International Journal of Computing, Science and New Technologies (IJCSNT)

AUTOMATIC GARAGE GATE OPENING SYSTEM USING SENSOR TECHNOLOGY

2026-05-31T04:46:20+00:00

The integration of sensor technology in automated gate systems has revolutionized access control and convenience in residential and commercial settings. This comprehensive research article examines the design, implementation, and optimization of automatic garage gate opening systems using various sensor technologies including ultrasonic, infrared (IR), Radio Frequency Identification (RFID), Passive Infrared (PIR), and Internet of Things (IoT)-enabled sensors. The study analyzes sensor selection criteria, system architecture, microcontroller integration, safety protocols, and performance metrics. Additionally, this article explores recent advancements in artificial intelligence and machine learning applications for predictive maintenance and enhanced security features. The findings demonstrate that multi-sensor fusion approaches combined with intelligent control algorithms significantly improve system reliability, response time, and user experience while maintaining robust safety standards. Implementation considerations, cost- benefit analysis, and future research directions are also discussed to provide a holistic framework for developing next-generation automated gate systems.

DESIGN AND IMPLEMENTATION OF A VOICE-ENABLED IOT-BASED MULTI-HAZARD DETECTION AND AUTOMATED CONTROL SYSTEM WITH VOICE PROMPT

2026-05-31T14:19:10+00:00

The increasing demand for intelligent safety and automation has accelerated the adoption of the Internet of Things in smart environments. This paper presents the design and implementation of a voice-enabled IoT-based multi-hazard detection and automated control system with cloud integration. The system employs multiple sensors to monitor critical conditions, including gas leakage, fire, water intrusion, and unauthorized motion. An embedded microcontroller processes sensor data and triggers automated responses such as alarm activation, gas shut-off, load isolation, and water pumping.

A voice prompt module is integrated to provide real-time auditory alerts, enhancing situational awareness and enabling hands-free interaction during emergency scenarios. The system is further connected to the ThingSpeak cloud platform for real-time data transmission, visualization, and remote monitoring. Experimental evaluation demonstrates detection accuracy exceeding 90% and response latency below 3 seconds. The proposed system provides a unified framework that integrates multi-hazard detection, automation, voice interaction, and cloud connectivity, offering improved efficiency and usability over conventional IoT-based systems. It is suitable for deployment in smart homes and safety-critical applications.

COMPARATIVE STUDY OF LIGHTWEIGHT CRYPTOGRAPHY ALGORITHMS FOR MEDICAL SENSOR NETWORKS: PERFORMANCE, ENERGY, AND SECURITY ANALYSES

2026-09-17T05:56:57+00:00

Medical sensor networks are rapidly transforming healthcare delivery through continuous remote monitoring of physiological signals; yet, the sensitive nature of patient data demands robust security mechanisms that must operate within severe resource constraints. While lightweight cryptography offers a viable security solution for such resource-constrained environments, no systematic comparative study has evaluated leading algorithms specifically under medical sensor network operating conditions. Existing work either tests limited algorithm sets against generic Internet of Things (IoT) metrics or proposes single hybrid schemes without cross-algorithm comparison. This paper presents a comprehensive comparative evaluation of seven lightweight cryptography algorithms - PRESENT, KASUMI, ASCON, SIMON, SPECK, LED, and GIFT - across eight performance and security metrics: throughput, power consumption, energy per bit, RAM footprint, ROM footprint, encryption latency, security level, and healthcare regulatory compliance. Evaluations were conducted on an ARM Cortex-M4 platform under ECG and EEG transmission workloads representative of real medical sensor deployments. Results demonstrated that ASCON and GIFT provided the most favourable balance of security strength and energy efficiency for wearable medical sensors, while PRESENT remains most suitable for ultra-constrained implantable devices. The study further introduced a healthcare compliance scoring rubric aligned with HIPAA and ISO/IEEE 11073, providing medical IoT system designers with evidence-based algorithm selection guidance.

SCORECRAFT: A LARGE LANGUAGE MODEL-BASED SYSTEM FOR AUTOMATED EXAMINATION MARKING AND FEEDBACK GENERATION

2026-09-17T06:07:06+00:00

Manual marking of examination scripts is time-consuming, prone to inconsistency, and often delays the delivery of feedback to students, particularly in large university classes. This paper presents ScoreCraft, a large language model (LLM)-based examination marking system designed to assist lecturers in evaluating student responses. The system allows lecturers to upload structured marking guides and student answer scripts, after which it generates draft scores and explanatory feedback for lecturer review and approval. The proposed system integrates marking guide ingestion, student response alignment, LLM-based scoring, feedback generation, and a human-in-the-loop moderation interface. By retaining the lecturer as the final authority over released scores, ScoreCraft is designed to support transparency, consistency, fairness, and accountability while reducing marking workload and turnaround time. The paper also presents an evaluation protocol based on agreement between system-generated scores and lecturer-assigned scores, using measures such as quadratic weighted kappa and mean absolute error. The proposed system provides a foundation for efficient and scalable examination assessment, with future extensions including multilingual assessment, handwriting recognition, and improved adaptation to diverse examination formats.

RAPID PROTOTYPING AS A DESIGN METHOD FOR MICROCONTROLLER-BASED SYSTEMS: PRINCIPLES, APPLICATIONS AND SCIENTIFIC EVALUATIONS

2026-09-24T06:43:39+00:00

This paper describes rapid prototyping as a practical design methodology for microcontroller-based embedded systems. Rapid prototyping shortens the hardware–firmware development cycle by letting engineers build, test, and refine working prototypes at each design stage rather than waiting until the end. The study covers the theoretical basis of the methodology, reviews major microcontroller platforms and development toolchains, introduces a structured eight-stage process model with a detailed flowchart, and summarises published evidence on how the approach affects development time, defect rates, cost, and product quality. An experimental prototype built around an STM32F407 Cortex-M4 microcontroller was used to validate the process. Measurements across all key electrical parameters came within ±1.4% of reference values. The system reached operational status in 1.8 seconds from cold start and maintained a 99.2% firmware upload success rate over 72 hours of continuous testing. The complete prototype cost roughly $31 in components, making it well-suited for university projects and small commercial work. The paper also discusses common difficulties, including timing bugs and platform portability, and offers straightforward recommendations for engineers, educators, and researchers.