Front Cover
Publication Month: January 2024, Page(s): A1 – A1
Editorial Board
Publication Month: January 2024, Page(s): B1 – B1
Editorial
Publication Month: January 2024, Page(s): C1 – C1
Table of Contents
Publication Month: January 2024, Page(s): D1 – D1
Neural Synchrony-Based State Representation in Liquid State Machines, an Exploratory Study
Nicolas Pajot, Mounir Boukadoum
J. Engg. Res. & Sci. 2(11), 1-14 (2023);
Solving classification problems by Liquid State Machines (LSM) usually ignores the influence of the liquid state representation on performance, leaving the role to the reader circuit. In most studies, the decoding of the internally generated neural states is performed on spike rate-based vector representations. This approach occults the interspike timing, a central aspect of biological neural coding, with potentially detrimental consequences on the LSM performance. In this work, we propose a model of liquid state representation that builds the feature vectors from temporal information extracted from the spike trains, hence using spike synchrony instead of rate. Using pairs of Poisson-distributed spike trains in noisy conditions, we show that such model outperforms a rate-only model in distinguishing two spike trains regardless of the sampling frequency of the liquid states or the noise level. In the same vein, we suggest a synchrony-based measure of the separation property (SP), a core feature of LSMs regarding classification performance, for a more robust and biologically plausible interpretation.
Baggage Cart with Weighing Mechanism for Hotels and Airlines
Vishal Verma, Kuldeep Kumar, Rashmi Aggarwal, Tanvi Verma
J. Engg. Res. & Sci. 3(1), 6-10 (2024);
In this article, it has been proposed a functional design of Baggage Cart with Weighing Mechanism for Hospitality Industry based on empirical observations. This design is expected to promote and become one of the high demand products which can later be used especially by hotels and respective airlines. It has been often observed that travelers are always concerned about the maximum weight of their baggage allowed in order to board a flight. Because as per norms of airlines one needs to carry a specific amount of baggage in the flight, in case of extra weighed baggage carried by a guest, he/she is charged extra amount as per KG by the respective airline. Keeping this point in mind, travelers are always conscious about the weight of their baggage before boarding a flight to avoid the last-minute hassle of paying huge extra money, and sometimes they need to drop the necessary items out from the baggage to adjust the weight of the baggage. In hotels, a guest request to weigh his/ her luggage is dealt with in the following ways: The front office associate/porter first needs to fetch the baggage from the guest room and bring it up to the bell desk or need to drag it up till time office / receiving area where it is weighed on a heavy-duty weighing scale. It is the traditional way of measuring baggage. The traditional way is time-consuming, uncomfortable, and less suitable to support the need of the traveler. Hence, to minimize the time and efforts, we wanted to invent a mechanism that will help and reduce the burden of managing the separate weighing machine, and therefore we came up with a baggage cart with a weighing mechanism in it.
Robust Localization Algorithm for Indoor Robots Based on the Branch-and-Bound Strategy
Huaxi (Yulin) Zhang, Yuyang Wang, Xiaochuan Luo, Baptiste Mereaux, Lei Zhang
J. Engg. Res. & Sci. 3(2), 22-42 (2024);
Robust and accurate localization is crucial for mobile robot navigation in complex indoor environments. This paper introduces a robust and integrated robot localization algorithm designed for such environments. The proposed algorithm, named Branch-and-Bound for Robust Localization (BB-RL), introduces an innovative approach that seamlessly integrates global localization, position tracking, and resolution of the kidnapped robot problem into a single, comprehensive framework. The process of global localization in BB-RL involves a two-stage matching approach, moving from a broad to a more detailed analysis. This method combines a branch-and-bound algorithm with an iterative nearest point algorithm, allowing for an accurate initial estimation of the robot’s position. For ongoing position tracking, BB-RL uses a local map-based scan matching technique. To address inaccuracies that accumulate over time in the local maps, the algorithm creates a pose graph which helps in loop-closure optimization. Additionally, to make loop-closure detection less computationally intensive, the branch-and-bound algorithm is used to speed up finding loop constraints. A key feature of BB-RL is its Finite State Machine (FSM)-based relocalization judgment method, which is designed to quickly identify and resolve the kidnapped robot problem. This enhances the reliability of the localization process. BB-RL’s performance was thoroughly tested in real-world situations using commercially available logistics robots. These tests showed that BB-RL is fast, accurate, and robust, making it a practical solution for indoor robot localization.
“Greenwashing” or “Helping”: ESG Performance and Chinese Firm Total Factor Productivity
Jing Zhang, Ziyang Liu
J. Engg. Res. & Sci. 3(3), 1-12 (2024);
As the market economy has continued to develop, businesses have consistently prioritized profits, excessively emphasizing income and financial gains while neglecting ecological conservation and financial fraud. Consequently, the phenomenon of “greenwashing” has emerged. How to prevent this “greenwashing” phenomenon while pursuing economic benefits and enabling high-quality business development has become a focal point. Therefore, this paper analyzes whether the ESG (Environmental, Social, and Governance) performance of listed companies has an impact on the enhancement of the Total Factor Productivity (TFP) of enterprises. This study aims to explore how companies, while striving to maximize economic interests, can more proactively undertake environmental protection and social responsibility, thereby promoting the green transformation of enterprises. Using A-share listed companies from 2012 to 2022 as the sample, through an empirical examination of the correlation between the ESG performance of listed companies in China and the TFP of enterprises, the following conclusions are drawn: (1) ESG performance significantly promotes the TFP of enterprises, indicating that higher ESG performance corresponds to higher TFP; (2) Through intermediary effect tests, it is found that corporate reputation plays a role in enhancing the TFP of enterprises. That is, through good ESG performance, a company’s reputation is improved, thereby leading to higher TFP; (3) Heterogeneity analysis demonstrates that the impact of good ESG performance on the enhancement of TFP is more significant in large-scale enterprises and state-owned enterprises.
Missile Guidance using Proportional Navigation and Machine Learning
Mirza Hodžić, Naser Prljača
J. Engg. Res. & Sci. 3(3), 19-26 (2024);
Variants of proportional navigation (PN) are perhaps mostly used guidance laws for tactical homing missiles. PN aims to generate commanding missile lateral acceleration proportional to line of sight (LOS) angular rate, so that missile velocity vector rotates in such a way to assure interception of a target. In order to generate commanding lateral accelerations, the guidance system needs measurements of LOS angular rate and the closing velocity between the missile and the target, or the missile velocity. A device which provides guidance information is referred to as the missile seeker. In the case of imaging based seekers (visible light (EO), infrared light (IIR)), LOS rate is estimated using imaging sensor, while closing or missile velocity is measured using appropriate sensors or guess estimated. In this paper, we present the design and simulation of a missile homing system which includes: true PN guidance law, linear multiloop acceleration autopilot, and gimbaled imaging based missile seeker. Target seeker uses advanced deep machine learning object detection YOLO (You only look once) model, for target detection and tracking as well as LOS rate estimation. Comprehensive simulation model, consisting of full 6DOF missile and controls dynamics, 3D world and camera model, is developed. Intensive simulation results show performances of the proposed missile homing system.
Estimation of Elbow Joint Movement Using ANN-Based Softmax Classifier
Abdullah Y. Al-Maliki, Kamran Iqbal, Gannon White
J. Engg. Res. & Sci. 3(4), 1-9 (2024);
Estimating the natural voluntary movement of human joints in its entirety is a challenging problem especially when high accuracy is desired. In this paper, we build a modular estimator to estimate the elbow joint motion including angular displacement and direction. Being modular, this estimator can be scaled for application to other joints. We collected surface Electromyographic (sEMG) signals and motion capture data from healthy participants while performing elbow flexion and extension in different arm positions and at different effort levels. We preprocessed the sEMG signals, extracted features array, and used it to train an ANN-based Softmax classifier to estimate the angular displacement and movement direction. When compared against the motion cap-ture data, the classifier achieved estimation accuracy ranging from 80% to 90% with a resolution of 5°, which translates into Pear-son Correlation Coefficient (PCC) ranging from 0.91 to 0.95. Such high PCC values in mimicking the voluntary movement of the upper limb may help toward building intuitive prostheses, exoskeletons, remote-controlled robotic arms, and other Human Ma-chine Interface (HMI) applications.
Comprehensive E-learning of Mathematics using the Halomda Platform enhanced with AI tools
Philip Slobodsky, Mariana Durcheva
J. Engg. Res. & Sci. 3(4), 10-19 (2024);
The method of assessment affects on learning by determining how students manage their time and prioritize subjects. It is widely accepted that students may demonstrate different skills in different assessment formats. The authors demonstrated how e-assessment through the Halomda educational platform can not only improve student learning outcomes, but also enrich their learning experiences. In addition, it is shown how ChatGPT integrated with two new math exploration tools into proprietary Chat-Mat™ module, can help students learn at home and in the classroom, as well as support teachers in their daily work of reviewing student assignments. The outcomes of teaching courses with Halomda not only reveal impressive student performance on final exams but also illustrate a strong correlation between exam scores and weekly assignment grades.
Mathematical Model of Optimum Management of the Customs Control Process and Expert System for Ensuring Data Reliability
Ilkhom Mukhtorov, Takhir Abduraxmonov, Abdusobir Saidov
J. Engg. Res. & Sci. 3(5), 1-13 (2024);
The article considers the issue of modeling the multi-step process of customs clearance of goods in foreign trade. A mathematical model of control of the process under consideration has been developed. A brief review of existing methods for solving the linear programming problem with variable coefficients of the target function is given. The essence of customs risks has been studied and a method for identifying customs risks of reliability using threshold matrixes has been proposed. An algorithm for controlling the reliability of the customs value of goods is developed and the results of the implementation of this algorithm are given
Analyzing the Impact of Optical Wireless Communication Technologies on 5G/6G and IoT Solutions: Prospects, Developments, and Challenges
Ramsha Khalid, Muhammad Naqi Raza
J. Engg. Res. & Sci. 3(5), 23-36 (2024);
The imminent 5G and 6G communication systems are projected to exhibit substantial advancements in comparison to the current 4G communication system. Several critical and prevalent concerns pertaining to the service quality of 5G and 6G communication systems encompass elevated capacity, extensive connectivity, minimal latency, robust security measures, energy efficiency, superior quality of user experience, and dependable connectivity. Undoubtedly, 6G communication is expected to offer markedly improved performance across these domains compared to 5G communication. The integration of the Internet of Things (IoT) within the framework of the tactile internet is anticipated to be a fundamental component of advanced communication systems, encompassing both 5G and beyond (5GB), such as 5G and 6G. Consequently, 5GB wireless networks will encounter various challenges in accommodating diverse types of heterogeneous traffic and meeting the specified parameters related to service quality. Optical wireless communication (OWC), alongside various other wireless technologies, emerges as a promising candidate to fulfill the requisites of 5G communication systems. This comprehensive review articulates the efficacy of OWC technologies, including Visible Light Communication (VLC), Light Fidelity (LiFi), Optical Camera Communication (OCC), and Free Space Optics (FSO) Communication, as a viable solution for the successful deployment of 5G/6G and IoT systems.
Using Artificial Intelligence Models to Predict the Wind Power to be fed into the Grid
Sambalaye Diop, Papa Silly Traore, Mamadou Lamine Ndiaye, Issa Zerbo
J. Engg. Res. & Sci. 3(6), 1-9 (2024);
The Taïba Ndiaye wind farm, connected to the SENELEC grid, plays a key role in offsetting shortfalls in electricity consumption, with an installed capacity of 158.7 MW. Moreover, as an intermittent power station, its production is highly dependent on the environmental conditions in the region. Bad weather can disrupt the electricity network, requiring forecasting methods to anticipate its production. This will make it easier to decide how much fossil energy to bring on stream to meet demand. The aim of this paper is to provide forecasts of wind generation at Taïba Ndiaye, subdividing the data into 80% for model training and 20% to assess its robustness to generalization to other situations. The aim is to quantify the energy produced and facilitate an optimal transition between intermittent and fossil energy sources. Two artificial intelligence models classified as machine learning (decision tree and random forest) are proposed in the study, with respective coefficients of determination of 0.92 and 0.938. The results, compared with the literature, demonstrate the reliability of the approach using only production data. These results promise significant benefits in terms of resource management.
Educational Applications and Comparative Analysis of Network Simulators: Protocols, Types, and Performance Evaluation
Nikolaos V. Oikonomou, Dimitrios V . Oikonomou
J. Engg. Res. & Sci. 3(6), 18-32 (2024);
This work explores the role of simulation in computer networks, discussing various network types, communication protocols, and the utilization of network simulators, with a focus on educational settings. We specifically analyze and compare five prominent network simulators: Cisco Packet Tracer, Riverbed Modeler Academic Edition, GNS3, NS-3, and Mininet. These tools are examined in terms of their functionality, user-friendliness, and suitability for educational purposes, assessing how they facilitate learning for students and trainees. The comparison extends to their operational capabilities, differences, effectiveness, and overall impact on networking education. The evaluation aims to highlight each simulator’s strengths and weaknesses, providing insights into their practical applications in an academic context.
Comprehensive Analysis of Software-Defined Networking: Evaluating Performance Across Diverse Topologies and Investigating Topology Discovery Protocols
Nikolaos V. Oikonomou, Dimitrios V. Oikonomou , Eleftherios Stergiou , Dimitrios Liarokapis
J. Engg. Res. & Sci. 3(6), 23-43 (2024);
Software-defined networking (SDN) represents an innovative approach to network architecture that enhances control, simplifies complexity, and improves operational efficiencies. This study evaluates the performance metrics of SDN frameworks using the Mininet simulator on virtual machines hosted on a Windows platform. The research objectives include assessing system performance across various predefined network topologies, investigating the impact of switch quantities on network performance, measuring CPU consumption, evaluating RAM demands under different network loads, and analyzing latency in packet transmission. Methods involved creating and testing different network topologies, including basic, hybrid, and custom, with the Mininet simulator. Performance metrics such as CPU and RAM usage, latency, and bandwidth were measured and analyzed. The study also examined the performance and extendibility of the OpenFlow Data Path (OFDP) protocol using the POX controller. Results indicate that balanced tree topologies consume the most CPU and RAM, while linear topologies are more efficient. Random topologies offer adaptability but face connection reliability issues. The POX controller and OFDP protocol effectively manage SDN network scalability. This research aims to analyze performance in a manner consistent with numerous previous studies, underscoring the importance of performance metrics and the scale of the network in determining the efficiency and reliability of SDN implementations. By benchmarking various topologies and protocols, the research offers a valuable reference for both academia and industry, promoting the development of more efficient SDN solutions. Understanding these performance metrics helps network administrators make informed decisions about implementing SDN frameworks to improve network performance and reliability.
