complexity and size. In this paper, we overcome this problem by operating at a high level of abstraction. The objective of this paper is to investigate the software merging at the level of UML activity diagrams, which is a new interesting issue. Its purpose is to merge activity diagrams instead of source code. The proposed approach, based on dependence analysis techniques, is illustrated through an appropriate case study.

Keywords: activity diagram, activity diagram slicing, dependency analysis, software merging

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Authors: Julia Ive, Lucia Specia

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Understanding and detecting toxicity is an important problem to support safer human interactions online. Our work focuses on the important problem of contextual toxicity detection, where automated classifiers are tasked with determining whether a short textual segment (usually a sentence) is toxic within its conversational context. We use “toxicity” as an umbrella term to denote a number of variants commonly named in the literature, including hate, abuse, offence, among others. Detecting toxicity in context is a non-trivial problem and has been addressed by very few previous studies. These previous studies have analysed the influence of conversational context in human perception of toxicity in controlled experiments and concluded that humans rarely change their judgements in the presence of context. They have also evaluated contextual detection models based on state-of-the-art Deep Learning and Natural Language Processing (NLP) techniques. Counterintuitively, they reached the general conclusion that computational models tend to suffer performance degradation in the presence of context. We challenge these empirical observations by devising better contextual predictive models that also rely on NLP data augmentation techniques to create larger and better data. In our study, we start by further analysing the human perception of toxicity in conversational data (i.e., tweets), in the absence versus presence of context, in this case, previous tweets in the same conversational thread. We observed that the conclusions of previous work on human perception are mainly due to data issues: The contextual data available does not provide sufficient evidence that context is indeed important (even for humans). The data problem is common in current toxicity datasets: cases labelled as toxic are either obviously toxic (i.e., overt toxicity with swear, racist, etc. words), and thus context does is not needed for a decision, or are ambiguous, vague or unclear even in the presence of context; in addition, the data contains labeling inconsistencies. To address this problem, we propose to automatically generate contextual samples where toxicity is not obvious (i.e., covert cases) without context or where different contexts can lead to different toxicity judgements for the same tweet. We generate toxic and non-toxic utterances conditioned on the context or on target tweets using a range of techniques for controlled text generation(e.g., Generative Adversarial Networks and steering techniques). On the contextual detection models, we posit that their poor performance is due to limitations on both of the data they are trained on (same problems stated above) and the architectures they use, which are not able to leverage context in effective ways. To improve on that, we propose text classification architectures that take the hierarchy of conversational utterances into account. In experiments benchmarking ours against previous models on existing and automatically generated data, we show that both data and architectural choices are very important. Our model achieves substantial performance improvements as compared to the baselines that are non-contextual or contextual but agnostic of the conversation structure.

Keywords: contextual toxicity detection, data augmentation, hierarchical text classification models, natural language processing

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Authors: X. Gumara Rigol, I. Martínez de Apellaniz Anzuola, A. Garcia Serrano, A. Franzi Cros, O. Vidal Calbet, A. Al Maruf

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Traditional federated architectures for data warehousing work well when corporations have existing regional data warehouses and there is a need to aggregate data at a global level. Schibsted Media Group has been maturing from a decentralised organisation into a more globalised one and needed to build both some of the regional data warehouses for some brands at the same time as the global one. In this paper, we present the architectural alternatives studied and why a custom federated approach was the notable recommendation to go further with the implementation. Although the data warehouses are logically federated, the implementation uses a single database system which presented many advantages like: cost reduction and improved data access to global users allowing consumers of the data to have a common data model for detailed analysis across different geographies and a flexible layer for local specific needs in the same place.

Keywords: data integration, data warehousing, federated architecture, Online Analytical Processing (OLAP)

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Authors: Jayson Vicente

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The young men and women of Philippine Military Academy Cadet Corps Armed Forces of the Philippines (PMA CCAFP) are bred to be front liners and last line of defense during war and times of peace; as such, they must be equipped with the most practical and most effective combat-ready Martial Arts and Combative skills to effectively fulfill their duty, as well as to protect and safeguard themselves to continue serving the people and their country. This study shall assess the current Martial Arts and Combative Program of the PMA CCAFP using descriptive methodology by interviews and floating questionnaires. The current Martial Arts and Combative Program of the PMA CCAFP with all of the subjects involved are more sports inclined rather than combat-equipped. Picking the best from each subject used in the program, this study seeks to recommend improvements or create a better Martial Arts and Combative Program that will satisfy the objective of producing Martial Arts combatant graduates. A good Martial Arts and Combative Program for PMA is essential to prepare them for what lies ahead, which is unforgiving and no rules to pacify threat.

Keywords: combative, martial arts, military, program

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Authors: Sadhvi Gupta, Charulatha S.

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Since the dawn of the early 1950s humans have launched numerous vehicles in space. Be it from rockets to rovers humans have done tremendous growth in the technology sector. While there is mostly upside for it for humans the only major downside which cannot be ignored now is the amount of junk produced in space due to it i.e. space debris. All this space junk amounts from objects we launch from earth which so remains in orbit until it re-enters the atmosphere. Space debris can be of various sizes mainly the big ones are of the dead satellites floating in space and small ones can consist of various things like paint flecks, screwdrivers, bolts etc. Tracking of small space debris whose size is less than 10 cm is impossible and can have vast implications. As the amount of space debris increases in space the chances of it hitting a functional satellite also increases. And it is extremely costly to repair or recover the satellite once hit by a revolving space debris. So the proposed solution is, Actively removing space debris while keeping space sustainability in mind. For this solution a total of 8 modules will be launched in LEO and in GEO and these models will be placed in their desired orbits through Hohmann transfer and for that calculating ΔV values is crucial. After which the modules will be placed in their designated positions in STK software and thorough analysis is conducted.

Keywords: space debris, Hohmann transfer, STK, delta-V

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Authors: Supaporn Wimonchailerk

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This research aims to study how to design and create the media to promote Thai cuisine. The study used qualitative research methods by using in-depth interview 3 key informants who have experienced in the production of food or cooking shows in television programs with an aspect of acknowledging Thai foods. The results showed that visual presentation is divided into four categories. First, the light meals should be presented in details via the close-up camera with lighting to make the food look more delicious. Then the curry presentation should be arranged a clear and crisp light focus on a colorful curry paste. Besides the vision of hot steam floating from the plate and a view of curry spread on steamed rice can call great attentions. Third, delivering good appearances of the fried or spicy foods, the images must allow the audiences to see the shine of the coat covering the texture of the food and the colorful of the ingredients. Fourth, the presentation of sweets is recommended to focus on details of food design, composition, and layout.

Keywords: media production, television, promote, Thai cuisine

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Authors: Chad Brown

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This paper establishes general conditions for the convergence rates of nonparametric sieve estimators with dependent data. We present two key results: one for nonstationary data and another for stationary mixing data. Previous theoretical results often lack practical applicability to deep neural networks (DNNs). Using these conditions, we derive convergence rates for DNN sieve estimators in nonparametric regression settings with both nonstationary and stationary mixing data. The DNN architectures considered adhere to current industry standards, featuring fully connected feedforward networks with rectified linear unit activation functions, unbounded weights, and a width and depth that grows with sample size.

Keywords: sieve extremum estimates, nonparametric estimation, deep learning, neural networks, rectified linear unit, nonstationary processes

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Authors: Riad

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As present world needs more and more energy in a low cost way, it needs to find out the optimal way of power generation. In the sense of low cost, renewable energy is one of the greatest sources of power generation. Water vapour of sea/river/lake can be used for power generation by using the greenhouse effect in a large flat type water chamber floating on the water surface. The water chamber will always be kept half filled. When water evaporates by sunlight, the high pressured gaseous water will be stored in the chamber. By passing through a pipe and by using aerodynamics it can be used for power generation. The water level of the chamber is controlled by some means. As a large amount of water evaporates, an estimation can be highlighted, approximately 3 to 4 thousand gallons of water evaporates from per acre of surface (this amount will be more by greenhouse effect). This large amount of gaseous water can be utilized for power generation by passing through a pipe. This method can be a source of power generation.

Keywords: renewable energy, greenhouse effect, water chamber, water vapour

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Authors: Adeep Hande, Shubham Agarwal

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This paper presents a study on identifying sexism in online texts using various state-of-the-art deep learning models based on BERT. We experimented with different feature sets and model architectures and evaluated their performance using precision, recall, F1 score, and accuracy metrics. We also explored the use of pseudolabeling technique to improve model performance. Our experiments show that the best-performing models were based on BERT, and their multilingual model achieved an F1 score of 0.83. Furthermore, the use of pseudolabeling significantly improved the performance of the BERT-based models, with the best results achieved using the pseudolabeling technique. Our findings suggest that BERT-based models with pseudolabeling hold great promise for identifying sexism in online texts with high accuracy.

Keywords: large language models, semi-supervised learning, sexism detection, data sparsity

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Authors: Eman I. Raslan, Haitham S. Hamza, Reda A. El-Khoribi

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Fiber-Wireless (FiWi) networks are a promising candidate for future broadband access networks. These networks combine the optical network as the back end where different passive optical network (PON) technologies are realized and the wireless network as the front end where different wireless technologies are adopted, e.g. LTE, WiMAX, Wi-Fi, and Wireless Mesh Networks (WMNs). The convergence of both optical and wireless technologies requires designing architectures with robust efficient and effective bandwidth allocation schemes. Different bandwidth allocation algorithms have been proposed in FiWi networks aiming to enhance the different segments of FiWi networks including wireless and optical subnetworks. In this survey, we focus on the differentiating between the different bandwidth allocation algorithms according to their enhancement segment of FiWi networks. We classify these techniques into wireless, optical and Hybrid bandwidth allocation techniques.

Keywords: fiber-wireless (FiWi), dynamic bandwidth allocation (DBA), passive optical networks (PON), media access control (MAC)

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Authors: Debajyoti Mukherjee, Arathy B. S., Arpita Sahu, Saranga P. Pogula

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A functional model to mimic the functional correctness of a neural network compute accelerator IP is very crucial for design validation. Neural network workloads are based on a Brain Floating Point (BF-16) data type. The major challenge we were facing was the incompatibility of GCC compilers to the BF-16 datatype, which we addressed with a native BF-16 generator integrated into our functional model. Moreover, working with big GEMM (General Matrix Multiplication) or SpMM (Sparse Matrix Multiplication) Work Loads (Dense or Sparse) and debugging the failures related to data integrity is highly painstaking. In this paper, we are addressing the quality challenge of such a complex neural network accelerator design by proposing a functional model-based scoreboard or software model using SystemC. The proposed functional model executes the assembly code based on the ISA of the processor IP, decodes all instructions, and executes as expected to be done by the DUT. The said model would give a lot of visibility and debug capability in the DUT, bringing up micro-steps of execution.

Keywords: ISA, neural network, Brain Float-16, DUT

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Authors: Mamdouh Yousef Saleh, Medhat Hosny El-Zahar, Shymaa El-Dosoky

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In most cases, the associated processing cost of dewatering sludge increase with the solid particles concentration. All experiments in this study were conducted on biological sludge type. All experiments help to reduce the greenhouse gases in addition, the technology used was faster in time and less in cost compared to other methods. First, the bubbling pressure was used to dissolve N₂ gas into the sludge, second alum was added to accelerate the process of coagulation of the sludge particles and facilitate their flotation, and third nitrogen gas was used to help floating the sludge particles and reduce the processing time because of the nitrogen gas from the inert gases. The conclusions of this experiment were as follows: first, the best conditions were obtained when the bubbling pressure was 0.6 bar. Second, the best alum dose was determined to help the sludge agglomerate and float. During the experiment, the best alum dose was 80 mg/L. It increased concentration of the sludge by 7-8 times. Third, the economic dose of nitrogen gas was 60 mg/L with separation efficiency of 85%. The sludge concentration was about 8-9 times. That happened due to the gas released tiny bubbles which adhere to the suspended matter causing them to float to the surface of the water where it could be then removed.

Keywords: nitrogen gas, biological treatment, alum, dewatering sludge, greenhouse gases

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Authors: Roukaya Dalbouchi, Abdelkrin Zitouni

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In this paper, we propose a novel design methodology for Dynamic Partial Reconfigurable (DPR) system. This type of system has the property of being able to be modified after its design and during its execution. The suggested design methodology is generic in terms of granularity, number of modules, and reconfigurable region and suitable for any type of modern application. It is based on the interconnection between several design stages. The recommended methodology represents a guide for the design of DPR architectures that meet compromise reconfiguration/performance. To validate the proposed methodology, we use as an application a video watermarking. The comparison result shows that the proposed methodology supports all stages of DPR architecture design and characterized by a high abstraction level. It provides a dynamic/partial reconfigurable architecture; it guarantees material efficiency, the flexibility of reconfiguration, and superior performance in terms of frequency and power consumption.

Keywords: dynamically reconfigurable system, block matching algorithm, partial reconfiguration, motion vectors, video watermarking

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Authors: Farzingohar Mehrnaz, Yasemi Mehran, Esmaili Zinat, Baharlouian Maedeh

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Aboozar (Ardeshir) and Bahregansar are the two important Iranian oilfields in Persian Gulf waters. The operation activities cause to create spills which impacted on the marine environment. Assumed spills are molded by ADIOS II (Automated Data Inquiry for Oil Spills) which is NOAA’s weathering oil software. Various atmospheric and marine data with different oil types are used for the modeling. Numerous scenarios for 100 bbls with mean daily air temperature and wind speed are input for 5 days. To find the model sensitivity in each setting, one parameter is changed, but the others stayed constant. In both fields, the evaporated and dispersed output values increased hence the remaining rate is reduced. The results clarified that wind speed first, second air temperature and finally oil type respectively were the most effective factors on the oil weathering process. The obtained results can help the emergency systems to predict the floating (dispersed and remained) volume spill in order to find the suitable cleanup tools and methods.

Keywords: ADIOS, modeling, oil spill, sensitivity analysis

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Authors: Kala Bharathan, R. Seshasayanan

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In this paper, a 1-bit Embedded Logic Full Adder (EFA) circuit in transistor level is proposed, which reduces logic complexity, gives low power and high speed. The design is further extended till 64 bits. To evaluate the performance of EFA, a 16, 32, 64-bit both Linear and Square root Carry Select Adder/Subtractor (CSLAS) Structure is also proposed. Realistic testing of proposed circuits is done on 8 X 8 Modified Booth multiplier and comparison in terms of power and delay is done. The EFA is implemented for different multiplier architectures for performance parameter comparison. Overall delay for CSLAS is reduced to 78% when compared to conventional one. The circuit implementations are done on TSMC 28nm CMOS technology using Cadence Virtuoso tool. The EFA has power savings of up to 14% when compared to the conventional adder. The present implementation was found to offer significant improvement in terms of power and speed in comparison to other full adder circuits.

Keywords: embedded logic, full adder, pdp, xor gate

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Authors: Maxwell Henderson, Tristan Cook, Justin Chan Jin Le, Mark Hodson, YoungJung Chang, John Novak, Daniel Padilha, Nishan Kulatilaka, Ansu Bagchi, Sanjoy Ray, John Kelly

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We present a method of using adiabatic quantum optimization (AQO) to solve a mixed integer nonlinear programming (MINLP) problem instance. The MINLP problem is a general form of a set of NP-hard optimization problems that are critical to many business applications. It requires optimizing a set of discrete and continuous variables with nonlinear and potentially nonconvex constraints. Obtaining an exact, optimal solution for MINLP problem instances of non-trivial size using classical computation methods is currently intractable. Current leading algorithms leverage heuristic and divide-and-conquer methods to determine approximate solutions. Creating more accurate and efficient algorithms is an active area of research. Quantum computing (QC) has several theoretical benefits compared to classical computing, through which QC algorithms could obtain MINLP solutions that are superior to current algorithms. AQO is a particular form of QC that could offer more near-term benefits compared to other forms of QC, as hardware development is in a more mature state and devices are currently commercially available from D-Wave Systems Inc. It is also designed for optimization problems: it uses an effect called quantum tunneling to explore all lowest points of an energy landscape where classical approaches could become stuck in local minima. Our work used a novel algorithm formulated for AQO to solve a special type of MINLP problem. The research focused on determining: 1) if the problem is possible to solve using AQO, 2) if it can be solved by current hardware, 3) what the currently achievable performance is, 4) what the performance will be on projected future hardware, and 5) when AQO is likely to provide a benefit over classical computing methods. Two different methods, integer range and 1-hot encoding, were investigated for transforming the MINLP problem instance constraints into a mathematical structure that can be embedded directly onto the current D-Wave architecture. For testing and validation a D-Wave 2X device was used, as well as QxBranch’s QxLib software library, which includes a QC simulator based on simulated annealing. Our results indicate that it is mathematically possible to formulate the MINLP problem for AQO, but that currently available hardware is unable to solve problems of useful size. Classical general-purpose simulated annealing is currently able to solve larger problem sizes, but does not scale well and such methods would likely be outperformed in the future by improved AQO hardware with higher qubit connectivity and lower temperatures. If larger AQO devices are able to show improvements that trend in this direction, commercially viable solutions to the MINLP for particular applications could be implemented on hardware projected to be available in 5-10 years. Continued investigation into optimal AQO hardware architectures and novel methods for embedding MINLP problem constraints on to those architectures is needed to realize those commercial benefits.

Keywords: adiabatic quantum optimization, mixed integer nonlinear programming, quantum computing, NP-hard

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Authors: Sajjad Rostami-Sani, Mojtaba Valinataj, Amir-Hossein Khojir-Angasi

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The cache has an important role in the reduction of access delay between a processor and memory in high-performance embedded systems. In these systems, the energy consumption is one of the most important concerns, and it will become more important with smaller processor feature sizes and higher frequencies. Meanwhile, the cache system dissipates a significant portion of energy compared to the other components of a processor. There are some elements that can affect the energy consumption of the cache such as replacement policy and degree of associativity. Due to these points, it can be inferred that selecting an appropriate configuration for the cache is a crucial part of designing a system. In this paper, we investigate the effect of different cache replacement policies on both cache’s performance and energy consumption. Furthermore, the impact of different Instruction Set Architectures (ISAs) on cache’s performance and energy consumption has been investigated.

Keywords: energy consumption, replacement policy, instruction set architecture, multicore processor

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Authors: Micheal Olaolu Arowolo, Muhammad Azam, Fei He, Mihail Popescu, Dong Xu

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As the quantity of biological articles rises, so does the number of biological route figures. Each route figure shows gene names and relationships. Annotating pathway diagrams manually is time-consuming. Advanced image understanding models could speed up curation, but they must be more precise. There is rich information in biological pathway figures. The first step to performing image understanding of these figures is to recognize gene names automatically. Classical optical character recognition methods have been employed for gene name recognition, but they are not optimized for literature mining data. This study devised a method to recognize an image bounding box of gene name as a photo using deep Siamese neural network models to outperform the existing methods using ResNet, DenseNet and Inception architectures, the results obtained about 84% accuracy.

Keywords: biological pathway, gene identification, object detection, Siamese network

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Authors: Tohari Ahmad, Hudan Studiawan, Iwang Aryadinata, Royyana M. Ijtihadie, Waskitho Wibisono

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A manual voting system has been implemented worldwide. It has some weaknesses which may decrease the legitimacy of the voting result. An electronic voting system is introduced to minimize this weakness. It has been able to provide a better result, in terms of the total time taken in the voting process and accuracy. Nevertheless, people may be reluctant to go to the polling location because of some reasons, such as distance and time. In order to solve this problem, mobile voting is implemented by utilizing mobile devices. There are many mobile voting architectures available. Overall, authenticity of the users is the common problem of all voting systems. There must be a mechanism which can verify the users’ authenticity such that only verified users can give their vote once; others cannot vote. In this paper, a challenge response-based authentication is proposed by utilizing properties of the users, for example, something they have and know. In terms of speed, the proposed system provides good result, in addition to other capabilities offered by the system.

Keywords: authentication, data protection, mobile voting, security

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Authors: Mohammed Alaa Alwafaie, Bela Kovacs

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With the rapid emergence and evolution of renewable energy sources like wind and solar power, there is an increasing demand for effective energy harvester architectures. This paper focuses on investigating the concept of energy harvesting using a wheel-motion energy source. The proposed method involves the placement of magnets and copper coils inside the hubcap rod of a wheel. When the wheel is set in motion, following Faraday's Law, the movement of the magnet within the coil induces an electric current. The paper includes an experiment to measure the output voltage of electromagnetics, as well as a numerical simulation to further explore the potential of this energy harvesting approach. By harnessing the rotational motion of wheels, this research aims to contribute to the development of innovative techniques for generating electrical power in a sustainable and efficient manner.

Keywords: harvesting energy, electromagnetic, hubcap rod wheel, magnet movement inside coil, faraday law

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Authors: Hrishi Rakshit, Pooneh Bagheri Zadeh

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This paper investigates the performance of drone classification methods using conventional DCNN with different hyperparameters, when additional drone audio data is embedded in the dataset for training and further classification. In this paper, first a custom dataset is created using different images of drones from University of South California (USC) datasets and Leeds Beckett university datasets with embedded drone audio signal. The three well-known DCNN architectures namely, Resnet50, Darknet53 and Shufflenet are employed over the created dataset tuning their hyperparameters such as, learning rates, maximum epochs, Mini Batch size with different optimizers. Precision-Recall curves and F1 Scores-Threshold curves are used to evaluate the performance of the named classification algorithms. Experimental results show that Resnet50 has the highest efficiency compared to other DCNN methods.

Keywords: drone classifications, deep convolutional neural network, hyperparameters, drone audio signal

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Authors: Mohammad A. Alia

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With the rapid evolution of the internet applications, cloud computing becomes one of today’s hottest research areas due to its ability to reduce costs associated with computing. Cloud is, therefore, increasing flexibility and scalability for computing services in the internet. Cloud computing is Internet based computing due to shared resources and information which are dynamically delivered to consumers. As cloud computing share resources via the open network, hence cloud outsourcing is vulnerable to attack. Therefore, this paper will explore data security of cloud computing by implementing chaotic cryptography. The proposal scenario develops a problem transformation technique that enables customers to secretly transform their information. This work proposes the chaotic cryptographic algorithms have been applied to enhance the security of the cloud computing accessibility. However, the proposed scenario is secure, easy and straightforward process. The chaotic encryption and digital signature systems ensure the security of the proposed scenario. Though, the choice of the key size becomes crucial to prevent a brute force attack.

Keywords: chaos, cloud computing, security, cryptography

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Authors: D. Pinto, L. Castro, M. L. Cruzat, S. Barros, J. Gironás, C. Oberli, M. Torres, C. Escauriaza, A. Cipriano

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Flash floods, together with landslides, are a common natural threat for people living in mountainous regions and foothills. One way to deal with this constant menace is the use of Early Warning Systems, which have become a very important mitigation strategy for natural disasters. In this work, we present our proposal for a pilot Flash Flood Early Warning System for Santiago, Chile, the first stage of a more ambitious project that in a future stage shall also include early warning of landslides. To give a context for our approach, we first analyze three existing Flash Flood Early Warning Systems, focusing on their general architectures. We then present our proposed system, with main focus on the decision support system, a system that integrates empirical models and fuzzy expert systems to achieve reliable risk estimations.

Keywords: decision support systems, early warning systems, flash flood, natural hazard

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Authors: Shathya Duobiene, Gediminas Račiukaitis

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Wireless Sensor Networks (WSNs) are an emerging technology that opens up a new field of research. The significant advance in WSN leads to an increasing prevalence of various monitoring applications and real-time assistance in labs and factories. Selective surface activation induced by laser (SSAIL) is a promising technology that adapts to the WSN design freedom of shape, dimensions, and material. This article proposes and implements a WSN-based temperature and humidity monitoring system, and its deployed architectures made for the monitoring task are discussed. Experimental results of newly developed sensor nodes implemented in university campus laboratories are shown. Then, the simulation and the implementation results obtained through monitoring scenarios are displayed. At last, a convenient solution to keep the WSN alive and functional as long as possible is proposed. Unlike other existing models, on success, the node is self-powered and can utilise minimal power consumption for sensing and data transmission to the base station.

Keywords: IoT, network formation, sensor nodes, SSAIL technology

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Authors: Nidhi Gaur, Padmaja Joshi, Vijay Jain, Rajeev Srivastava

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Web application architecture is important to achieve the desired performance for the application. Performance analysis studies are conducted to evaluate existing or planned systems. Web applications are used by hundreds of thousands of users simultaneously, which sometimes increases the risk of server failure in real time operations. We use Coloured Petri Net (CPN), a very powerful tool for modelling dynamic behaviour of a web application system. CPNs extend the vocabulary of ordinary Petri nets and add features that make them suitable for modelling large systems. The major focus of this work is on server side of web applications. The presented work focuses on modelling restructuring aspects, with major focus on concurrency and architecture, using CPN. It also focuses on bringing out the appropriate architecture for web and database servers given the number of concurrent users.

Keywords: coloured Petri Nets (CPNs), concurrent users, per- formance modelling, web application architecture

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Authors: Debajyoti Mukherjee, Arathy B. S., Arpita Sahu, Saranga P. Pogula

Abstract:

A Functional Model to mimic the functional correctness of a Neural Network Compute Accelerator IP is very crucial for design validation. Neural network workloads are based on a Brain Floating Point (BF-16) data type. The major challenge we were facing was the incompatibility of gcc compilers to BF-16 datatype, which we addressed with a native BF-16 generator integrated to our functional model. Moreover, working with big GEMM (General Matrix Multiplication) or SpMM (Sparse Matrix Multiplication) Work Loads (Dense or Sparse) and debugging the failures related to data integrity is highly painstaking. In this paper, we are addressing the quality challenge of such a complex Neural Network Accelerator design by proposing a Functional Model-based scoreboard or Software model using SystemC. The proposed Functional Model executes the assembly code based on the ISA of the processor IP, decodes all instructions, and executes as expected to be done by the DUT. The said model would give a lot of visibility and debug capability in the DUT bringing up micro-steps of execution.

Keywords: ISA (instruction set architecture), NN (neural network), TLM (transaction-level modeling), GEMM (general matrix multiplication)

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Authors: Oussama Yadine, Abdellah Jamali

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The rapid evolution of today's technology ecosystem, marked by the fusion of cloud computing, IoT, and distributed systems, has introduced complex security challenges in network and systems administration. Our research develops a framework that seamlessly merges contemporary systems administration with advanced security engineering methodologies, particularly focusing on DevSecOps implementation and zero-trust architectural principles. Comprehensive testing and analysis across diverse organizational environments reveal that this unified approach achieves remarkable results: a 47% decrease in security-related incidents while consistently maintaining 99.9% system uptime. The framework delivers actionable guidelines for deploying secure infrastructure architectures, automating compliance oversight, and implementing dynamic security protocols. This integration effectively eliminates the historical divide between systems administration and security engineering, fostering an environment where operational efficiency and security resilience coexist harmoniously.

Keywords: network security, systems administration, security engineering, infrastructure resilience

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Authors: Rabindranath Bag, Surjeet Singh

Abstract:

Materials in which the electrons are strongly correlated provide some of the most challenging and exciting problems in condensed matter physics today. After the discovery of high critical temperature superconductivity in layered or two-dimensional copper oxides, many physicists got attention in cuprates and it led to an upsurge of interest in the synthesis and physical properties of copper-oxide based material. The quest to understand superconducting mechanism in high-temperature cuprates, drew physicist’s attention to somewhat simpler compounds consisting of spin-chains or one-dimensional lattice of coupled spins. Low-dimensional quantum magnets are of huge contemporary interest in basic sciences as well emerging technologies such as quantum computing and quantum information theory, and heat management in microelectronic devices. Spin ladder is an example of quasi one-dimensional quantum magnets which provides a bridge between one and two dimensional materials. One of the examples of quasi one-dimensional spin-ladder compounds is Sr14Cu24O41, which exhibits a lot of interesting and exciting physical phenomena in low dimensional systems. Very recently, the ladder compound Sr14Cu24O41 was shown to exhibit long-distance quantum entanglement crucial to quantum information theory. Also, it is well known that hole-compensation in this material results in very high (metal-like) anisotropic thermal conductivity at room temperature. These observations suggest that Sr14Cu24O41 is a potential multifunctional material which invites further detailed investigations. To investigate these properties one must needs a large and high quality of single crystal. But these systems are showing incongruently melting behavior, which brings many difficulties to grow a large and quality of single crystals. Hence, we are using TSFZ (Travelling Solvent Floating Zone) method to grow the high quality of single crystals of the low dimensional magnets. Apart from this, it has unique crystal structure (alternating stacks of plane containing edge-sharing CuO2 chains, and the plane containing two-leg Cu2O3 ladder with intermediate Sr layers along the b- axis), which is also incommensurate in nature. It exhibits abundant physical phenomenon such as spin dimerization, crystallization of charge holes and charge density wave. The maximum focus of research so far involved in introducing defects on A-site (Sr). However, apart from the A-site (Sr) doping, there are only few studies in which the B-site (Cu) doping of polycrystalline Sr14Cu24O41 have been discussed and the reason behind this is the possibility of two doping sites for Cu (CuO2 chain and Cu2O3 ladder). Therefore, in our present work, the crystals (pristine and Cu-site doped) were grown by using TSFZ method by tuning the growth parameters. The Laue diffraction images, optical polarized microscopy and Scanning Electron Microscopy (SEM) images confirm the quality of the grown crystals. Here, we report the single crystal growth, magnetic and transport properties of Sr14Cu24O41 and its lightly doped variants (magnetic and non-magnetic) containing less than 1% of Co, Ni, Al and Zn impurities. Since, any real system will have some amount of weak disorder, our studies on these ladder compounds with controlled dilute disorder would be significant in the present context.

Keywords: low-dimensional quantum magnets, single crystal, spin-ladder, TSFZ technique

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