Manuel Eugenio Morocho-Cayamcela

Verified

Manuel Eugenio verified their affiliation via an institutional email.

Learn more

Verified

Manuel Eugenio verified their affiliation via an institutional email.

Learn more

• Doctor of Philosophy
• Professor at Universidad Yachay Tech

Metasurfaces can be engineered to guide surface waves in a homogeneous path, where sub-wavelength size printed patches are etched on a grounded high-frequency laminate. When the homogeneity of the patches is compromised or it is inappropriately excited, leakage takes place. This effect can be exploited to design leaky-wave antennas for a wide range...

A fully operative and efficient 5G network cannot be complete without the inclusion of artificial intelligence (AI) routines. Existing 4G networks with all-IP (Internet Protocol) broadband connectivity are based on a reactive conception, leading to a poorly efficiency of the spectrum. AI and its sub-categories like machine learning and deep learnin...

Driven by the demand to accommodate today’s growing mobile traffic, 5G is designed to be a key enabler and a leading infrastructure provider in the information and communication technology industry by supporting a variety of forthcoming services with diverse requirements. Considering the everincreasing complexity of the network, and the emergence o...

Multi-hop relay selection is a critical issue in vehicle-to-everything networks. In previous works, the optimal hopping strategy is assumed to be based on the shortest distance. This study proposes a hopping strategy based on the lowest propagation loss, considering the effect of the environment. We use a two-step machine learning routine: improved...

Wireless propagation loss modeling has gained significant attention due to its critical importance in forthcoming dynamic wireless technologies. Stochastic and map-based propagation models require more information (elevation extension, statistical scattering characteristics) than required by empirical models (i.e., operating frequency, distance bet...

Currently, artificial intelligence (AI), particularly computer vision (CV), has numerous applications in agriculture. In this field, the production and consumption of strawberries have experienced great growth in recent years, which makes meeting the growing demand a challenge that producers must face. However, one of the main problems regarding th...

Pregnancy complications significantly impact maternal and fetal health, requiring accurate and timely diagnostic methods for life-saving interventions. Traditional manual analysis of cardiotocography (CTG) tests, a commonly used technique for fetal health monitoring, is labor-intensive and prone to variability. This study addresses this issue by ap...

This study examines the potential interconnection between genetic mutations frequently observed in various types of cancer and depression, aiming to elucidate shared genetic factors that may influence the etiology and treatment of both conditions. Genomic profiles from cancer patients were extracted using cBioPortal to perform comparisons with a su...

This paper presents a simulation and evaluation of vehicle-to-everything (V2X) communication within a real urban environment tailored for fifth-generation (5G) networks. The main contributions are: (i) utilizing a real map segment of Barcelona, Spain, from OpenStreetMap; (ii) generating vehicular mobility patterns using the Simulation of Urban MObi...

Climate change is accelerating the evolution of species as global warming intensifies and natural environments undergo significant alterations. This study addresses the urgent need to understand how these environmental changes impact avian populations, specifically focusing on the physiological adaptation of finch beaks on the Galápagos Islands. By...

Pancreatic cancer, a serious disease, is challenging to detect early, hindering timely treatments. Medical imaging, especially computer tomography, is vital for early diagnosis. This study focused on accurate pancreatic segmentation in medical images using subtle changes in 2D U-Net architecture to aid in diagnosis and treatment. Additionally, we p...

The growing global population and the increasing demand for food have made food production a critical concern. To meet this challenge, techniques like vertical farming and aquaponics have been proposed to maximize output while conserving resources and space. However, there is still room for improvement in crop care processes. Deep learning, particu...

Contemporary neural networks frequently encounter the challenge of catastrophic forgetting, wherein newly acquired learning can overwrite and erase previously learned information. The paradigm of continual learning offers a promising solution by enabling intelligent systems to retain and build upon their acquired knowledge over time. This paper int...

This study explores the effectiveness of the ConvNeXt model, an advanced computer vision architecture, in the task of image captioning. We integrated ConvNeXt with a Long Short-Term Memory network that includes a visual attention module, focusing on assessing its performance across different scenarios. Experiments were conducted using various ConvN...

Modern electrical systems are evolving with data communication networks, ushering in upgraded electrical infrastructures and enabling bidirectional communication between utility grids and consumers. The selection of communication technologies is crucial, where wireless communications have emerged as one of the main benefactor technologies due to it...

In the current educational landscape, the transition from traditional paradigms to more interactive and personalized learning experiences has been accelerated by technological advancements, particularly in artificial intelligence (AI). This paper explores integrating large language models (LLMs) with retrieval augmented generation techniques (RAG)...

In August 2020, the World Health Assembly launched a global initiative to eliminate cervical cancer by 2030, setting three primary targets. One key goal is to achieve a 70% screening coverage rate for cervical cancer, primarily relying on the precise analysis of Papanicolaou (Pap) or digital Pap smears. However, the responsibility of reviewing Pap...

In August 2020, the World Health Assembly launched a global initiative to eliminate cervical cancer by 2030, setting three primary targets. One key goal is to achieve a 70% screening coverage rate for cervical cancer, primarily relying on the precise analysis of Papanicolaou (Pap) or digital Pap smears. However, the responsibility of reviewing Pap...

This work explores the segmentation and detection of tomatoes in different maturity states for harvesting prediction by using the laboro tomato dataset to train a mask R-CNN and a YOLOv8 architecture. This work aims to test the mask R-CNN architecture and the proposed methodology efficiency on the benchmark paper [12]. The evaluation metric interse...

In recent years, action recognition has seen significant advancements in using Convolutions Neural Networks (CNNs) models for video analysis. One of the essential fields in this area is violence detection, which determines whether or not violent scenes use videos from surveillance cameras. One popular approach to handle this is the Flow Gated Netwo...

Deep learning has been receiving a lot of attention lately, specially in the computer vision research community. In particular, the image segmentation task has revolutionized the treatment of medical imagery for diagnose and illness prediction. U-Net is one of the most extensively employed convolutional neural network (CNN) architectures for medica...

In August 2020, the World Health Assembly (WHA) established three global targets aimed at eliminating cervical cancer by 2030. One of these targets involves achieving 70% coverage of cervical cancer screening, which relies on the accurate analysis of Papanicolaou or digital Pap smears. However, the task of examining Pap smear images to detect suspi...

This paper implements airy disk smoothing, Poisson noise, Gaussian smoothing, Hanser’s phase term, and Zernike polynomial phase term as degradation techniques on images from the DIV2K dataset. These actions allows the generation of gray-scale degraded images to study the performance of the inverse filter, Wiener filter, and the Richardson-Lucy algo...

The American sign language is the most popular and widely-accepted sign language for people with hearing difficulties. Computer vision techniques, such as skeleton recognition, depth recognition, 3D model recognition, or deep learning recognition, have helped to develop better systems for sign language classification and detection. Despite the prom...

Face mask detection has become a great challenge in computer vision, demanding the coalition of technology with COVID-19 awareness. Researchers have proposed deep learning models to detect the use of face masks. However, the incorrect use of a face mask can be as harmful as not wearing any protection at all. In this paper, we propose a compound con...

The detection of plant diseases has been a hot research topic lately, specially since deep learning models and state-of-the-art convolutional neural networks (CNNs) architectures came into play. For this reason, this paper aims to compare several multitask CNN architectures used for: (i) classifying the environmental stress of coffee leaves, and (i...

The automatic recognition of license plates has been a widely research area, in special for surveillance scenarios. With the evolution of vigilance cameras and embedded devices, many computer vision models are being used to identify license plates by recognizing its characters. In this work, we present a computer vision model based on convolutional...

Introducción: El peso al nacer es uno de los principales indicadores pronóstico de mortalidad neonatal, en el que influyen factores asociados con la madre, el neonato, y también con las características socioeconómicas del núcleo familiar. Los factores de riesgo implican comorbilidades al momento del nacimiento, por lo que, la intervención adecuada...

Parkinson’s disease is the second most common neurological disorder after Alzheimer. Several limitations and challenges have arisen when aiming to diagnose this disease. In this regard, a computer-aided diagnosis system is enforced for the early detection of any abnormalities. Prominent research efforts have been developed based on speech and gait...

This paper focuses on visual attention , a state-of-the-art approach for image captioning tasks within the computer vision research area. We study the impact that different hyperparemeter configurations on an encoder-decoder visual attention architecture in terms of efficiency. Results show that the correct selection of both the cost function and...

An economic group is a collection of parent and subsidiary corporations that operates as a single economic organism under the same legislature of control. The decisions taken by the economic groups in any country are among the most influential factors that impact its market and the country’s economic political scenario. This work studies the impact...

Considering the historical trajectory and evolution of image captioning as a research area, this paper focuses on visual attention as an approach to solve captioning tasks with computer vision. This article studies the efficiency of different hyperparameter configurations on a state-of-the-art visual attention architecture composed of a pre-trained...

Issues in spectrum allocation between wireless network users have arisen due to the fast increase in the number of broadband services. Such issues include the failure to maximize the performance of all users by considering only a particular category of users. Specifically, a previously adopted selfish algorithm for spectrum allocation considers onl...

The concept of drone base stations (DBSs) has been applied to reduce the distance of the wireless link between a macro base station and its active users under diverse scenarios in military communications, smart industries, and high-density networks, and to provide service in topologies with damaged infrastructure. In this paper, we address the opti...

The analysis of plantar pressure through podometry has allowed analyzing and detecting different types of disorders and treatments in child patients. Early detection of an inadequate distribution of the patient’s weight can prevent serious injuries to the knees and lower spine. In this paper, an embedded system capable of detecting the presence of...

In this letter, we introduce CGDNet, a cost-efficient hybrid neural network composed of a shallow convolutional network, a gated recurrent unit, and a deep neural network, for robust automatic modulation recognition for cognitive radio services of modern communication systems. Our model employs pooling layers, small filter sizes, Gaussian dropout l...

The concept of using autoencoders (AEs) to represent wireless communication systems as an end-to-end reconstruction task that optimizes the transmitter and receiver components simultaneously in a single process has attracted the attention of wireless practitioners worldwide. This is attributable to the flexibility, and convenience of representing c...

Personal identification number (PIN) passwords are the preferred authentication method for visually impaired users to access digital devices like automated teller machines (ATMs), digital door locks, and cellular phones. The latest PIN input techniques have shown vulnerability to security breaches via shoulder-surfing, screen recording, and smudge...

In this paper, we study a deep learning (DL)-based multimodal technology for military, surveillance, and defense applications based on a pixel-by-pixel classification of soldier's image data-set. We explore the acquisition of images from a remote tactical-robot to a ground station, where the detection and tracking of soldiers can help the operator...

Issues in spectrum allocation between wireless network users have arisen due to the fast increase in the number of broadband services. Such issues include the failure to maximize the performance of all users, by considering only a particular category of users. Specifically, a previously adopted selfish algorithm for spectrum allocation considers on...

This thesis addresses the environmental uncertainty in satellite images as a computer vision task using semantic image segmentation. We focus in the reduction of the error caused by the use of a single-environment models in wireless communications. We propose to use computer vision and image analysis to segment a geographical terrain in order to em...

The authors present a model-free policy-based reinforcement learning model that introduces perturbations on the pattern of a metasurface. The objective is to learn a policy that changes the size of the patches, and therefore the impedance in the sides of an artificially structured material. The proposed iterative model assigns the highest reward wh...

In this paper, we propose a radar signal modulation algorithm to recognize three different radar signals amidst other wireless communication waveforms, including Barker, linear frequency modulation, and rectangular codes. First we extract the features of the original signal by computing its smoothed pseudo Wigner-Ville distribution. Second, we cons...

With the current situation of the increasing pandemic all over the world, thermal cameras detecting temperature gives a huge advantage of measuring an individual's temperature. Cascade has been widely used in face detection, where a classifier with low computation cost can be firstly used to shrink most of the background while keeping the recall. C...

In this paper, we propose a radar signal modulation algorithm to recognize three different radar signals amidst other wireless communication waveforms, including Barker, linear frequency modulation, and rectangular codes. First, we extract the features of the original signal by computing its smoothed pseudo Wigner-Ville distribution. Second, we con...

측정되는 데이터는 우리가 원하는 값 외에도 잡음이나 측정 오차와 같은 원하지 않는 정보를 포함하고 있다. 필터링은 잡음 등 불필요한 데이터가 혼합된 측정 데이터로부터 원하는 데이터만을 걸러내는 데이터 처리 과정이다. 필터링되는 잡음이나 오차는 일정한 값을 가지지 않고 확률적으로 변화하기 때문에 확률적 모델의 상태를 예측해 필터링할 필요가 있지만, 확률적 잡음 모델의 추정 및 예측은 쉽지 않다. 따라서 확률적 잡음 모델의 상태 추정을 위한 필터링 방법들이 연구되고 있으며, 대표적인 방법으로 칼만 필터가 있다.[1] 칼만 필터는 정확도가 높은 방법이지만, 선형시스템과 가우시안 잡음이 있는 경우에 그 정확도가 보장되는 단점이...

Unmanned aerial vehicles have the potential to be used as aerial base stations (ABSs) for the telecommunication industry to serve locations where a high number of users convene temporally (i.e., stadiums, concerts, or other massive attendance events). However, the positioning of the ABSs is still an open research area. In this study, we propose a p...

Traditional wireless communication theory is based on complex probabilistic models and fixed conjectures, which limit the optimal utilization of spectrum resources. Deep learning has been used to design end-to-end communication systems using an encoder to replace the transmitter and a decoder for the receiver. We address the challenge to update the...

This paper analyses the feasibility of using implantable antennas to detect and monitor tumors. We analyze this setting according to the wireless propagation loss and signal fading produced by human bodies and their environment in an indoor scenario. The study is based on the ITU-R propagation recommendations and prediction models for the planning...

One of the most critical challenge in a vehicle-to-vehicle (V2V) scenario is the transmission safety messages (BSMs) e.g., geographical location, braking information, speed, the status of the turn signal, and direction of travel. The protocol adopted to transmit BSMs in V2V is refered as Dedicated Short-Range Communications (DSRC). The limited comm...

The design and implementation of conventional communication systems are based on strong probabilistic models and assumptions. These fixed and conventional communication theories exhibit limitations in the utilization of the limited spectrum resources and the complexity of optimization for emerging wireless applications. Currently, new generations o...

Deep learning has been helping communication networks to reconfigure and heal themselves dynamically. Self-organizing maps (SOM) have been used for this purpose in order to create self-organizing networks (SON) to meet the requirements of the actual fifth-generation (5G) network. In this paper, we create hexagonal and random topologies to simulate...

Due to the ever-increasing demand by bandwidth-hungry mobile applications and the prevalent growth in wireless communication, effective spectrum management continues to constitute an important issue. So many spectrum management techniques have been employed in different areas including broadband satellite systems, cognitive acoustic networks, railw...

Vehicle-to-vehicle (V2V) technology generally adopts Dedicated Short-Range Communications (DSRC) to transmit based safety messages (BSMs) e.g., geographical location, braking information, speed, the status of the turn signal, and direction of travel. Specific propagation and wireless communications channel models have been proposed from industry an...

In this paper, we study a deep learning (DL)-based application for military, surveillance and defence schemes, based on a pixel-by-pixel classification of a custom soldier's image dataset. We explore the acquisition of images from a remote tactical-robot to a ground station, where the detection and tracking of soldiers can help the operator to take...

In this paper, we present a real-time American Sign Language (ASL) hand gesture recognizer based on an artificial intelligence execution, instead of the classical and outdated image processing modalities. Our approach uses a Convolutional Neural Network (CNN) to train a dataset of hundreds of instances from the ASL alphabet, extracting the features...

In this paper, we describe an artificial intelligence (AI)-based application for military and defence purposes, based on the detection of the patterns of soldier's uniforms. Our approach uses two convolutional neural networks (CNN) to generate a segmentation network (SegNet) capable of being trained to perform semantic segmentation of any image pix...

This article analyzes the use, access, and development of the Information and Communication Technologies (ICTs) in the Ecuadorian manufacturing companies during the period 2012-2014. The theoretical aspects around ICT, its importance, limitations, and challenges are summarized. Furthermore, this manuscript proves the lineal correlation between the...

This paper combines two of the most recent research topics for consumer electronic devices: Internet of Things (IoT) and Artificial Intelligence (AI), applied to solve an image classification problem for an electrical appliance's recipe database. The first part of this article presents the development of an Android mobile application containing all...

The development of wearable technologies has been boosted considerably. On one hand, due to Internet of Things market expansion, and on the other, owing to health awareness on users demanding the convergence of wearable technology with applications that track their activity during the day, providing feedback on how to improve their consuming experi...

Acoustic-based source localization is being widely developed in target localization due to its advantages over visual-based localization. In this paper, a comparison of CC-PHAT and RP-PHAT methods for acoustic source localization is introduced in order to determine the accuracy and response in speed for acoustic source localization applications. Th...

This paper combines two of the most recent research topics for consumer electronic devices: Internet of Things (IoT) and Artificial Intelligence (AI), applied in a commercial Whole Slow Juicer. The article presents the development of the Android mobile application containing all the options from its recipe book manual. The problem addresses its inc...

This paper proposes a methodological design practice for the estimations of fading and propagation losses for an implant-to-bedside short-range wireless link. The breakdown is based on the ITU-R propagation data and prediction models for the planning of indoor radio communication systems and radio local area networks in the frequency range of 300 M...

In this paper we analyze the integration of HSI, VoIP and IPTV services into the optical network owned by the National Telecommunications Corporation of Ecuador; across the study of FTTx network topologies, convergence of technologies and access to the company services from the nodes. We overhaul the implementation process of the contract for insta...