https://repositori.mypolycc.edu.my/jspui/handle/123456789/6661 2026-04-17T18:11:42Z 2026-04-17T18:11:42Z Medina-Salas, Erick Iván Laureano-Cruces, Ana Lilia Lárraga-Ramírez, Ma. Elena https://repositori.mypolycc.edu.my/jspui/handle/123456789/9474 2026-04-16T03:00:49Z 2023-11-24T00:00:00Z

Title: A NEW MODEL FOR SPREADING MALWARE OVER SMS USING NETWORK AUTOMATA Authors: Medina-Salas, Erick Iván; Laureano-Cruces, Ana Lilia; Lárraga-Ramírez, Ma. Elena Abstract: By the year 2026, it is estimated that the number of smartphone users in Mexico will be approximately 118.1 million. Each smartphone has the functionality of sending and receiving SMS (Short Message Service) messages, which pose a significant threat to all users, as it makes any device vulnerable to a malware attack. In particular, worm-type malware takes advantage of this means of communication in order to spread. Studying the dynamics of mal ware propagation can help understand and prevent massive contagion between mobile devices. In this work, a model based on Network Automata and compartmental epidemiological models is presented, aiming to simulate, analyze and study the spread of worm-like malware through sending SMS on smart phones.

2023-11-24T00:00:00Z Adjardjah, Winfred Addor, John Awuah Opare, Wisdom Ayipeh, Isaac Mensah https://repositori.mypolycc.edu.my/jspui/handle/123456789/9473 2026-04-16T03:00:57Z 2023-09-28T00:00:00Z

Title: DESIGN AND SIMULATION OF AN AUDIO SIGNAL ALERTING AND AUTOMATIC CONTROL SYSTEM Authors: Adjardjah, Winfred; Addor, John Awuah; Opare, Wisdom; Ayipeh, Isaac Mensah Abstract: A large part of our daily lives is spent with audio information. Massive obstacles are frequently presented by the colossal amounts of acoustic information and the incredibly quick processing times. This results in the need for applications and methodologies that are capable of automatically analyzing these contents. These technologies can be applied in automatic content analysis and emergency response systems. Breaks in manual communication usually occur in emergencies leading to accidents and equipment damage. The audio signal does a good job by sending a signal underground, which warrants action from an emergency management team at the surface. This paper, therefore, seeks to design and simulate an audio signal alerting and automatic control system using Unity Pro XL to substitute manual communication of emergencies and manual control of equipment. Sound data were trained using the neural network technique of machine learning. The metrics used are Fast Fourier trans form magnitude, zero crossing rate, root mean square, and percentage error. Sounds were detected with an error of approximately 17%; thus, the system can detect sounds with an accuracy of 83%. With more data training, the system can detect sounds with minimal or no error. The paper, therefore, has critical policy implications about communication, safety, and health for underground mine.

2023-09-28T00:00:00Z Jamal Habibi Markani Syed Ibtehaj Raza Rizvi Abdessamad Amrhar Gagné, Jean-Marc Landry, René Jr. https://repositori.mypolycc.edu.my/jspui/handle/123456789/9472 2026-04-16T03:00:38Z 2023-05-09T00:00:00Z

Title: ADS-B RECEPTION ERROR CORRECTION BASED ON THE LSTM NEURAL-NETWORK MODEL Authors: Jamal Habibi Markani; Syed Ibtehaj Raza Rizvi; Abdessamad Amrhar; Gagné, Jean-Marc; Landry, René Jr. Abstract: Standard automatic dependent surveillance broadcast (ADS-B) reception algorithms offer considerable performance at high signal-to-noise ratios (SNRs). However, the performance of ADS-B algorithms in applications can be problematic at low SNRs and in high interference situations, as detecting and de coding techniques may not perform correctly in such circumstances. In addition, conventional error correction algorithms have limitations in their ability to correct errors in ADS-B messages, as the bit and confidence values may be declared inaccurately in the event of low SNRs and high interference. The principal goal of this paper is to deploy a Long Short-Term Memory (LSTM) recurrent neural network model for error correction in conjunction with a conventional algorithm. The data of various flights are collected and cleaned in an initial stage. The clean data is divided randomly into training and test sets. Next, the LSTM model is trained based on the training dataset, and then the model is evaluated based on the test dataset. The proposed model not only improves the ADS-B In packet error correction rate (PECR), but it also enhances the ADS-B In terms of sensitivity. The performance evaluation results reveal that the proposed scheme is achievable and efficient for the avionics industry. It is worth noting that the proposed algorithm is not dependent on conventional algorithms’ prerequisites.

2023-05-09T00:00:00Z Ju, Zhiyong Shui, Jiacheng Huang, Jiameng https://repositori.mypolycc.edu.my/jspui/handle/123456789/9471 2026-04-16T03:00:25Z 2025-09-27T00:00:00Z

Title: GLDS-YOLO: AN IMPROVED LIGHTWEIGHT MODEL FOR SMALL OBJECT DETECTION IN UAV AERIAL IMAGERY Authors: Ju, Zhiyong; Shui, Jiacheng; Huang, Jiameng Abstract: To enhance small object detection in UAV aerial imagery suffering from low resolution and complex backgrounds, this paper proposes GLDS-YOLO, an improved lightweight detection model. The model integrates four core modules: Group Shuffle Attention (GSA) to strengthen small-scale feature perception, Large Separable Kernel Attention (LSKA) to capture global semantic context, DCNv4 to enhance feature adaptability with reduced parameters, and further proposes a novel Small-object-enhanced Multi-scale and Structure Detail Enhancement (SMSDE) module, which enhances edge-detail representation of small objects while maintaining lightweight efficiency. Experiments on VisDrone2019 and DOTA1.0 demonstrate that GLDS-YOLO achieves superior detection performance. On VisDrone2019, it improves mAP@0.5 and mAP@0.5:0.95 by 12.1% and 7%, respectively, compared with YOLOv11n, while maintaining competitive results on DOTA. These results confirm the model’s effectiveness, robustness, and adaptability for complex small object detection tasks in UAV scenarios.

2025-09-27T00:00:00Z