School of Engineering & Technology

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    Solidification and Stabilization of Spent Pine cone Biochar using Chemically Bonded Phosphate Cement
    (2023) Tyagi, S; Annachhatre, A
    Spent biochar is produced after adsorption of heavy metal which is hazardous by nature. A suitable disposal technique is required to prevent the leaching of heavy metals from spent biochar into the environment. This study highlights the solidification and stabilization (S/S) of copper loaded spent pine-cone biochar by chemically bonded phosphate cement (CBPC). The response surface methodology (RSM) was used to conduct S/S experiments in order to evaluate the compressive strength of CBPC products. The CBPC samples were prepared by varying biochar content (5-50 wt. %); W:S (0.15-0.3) and curing time(3-28d). Results illustrated that CBPC products containing biochar had higher compressive strength upto 12.8 MPa in comparison to CBPC without biochar i.e., upto 10.8 MPa. XRD and SEM analysis confirmed the presence of K-struvite (MgKPO4.6H2O), copper containing phases (Ca Cu-Si), copper phosphate precipitates (Cu3(PO4)2) and filling of pore spaces by spent biochar. Highest compressive strength of 12.8 MPa was obtained at an optimized biochar content of 25%, W:S of 0.18 and curing time of 28 d. The evaluation of leaching potential by TCLP illustrated that stabilization of Cu (II) upto 99.9% was achieved in CBPC product. The risk assessment study revealed that there is no significant danger due to leaching of heavy metals from final CBPC product indicating that it can be readily disposed in the hazardous landfill sites.
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    Simultaneous Placement of Multiple Rooftop Solar PV Integrated Electric Vehicle Charging Stations for Reliability Benefits
    (2023) Reddy, G; Rani, D; Gope, S; Narayana, B
    Electric Vehicles (EVs) are known to be future mode of transportation because of their environment-friendly nature. The increase in electric vehicle (EV) penetration needs to set up the new charging stations to meet the demand. The EV charging shows the negative impact on distribution system and system failures lead to the unavailability of power to charge EVs. The EVs not charging due to system failure is to be considered but ignored in the previous studies. Incorporating the Vehicle-to-Grid (V2G) technologies into charging station (CS) improves the system reliability. In this paper, solar rooftop PV units are integrated with CSs to overcome the negative impacts of EV charging and further enhance the reliability of the system. To extracts the maximum benefits from the solar PV integrated charging stations (PVCS), optimal placement is done with objective of reliability improvement. EV reliability is evaluated by using a novel index called as expected energy not charged (EENC). The reliability of both distribution system and EVs are considered as objective functions simultaneously, hence, placement problem becomes multi-objective. The optimal placement is done by considering different EV penetration levels. A multi-objective Grasshopper optimization algorithm (MOGOA) is applied to solve the optimal placement problem of PVCS. The EENS value is improved by 6.18% and 13.9% as compared to base case for case 1 and case 2 respectively. The EENC is improved by 11.51% in case 2 as compared to case 1.
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    Simulation of thermal comfort and energy demand in buildings of sub-Himalayan eastern India - Impact of climate change at mid (2050) and distant (2080) future
    (2023-02) Thapa, S; Rijal, H; Pasut, W; Singh, R
    The global warming associated with climate change predicted by the Intergovernmental Panel on Climate Change (IPCC) is expected to deteriorate the indoor climate of free-running buildings. Features like proper orientation and wall thickness are important for the design of a building that is resilient to the impact of climate change. However, the implementation of these features is sometimes difficult, especially in a rugged hilly location. A whole building simulation was per formed using DesignBuilder for an existing 3-storey free running multi-family concrete building located in the sub-Himalayan region of eastern India, for thermal comfort and energy demand during the present and the climate change scenarios of 2050 and 2080. The results show an increasing trend in the indoor operative temperature during the future climatic scenario, with the condition inside the top roof-exposed floor deteriorating the most. A decrease of 59.8% and 81.2% in the annual heating energy and an increase of 221.9% and 467.0% in the annual cooling energy were predicted for the future climate of 2050 and 2080 compared to the present. Para metric analysis performed considering orientation, wall U-value, infiltration rate and window-to wall ratios revealed that the east/south-east facing orientation would perform the best with re gard to overheating due to climate change. Further, the use of autoclaved aerated concrete (AAC) brick is recommended along with the decrease in air infiltration rate and window-to-wall ratio to improve the thermal performance of the indoor environment. In addition, we have also proposed a method to assess the under-cooling of an indoor environment.
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    ProRE: An ACO- based programmer recommendation model to precisely manage software bugs
    (2022-12) Kukkar, A; Lilhore, U; Goyal, N; Sandhu, J; Kumar, A
    The process of assigning bugs to particular programmers is called bug assignment in software engineer ing. The programmer can fix the bugs by applying their knowledge. This research article presents an Ant colony optimization-based programmer recommendation model (ProRE) to manage software bugs pre cisely. The proposed ProRE model performs four operations: data pre-processing, i.e., data Pre processing, extraction, feature selection, and programmer recommendation process. The feature selection stage utilized the Ant colony optimization (ACO) method to determine the appropriate subsets of features from all features. In the programmer recommendation stages, three programmer metrics, i.e., function ality ranking, bug occurrence, and mean Bug fixing time, are utilized for the recommendation assignment. The effectiveness of the proposed programmer recommendation system is assessed using datasets from Mozilla, Eclipse, Firefox, JBoss, and OpenFOAM. It is noted that the proposed model offers a better recom mendation strategy over the other available systems. The simulation findings of the proposed ProRE model are also analyzed with well-known available ML methods, i.e., SVM, NB, and C4.5. It is observed that the recommendation results have improved by an average of 4%, 10%, and 12% compared to SVM, C4.5, and NB-based models. Programmer recommendation software is implemented for allocating the bugs to accurate programmers. It has been found that the proposed ProRE model generates more opti mistic outcomes than existing ones.
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    Pre-Trained Deep Neural Network-Based Features Selection Supported Machine Learning for Rice Leaf Disease Classification
    (2023-04) Aggarwal, M; Khullar, V; Goyal, N
    Rice is a staple food for roughly half of the world’s population. Some farmers prefer rice cultivation to other crops because rice can thrive in a wide range of environments. Several studies have found that about 70% of India’s population relies on agriculture in some way and that agribusiness accounts for about 17% of India’s GDP. In India, rice is one of the most important crops, but it is vulnerable to a number of diseases throughout the growing process. Farmers’ manual identification of these diseases is highly inaccurate due to their lack of medical expertise. Recent advances in deep learning models show that automatic image recognition systems can be extremely useful in such situations. In this paper, we propose a suitable and effective system for predicting diseases in rice leaves using a number of different deep learning techniques. Images of rice leaf diseases were gathered and processed to fulfil the algorithmic requirements. Initially, features were extracted by using 32 pre-trained models, and then we classified the images of rice leaf diseases such as bacterial blight, blast, and brown spot with numerous machine learning and ensemble learning classifiers and compared the results. The proposed procedure works better than other methods that are currently used. It achieves 90–91% identification accuracy and other performance parameters such as precision, Recall Rate, F1-score, Matthews Coefficient, and Kappa Statistics on a normal data set. Even after the segmentation process, the value reaches 93–94% for model EfficientNetV2B3 with ET and HGB classifiers. The proposed model efficiently recognises rice leaf diseases with an accuracy of 94%. The experimental results show that the proposed procedure is valid and effective for identifying rice diseases.
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    Photovoltaic (PV) Parameter Extraction using a Hybrid Algorithm based on Spotted Hyena-Ant Lion Optimization
    (2023-12) Kumar, P; Kumar, M; Bansal, A
    The parameter extraction of Photovoltaic (PV) cell and module is a necessary to simulate and evaluate the performance of the PV system. The parameter extraction is a complex and challenging task due to its non-linear nature. Researchers are used several metaheuristic algorithms to solve the non-linear problem of parameter extraction. However, the demand for most accurate and reliable methods is increasing to get precise estimation of parameters. In this paper, a novel hybrid optimization algorithm is proposed based on the Spotted-Hyena optimization (SHO) and Ant Lion Optimization (ALO). The hybrid method is called as Spotted Hyena – Ant Lion (SH-AL) optimization. The optimization algorithm is applied in two stages. In stage 1, essential parameters are identified and extracted using SHO and passed to stage 2. In stage 2, identified parameters are optimized using ALO for accurate model of PV cell. Different type of PV cells such as thin film, mono and multi crystalline are examined under various irradiance conditions to extract the parameters. The proposed algorithm is validated by comparing the results with other algorithms and proposed algorithm is proved its superiority.
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    Performance Evaluation of Ingenious Crow Search Optimization Algorithm for Protein Structure Prediction
    (2023-05) Alshamrani, A; Saxena, A; Shekhawat, S; Zawbaa, H
    Protein structure prediction is one of the important aspects while dealing with critical diseases. An early prediction of protein folding helps in clinical diagnosis. In recent years, applications of metaheuristic algorithms have been substantially increased due to the fact that this problem is computationally complex and time-consuming. Metaheuristics are proven to be an adequate tool for dealing with complex problems with higher computational efficiency than conventional tools. The work presented in this paper is the development and testing of the Ingenious Crow Search Algorithm (ICSA). First, the algorithm is tested on standard mathematical functions with known properties. Then, the application of newly developed ICSA is explored on protein structure prediction. The efficacy of this algorithm is tested on a bench of artificial proteins and real proteins of medium length. The comparative analysis of the optimization performance is carried out with some of the leading variants of the crow search algorithm (CSA). The statistical comparison of the results shows the supremacy of the ICSA for almost all protein sequences.
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    Novel Real-Time Decision-Based Carrier Tracking for Software-Defined Radios Using M-ARY QAM Modulation
    (2023) Sangwan, A; Marriwala, N
    In this paper, a scalable and real-time decision-based carrier tracking for software defined radio (SDR) for M-ary QAM modulation has been proposed and tested in real-time using the universal software radio peripheral (USRP)-2922. The proposed system provides real-time decision-based carrier tracking for improved efficiency and performance. The real-time transfer of data has been done using the USRP-2922 hardware using the M-ary QAM modulation scheme for data transmission. The novelty of the proposed system is that one can transfer random data (binary bits), text or an image which can be encoded using the desired forward error correction codes (FEC) namely the convolution coding/ Viterbi soft decision decoding and the turbo coding/decoding. The proposed novel SDR transceiver system with real-time decision-based carrier tracking shows an improved performance for different parameters such as the bit error rate (BER) and signal to noise ratio (Eb/No).
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    Need for rating system for assessing sustainability of built environment during construction stage
    (2023-06) Dubey, M; Raj, V; Kumar, M; Garg, V
    Sustainability in construction has gained attention in recent years as the construction industry adversely impacts the environment and green construction has been emphasized. Various rating systems have been devel oped by various organizations across the world like LEED, USA; CASBEE), Japan; BREEMA, UK; GBCSL, Sri Lanka, IGBC, India; etc. to certify construction projects under the green projects category, based on parameters majorly considering three factors environmental, societal & economic impact during the entire span of a project from inception stage to demolition stage. In these sustainability assessment rating systems, the parameters rel evant to the construction phase have not been emphasized except for a few factors which have been considered under pre‐requisite. Though the construction phase has a comparatively shorter duration, it adversely impacts sustainability if considered collectively. So, a holistic approach is needed for sustainability assessment which shall include construction stage‐based sustainability parameters too. In this paper, with a questionnaire survey & interaction with experts, the need for construction‐based sustainability parameters has been discussed for making a holistic sustainability assessment rating system. The survey & interaction data were analyzed with an analytical hierarchical process (AHP) to understand the need for the additional parameter during the con struction stage of the project
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    Modeling and parameters optimization of biocomposite using box-Behnken response surface methodology
    (2023-12) Mittal, M; Phutela, K
    The primary objective of this work includes modeling and optimization of the mechanical properties of natural fiber biocomposites using three-factor, three-level Box-Behnken design (BBD). In this context, the effect of three independent performance parameters; pineapple leaf fiber (PALF) content, fiber length, and polyethylene-grafted-maleic anhydride (MAPE) compatibilizer load have been investigated on the mechanical properties of PALF/HDPE/MAPE biocomposite. The sequential model sum of squares, lack of fit, and normal probability plots showed a good agreement in between the experimental results and those predicted by mathematical models (95% confidence level). The optimization results obtained in Design-Expert software revealed that the most optimal value of tensile strength, tensile modulus, flexural strength, flexural modulus, and impact strength as 32.35 MPa, 1475 MPa, 49.21 MPa, 1659.04 MPa, and 58.24 J/m respectively, at fiber length of 13.67 mm, PALF content of 16.84 wt.%, and MAPE load of 2.95 wt.%. To verify the mathematical models, validation tests were also performed which showed that the response surface methodology (RSM) based BBD and ANOVA tools are adequate for analytically evaluating the performance of biocomposites.
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    Mathematical modeling and parameter analysis of quantum antenna for IoT sensor-based biomedical applications
    (2023-08) Goyal, N; Rana, A; Singh, H; Krishna, R
    In this paper, an equivalent combination of series and parallel R-L-C high-pass filter circuit is derived for a nano (quantum) antenna for the Internet of thing (IoT) based sensors for speedy data or organ image displaying in medical line surgeries. The proposed method utilized the sample frequency behavior of characteristics mode to develop a fundamental building block that superimposes to create the complete response. The resonance frequency, input impedance, and quality factor have been evaluated along with basic and higher-order resonating modes. The relation between quality factor, bandwidth, resonance frequency, and selectivity for higher order, increases the quantum circuits in terms of increased order of a filter, quality factor, and odd and even harmonics factors. Therefore, the basic circuits derivation factor of frequency coefficients are expanded in terms of polynomials and then they are expressed as a simple rational function from which the basic circuit parameters are calculated. In this circuit input impedance of each circuit’s element is complex. The real part of input impedance depends on frequency, depending on the frequency positive or negative value of the resistor, and the imaginary part of impedance modelling an inductor or capacitor due to the value of frequency. At cutoff frequency 511 THz, z11 and VSWR parameters are 34 Ω and 1.11, respectively. The proposed quantum DRA is tested at 5 THz, 10 THz, and 500 THz by calculating the electrical parameters like R, L, C and model performance is quite good as compared to existing ones. The dynamic impedance is dependent on the skin effect and enhances the detailed discussion below. The utilization of optical or quantum DRAs is as optical sensors in biomedical engineering, speedy wireless communication, and optical image solutions. Analyte material has been used for monitoring frequency deviation.
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    Lightweight Federated Learning for Rice Leaf Disease Classification Using Non Independent and Identically Distributed Images
    (2023-08) Aggarwal, M; Khullar, V; Goyal, N
    Rice (Oryza sativa L.) is a vital food source all over the world, contributing 15% of the protein and 21% of the energy intake per person in Asia, where most rice is produced and consumed. However, bacterial, fungal, and other microbial diseases that have a negative effect on the health of plants and crop yield are a major problem for rice farmers. It is challenging to diagnose these diseases manually, especially in areas with a shortage of crop protection experts. Automating disease identifi cation and providing readily available decision-support tools are essential for enabling effective rice leaf protection measures and minimising rice crop losses. Although there are numerous classification systems for the diagnosis of rice leaf disease, no reliable, secure method has been identified that meets these needs. This paper proposes a lightweight federated deep learning architecture while maintaining data privacy constraints for rice leaf disease classification. The distributed client–server design of this framework protects the data privacy of all clients, and by using independent and identically distributed (IID) and non-IID data, the validity of the federated deep learning models was examined. To validate the framework’s efficacy, the researchers conducted experiments in a variety of settings, including conventional learning, federated learning via a single client, as well as federated learning via multiple clients. The study began by extracting features from various pre-trained models, ultimately selecting EfficientNetB3 with an impressive 99% accuracy as the baseline model. Subsequently, experimental results were conducted using the federated learning (FL) approach with both IID and non-IID datasets. The FL approach, along with a dense neural network trained and evaluated on an IID dataset, achieved outstanding training and evaluated accuracies of 99% with minimal losses of 0.006 and 0.03, respectively. Similarly, on a non-IID dataset, the FL approach maintained a high training accuracy of 99% with a loss of 0.04 and an evaluation accuracy of 95% with a loss of 0.08. These results indicate that the FL approach performs nearly as well as the base model, EfficientNetB3, highlighting its effectiveness in handling both IID and non-IID data. It was found that federated deep learning models with multiple clients outperformed conventional pre-trained models. The unique characteristics of the proposed framework, such as its data privacy for edge devices with limited resources, set it apart from the existing classification schemes for rice leaf diseases. The framework is the best alternative solution for the early classification of rice leaf disease because of these additional features.
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    Improvement of power quality of a 200 kW grid-connected PV system
    (2023-09) Saini, M; Khan, S; Sharma, A
    In order to enhance electricity quality, a grid-connected photovoltaic (PV) system simulation is presented in this study. A 200 kW photovoltaic system is integrated to a utility grid and loads. A 25 kV 3-level insulated gate bipolar transistor (IGBT) bridge converter is used. Through this system integration of a renewable energy source with a non-renewable source is achieved. This system also wins over the intermittent nature of the renewable source (solar energy) and non-reliability of conventional sources (utility grid). Power and signal quality for various signals such as ripple factor and total harmonic distortion has improved. The suggested system is investigated using MATLAB/Simulink environment. Total harmonic distortion (THD) in voltage and current, ripple factor correction is also incorporate in the MATLAB model.
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    GMPP Tracking of Solar PV system using Spotted Hyena and Quadratic Approximation based Hybrid Algorithm under Partially shaded conditions
    (2023) Kumar, P; Kumar, M; Bansal, A
    The demand for clean and renewable energy is increasing due to environmental concerns. Photovoltaic (PV) system is very popular from the renewable sources and its usage is increasing. The PV system power transfer efficiency depends on several factors such electrical characteristics of loads, solar irradiation, shading condition and panel temperature. The process of transferring maximum possible power to load from PV system by adjusting the system parameters is called maximum power point tracking (MPPT). Under the partially shaded conditions, multiple power peaks are created in the PV curves and makes the MPPT is a non-convex problem. It is a challenging problem to track the global maximum power point (GMPP) under partially shaded conditions. It is essential to recognize the GMPP and restart the string as early as possible to prevent the physical as well as economical damage. In this paper, a new hybrid algorithm for MPPT is proposed and called as QASHO, a combination of Spotted Hyena optimization (SHO) and Quadrature approximation (QA) technique. The QASHO is used to track the GMPP under different weather conditions such as partial shading. The simulation results are compared with Perturb and Observe (P&O) algorithm and Cuckoo Search optimization (CSO) algorithm and promised for better performance in tracking GMPP. The experimental results are provided to validate the proposed MPPT methodology.
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    Federated Transfer Learning for Rice-Leaf Disease Classification across Multiclient Cross-Silo Datasets
    (2023-09) Aggarwal, M; Khullar, V; Goyal, N; Gautam, R; Singh, A
    Paddy leaf diseases encompass a range of ailments affecting rice plants’ leaves, arising from factors like bacteria, fungi, viruses, and environmental stress. Precision agriculture leverages technologies for enhanced crop production, with disease detection being a vital element. Prompt identification of diseases in paddy leaves is critical for curtailing their propagation and reducing crop damage. However, manually diagnosing paddy diseases in regions with vast agricultural areas and limited experts proves immensely difficult. The utilization of machine learning (ML) and deep learning (DL) for diagnosing diseases in agricultural crops appears to be effective and well-suited for widespread application. These ML/DL methods cannot ensure data privacy, as they involve sharing training data with a central server, overlooking competitive and regulatory considerations. As a solution, federated learning (FL) aims to facilitate decentralized training to tackle the identified limitations of centralized training. This paper utilizes the FL approach for the classification of rice-leaf diseases. The manuscript presents an effective approach for rice-leaf disease classification with a federated architecture, ensuring data privacy. We have compiled an unbalanced dataset of rice-leaf disease images, categorized into four diseases with their respective image counts: bacterial blight (1584), brown spot (1440), blast (1600), and tungro (1308). The proposed method, called federated transfer learning (F-TL), maintains privacy for all connected devices using a decentralized client-server setup. Both IID (independent and identically distributed) and non-IID datasets were utilized for testing the F-TL framework after preprocessing. Initially, we conducted an effectiveness analysis of CNN and eight transfer learning models for rice-leaf disease classification. Among them, MobileNetV2 and EfficientNetB3 outperformed the other transfer-learned models. Subsequently, we trained these models using both IID and non-IID datasets in a federated learning environment. The framework’s performance was assessed through diverse scenarios, comparing it with traditional and federated learning models. The evaluation considered metrics like validation accuracy, loss as well as resource utilization such as CPU and RAM. EfficientNetB3 excelled in training, achieving 99% accuracy with 0.1 loss for both IID and non-IID datasets. MobilenetV2 showed slightly lower training accuracy at 98% (IID) and 90% (non-IID) with losses of 0.4 and 0.6, respectively. In evaluation, EfficientNetB3 maintained 99% accuracy with 0.1 loss for both datasets, while MobilenetV2 achieved 90% (IID) and 97% (non-IID) accuracy with losses of 0.6 and 0.2, respectively. Results indicated the F-TL framework’s superiority over traditional distributed deep-learning classifiers, demonstrating its effectiveness in both single and multiclient instances. Notably, the framework’s strengths lie in its cost-effectiveness and data-privacy assurance for resource-constrained edge devices, positioning it as a valuable alternative for rice-leaf disease classification compared to existing tools.
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    Enhance traffic flow prediction with Real-Time Vehicle Data Integration
    (2023-08) Jain, R; Dhingra, S; Joshi, K; Rana, A; Goyal, N
    This study examines how sophisticated traffic control systems affect traffic flow. These cutting-edge solutions use real-time traffic data to increase road networks’ intelligence. These technologies enable the creation of a smoother and more efficient traffic flow by enhancing traffic signal timings and automatically rerouting cars towards less crowded routes. Notably, these innovations significantly lower air pollution, greenhouse gas emissions, and fuel consumption while also minimizing the financial and time expenses related to traffic congestion. Our unique Real-Time Vehicle Data Integration (RTVDI) algorithm is being used to portray the potential of intelligent traffic control systems. These technologies have the potential to revolutionize traffic management procedures by using real-time data and complex processes. They have the potential to improve commuter safety, increase road efficiency, and improve traffic flow.
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    Electric vehicle integration’s impacts on power quality in distribution network and associated mitigation measures: a review
    (2023) Srivastava, A; Manas, M; Dubey, R
    The problem of global warming, along with environmental concerns, has already led governments to replace fossil-fuel vehicles with low-emission electric vehicles (EVs). The energy crisis and environmental problems, such as global warming and air pollution, are essential reasons for the development of electric vehicles (EVs). Electric vehicles are one of the most fascinating and essential felds to emerge in recent years. According to the current report, electric vehicles are attempting to replace older, tra ditional automobiles. These vehicles not only help to reduce pollution but also to save natural resources. The presence of electric vehicles may cause several problems for the conventional electrical grid due to their grid-to-vehicle (G2V) and vehicle-to-grid (V2G) charging and discharging capabilities. With increased EV adoption, many power quality (PQ) issues in the electrical distribution system arise. With the penetration of EVs in dis tribution networks, power quality issues such as voltage imbalance, transformer failure, and harmonic distortion are expected to arise. The focus of this research is on exploring and reviewing the issues that the integration of EVs poses for electrical networks. The existing and future situations of electric vehicles’ integration, as well as new research on the subjects, have been reviewed in this paper. This study provides a thorough exami nation of power quality issues and their mitigating approaches
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    Development of Grey Machine Learning Models for Forecasting of Energy Consumption, Carbon Emission and Energy Generation for the Sustainable Development of Society
    (2023-03) Saxena, A; Zeineldin, R
    Energy is an important denominator for evaluating the development of any country. Energy consumption, energy production and steps towards obtaining green energy are important factors for sustainable development. With the advent of forecasting technologies, these factors can be accessed earlier, and the planning path for sustainable development can be chalked out. Forecasting technologies pertaining to grey systems are in the spotlight due to the fact that they do not require many data points. In this work, an optimized model with grey machine learning architecture of a polynomial realization was employed to predict power generation, power consumption and CO2 emissions. A nonlinear kernel was taken and optimized with a recently published algorithm, the augmented crow search algorithm (ACSA), for prediction. It was found that as compared to conventional grey models, the proposed framework yields better results in terms of accuracy.
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    Combining Forth and Rust: A Robust and Efficient Approach for Low-Level System Programming †
    (2023-12) Gupta, P; Rahar, R; Yadav, R; Singh, A
    Abstract: Rust is a modern programming language that addresses the drawbacks of earlier languages by providing features such as memory safety at compilation and high performance. Rust’s memory safety features include ownership and borrowing, which makes it an ideal choice for systems programming, where memory safety is critical. Forth is a stack-based programming language that is widely used for low-level system programming due to its simplicity and ease of use. This research paper aims to explore the combination of Forth and Rust programming languages to create a more robust and efficient solution for low-level system programming. The primary objective is to demonstrate the implementation of essential Forth operations, including addition, subtraction, assignment, comparison, and if-else statements, while demonstrating loops, push operations, and dump operations in Rust. The implementation of these operations in Rust is demonstrated using code from actual implementation. This research paper also discusses the advantages of using Rust for low-level system programming. Rust’s memory safety features, coupled with its high performance, make it an ideal choice for systems programming, where memory safety and performance are critical. The combination of Forth and Rust provides a more efficient and safer solution for low-level system programming, making the implementation more robust. Our implementation tries to leverage these properties of both languages to make a memory-safe and low-level system programming language. This research paper also includes code snippets to provide a practical demonstration of how the Forth operations can be implemented in Rust.
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    CAQoE: A Novel No-Reference Context-aware Speech Quality Prediction Metric
    (2023-01) JAISWAL, R; DUBEY, R
    The quality of speech degrades while communicating over Voice over Internet Protocol applications, for exam ple, Google Meet, Microsoft Skype, and Apple FaceTime, due to different types of background noise present in the surroundings. It reduces human perceived Quality of Experience (QoE). Along this line, this article proposes a novel speech quality prediction metric that can meet human’s desired QoE level. Our motivation is driven by the lack of evidence showing speech quality metrics that can distinguish different noise degra dations before predicting the quality of speech. The quality of speech in noisy environments is improved by speech enhancement algorithms, and for measuring and monitoring the quality of speech, objective speech quality metrics are used. With the integration of these components, a novel no-reference context-aware QoE prediction metric (CAQoE) is proposed in this article, which initially identifies the context or noise type or degradation type of the input noisy speech signal and then predicts context-specific speech quality for that input speech signal. It will have of great importance in deciding the speech enhancement algorithms if the types of degradations causing poor speech quality are known along with the quality metric. Results demon strate that the proposed CAQoE metric outperforms in different contexts as compared to the metric where contexts are not identified before predicting the quality of speech, even in the presence of limited size speech corpus having different contexts available from the NOIZEUS speech database.