Computer Engineering

Computer engineering is the field that combines computer science and electrical engineering to design, build, and optimize computer systems — from tiny embedded devices to large-scale networks and supercomputers. It focuses on both hardware (the physical components) and software (the logic and programs) that make computers and digital systems work together efficiently.

Learning to Code, One Helpful Nudge at a Time

~ Manuel Castañón-Puga

If you’ve ever opened a coding tutorial and felt lost by line two, you’re not alone. Hurtado et al. describe a simple idea that helps: teaching beginners with a platform that guides you step by step, provides clear feedback, and recommends the next thing to learn based on your progress. Their tool, Protoboard, suggests learning materials by combining teacher input with intelligent rules about difficulty, and it adapts to each student rather than presenting the same content to everyone. Think of it like a friendly playlist for studying Java: it starts with easier “tracks,” then levels you up as you demonstrate your readiness. The system uses fuzzy-rule recommendations tied to beginner, medium, and advanced learning objects, along with basic metadata such as audience and format, to determine what you should see next.

When you open a unit, Protoboard prompts you to read the short lesson first and then try two types of practice: one where you fill in missing code and another where you start from a blank page. This order matters because it builds confidence before throwing you into the deep end. The app also checks for good habits—clear variable names, proper use of brackets, clean structure—and points out exactly what went wrong when you slip. That means your mistakes turn into quick lessons instead of long detours on Stack Overflow. In plain terms: you see what to fix, why it matters, and what “good” looks like.

Does this approach actually help? Hurtado et al. tested it with 112 students across two universities, focusing on classic control structures like if/else, switch, while, do-while, and for. After studying a topic, each student completed a pair of exercises (one “complete the code,” one “from scratch”). On average, students needed roughly one to three tries to get programs right—evidence that the feedback and structure were doing their job. The trickiest bits were usually the if/else cases, which makes sense for beginners; still, most learners landed the solution in just a few attempts.

Why should you care if you’re just starting out? This study suggests a smoother and less frustrating way to learn. A tool that nudges you to read first, practice right after, and adopt clean habits can save you time and make your code easier to grow later. Teachers benefit too—they can see how many attempts a task takes and adjust lessons or add new examples where people stumble. For you, that means clearer instructions, more tailored practice, and faster progress. If you’re curious about coding, look for resources that copy these ideas: short lessons, immediate practice, precise feedback, and gradual difficulty. Small wins stack up, and with the right nudges, you’ll go from “What is this bracket doing?” to “I’ve got this” much faster than you think.

Reference:
Hurtado, C., Licea, G., García-Valdez, M., Quezada, A., & Castañón-Puga, M. (2020). Teaching computer programming as well-defined domain for beginners with protoboard. Advances in Intelligent Systems and Computing, 1160 AISC, 262–271. https://doi.org/10.1007/978-3-030-45691-7_25

How Tech Can Read the Room—and Make Your Experience Better

~ Manuel Castañón-Puga

Ever felt like a game, app, or museum exhibit “just gets you” and reacts at the right moment? Rosales et al. explain a simple idea behind that feeling: measure how you’re interacting, then use that to adapt what you see. They lean on a classic set of eight clues about your behavior—presence, interactivity, control, feedback, creativity, productivity, communication, and adaptation—to describe your “level of interaction.” Think of them as vibes the system watches for: Are you engaged? Are you trying new things? Are you getting responses when you press buttons? These signals help the system learn what to show next, so you don’t get bored or lost.

To test this in real life, the team visited an interactive science museum in Tijuana, where people—especially children and teenagers—play to learn. They tracked everyday details, such as how long someone stayed, where they moved, whether they read information labels, and if they returned to the same spot. That may sound small, but together, those bits tell a story about attention and curiosity, helping designers make labels clearer, stations easier to use, and activities more enjoyable. Imagine a driving or flight station that notices you’re stuck and gives a quick tip, or speeds things up when you’re clearly nailing it—that’s the goal.

Under the hood, Rosales et al. use a fuzzy logic system—don’t worry, it’s just math that handles “in-between” values instead of only yes/no. Each of the eight clues gets a score between 0 and 1, and the system groups those scores into levels from “very bad” up to “excellent.” Then it determines your overall interaction level, ranging from 0 to 5, much like a skill tier in a game. If your level is near the next tier, it nudges you upward and updates its knowledge of you for the next step. In plain terms, the exhibit watches what you do, estimates your current mood, and adapts so you can keep learning without zoning out.

Does it work? They tried it with data from 500 visitors. The team split the group in half—one half to set up the tool and the other half to test it—and compared the system’s calls with human judgments. The results were close most of the time, with about 76% accuracy, which is decent for a first pass. For everyday life, that means smarter exhibits, apps, and games that can sense when to give you hints, when to challenge you, and when to switch things up. It’s the same idea you can use yourself: notice your own signals—am I engaged, getting feedback, learning something new?—and tweak your setup, whether that’s changing a study app’s difficulty, turning on captions, or picking a different mode in a game. Small cues add up to a better experience.

Suggested by Gayesky and Williams’ level idea and brought to life by Rosales et al., this approach is about meeting you where you are and moving with you. The more systems pay attention to those eight everyday clues—and the more they adjust in the moment—the more tech feels like a helpful guide, rather than a hurdle. Next time a tool feels smooth and responsive, there’s a good chance it’s quietly reading the room and adapting to keep you in the zone.

Reference:
Rosales, R., Ramírez-Ramírez, M., Osuna-Millán, N., Castañón-Puga, M., Flores-Parra, J. M., & Quezada, M. (2019). A fuzzy inference system as a tool to measure levels of interaction. In Advances in Intelligent Systems and Computing (Vol. 931). https://doi.org/10.1007/978-3-030-16184-2_52

How Rumors Actually Travel in Your Group Chats

~ Manuel Castañón-Puga

Think of your friends as dots and the relationships between you as lines. That picture—a network—can tell us a great deal about how a message travels. Raya-Díaz and colleagues explain that we can describe who’s connected to whom with something called an adjacency matrix, which is just a grid that marks a 1 when two people are linked and 0 when they aren’t. From that grid, you can spot who knows lots of people (their “degree”) and even find “hubs,” those super-connected folks who shrink distances in a network and speed things up. In simple terms, if a rumor hits a hub, it can quickly jump to many others.

But not all groups look the same. One shape the authors use is the “barbell” network: two tightly connected friend groups, separated by a thin path—perhaps that one person who belongs to both circles. In this setup, what happens to a rumor depends a lot on the people sitting on the bridge between the two sides. If the bridging person doesn’t pass things on, one whole half may never hear the news. That’s why “betweenness centrality”—basically, how often someone sits on the shortest route between others—matters so much for real communication. The higher your betweenness, the more you act like a hallway everyone has to walk through.

The team modeled a classroom to show this in action. Each student had a few simple traits: the number of connections they had (degree), how close they were to everyone else, whether they were on those in-between routes (betweenness), and—crucially—whether they chose to cooperate by passing the message along. One student initially received the rumor; after that, its spread depended on two factors: their willingness to share and the degree of betweenness of the neighbors they spoke to. When the “bridge” students cooperated, the message flowed to both sides; when they didn’t, it stalled, even if plenty of people on each side were chatty. You’ve seen this in everyday life: a club hears about an event only if the one friend who’s in both the club and your class actually tells them.

So what can you do with this? First, notice the bridges in your world—the friend who hops between group chats, the classmate in multiple circles, the teammate who also runs student council. If you want something to spread fast (a study guide, a show, a fundraiser), talk to them early. If you want to keep something confidential, be cautious about sharing it with people who belong to different groups. And remember, spreading isn’t automatic; it’s a choice. In the authors’ simulations, flipping cooperation on or off at the bridge changed everything—proof that a single person can shape what the whole network knows. That awareness helps you share more effectively, avoid misinformation, and ensure the right people actually hear what matters.

Reference:
Raya-Díaz, K., Gaxiola-Pacheco, C., Castañón-Puga, M., Palafox, L. E., & Rosales Cisneros, R. (2018). Influence of the Betweenness Centrality to Characterize the Behavior of Communication in a Group. In Computer Science and Engineering Theory and Applications, Studies in Systems, Decision and Control (Vol. 143, pp. 89–101). https://doi.org/10.1007/978-3-319-74060-7_5

From Taps to Talk: How Smart Exhibits Learn From You

~ Manuel Castañón-Puga

When you visit a museum or use an app, you’re not just a spectator—you’re part of a conversation. Rosales et al. show that the quality of that conversation depends on a few simple things: Are you actually there and paying attention? Do you get to control anything? Do you receive feedback that helps you keep going? Can you adapt what you’re doing, be creative, or even “talk” back to the system? Those ideas—presence, interactivity, control, feedback, creativity, productivity, communication, and adaptation—can be observed and visualized to create a picture of how engaged you are. The goal is to use that picture to serve you better in the moment, not after the fact.

To make this practical, the authors describe six easy-to-grasp “levels” of interaction, ranging from simply being present (Level 0) to full-on back-and-forth interactions where you create, choose, adapt, and receive instant responses (Level 5). Imagine the difference between glancing at a welcome screen and actually steering what happens next. At the high end, you can pick what you see, change the order, give input, and get tailored replies—more like a game than a poster. Thinking in levels helps designers ask: what tiny tweak would move someone up one step—add a button, a hint, a quick “nice job,” or a way to choose the next challenge?

The team tested these ideas in a Mexican science museum called “El Trompo,” watching 500 visitors try a four-screen exhibit where you control a car, plane, bike, or balloon. Their system treated each exhibit and each visitor like “agents” that sense what’s happening and adjust the experience. Think of it as a low-key guide that notices if you’re lost, bored, or excited and then nudges the content—more instructions if you’re stuck, more freedom if you’re cruising. This isn’t sci-fi; it’s built with rules that translate fuzzy, human behavior into clear decisions about what to show you next.

What’s the practical takeaway for your everyday tech life? If you’re building a school project, a club website, or a small app, aim to lift people one level at a time. Offer quick feedback so they know a tap or swipe “worked.” Offer small choices so they feel in control, such as picking the next topic or difficulty level. Let them adapt the path, not just the pace. And if you’re the user, look for tools that “listen”—ones that react when you linger, explore, or ask for more. In the study, a learning approach called a neuro-fuzzy system performed the best in recognizing the level of people, which helped the system respond more accurately. In plain terms, the tech learned to read the room and act accordingly, which made the experience smoother and more enjoyable.

Reference:
Rosales, R., Castañón-Puga, M., Lara-Rosano, F., Flores-Parra, J. M., Evans, R., Osuna-Millan, N., & Gaxiola-Pacheco, C. (2018). Modelling the interaction levels in HCI using an intelligent hybrid system with interactive agents: A case study of an interactive museum exhibition module in Mexico. Applied Sciences (Switzerland), 8(3), 1–21. https://doi.org/10.3390/app8030446

When Your Wi-Fi Argues with Itself (and Wins): How Smart Networks Keep Things Flowing

~ Manuel Castañón-Puga

Imagine a city where traffic lights talk to each other. When one intersection becomes crowded, nearby lights adjust to keep cars moving. Raya et al. describe a network that behaves like that—except the “cars” are your video calls, game updates, and voice messages, and the “traffic lights” are tiny software agents that make quick decisions without waiting for a human. The idea is simple: detect when parts of the network are congested, then negotiate smarter routes so that the most important data gets through first. This self-managing style relies on four habits that every teen can recognize from life: setting yourself up, continually improving, addressing problems early, and protecting yourself. In network terms, these are referred to as self-configuration, self-optimization, self-repair, and self-protection.

To pull this off, the network sees itself as a web of “nodes” (devices) and “links” (connections). Some nodes are social butterflies with numerous connections; others are more reserved. By measuring who’s connected to whom and who sits in the “center” of things, the system spots the best places to send traffic when there’s trouble—think of texting a friend who knows everyone to spread the word fast. These ideas originate from graph theory, but you can visualize them as group chats and mutual connections: the more meaningful connections a node has, the more influence it holds in keeping the conversation moving.

Here’s the everyday win. When a device’s queue starts to overflow—like unread messages piling up—the system flags congestion and triggers a quick “vote” among nearby nodes about where to send each flow next. Flows get simple labels: video, voice, or data, each with a priority. The network then shares bandwidth based on that priority (for example, a higher share for top-priority traffic), so your voice call won’t stutter just because a background download got greedy. Only congested spots initiate this negotiation, and the choices aim to match each neighbor’s preferences and capacity, using straightforward rules such as first-in, first-out lines and a clear threshold for when to act. In short: notice the jam, ask the neighbors, and direct the flow where it will be treated most effectively.

The team built and tested this in a simulator to watch what happens over time. When parts of the network got crowded, the agents stepped in and rerouted traffic according to those priorities. Voice often came out ahead—useful when you care about smooth calls—while video and general data took turns depending on the situation. The big takeaway for daily life: smarter, fairer sharing means fewer glitches during the moments you actually notice, like streaming or chatting, while quieter tasks adapt in the background. It’s like having a friend group that instinctively gives the mic to whoever needs it most, then rotates it back. This approach makes networks more resilient and hands-off, allowing them to keep up with whatever you throw at them—without requiring you to think about it.

Reference:
K. Raya-Díaz, C. Gaxiola-Pacheco, & M. Castañón-Puga. (2018). Agent-Based Model for Self-Management of Network Flows using Negotiation. IEEE LATIN AMERICA TRANSACTIONS, 16(1), 204–209. https://doi.org/10.1109/TLA.2018.8291475