This is a summary of the book titled “If it’s smart, it’s vulnerable” written by cybersecurity expert Mikko Hypponen and published by Wiley in 2022. His book is a gripping, insightful journey through the evolution of malware and the ever-expanding battlefield of cybersecurity. With decades of experience and a sharp narrative style, he traces the arc from the earliest computer viruses to the sophisticated cyberweapons of today, revealing how the internet—our most transformative invention—has also become a playground for criminals, spies, and rogue states. He also advises on how businesses and individuals can protect themselves online. 

The book opens with a historical lens, recounting how viruses first emerged in the 1980s, spreading via floppy disks among early personal computers. The real turning point came with the IBM PC’s open architecture, which allowed widespread software development and, inadvertently, the proliferation of malware. As modems and network cards connected users to bulletin board systems (BBSs), new infection vectors emerged, leading to the rise of file viruses and, eventually, internet-based threats. 

Hypponen categorizes malware into distinct types: macro viruses that tamper with shared documents, email worms that exploit trust between contacts, and internet worms like Slammer, which infected systems globally in mere minutes. He explains how exploit kits and ransomware trojans evolved to target users more aggressively, encrypting data and demanding payment—often in bitcoin, the preferred currency of cybercriminals due to its anonymity and irreversibility. 

The narrative then shifts to the economics of cybercrime. He paints a chilling picture of a booming underground industry, where ransomware attacks and spam campaigns generate billions annually. He recounts infamous cases like CryptoLocker and FileFixer, which tricked users into paying for fake recovery tools, and shows how cryptocurrencies have enabled criminals and even nations like North Korea to bypass traditional financial systems. 

Cyberwarfare emerges as a central theme, with him detailing how malware has become a strategic weapon. The Stuxnet worm, allegedly developed by the US and Israel, sabotaged Iran’s nuclear program with surgical precision. Other attacks, like NotPetya and WannaCry, masqueraded as ransomware but were actually state-sponsored sabotage campaigns, causing massive financial damage across industries. 

Law enforcement, too, has entered the malware arena—not to harm, but to investigate. He describes how police agencies deploy malware to intercept communications before encryption, often by physically accessing devices or collaborating with internet providers. Yet even with advanced tools, human error remains the weakest link. Simple mistakes—like reusing passwords or clicking suspicious links—continue to enable breaches. 

As the Internet of Things expands, even mundane devices like toasters and dishwashers will become vulnerable. He warns that security must evolve beyond firewalls and antivirus software. He advocates for proactive monitoring, bait networks, and regulatory accountability for manufacturers of smart devices. 

He ends with a clear message: the smarter our technology becomes, the more exposed we are. But with awareness, vigilance, and smarter security practices, we can navigate this digital minefield. His book is both a wake-up call and a guide for anyone living in our increasingly connected world. 

#codingexercise: CodingExercise-03-29-2025.docx

 This is a summary of the book titled “Pattern Breakers: why some start-ups change the future” written by Peter Ziebelman and Mike Maples Jr. and published by Public Affairs in 2024. When people hesitated to open their vehicle or homes to strangers, companies like Uber, Lyft and AirBnb upended these assumptions. The authors advocate to have a vision that agrees with the future even if it does not fall within the current pattern. They say best practices do not help start-ups create pattern-breaking ideas. Identify the inflections that are worthy of your attention today. Non-consensus insights help you outcompete the status quo. Achieve insights by living the future and finding what’s missing. Test your insights with early adopters to gauge interest. Gather stakeholders from team members, customers and investors. Build your movement by telling a provocative hero story. Embracing pattern-breaking ideas is not just for startups.

The book highlights the importance of living in the future to discover what is missing. By immersing themselves in cutting-edge technologies and trends, founders can gain valuable insights. These insights should be tested with early adopters to gauge interest and refine the concept. The authors stress the need for start-ups to gather stakeholders, including team members, customers, and investors, who believe in the vision and can help build a movement.

Corporations, too, can innovate by understanding inflections and leveraging their existing strengths. The book provides examples such as Lockheed Martin's development of a groundbreaking fighter jet during World War II and Facebook's acquisition of Instagram, which grew exponentially with the help of Facebook's global reach. The authors argue that large corporations often become too reliant on their past successes, leading to biases that favor established patterns and resistance to breakthrough ideas .

Disagreeableness is presented as an asset for founders, enabling them to say "no" to decisions that dilute their groundbreaking ideas. The right amount of disagreeableness helps founders develop resilience in the face of rejection and avoid the conformity trap. The book advises founders to work with executive coaches to find their most functional level of disagreeableness and reorient themselves toward their central mission.

The authors also emphasize the importance of storytelling in building a movement. Founders should create a hero's journey with co-conspirators as the heroes, the start-up founder as the mentor, and the status quo as the enemy. A powerful story centered on a higher purpose can inspire radical change and unite people in a shared belief in a better future. The book cites Tesla's mission to "accelerate the world's transition to sustainable energy" as an example of a compelling story that attacks the status quo.

To enlist early believers, start-ups should focus on those who align with their vision and can help overcome resistance to new ideas. The book advises founders to seek feedback from those who share their core vision and to uncover surprises that can refine their concept. Positive surprises indicate a genuine craving for the product, while negative surprises suggest issues with implementation, audience, or insight.

The authors conclude that the true artistry of breakthrough founders lies in discovering compelling insights that leverage inflections to create new games with new rules. By embracing inflection theory and focusing on non-consensus insights, start-ups can develop pattern-breaking ideas that change the world .

#codingexercise: https://1drv.ms/w/c/d609fb70e39b65c8/EVSR0785qI5JpCaOv-gObmsBWKWvJQQUwZFwVgR8w2kwOw?e=7bx7PD 

 Extending Radial Basis Function Neural Networks to Azure Cloud Analytics for UAV Swarm Control

Radial Basis Function Neural Networks (RBFNNs) are particularly well-suited for modeling uncertain dynamics in UAV swarm formation control due to their localized activation functions and strong interpolation capabilities. Traditionally deployed on-device, RBFNNs offer fast approximation of nonlinearities but are constrained by limited computational resources, which restricts their scalability and responsiveness in dynamic environments. By integrating RBFNNs into Azure’s cloud infrastructure, we can significantly enhance their utility and operational impact across UAV swarms.

In decentralized UAV swarm systems, each drone typically runs a lightweight RBFNN to adapt its control signals based on local observations. However, this localized inference lacks global awareness and is vulnerable to noise, latency, and model drift. By shifting the RBFNN computation to Azure, UAVs can stream telemetry data to a centralized model that aggregates swarm-wide inputs, performs high-fidelity function approximation, and returns optimized control signals in real time. Azure’s GPU-accelerated environments allow for deeper RBFNN architectures and ensemble modeling, which are infeasible on embedded systems.

For example, in leader-follower scenarios where follower UAVs must track a dynamic leader, Azure-hosted RBFNNs can continuously learn and refine the leader’s trajectory model using historical and real-time data. This enables predictive control strategies that anticipate future states rather than react to current ones. Similarly, in constrained environments with unknown obstacles, cloud-based RBFNNs can integrate geospatial data, environmental maps, and swarm telemetry to generate adaptive control laws that are both collision-aware and formation-preserving.

Azure’s edge computing stack—particularly Azure IoT Edge and Azure Percept—can be used to deploy lightweight inference modules on UAVs that receive periodic updates from the cloud-hosted RBFNN. This hybrid architecture ensures low-latency responsiveness while maintaining the benefits of centralized learning. Moreover, Azure’s support for continuous integration and deployment (CI/CD) pipelines allows for real-time model updates, ensuring that the RBFNN evolves with mission demands and environmental changes.

Security and reliability are also enhanced in this cloud-augmented framework. Azure’s built-in compliance with aviation-grade standards and its support for encrypted data channels ensure that control signals and telemetry remain secure throughout the feedback loop. Additionally, Azure Monitor and Application Insights can be used to track model performance, detect anomalies, and trigger automated retraining when drift is detected.

In summary, migrating RBFNN-based UAV swarm control to Azure cloud analytics transforms a reactive, localized control strategy into a predictive, globally optimized system. This approach enhances formation stability, obstacle avoidance, and mission adaptability—while preserving the real-time responsiveness required for aerial operations.

 Extending DRL-based UAV Swarm Formation Control to Azure Cloud Analytics 

Deep Reinforcement Learning (DRL) has emerged as a powerful paradigm for autonomous UAV swarm control, enabling agents to learn optimal policies through interaction with dynamic environments. Traditionally, these DRL models are trained and executed on-device, which imposes significant constraints on sample efficiency, model complexity, and real-time adaptability. By integrating Azure cloud analytics into the control loop, we can overcome these limitations and unlock a new tier of intelligent swarm coordination. 

In conventional setups, algorithms like Deep Q-Networks (DQN), Momentum Policy Gradient (MPG), Deep Deterministic Policy Gradient (DDPG), and Multi-Agent DDPG (MADDPG) are deployed locally on UAVs. These models must balance computational load with battery life, often resulting in shallow architectures and limited exploration. Azure’s cloud infrastructure allows for centralized training of deep, expressive DRL models using vast datasets—including historical flight logs, environmental simulations, and real-time telemetry—while enabling decentralized execution via low-latency feedback loops. 

For instance, DQN-based waypoint planning can be enhanced by hosting the Q-function approximation in Azure. UAVs transmit their current state and receive action recommendations derived from a cloud-trained policy that considers global swarm context, terrain data, and mission objectives. This centralized inference reduces redundant exploration and improves convergence speed. Similarly, MPG algorithms can benefit from cloud-based momentum tracking across agents, enabling smoother policy updates and more stable learning in sparse-reward environments. 

DDPG and MADDPG, which are particularly suited for continuous action spaces and multi-agent coordination, can be scaled in the cloud to model inter-agent dependencies more effectively. Azure’s support for distributed training and federated learning allows each UAV to contribute local experiences to a shared policy pool, which is periodically synchronized and redistributed. This architecture supports centralized critics with decentralized actors, aligning perfectly with MADDPG’s design philosophy. 

Moreover, Azure’s integration with edge services like Azure IoT Edge and Azure Digital Twins enables real-time simulation and feedback. UAVs can simulate potential actions in the cloud before execution, reducing the risk of unsafe behaviors during exploration. Safety constraints, such as collision avoidance and energy optimization, can be enforced through cloud-hosted reward shaping modules that adapt dynamically to mission conditions. 

Metrics that can be used to measure gains using this strategy include: 

Policy Convergence Rate: Faster Convergence due to centralized training and shared experience across agents 

Sample efficiency: Improved Learning from fewer interactions via cloud-based replay buffers and prioritized experience 

Collision Avoidance Rate: Higher success rate through global awareness and cloud-enforced safety constraints 

Reward Optimization Score: Better long-term reward accumulation from cloud-tuned reward shaping and mission-aware feedback 

Exploration Stability Index: Reduced variance in learning behavior due to centralized critics and policy regularization 

Mission Completion Time: Shorter execution time through optimized waypoint planning and co-operative swarm behavior. 

In summary, extending DRL-based UAV swarm control to Azure cloud analytics transforms the learning paradigm from isolated, resource-constrained agents to a collaborative, cloud-augmented intelligence network. This approach enhances sample efficiency, stabilizes training, and enables real-time policy refinement—ultimately leading to more robust, scalable, and mission-aware swarm behaviors. 

 Introduction

The evolution of drone technology has catalyzed a diverse body of research spanning autonomous flight, swarm coordination, and distributed sensing. Much of the existing literature emphasizes the increasing sophistication of onboard capabilities and collaborative behaviors among UAVs, particularly in swarm configurations. Adoni et al. [11] present a comprehensive framework for intelligent swarms based on the leader–follower paradigm, demonstrating how standardized hardware and improved communication protocols have lowered barriers to swarm deployment. Their work highlights the operational advantages of swarms in mission-critical applications, such as fault-tolerant navigation, dynamic task allocation, and consensus-based decision making [37,47,53].

Swarm intelligence, as defined by Schranz et al. [37], involves a set of autonomous UAVs executing coordinated tasks through local rule sets that yield emergent global behavior. This includes collective fault detection, synchronized motion, and distributed perception—capabilities that are particularly valuable in environments requiring multitarget tracking or adaptive coverage. These behaviors are often supported by consensus control mechanisms [38,39], enabling UAVs to converge on shared decisions despite decentralized architectures. Such systems are robust to individual drone failures and can dynamically reconfigure based on mission demands.

In parallel, recent advances in UAV swarm mobility have addressed challenges related to spatial organization, collision avoidance, and energy efficiency. Techniques such as divide-and-conquer subswarm formation [11,74] and cooperative navigation strategies [44,47,75] have been proposed to enhance swarm agility and resilience. These mobility frameworks are critical for applications ranging from environmental monitoring [8,32] to collaborative transport [20,21], where drones must maintain formation and communication integrity under dynamic conditions.

While these studies underscore the importance of onboard intelligence and inter-UAV coordination, a complementary line of research has emerged focusing on networked decision-making and edge-based analytics. Jung et al. [Drones 2024, 8, 582] explore the integration of edge AI into UAV swarm tactics, proposing adaptive decision-making frameworks that leverage reinforcement learning (RL) algorithms such as DDPG, PPO, and DDQN [25–35]. These approaches enable drones to learn optimal behaviors in real time, adjusting to environmental feedback and peer interactions. Their work also addresses limitations in traditional Flying Ad Hoc Networks (FANETs) and Mobile Ad Hoc Networks (MANETs), proposing scalable routing protocols and adaptive network structures to support high-mobility drone swarms [12–22].

Despite the promise of RL-based control and swarm intelligence, both paradigms often rely on extensive onboard computation or pre-trained models tailored to specific tasks. This tight coupling between the drone’s hardware and its analytical stack can limit flexibility and scalability. In contrast, the present work proposes a shift toward cloud-native analytics that operate independently of drone-specific configurations. By treating the drone as a mobile sensor and offloading interpretation to external systems, we aim to reduce the dependency on custom models and instead utilize agentic retrieval techniques to dynamically match raw video feeds with relevant analytical functions.

This approach aligns with broader efforts to democratize UAV capabilities by minimizing hardware constraints and emphasizing software adaptability. It complements swarm-based methodologies by offering an alternative path to autonomy—one that leverages scalable infrastructure and flexible analytics rather than bespoke onboard intelligence. As such, our work contributes to the growing discourse on UAV-enabled sensing and control, offering a lightweight, analytics-driven framework that can coexist with or substitute traditional swarm intelligence and RL-based decision systems.

Extending DRL-based UAV Swarm Formation Control to Azure Cloud Analytics

Deep Reinforcement Learning (DRL) has emerged as a powerful paradigm for autonomous UAV swarm control, enabling agents to learn optimal policies through interaction with dynamic environments. Traditionally, these DRL models are trained and executed on-device, which imposes significant constraints on sample efficiency, model complexity, and real-time adaptability. By integrating Azure cloud analytics into the control loop, we can overcome these limitations and unlock a new tier of intelligent swarm coordination.

In conventional setups, algorithms like Deep Q-Networks (DQN), Momentum Policy Gradient (MPG), Deep Deterministic Policy Gradient (DDPG), and Multi-Agent DDPG (MADDPG) are deployed locally on UAVs. These models must balance computational load with battery life, often resulting in shallow architectures and limited exploration. Azure’s cloud infrastructure allows for centralized training of deep, expressive DRL models using vast datasets—including historical flight logs, environmental simulations, and real-time telemetry—while enabling decentralized execution via low-latency feedback loops.

For instance, DQN-based waypoint planning can be enhanced by hosting the Q-function approximation in Azure. UAVs transmit their current state and receive action recommendations derived from a cloud-trained policy that considers global swarm context, terrain data, and mission objectives. This centralized inference reduces redundant exploration and improves convergence speed. Similarly, MPG algorithms can benefit from cloud-based momentum tracking across agents, enabling smoother policy updates and more stable learning in sparse-reward environments.

DDPG and MADDPG, which are particularly suited for continuous action spaces and multi-agent coordination, can be scaled in the cloud to model inter-agent dependencies more effectively. Azure’s support for distributed training and federated learning allows each UAV to contribute local experiences to a shared policy pool, which is periodically synchronized and redistributed. This architecture supports centralized critics with decentralized actors, aligning perfectly with MADDPG’s design philosophy.

Moreover, Azure’s integration with edge services like Azure IoT Edge and Azure Digital Twins enables real-time simulation and feedback. UAVs can simulate potential actions in the cloud before execution, reducing the risk of unsafe behaviors during exploration. Safety constraints, such as collision avoidance and energy optimization, can be enforced through cloud-hosted reward shaping modules that adapt dynamically to mission conditions.

Metrics that can be used to measure gains using this strategy include:

Policy Convergence Rate: Faster Convergence due to centralized training and shared experience across agents

Sample efficiency: Improved Learning from fewer interactions via cloud-based replay buffers and prioritized experience

Collision Avoidance Rate: Higher success rate through global awareness and cloud-enforced safety constraints

Reward Optimization Score: Better long-term reward accumulation from cloud-tuned reward shaping and mission-aware feedback

Exploration Stability Index: Reduced variance in learning behavior due to centralized critics and policy regularization

Mission Completion Time: Shorter execution time through optimized waypoint planning and co-operative swarm behavior.

In summary, extending DRL-based UAV swarm control to Azure cloud analytics transforms the learning paradigm from isolated, resource-constrained agents to a collaborative, cloud-augmented intelligence network. This approach enhances sample efficiency, stabilizes training, and enables real-time policy refinement—ultimately leading to more robust, scalable, and mission-aware swarm behaviors.

 Extending ANN-Based UAV Swarm Formation Control to Azure Cloud Analytics

Artificial Neural Networks (ANNs) have long been central to on-device UAV swarm formation control due to their ability to approximate nonlinear dynamics, adapt to environmental changes, and generalize across mission scenarios. However, the reliance on embedded computation within UAVs introduces limitations in scalability, energy efficiency, and model complexity. By shifting the analytical workload to the Azure public cloud—where computational resources are virtually limitless—we can significantly enhance the depth and responsiveness of ANN-driven swarm control.

In traditional on-device implementations, radial basis function networks, Chebyshev neural networks, and recurrent neural networks are used to approximate uncertain dynamics, estimate nonlinear functions, and predict future states. These models are constrained by the onboard hardware’s memory and processing power, often requiring simplifications that reduce fidelity. By offloading these computations to Azure, UAVs can transmit real-time telemetry and imagery to cloud-hosted ANN models that are deeper, more expressive, and continuously retrained using federated learning or centralized datasets.

For example, instead of each UAV running a lightweight radial basis function network to adapt to unknown dynamics, the Azure cloud can host a high-resolution ensemble model that receives state data from all swarm members, performs centralized inference, and returns optimized control signals. This enables richer modeling of inter-agent dependencies and environmental constraints. Similarly, Chebyshev neural networks, which benefit from orthogonal polynomial approximations, can be scaled in the cloud to handle more complex formations and dynamic reconfigurations without overburdening UAV processors.

Recurrent neural networks, particularly those used for leader-follower consensus or predictive control, can be extended into cloud-based long short-term memory (LSTM) or transformer architectures. These models can ingest historical flight data, weather patterns, and mission objectives to generate predictive trajectories that are fed back into the swarm’s control loop. Azure’s real-time streaming and edge integration capabilities (e.g., Azure IoT Hub, Azure Stream Analytics) allow UAVs to receive low-latency feedback, ensuring that cloud-derived insights are actionable within the swarm’s operational timeframe.

Metrics that can be used to measure gains using this strategy include:

Formation Stability Index: Reduced deviation from desired formation due to centralized coordination and richer model generalization.

Function Approximation Error: Lower error in modeling nonlinear dynamics thanks to deeper, cloud-hosted ANN architectures.

Control Signal Latency: Maintained sub-100ms latency via Azure IoT Edge integration, ensuring real-time responsiveness.

Energy Consumption per UAV: Reduced onboard compute load, extending flight time and reducing thermal stress.

Model Update Frequency: Increased frequency of retraining and deployment using Azure ML pipelines for adaptive control.

Adaptability Score: Faster response to environmental changes due to cloud-based retraining and swarm-wide context awareness.

In summary, migrating ANN-based formation control from on-device computation to Azure cloud analytics unlocks higher model complexity, centralized learning, and real-time collaborative inference. This paradigm shift transforms UAV swarms from isolated agents into a cloud-augmented collective, capable of executing more intelligent, adaptive, and mission-aware behaviors.

 In the evolving landscape of autonomous aerial systems, coordinating UAV swarms in dynamic environments presents a formidable challenge. Traditional centralized control models often struggle with scalability and adaptability, especially when navigating complex terrains or responding to unpredictable obstacles. To address this, a promising approach involves blending Self-Organizing Maps (SOMs) with Deep Q-Networks (DQNs)—a hybrid architecture that leverages unsupervised spatial abstraction alongside reinforcement-driven decision-making.

At the heart of this system lies a decentralized swarm of UAV agents, each equipped with onboard sensors to capture environmental data such as terrain features, obstacle proximity, and traffic density. This raw data is first processed through a SOM, which clusters high-dimensional inputs into a topological map. The SOM acts as a spatial encoder, reducing complexity and revealing latent structure in the environment—essentially helping each UAV “see” the world in terms of navigable zones, threat clusters, and flow corridors.

Once the SOM has abstracted the environment, its output feeds into a Deep Q-Network. The DQN uses this simplified state representation to learn optimal actions—whether to move, rotate, ascend, or hold position—based on a reward function tailored to swarm objectives. These objectives include maintaining formation integrity, avoiding collisions, minimizing energy consumption, and maximizing throughput through constrained airspace. The reward engine dynamically adjusts feedback based on real-time metrics like deviation from formation, proximity to obstacles, and overall swarm flow efficiency.

A key advantage of this hybrid model is its ability to support leader-follower dynamics within the swarm. The SOM helps follower UAVs interpret the leader’s trajectory in context, abstracting both environmental constraints and formation cues. This enables fluid reconfiguration when conditions change—say, a sudden wind gust or a moving obstacle—without requiring centralized recalibration. The SOM re-clusters the environment, and the DQN re-plans the agent’s next move, all in real time.

To evaluate the system, simulations can be run in urban grid environments with variable wind, dynamic obstacles, and no-fly zones. Metrics such as formation deviation, collision rate, and flow efficiency provide quantitative insight into performance. Compared to vanilla DQN models or rule-based planners, the SOM-DQN hybrid is expected to demonstrate superior adaptability and throughput, especially in congested or unpredictable settings.

Technically, the system can be implemented using Python-based SOM libraries like MiniSom, paired with PyTorch or TensorFlow for the DQN. Simulation platforms such as AirSim or Gazebo offer realistic environments for testing swarm behavior under diverse conditions.

Ultimately, this architecture offers a scalable, intelligent framework for UAV swarm coordination—one that balances spatial awareness with strategic action. By fusing the pattern-recognition strengths of SOMs with the decision-making power of DQNs, it opens the door to more resilient, efficient, and autonomous aerial systems.

 Deep Q-Networks (DQNs) have emerged as a transformative approach in the realm of autonomous UAV swarm control, particularly for waypoint determination and adherence. At their core, DQNs combine the strengths of Q-learning—a reinforcement learning technique—with deep neural networks to enable agents to learn optimal actions in complex, high-dimensional environments. This fusion allows UAVs to make intelligent decisions based on raw sensory inputs, such as position, velocity, and environmental cues, without requiring handcrafted rules or exhaustive programming.

In the context of UAV swarms, waypoint determination refers to the process of selecting a sequence of spatial coordinates that each drone must follow to achieve mission objectives—be it surveillance, search and rescue, or environmental monitoring. Traditional methods for waypoint planning often rely on centralized control systems or pre-defined trajectories, which can be rigid and vulnerable to dynamic changes in the environment. DQNs, however, offer a decentralized and adaptive alternative. Each UAV can independently learn to navigate toward waypoints while considering the positions and behaviors of its neighbors, obstacles, and mission constraints.

One of the key advantages of DQNs in swarm coordination is their ability to model the waypoint planning problem as a Markov Decision Process (MDP). In this framework, each UAV observes its current state (e.g., location, heading, proximity to obstacles), selects an action (e.g., move to a neighboring grid cell), and receives a reward based on the outcome (e.g., proximity to target, collision avoidance). Over time, the DQN learns a policy that maximizes cumulative rewards, effectively guiding the UAV through optimal waypoints. This approach has been successfully applied in multi-agent scenarios where drones must maintain formation while navigating complex terrains.

For example, Xiuxia et al. proposed a DQN-based method for multi-UAV formation transformation, where the swarm adapts its configuration from an initial to a target formation by learning optimal routes for each drone. The system models the transformation as an MDP and uses DQN to determine the best movement strategy for each UAV, ensuring collision-free transitions and minimal energy expenditure. Similarly, Yilan et al. implemented a DQN-driven waypoint planning system that divides the 3D environment into grids. Each UAV selects its next move based on DQN predictions, optimizing path efficiency and obstacle avoidance.

To enhance learning efficiency, modern DQN implementations often incorporate techniques like prioritized experience replay and target networks. Prioritized experience replay allows UAVs to learn more effectively by focusing on experiences with high temporal difference errors—those that offer the most learning value. Target networks stabilize training by decoupling the Q-value updates from the current network predictions, reducing oscillations and improving convergence.

Moreover, DQNs support scalability and robustness in swarm operations. Because each UAV learns independently using local observations and shared policies, the system can accommodate large swarms without overwhelming communication channels or computational resources. This decentralized learning paradigm also enhances fault tolerance; if one UAV fails or deviates, others can adapt without compromising the entire mission.

In real-world deployments, DQN-based swarm control has shown promise in dynamic environments such as urban landscapes, disaster zones, and contested airspaces. By continuously learning from interactions, UAVs can adjust their waypoint strategies in response to changing conditions, such as wind patterns, moving obstacles, or evolving mission goals.

There is speculation that self-organizing maps (SOM) can be integrated with DQN where UAV swarm must optimize formation under environmental constraints. SOMs can preprocess high-dimensional state spaces into simplified input for Q-network, cluster environmental features such as terrain obstacles, traffic density, to guide UAVs towards optimal formations, improves exploration efficiencies by identifying promising regions in the state-action space. When combined with Multi-agent Reinforcement Learning (MARL) for decentralized decision-making and GNN for modeling inter-agent relationships and spatial topology, MARL-SOM-GNNs architecture enables a UAV swarm to dynamically adapt formation based on clustered environmental features, maximize flow and coverage in constrained environments and maintain robust co-ordination even with partial observability or noisy data.

Finally, Deep Q-Networks offer a powerful, flexible, and scalable solution for UAV swarm waypoint determination and adherence. By enabling autonomous learning and decision-making, DQNs pave the way for intelligent aerial systems capable of executing complex missions with minimal human intervention.

 Boids algorithm

The Boids algorithm, originally developed by Craig Reynolds in 1986, is a computational model that simulates the flocking behavior of birds through three simple rules: separation (avoid crowding neighbors), alignment (steer towards the average heading of neighbors), and cohesion (move toward the average position of neighbors). Though deceptively simple, these rules give rise to complex, emergent group behaviors that have inspired a wide range of applications—including the coordination of Unmanned Aerial Vehicles (UAVs).

In the context of UAV operations, especially in swarm scenarios, the Boids algorithm offers a biomimetic approach to decentralized control. Traditional UAV control systems rely heavily on centralized Ground Control Stations (GCSs) or direct remote control, which become increasingly inefficient and fragile as the number of drones scales up. Communication bottlenecks, latency, and the risk of packet loss can severely compromise mission success. The Boids model, by contrast, enables each drone to act autonomously based on local information, reducing reliance on centralized coordination and enhancing robustness.

Recent research has demonstrated the viability of Boids-inspired algorithms for UAV formation control and obstacle avoidance. For instance, Lu et al. proposed a Boids-based integration algorithm that allows UAVs to autonomously switch between formation mode and obstacle avoidance mode depending on environmental stimuli. In formation mode, drones use a virtual structure method to maintain their positions relative to the group, while in obstacle avoidance mode, they employ artificial potential fields to navigate safely around hazards. This dual-mode flexibility ensures that UAV swarms can adapt dynamically to changing conditions while maintaining mission integrity.

Moreover, the Boids algorithm has been successfully implemented in real-world UAV systems using platforms like the Robot Operating System (ROS). Hauert et al. created a flock of ten drones that mimicked Boids behavior both in simulation and physical flight, although with some limitations in separation due to altitude constraints. Braga et al. extended this work by developing a leader-following Boids-inspired algorithm for multi-rotor UAVs, demonstrating its effectiveness in both simulated and real environments.

One of the most compelling advantages of Boids-based UAV control is its scalability. Because each drone only needs to consider its immediate neighbors, the system can scale to hundreds or even thousands of units without overwhelming computational or communication resources. This makes it particularly suitable for applications like search and rescue, environmental monitoring, and large-scale aerial displays, where coordinated movement and adaptability are crucial.

The integration of Boids with reinforcement learning (RL) further enhances its capabilities. In pursuit-evasion scenarios, for example, researchers have combined Boids principles with deep RL algorithms to enable drones to learn optimal strategies for tracking or evading targets in complex environments. The Boids-PE framework, hosted on GitHub, exemplifies this hybrid approach by merging Boids dynamics with Apollonian circle strategies for multi-agent coordination.

In summary, the Boids algorithm provides a powerful, nature-inspired framework for decentralized UAV swarm control. Its simplicity, adaptability, and compatibility with modern AI techniques like reinforcement learning make it a cornerstone for next-generation autonomous aerial systems. As drone operations continue to expand in scale and complexity, Boids-based models offer a promising path toward resilient, intelligent, and cooperative UAV behavior.

 A previous article explained the approach for UAV Swarm video sensing and this article explains the differences between stream and batch processing of the input. Most video sensing applications make use of one or the other form of processing depending on how intensive they want the analytics to be or how fast they want to study the images such as to track an object. Our approach was that of a platform across use cases such that there is deep data analysis and resource scheduling with ways to beat trade-offs in latency and flexibility. It also comes with the benefits of improved data quality, offline features, minimal supervision, greater efficiency, simplified processes and the ability to query both with structured query operators as well as natural language queries. 

This is not to say stream processing must be avoided but that the analysis of each and every image as a datapoint could be avoided with little loss of fidelity in the responses to the queries from the video sensing applications. Stream processing manages endless flow of data while swiftly identifying and retaining the most important information with security and scalability, so use cases that do without it are not in scope but we do extend the boundary in that direction. We believe the gains from the batch processing characteristics such as being less critical, more fault-tolerant, simpler to implement and extend, and flexibility in defining batches reduce the Total Cost of Ownership and in a way that frees up the video sensing application from the infrastructure concerns.

In this regard, we cite the following comparisons via charts:

Use cases Latency from Streaming Latency from Batch

Occurrences of object

Object description such as circular roof

Distance between objects

Location information

Tracking of objects N/A

Color based search of objects such as red car

Shape based search such as triangular parking/building

Time lapse of a location N/A

Use cases Cost from Streaming in terms of (tokens, api calls, size of digital footprint) Cost from Batch in terms of (tokens, api calls, size of digital footprint)

Occurrences of object

Object description such as circular roof

Distance between objects

Location information

Tracking of objects

Color based search of objects such as red car

Shape based search such as triangular parking/building

Time lapse of a location

 There are N points (numbered from 0 to N−1) on a plane. Each point is colored either red ('R') or green ('G'). The K-th point is located at coordinates (X[K], Y[K]) and its color is colors[K]. No point lies on coordinates (0, 0).

We want to draw a circle centered on coordinates (0, 0), such that the number of red points and green points inside the circle is equal. What is the maximum number of points that can lie inside such a circle? Note that it is always possible to draw a circle with no points inside.

Write a function that, given two arrays of integers X, Y and a string colors, returns an integer specifying the maximum number of points inside a circle containing an equal number of red points and green points.

Examples:

1. Given X = [4, 0, 2, −2], Y = [4, 1, 2, −3] and colors = "RGRR", your function should return 2. The circle contains points (0, 1) and (2, 2), but not points (−2, −3) and (4, 4).

class Solution {

    public int solution(int[] X, int[] Y, String colors) {

        // find the maximum

        double max = Double.MIN_VALUE;

        int count = 0;

        for (int i = 0; i < X.length; i++)

        {

            double dist = X[i] * X[i] + Y[i] * Y[i];

            if (dist > max)

            {

                max = dist;

            }

        }

        for (double i = Math.sqrt(max) + 1; i > 0; i -= 0.1)

        {

            int r = 0;

            int g = 0;

            for (int j = 0; j < colors.length(); j++)

            {

                if (Math.sqrt(X[j] * X[j] + Y[j] * Y[j]) > i)

                {

                    continue;

                }

                if (colors.substring(j, j+1).equals("R")) {

                    r++;

                }

                else {

                    g++;

                }

            }

            if ( r == g && r > 0) {

                int min = r * 2;

                if (min > count)

                {

                    count = min;

                }

            }

        }

        return count;

    }

}

Compilation successful.

Example test: ([4, 0, 2, -2], [4, 1, 2, -3], 'RGRR')

OK

Example test: ([1, 1, -1, -1], [1, -1, 1, -1], 'RGRG')

OK

Example test: ([1, 0, 0], [0, 1, -1], 'GGR')

OK

Example test: ([5, -5, 5], [1, -1, -3], 'GRG')

OK

Example test: ([3000, -3000, 4100, -4100, -3000], [5000, -5000, 4100, -4100, 5000], 'RRGRG')

OK

 This is a summary of the book titled “Account-Based Marketing: The definitive handbook for B2B marketers” written by Bev Burgess and published by Kogan Page in 2025. In this comprehensive guide, she draws out the landscape and advises both new and experienced practitioners in implementing the five types: “Strategic, Scenario, Segment, Programmatic, and Pursuit marketing”.

At its core, account-based marketing (ABM) is about shifting from broad, generic campaigns to highly targeted, personalized strategies that treat each account as a market of one. This approach, first introduced in 2003, has evolved into a powerful engine for B2B growth, especially in a world where customer expectations are rising due to AI, sustainability concerns, and generational shifts in decision-making.

ABM delivers outsized returns by focusing on the accounts that matter most. Burgess highlights the fractal nature of the 80/20 rule, noting that the top 4% of customers can drive up to 64% of revenue. This makes ABM not just a marketing tactic, but a strategic imperative. It requires deep alignment between marketing and sales, a nuanced understanding of each account’s context, and a commitment to long-term relationship building. The goal isn’t just to generate leads — it’s to drive business outcomes like revenue growth, market share, and customer lifetime value.

The book outlines five distinct ABM types, each suited to different business contexts:

Strategic ABM is the most intensive, designed for top-tier accounts with high revenue potential. It involves a dedicated marketer embedded in the account team, acting almost like a CMO for the client. Success depends on shared goals, agile collaboration, and a five-step process from ambition-setting to activation.

Scenario ABM offers a scalable version of Strategic ABM, focusing on a single outcome within a defined timeframe. It’s ideal for existing clients and leverages recurring scenarios to streamline execution. AI and unified data play a key role in identifying opportunities and personalizing outreach.

Segment ABM clusters similar accounts based on shared priorities or contexts. It’s the most widely used format today, balancing personalization with scalability. Campaigns are often lightly customized or curated, using digital channels and tools like Folloze to deliver semi-personalized experiences.

Programmatic ABM targets large groups of similar accounts through digital engagement. Though once debated as “true” ABM, its growing sophistication — especially with AI-driven platforms — has made it a staple for reaching new prospects and lower-tier clients efficiently.

Pursuit Marketing is a high-stakes, high-effort approach aimed at winning major deals, often with existing clients. It demands rigorous qualification, deep competitive insight, and compelling storytelling to differentiate and win complex bids.

Burgess emphasizes that building ABM capability is a company-wide endeavor. It requires clear governance, strong infrastructure, and a blend of technical and soft skills. Organizations often establish Centers of Excellence to manage ABM programs, ensuring alignment across teams and readiness for AI-driven scalability.

Ultimately, ABM is not just a marketing function — it’s a strategic growth engine. When done right, it orchestrates the best of what a company can offer to help its clients succeed, creating lasting value on both sides of the relationship.

#codingexercise: https://1drv.ms/w/c/d609fb70e39b65c8/EVnNhxC3YQpEgdMWPXrmI4UBJeMqJ55GqLUHEg2iaAVtwA?e=Ws6dv8 

 Problem:  

Given a string containing some characters from ‘a’ to ‘z’ with repetitions, find the maximum frequency deviation between character occurrences where the deviation is computed as the difference between the frequencies of the most occurring character and the least occurring character.   

import java.util.*;  

import java.lang.*;  

import java.io.*;  

class Ideone  

{  

public static void main (String[] args) throws java.lang.Exception  

{  

System.out.println(getMaxDiffFrequencyDistribution("abcdeefggggghhhiij"));  

}  

private static int getMaxDiffFrequencyDistribution(String input)  

{  

int max = Integer.MIN_VALUE;  

int min = max;  

int diff = max – min;  

Map<Character, Integer> countMap = new HashMap<Character, Integer>();  

for (int i = 0; i < input.length(); i++)  

{  

int val = 1;  

if (countMap.containsKey(input.charAt(i)) {  

val = countMap.get(input.charAt(i));  

val += 1;  

}  

else {  

min = 1;  

}  

  

if (val > max) {  

max = val;  

}  

countMap.put(input.charAt(i), val);  

}  

If (max – min > diff) {  

diff = max – min;  

}  

return diff;  

}  

}  

  

Test Case  

“a” => 0  

“abcdeefggggghhhiij” => 4