COE 592Lecture 00Glossary
Glossary
Every term in Course overview, defined once and used the same way in every part. Each entry links to the slides where the idea appears.
- Terms
- 57
- Letters
- 20
A
- Activations
The intermediate outputs each layer produces for one input; during inference they must be held in memory alongside the weights, and during training they are also stored for the backward pass.
- Adversarial machine learning
The study of attacks that manipulate a model's inputs or training to make it fail, and of defenses that make models robust to them.
- Automated visual inspection
Using cameras and vision models to find defects, such as cracks or missing parts on rail car undercarriages, without manual inspection.
B
- Bit width
The number of bits used to store one weight or activation; weight size in bytes is parameter count times bit width divided by eight.
- Blackboard
KFUPM's learning management system, where the COE 592 syllabus is posted.
C
- Confidence score
The number a detector attaches to each box, such as UAV 0.93, expressing how certain it is of the class; boxes below a chosen threshold are discarded.
- Confidence threshold
The cut-off below which a detector discards its boxes; raising it removes low-confidence false positives but also drops correct boxes that scored low.
- Convolutional neural network
A neural network that learns filters applied across its input by convolution, widely used for images and adapted in PNet-IDS to network traffic features.
D
- Deployment
Implementing a model on the target hardware and software stack so it runs in the real application.
- Distribution shift
A change between the data a model was trained on and the data it meets in deployment, such as rain-degraded images or unseen network traffic; also called domain shift.
E
- EcoWeedNet
A lightweight, computationally and energy-efficient weed detection model proposed for low-carbon consumer electronics in precision agriculture.
- Edge AI
Running machine learning inference on or near the device that captures the data, instead of sending the data to a distant cloud server.
- Efficiency metrics
Measures of the cost of deep learning computing, such as model size, memory, compute, latency, throughput and energy, used alongside accuracy.
- Embedded system
A computer built into a larger device to perform a dedicated function under tight limits on memory, compute, power and cost, such as the processor on a drone, robot or IoT sensor.
F
- False positive
A detection of something that is not there or is the wrong class, such as YOLOv5-m labelling a bird as a UAV with confidence 0.29.
- Flash memory
The non-volatile storage on a microcontroller that holds program code and, in a deployed model, the weights; its size caps the model size that can be stored.
G
I
- Intelligent transportation systems
Applying sensing, communication and analytics to road and rail transport to improve safety and efficiency, for example detecting conflicts between turning vehicles and pedestrians.
- Internet of Things
Networks of connected physical devices and sensors that exchange data, typically with limited compute and energy.
- Intrusion detection system
A system that monitors network traffic or host activity and flags malicious behaviour or attacks.
K
- Knowledge distillation
Training a small student network to reproduce the outputs of a larger teacher network, so the student keeps most of the teacher's accuracy at a fraction of its size; PNet-IDS uses it to hold up under distribution shift.
L
- Latency
The time from one input reaching a model to its output being ready; for a moving vehicle or a passing wagon the scene changes while it elapses, so it is a safety budget as well as a performance metric.
- Left-turn pedestrian conflict
The intersection safety pattern in which a vehicle turning left across oncoming traffic enters a crosswalk a pedestrian is already using, drawn on slide 7 as a red vehicle arrow crossing a blue pedestrian arrow.
- LiDAR
Light detection and ranging: a sensor that measures distance with laser pulses and produces a 3D point cloud of the scene.
- Lightweight model
A neural network designed with few parameters and low compute so it can run on constrained hardware with acceptable accuracy.
M
- mAP
Mean average precision: the standard object detection accuracy score, averaging precision over recall levels and classes; mAP@0.5 counts a box correct at 50% overlap with the truth, and mAP50-95 averages over overlap thresholds from 50% to 95%.
- Microcontroller
A single chip that combines a small processor, flash memory and SRAM for a dedicated task, typically with kilobytes to a few megabytes of memory and no operating system; the smallest deployment target in the course.
- Model compression
Techniques that shrink a trained model's size and compute, such as pruning and quantization, so it can be deployed on constrained devices.
- Multi-sensor platform
A vehicle or UAV carrying several sensors, such as visual and thermal cameras, LiDAR and GPS, whose data are combined for inspection.
N
- Neural architecture search
Automatically searching over network designs, such as depth, width and layer types, to find architectures that meet an accuracy target within a hardware budget.
O
- Object detection
The task of locating objects in an image with bounding boxes and assigning each box a class label and a confidence score.
- Optimization tradeoff
The balance between the benefits of an optimization technique, such as smaller size or lower latency, and its costs, such as lost accuracy or extra engineering.
- Orthomosaic
A top-down image of an area assembled from many overlapping aerial photographs, each corrected for perspective and placed by its GPS position, such as the track corridor strips on slide 3.
P
- Parameter count
The number of learnable weights and biases in a model, usually quoted in millions; it sets the memory needed to store the model, such as 2.6M for YOLO11n.
- PNet-IDS
A lightweight and generalizable convolutional neural network for intrusion detection in the Internet of Things, published in IEEE Access in 2025.
- Point cloud
A set of 3D points sampled from surfaces in a scene, produced by LiDAR or photogrammetry and often coloured by elevation.
- Pre-course survey
The form students fill in at the start of the course so the instructor learns their background, skills and expectations.
- Precision
The fraction of a detector's reported boxes that are correct: true positives divided by true positives plus false positives.
- Precision agriculture
Farming that uses sensors, robots and data analysis to act on individual plants or small areas, for example automated weed detection.
Q
- Quantization
Storing and computing a network's weights and activations in fewer bits, for example 8-bit integers instead of 32-bit floats, which divides model size and speeds up arithmetic at some accuracy cost.
R
- Recall
The fraction of the real objects a detector finds: true positives divided by true positives plus false negatives.
- Resource-constrained device
Hardware whose memory, storage, compute and energy budget are small enough that a deep learning model must be optimized before it can run on it.
- Rolling stock
The wheeled vehicles that run on a railway, such as wagons and carriages, whose undercarriages are photographed for inspection on slide 8.
S
- SDAIA-KFUPM JRC for AI
The SDAIA-KFUPM Joint Research Center for Artificial Intelligence, where the instructor is a research scholar.
T
- Thermal camera
A camera that images infrared radiation, so it shows temperature differences rather than visible colour; labelled IR camera on the slide.
- Throughput
The number of inferences a system completes per second; it can rise through batching while the latency of each individual input stays the same or grows.
- TinyML
Machine learning that runs on microcontrollers and other devices with kilobytes of memory and milliwatts of power; the field this course's deployment work belongs to.