Whether approving loans, supporting hiring decisions, prioritizing healthcare interventions, or assisting criminal justice systems, machine learning models are now operating in domains where fairness is not optional — it is essential.

Yet a persistent challenge remains:

How do we build models that are both accurate and fair?

For years, this has often been framed as a trade-off: improve fairness and risk losing predictive performance, or maximize accuracy and accept possible bias.

But what if that trade-off can be navigated more intelligently?

Our recent research, “Balancing Fairness and Accuracy Using Grammatical Evolution,” explores precisely that question through an unusual but powerful lens: evolutionary computation combined with causal reasoning.

Why Fairness in AI Is Still Hard

Modern machine learning systems often inherit historical and structural biases embedded in data. Even highly accurate models can generate systematically unequal outcomes for different groups.

This is particularly problematic in high-stakes settings.

Traditional fairness interventions often happen after a model is built:

• Pre-process the data
• Adjust the training objective
• Post-process predictions to satisfy fairness constraints

While valuable, these approaches often treat fairness as a corrective patch.

We wanted to explore a different idea:

What if fairness could be part of the search process itself?

Enter Grammatical Evolution

At the center of our work is Grammatical Evolution (GE) — a form of evolutionary computation that evolves structured solutions using grammar-based representations.

Rather than simply optimizing parameters, GE can evolve causal graph structures, enabling models that are:

• Predictive
• Fairness-aware
• Interpretable

That last property matters enormously.

In many fairness discussions, interpretability is often overlooked. But understanding why a model behaves fairly (or unfairly) is just as important as measuring the fairness outcome.

From Optimization to Fairness-Aware Evolution

Our work introduces three complementary approaches.

GE-α: Constrained Fairness Optimization

The first approach embeds fairness directly as a constraint during evolution.

Instead of evolving models solely for predictive accuracy, we ask:

Can evolution search for models that satisfy fairness criteria while remaining accurate?

Using Equalized Odds Difference as the fairness objective, GE-α explicitly searches for balanced solutions rather than treating fairness as an afterthought.

This turns fairness into part of the fitness landscape.

And that changes everything.

GE-β: A Minimax Perspective on Fairness

The second approach pushes further.

Rather than optimizing average behavior, GE-β uses a minimax formulation that focuses on worst-case trade-offs between error and unfairness.

This is important because responsible AI should not only perform well on average — it should be robust under difficult conditions.

The minimax perspective introduces a stronger notion of balance:

Not just “good enough” fairness.

But fairness that holds under pressure.

GE-γ: Causal Interventions for Explainable Fairness

This is perhaps the most exciting part of the work.

With GE-γ, we integrate causal reasoning through Average Causal Effect (ACE) and intervention-based node pruning.

The goal is not only to improve fairness metrics but to simplify and clarify the learned causal structures themselves.

Low-impact nodes can be removed.

Causal roles like confounders, mediators, and colliders are preserved.

The resulting models become more interpretable without sacrificing performance.

This opens an intriguing possibility:

Using evolution not only to optimize models, but to evolve explanations.

That is a very different vision of machine learning.

Why This Matters

What excites me most about this work is not just the fairness results.

It is the broader idea that evolutionary computation may be an underexplored engine for Responsible AI.

We often associate evolutionary algorithms with optimization benchmarks.

But they may also help us tackle problems involving:

• Fairness
• Transparency
• Robustness
• Causal structure discovery
• Multi-objective ethical trade-offs

That is a much richer role.

And perhaps a much more important one.

Beyond Accuracy-Centric AI

There is a quiet shift happening in AI research.

We are moving beyond asking:

“How accurate is the model?”

Toward asking:

• Is it fair?
• Is it explainable?
• Is it trustworthy?
• Can we intervene when harms emerge?

Those questions require new computational ideas.

We believe evolutionary methods have something valuable to contribute.

A Broader Vision

This work also points toward something larger:

A future where machine learning systems are not merely optimized for performance, but evolved under principles of responsibility.

Imagine search processes where objectives include:

• Accuracy
• Fairness
• Interpretability
• Causal validity
• Societal constraints

That begins to look less like conventional model training—

and more like engineering trustworthy intelligence.

Final Thoughts

Fairness and accuracy do not have to be opposing goals.

Sometimes the real opportunity lies in changing how we search for solutions altogether.

That is what makes evolutionary approaches so compelling.

They do not simply tune models.

They explore possibility spaces.

And in those spaces, we may discover better ways to build AI systems worthy of trust.

Our work on Balancing Fairness and Accuracy Using Grammatical Evolution is one small step in that direction.

I believe there is much more ahead.

If you work in fairness, causal machine learning, evolutionary computation, or Responsible AI, I’d love to hear your thoughts.

How should we evolve the next generation of trustworthy AI?

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#ArtificialIntelligence #ResponsibleAI #FairnessInML #EvolutionaryComputation #CausalInference #ExplainableAI #MachineLearning #Research

The future of autonomous aerial systems is not arriving suddenly—it is being carefully engineered, tested, and refined in simulation environments that mirror the complexity of the real world.

Our latest IEEE research explores this future through the development of a distributed reinforcement learning testbed for UAV target tracking, where multiple autonomous drones learn to coordinate in real time to follow a dynamic airborne target.

At its core, this work investigates a simple but powerful question:

How can UAV swarms learn to track moving targets more efficiently in unpredictable environments?

The answer lies in combining realistic flight simulation, distributed networking, and modern reinforcement learning algorithms.

Why UAV Swarm Target Tracking Matters

Target tracking is one of the most important capabilities in autonomous drone systems.

Whether the mission involves:

• search and rescue
• disaster monitoring
• perimeter surveillance
• defense simulation
• intelligent logistics
• environmental observation

…the ability for multiple UAVs to collaboratively maintain awareness of a moving target is essential.

Traditional rule-based control methods often struggle when the target behaves unpredictably or when the environment becomes dynamic.

This is where reinforcement learning (RL) becomes transformative.

Instead of following hard-coded instructions, UAVs learn through interaction with the environment, continuously improving their decision-making policies based on experience.

A Realistic Testbed Built on FlightGear and JSBSim

To study this problem, we developed a distributed UAV simulation testbed using:

• FlightGear for high-fidelity 3D flight simulation
• JSBSim for realistic flight dynamics modeling
• UDP-based distributed communication
• real-time reinforcement learning control loops

The architecture allows multiple UAVs to operate as independent learning agents while exchanging state information such as:

• positional coordinates
• orientation
• velocity
• control signals

This creates a highly scalable framework for testing swarm intelligence strategies under near-realistic conditions.

In our experimental setup:

• one UAV acts as the autonomous target
• multiple UAVs act as tracking agents
• distributed reinforcement learning coordinates the swarm in real time

Comparing Modern Reinforcement Learning Models

The study compares three influential RL methods:

• A2C (Advantage Actor-Critic)
• A3C (Asynchronous Advantage Actor-Critic)
• PPO (Proximal Policy Optimization)

Each algorithm contributes different strengths.

A2C for the Target UAV

A2C was used to control the target UAV, generating complex motion patterns that make the tracking task challenging and realistic.

A3C for Distributed Swarm Coordination

A3C enables multiple worker agents to learn asynchronously, making it highly suitable for swarm UAV coordination where multiple trackers operate in parallel.

PPO for Stable Policy Learning

PPO was used to provide robust and stable policy optimization, particularly useful in dynamic environments where abrupt policy updates can destabilize learning.

The Role of Intelligent Exploration

One of the biggest challenges in reinforcement learning is the sparse reward problem.

In target tracking, useful feedback may not arrive frequently enough for agents to learn efficiently.

This means UAVs may spend too much time exploring ineffective strategies before discovering successful behaviours.

To address this, our work integrates an Intrinsic Curiosity Module (ICM), which generates internal rewards whenever the agent encounters novel or difficult-to-predict states.

This mechanism encourages:

• better exploration
• faster discovery of useful strategies
• improved adaptation to unfamiliar target behaviour
• more efficient learning in dynamic environments

Rather than waiting for explicit environmental rewards, the swarm develops an internal motivation to learn.

This significantly improves learning speed and robustness.

What the Results Showed

The results were highly encouraging.

Across multiple simulation runs, the UAV swarm agents enhanced with curiosity-driven exploration demonstrated:

• faster convergence
• higher cumulative rewards
• smoother actor-critic losses
• stronger policy stability
• improved entropy-driven exploration
• better generalisation to dynamic target motion

Among all tested models, A3C integrated with curiosity mechanisms showed the strongest overall performance, delivering the most stable and effective swarm target tracking.

This is particularly significant because asynchronous distributed learning closely mirrors how real swarm systems may operate across multiple compute nodes or edge devices.

Why This Matters Beyond Simulation

The importance of this research extends far beyond virtual flight environments.

The same principles can directly influence real-world systems in:

• disaster response drones
• persistent surveillance
• maritime monitoring
• intelligent border systems
• military training simulation
• autonomous delivery fleets
• environmental hazard assessment

The ability of UAVs to learn collaboratively, adapt to novelty, and coordinate under uncertainty is central to the next generation of autonomous aerospace systems.

Simulation-first research provides a safe, cost-effective pathway to develop these capabilities before real deployment.

Looking Ahead

This work represents an important step toward truly intelligent UAV swarms.

As reinforcement learning continues to mature, the combination of:

• distributed simulation
• curiosity-driven exploration
• asynchronous swarm learning
• realistic flight dynamics
• scalable communication architectures

…will become increasingly important for building resilient autonomous systems.

The sky is no longer the limit.

It is the next intelligent frontier.

The post Building Smarter UAV Swarms: How Reinforcement Learning is Transforming Autonomous Target Tracking first appeared on Psyops Prime.]]> https://psyopsprime.com/ideas/building-smarter-uav-swarms-how-reinforcement-learning-is-transforming-autonomous-target-tracking/feed/ 0 https://creativecommons.org/licenses/by-nc-nd/4.0/ 2730 https://psyopsprime.com/ideas/teaching-machines-to-be-curious-a-step-toward-intelligent-uav-swarms/?utm_source=rss&utm_medium=rss&utm_campaign=teaching-machines-to-be-curious-a-step-toward-intelligent-uav-swarms https://psyopsprime.com/ideas/teaching-machines-to-be-curious-a-step-toward-intelligent-uav-swarms/#respond Wed, 25 Mar 2026 17:29:08 +0000 https://psyopsprime.com/?p=2723 Autonomous systems are often described as the future—but in many ways, they are still struggling with a very human problem: learning from delayed consequences. In

Autonomous systems are often described as the future—but in many ways, they are still struggling with a very human problem: learning from delayed consequences.

In reinforcement learning, this challenge is known as the delayed reward problem. An agent performs a sequence of actions, but the reward—or feedback—arrives much later. By then, it becomes difficult to determine which action actually led to success or failure. For systems operating in complex, dynamic environments—like unmanned aerial vehicles (UAVs)—this problem becomes even more pronounced.

In this post, I want to share insights from a research project focused on addressing this challenge in the context of multi-UAV systems, and how introducing a concept as simple—and as powerful—as curiosity can significantly improve learning.

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The Problem with Learning Too Late

Imagine trying to learn how to fly a drone, but you only receive feedback minutes after making a mistake. You wouldn’t know what exactly went wrong. Reinforcement learning agents face a similar issue.

In UAV tracking tasks, for example:

• A drone may take dozens of actions before receiving a reward
• The learning signal becomes weak and noisy
• Training becomes unstable and slow

This is particularly problematic in real-time systems, where decisions must be made continuously and reliably.

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Building a Realistic UAV Testbed

To study this problem, we developed a multi-UAV testbed that combines:

• A high-fidelity flight simulator (FlightGear)
• A Flight Dynamics Model (JSBSim)
• A real-time communication layer using UDP
• Reinforcement learning models integrated directly into the control loop

This setup allows UAVs to:

• Interact with a realistic environment
• Learn from continuous feedback
• Be evaluated under dynamic flight conditions

The goal was not just to simulate intelligence—but to create a platform where intelligent behavior can emerge.

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A Hybrid Learning Approach

One of the key design decisions was to use different reinforcement learning strategies for different roles:

• The target UAV is controlled using Advantage Actor-Critic (A2C)
  → This ensures stable and predictable flight behavior
• The tracking UAV is controlled using Asynchronous Advantage Actor-Critic (A3C)
  → This enables parallel exploration and faster learning

This separation is important. In multi-agent systems, if all agents behave unpredictably, the environment becomes chaotic. By keeping one agent stable and allowing the other to explore, we create a balanced learning ecosystem.

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Introducing Curiosity into Machines

The real breakthrough comes from integrating an Intrinsic Curiosity Module (ICM) into the learning process.

Instead of relying only on external rewards (e.g., “you successfully tracked the target”), the UAV also receives intrinsic rewards based on how surprised it is by new experiences.

In simple terms:

• If the UAV encounters something unexpected → it gets rewarded
• If it explores new states → it gets encouraged
• If it keeps doing the same thing → rewards diminish

This transforms learning in a fundamental way.

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From Sparse Rewards to Continuous Learning

By combining external and intrinsic rewards, we effectively turn:

Sparse, delayed feedback
into
Continuous, meaningful learning signals

This allows the UAV to:

• Keep learning even when external rewards are absent
• Explore more effectively
• Adapt to changing environments in real time

Curiosity acts as a bridge over the gap created by delayed rewards.

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What We Observed

The results were both encouraging and insightful:

• Traditional methods showed initial learning followed by instability
• The curiosity-driven approach demonstrated:
  • Smoother learning curves
  • Better exploration
  • More reliable tracking behavior

In practical terms, the tracking UAV was able to:

• Maintain pursuit more effectively
• Adapt to variations in the target’s movement
• Continue learning even in uncertain conditions

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Why This Matters

Most UAV research focuses on:

• Flight control
• Navigation
• Multi-agent coordination

But relatively little attention is given to how these systems actually learn over time, especially under imperfect conditions.

This work highlights an important shift:

Instead of designing systems that rely solely on external feedback, we can build systems that motivate themselves to learn.

This idea has implications far beyond UAVs:

• Autonomous vehicles
• Robotics
• Smart surveillance systems
• Distributed AI systems

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Looking Ahead

There is still much to explore.

Future directions include:

• Expanding to multi-UAV swarm coordination
• Incorporating vision-based perception
• Exploring advanced algorithms like Proximal Policy Optimization (PPO)
• Moving toward real-world deployment and digital twins

Each of these steps brings us closer to systems that are not just automated—but truly autonomous.

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Final Thoughts

Curiosity is often seen as a uniquely human trait—the drive to explore, to learn, to understand the unknown.

But what happens when machines begin to exhibit the same behavior?

This research suggests that by embedding curiosity into artificial systems, we can overcome some of the most persistent challenges in learning—transforming hesitation into exploration, and delay into discovery.

And perhaps, in doing so, we move one step closer to building machines that don’t just follow instructions—but learn how to think for themselves.

The post Teaching Machines to Be Curious: A Step Toward Intelligent UAV Swarms first appeared on Psyops Prime.]]>

In the grand tapestry of the Indian subcontinent’s history, there is a persistent tension between the “history of the sword” and the “history of the heart.” While the 12th and 13th centuries are often remembered for the thundering hooves of Central Asian cavalry, the true civilizational shift was occurring in the quiet, mud-walled Khanqahs (hospices) of the Sufis. At the center of this spiritual revolution stood Hazrat Qutbuddin Bakhtiar Kaki (RA).

The premier disciple of Khwaja Moinuddin Chishti, Bakhtiar Kaki did not just bring a religion to Delhi; he brought a set of Superior Values—independence, radical poverty, and ecstatic love—that proved more enduring than any dynasty.

The Man of the “Bread of Grace”

Born in Osh (modern-day Kyrgyzstan), Bakhtiar Kaki’s life was defined by the concept of Tawakkul (absolute trust in the Divine). His title, “Kaki,” stems from a narrative of profound dignity in the face of poverty.

Legend tells of a time when his family faced starvation because they refused to accept stipends from the Sultan. When his wife was taunted for her poverty by a local baker, the Khwaja pointed to a corner of his house, telling her to take what she needed. From that corner, hot cakes (Kak) miraculously appeared.

The Conclusion for the Modern Reader: This miracle wasn’t just about magic; it was a political statement. By refusing to “eat the bread of the state,” the Chishtis established themselves as a third pole of power—independent of the king and the military. This gave them the moral authority to speak for the common man.

The Refusal of the “Sheikh-ul-Islam”

When Sultan Iltutmish—himself a man of deep faith—begged the Khwaja to accept the official title of Sheikh-ul-Islam, the saint declined. He understood a truth that the “nomadic warlords” did not: Divine authority cannot be legislated; it must be earned.

While the Sultans built fortresses to keep people out, Bakhtiar Kaki kept his doors open to everyone—the pompous Rajput warrior, the disenfranchised laborer, and the curious seeker. He demonstrated that Islamic egalitarianism wasn’t a theory found in books, but a lived reality where the prince and the pauper shared the same meal.

The Miracle of Gandhak ki Baoli

The “superiority” of the Sufi path was often demonstrated through service (Khidmat). When Delhi faced a crippling water crisis, it was the Khwaja’s spiritual intervention that led to the discovery of a fresh spring. The resulting Gandhak ki Baoli (Sulphur Stepwell) was more than an engineering feat; it was a symbol of the Sufi as a provider for the community.

The Martyrdom of Love

The most striking aspect of Bakhtiar Kaki’s persona was his devotion to Sama (devotional music). His end came not on a battlefield, but in a state of spiritual ecstasy. While listening to a verse about “surrendering to the dagger of God’s will,” he entered a trance so deep that his soul departed his body.

This “Martyrdom of Love” offered the warrior clans of India—the Rajputs, Janjuas, and Bhattis—a new archetype of heroism. They saw a man who had more courage in his silence and prayer than a general had in his army.

Why He Matters to Our Collective Conscience

As we reflect on “A Tale of a Thousand Years,” we must conclude that the conversion of the Indian warrior tribes was not a “surrender to an invader,” but an alignment with a higher moral code. Hazrat Qutbuddin Bakhtiar Kaki remains the “Qutub” (Pole Star) of Delhi because he represented the best of Islamic values:

• Austerity over Greed.

• Mercy over Vengeance.

• Spiritual Sovereignty over Political Subjugation.

His legacy reminds us that while the sword may change the name of the ruler, it is the spirit of the saint that changes the identity of the people.

Key Fact Sheet for Post Summary:

• Order: Chishtiyya (The first to establish a major presence in Delhi).

• Era: Early 13th Century (Reign of Sultan Iltutmish).

• Spiritual Master: Khwaja Moinuddin Chishti of Ajmer.

• Most Notable Successor: Baba Farid Ganjshakar of Pakpattan.

• Core Philosophy: Sulh-e-Kul (Peace with All) and Faqr (Voluntary Poverty).

I have written quite a lot about the rather messed-up human history. To this end, I have written extensively about the often-bad role of medieval Muslim nomadic warriors in Asia. I have argued against the conquests of the Iberian Peninsula and India. I have been thinking for a long time about writing about the role of Sufism in transforming India. This is the first article in that spirit. I would like to acknowledge that parts of the following text was generated by Google’s Gemini after various interactions with it. I have also now arranged all the articles of the journey to Jerusalem series under a common tag of this name. If you are looking for a particular article in this series, or what is in this series at all, please go to the tag cloud on my website and look for Journey to Jerusalem. My overall ambition is that warfare and conquering other peoples countries is a bad idea and it can never lead to peace. And a tolerance and acceptance of others is much better, along with intercommunal and interfaith harmony. I personally prefer the Sufi path. And I am sure if you read enough about it, you will begin to love it too. It is a path of love of God and love of humanity. This is what we need in our turmoiled world.

This is the best of the times, this is the worst of the times. Isn’t it? Indeed, it is. We have never lived in such times before. We have fun, we have war. We have entertainment. We have tragedies. Lives are long, problems are fewer, yet there is the fear of the unprecedented. We live in an era where advanced countries care about human rights. Yet, organised rings for rapes of minors, child abuse and infant eating are being exposed. Geopolitical pundits are warning us about the eruption of the Third World War. Indeed, American armadas have surrounded Persia from all sides. Air bases have been filled with stealth planes. Negotiations are happening to avert this. Rhetorics are belligerent. Aspirations for underhanded dealmaking are high.

Over the past couple of years, I have been writing about certain historical matters that I hope will help heal humanity’s collective conscience. I have tried to be objective. As a Muslim person, I have criticised the many conquests of the so-called Mulim forces of the medieval era. I have argued that the conquests of Spain and India by nomadic Mulim warlords were wrong. One of my previous articles in the journey to Jerusalem series was a chronological critique of the conquests of the Indian subcontinent. And I am committed to all that.

My today’s article has a slightly different spirit. In this, I would like to reflect on the various groups and clans of India that converted to Islam over more than a thousand years. To be more precise, I want to reflect on the groups or clans of India that came in conflict with those Muslim warlords and they later converted to Islam. This is a long list victors and loosers, and it is interesting because it also contains the former category of clans.

India is a caste-based society. Muslims confronted various caste groups in the subcontinent from 712 AD till the middle of the nineteenth century. Let’s begin with Raja Daahir. He was the first one to bear the siege by Arabs. I have written about him. He stood firm against the Arabs as any brave king would. And he tried to defend Sindh to the best of his abilities. It is a pity that he lost. However, his progeny or tribe still live in Sindh and they embraced Shia Islam.

If you read my article on the conquests of India by the Muslims, the second clash I mention there was between Sultan Mahmood of Ghazni and the Northern Indian king Jaya Pala. Jaya Pala was a Rajput from the famous Janjua clan that is still based in the salt range of Pakistan. Jaya Pala lost the battle of Peshawar and later committed suicide. Almost all Janjuas are Muslims, and many of them acquired notable sociopolitical positions in Pakistan.

Sultan Mahmood is famous for his seventeen attacks on India. During his last attack, he was pursued by a local Indian king, Raja Bhoj. Bhoj belonged to the Paanwar Rajput tribe and was famous for his ferocity, among other qualities. Many Paanwars converted to Islam, and they also migrated to Pakistan after 1947. They acquired notable positions in various services in Pakistan. A sub-tribe of Paanwars is Naarma. All of it is Muslim, and they live in Pakistan.

During his campaigns, he also invaded the region of Sindh. Jaats of Sindh drove him out of Sindh. Almost all Jaats in SIndh are Muslims.

Then comes the turn of the clash between Sultan Muhammad of Ghaur and Prithvi Raj Chohan. Although I have argued that the clash between Mehmood of Ghazni and Jaya Pala was a fair game on the part of the erstwhile, I would assert that Sultan Muhammad of Ghauri beat Prithvi Raj Chohan with deception. Chohans are also Rajputs of India. Many of them are settled in Pakistan. And among the many who are in Pakistan are devout Muslims. Later, Sultan Muhammad was ambushed and assassinated by another northern Indian clan called Khokhars. Khokhars also identify themselves as Rajputs. The ones from northern India have historically lived in the salt range of Pakistan and they are largley Muslim. Legend has it that Raja Porus, who clashed with Alexander the Great in the fourth century BC in the famous battle of Hydaspes, was also a Khokhar.

When Babar invaded India, he made a fiery speech in Sohawa (a town in Northern Punjab, Pakistan). He mustered the support of local Gujjars and took them along with their military to invade Delhi. Gujjar is a scheduled caste of India. Almost every Gujjar who lives in Pakistan (including Sohawa and its suburbs) is a Muslim. Legend has it that much of the victory of the throne of India can be attributed to the support lent to Babar by the Gujjars.

Akbar made political alliances with Indians through marriages. He married in a Rajput clan, and hence the later progeny of Mughals had Rajput genetic code in them.

Maharaja Ranjit Singh established the SIkh dynasty in Northern India. He and his army were known for their ferocity. Afghans used to fear Hari Singh Nalwa for a long time. Ranjit belonged to the Sainsi subtribe of the Bhatti Rajputs. He was from Gujranwala. Sainsi Bhattis can still live there. They are all Muslims.

It is interesting to note that almost all the local Indian clans who came into conflict with Muslims converted to Islam, irrespective of whether they won or lost against them. This fact is additionally noteworthy because there is a notion that Muslim nomads subjugated the local populations and forced them to convert. I am not denying this. However, my position is that even the victors converted to Islam.

Why did this happen? Conquering other peoples land is bad unless there is a noble purpose behind it. And I have already argued that conquest of India by nomadic Muslims was bad.. This is my very firm position and it is one of the important theses of my life. Not only that, I am also against the conquest of Spain, Sicily, Central Asia and other places. Actually there is a strong argument that when various Arab factions conspired against the household of Prophet Muhammad PBUH, and litterally assassinated son upon son of noble Imams, to divert peoples’ attention from their heinous deeds, they started a new project of conquest of the known world as a noble cause. I have argued about this in several of my previous articles. My question is that how was it possible that the defenders of India, who were hostile to Muslim warlords, converted to Islam irrespective of whether they lost or won against a certain campaigner.

Islam has a strong epistemology. The Holy Quran and the Hadith of Prophet Muhammad PBUH speak about the past Prophets and try to straighten human view of them. Especially, much of the Holy Quran revolves around setting the status of Jesus Christ PBUH straight. In the Middle East, he was either loved and revered to the extent of worship or loathed. And the haters had the excuse to do so because he was worshipped. Monotheists couldn’t agree with him, and hence they would strip him off of his divine title as the Christ. Chapters of the Holy Quran and Hadith of Prophet Muhammad PBUH set his status right in the conscience of the common man. The Holy Quran asserts that he was not the son of God, but a Holy Prophet. In doing this, God also eliminates any excuse to loathe him.

Apart from this, Islamic prophecies about the future of mankind are also strong. Moreover, the Islamic values are quite commendable. The ideas of human equality and justice in Islam are indeed universal. But above all, it was the arrival of Sufi Saints that changed human hearts in Islam. Rajputs of India are not an easy bunch to succumb to any threat or domineering entity. Actually, the Rajput caste in India crystallised with the advent of the Islamic invaders in India. They stood up to defend India against Muslim invasion. And many of them were quite successful in their assignments. However, Sufi saints moulded hearts. It is said that one of the sons of Prithviraj Chohan converted to Islam at the hands of Hazrat Khawaja Moin ud Din Chishti RA. He is considered the patron saint of India. Around half a million Rajputs converted to Islam on the hands of Hazrat Bahaudin Zakriya RA. And there is no chance that all of these millions of people will convert to any other religion, even if given any choice. So if a future clash between India and Pakistan happens (God forbid), it will be mostly between local Hindus and Muslims of Indian origin.

Swayamvar has been an old Indian tradition. In this, when a princess had to be married, princes would be invited to view the princess from far flung Indian territories. The princess instead would review the princes. She would select one and reject the rest. Rest of the disheartened princes would get against the state of the princess. This would lead to political conflicts between different states, leading to disloyalties, animosities and war. Most of the invaders of India were helped by local Indians. And many local Indians also converted to Islam due to the universal ideas of human egalitarianism and justice. 

While the sword may have changed the political boundaries and the ruling elite, it was the Sufi influence and social values of Islam coupled with the fragmented Indian society that actually converted the populace and sustained the faith over centuries. Military force often breeds resentment rather than genuine belief. The sword could demand taxes or submission, but it couldn’t necessarily mould hearts. In a highly stratified, caste-based society, the Islamic concepts of egalitarianism and divine justice offered a radical alternative. The superior values acted as a psychological and social pull factor, especially for those looking for a sense of human equality that was historically denied to them.

This is where the victors and losers both converting becomes most relevant. Sufi saints often lived outside the royal courts and reached out to the common people, including the warrior classes (Rajputs, Khokhars, etc.). Sufis often used local idioms, music, and poetry, making the transition to Islam feel less like a foreign imposition and more like a spiritual evolution.

The post A Tale of a Thousand Years first appeared on Psyops Prime.]]> https://psyopsprime.com/consciousness/a-tale-of-a-thousand-years/feed/ 0 https://creativecommons.org/licenses/by-nc-nd/4.0/ 2693 https://psyopsprime.com/digital-signal-processing/demystifying-the-imaginary-how-j-and-the-complex-plane-make-dsp-click/?utm_source=rss&utm_medium=rss&utm_campaign=demystifying-the-imaginary-how-j-and-the-complex-plane-make-dsp-click https://psyopsprime.com/digital-signal-processing/demystifying-the-imaginary-how-j-and-the-complex-plane-make-dsp-click/#respond Thu, 29 Jan 2026 13:06:16 +0000 https://psyopsprime.com/?p=2687 If you’ve ever dipped your toes into Digital Signal Processing (DSP), you’ve probably encountered the mythical beast known as the “complex plane” and its equally

If you’ve ever dipped your toes into Digital Signal Processing (DSP), you’ve probably encountered the mythical beast known as the “complex plane” and its equally enigmatic resident, the imaginary number $j$ (or $i$ in math circles). For many, these concepts are where DSP goes from “a bit tricky” to “utterly bewildering.”

But what if I told you that, for engineers, j isn’t really “imaginary” at all? What if it’s actually one of the most practical, intuitive tools we have?

Richard Lyons, in his seminal “Understanding Digital Signal Processing,” has a gift for making these intimidating topics not just understandable, but obvious. Let’s unpack his wisdom and reveal the real-world power of j and the complex plane.

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Step 1: Beyond the Number Line – The Complex Plane

Forget the simple, one-dimensional number line we learned in grad school. That line is great for showing “how much,” but in DSP, we often need to know “how much” and “where it’s at” in a cycle. Think of a spinning wheel: you need to know its speed and its current position.

Enter the Complex Plane.

Instead of a line, imagine a two-dimensional grid:

• The Horizontal Axis (Real Axis): This is your familiar number line. We use it to represent the “in-phase” part of a signal.

• The Vertical Axis (Imaginary Axis): This is the new kid on the block. It represents the “quadrature” (or 90-degree shifted) part of a signal.

Now, instead of just a single number, any point on this plane is a complex number, made up of a real part and an imaginary part. We write it as

a + jb

, where

a

is the real part and

jb

is the imaginary part.

Step 2: j – Not Imaginary, But a Rotational Operator!

Here’s where Lyons flips the script. Stop thinking of j as “the square root of -1” in a purely abstract sense. In DSP, j has a concrete, visual job: it’s a 90-degree counter-clockwise rotation operator ¹.

Let’s see it in action:

1. Start with a real number, say

   1

   , on the positive Real Axis.

2. Multiply

   1

   by j:

   1 * j = j

   . You’ve rotated

   1

   by 90 degrees counter-clockwise, and now you’re at

   j

   on the positive Imaginary Axis.

3. Multiply

   j

   by j again:

   j * j = j^2

   . You’ve rotated another 90 degrees. You’re now at

   -1

   on the negative Real Axis.

4. Multiply

   -1

   by j:

   -1 * j = -j

   . Another 90-degree rotation. You’re now at

   -j

   on the negative Imaginary Axis.

5. Multiply

   -j

   by $j$:

   -j * j = -j^2 = -(-1) = 1

   . One more 90-degree rotation brings you back to

   1

   on the positive Real Axis.

See? j isn’t making things disappear into some alternate reality. It’s simply turning things on a two-dimensional surface. This is the core insight that unlocks so much of DSP. This is the power of j. And j has brought us a long way.

Step 3: Spinning Signals – The Power of Phasors

Now, let’s bring it back to signals. Many real-world signals, like sound waves or radio waves, are sinusoidal—they oscillate up and down. Instead of describing them with messy sines and cosines, DSP engineers represent them as phasors: points that spin around the origin of the complex plane. Basically such waves do not oscillate up and down. They propagate from source to the sink in a helical manner.

The magic comes with Euler’s Equation:

$$e^{j\theta} = \cos(\theta) + j\sin(\theta)$$

This deceptively simple equation is the bedrock of modern DSP. It tells us that a complex exponential (the left side, which describes something spinning) is made up of a cosine wave (its projection onto the Real axis) and a sine wave (its projection onto the Imaginary axis).

Why is this a big deal?

• Simplification: Multiplying complex exponentials (which involves just adding their angles) is far easier than wrestling with trigonometric identities. Want to shift a signal’s phase? Just add an angle to its complex representation.

• Complete Picture: A single complex number can encapsulate both the amplitude (how big the signal is, represented by the length of the phasor) and the phase (where it is in its cycle, represented by its angle on the complex plane).

Step 4: The “Mystery” of Negative Frequency Solved

With the complex plane and spinning phasors in hand, “negative frequency” also becomes intuitive.

Imagine our phasor spinning.

• Positive Frequency: The phasor spins counter-clockwise.

• Negative Frequency: The phasor spins clockwise.

A real-world sine wave, the kind you measure with an oscilloscope, is actually the sum of two complex phasors: one spinning counter-clockwise (positive frequency) and one spinning clockwise (negative frequency). Their imaginary components cancel out, leaving only the real oscillation we observe.

$$\cos(\omega t) = \frac{e^{j\omega t} + e^{-j\omega t}}{2}$$

On a spectrum analyzer, when you see a spike at 100 Hz, you’ll also see a mirrored spike at -100 Hz for a real signal. They are two sides of the same coin, both necessary to describe that single real cosine wave.

The Practical Benefit: By converting real signals into purely complex (or “analytic”) signals through techniques like Hilbert transforms, we can eliminate one of these mirrored frequencies. This effectively doubles our usable bandwidth and simplifies many advanced DSP operations, especially in communications systems (like radio and cellular data).

The Takeaway

Richard Lyons strips away the fear surrounding “imaginary” numbers by showing their practical utility. The complex plane isn’t an abstract mathematical playground; it’s a two-dimensional whiteboard where we can draw and manipulate signals more effectively.

Understanding j as a rotation, and grasping how phasors spin on this plane, unlocks the intuition behind powerful concepts like Euler’s Equation and negative frequency. These aren’t just theoretical constructs—they are the foundational tools that allow us to build everything from your smartphone to high-fidelity audio systems.

So, next time you see j, don’t be intimidated. Just remember: it’s simply turning things around, making the complex world of signals a whole lot clearer!

The post Demystifying the “Imaginary”: How “j” and the Complex Plane Make DSP Click first appeared on Psyops Prime.

1    However, I would like to emphasize that rotation happens due to the virtue of it being the square root of -1.

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Artificial Intelligence systems are no longer confined to research labs. They influence decisions about loans, employment, healthcare, and criminal justice—domains where fairness is not optional. Yet, much of modern machine learning still treats fairness as a secondary concern: something to be fixed after a model has already learned its patterns.

One of the most persistent assumptions in this space is that fairness and accuracy are inherently at odds. Improve one, and the other must suffer. But is this trade-off inevitable—or is it simply a limitation of how we frame the problem?

In our recent work, A Multi-Objective Approach to Balance Fairness and Accuracy, we argue for a different perspective: fairness should be treated not as a constraint or post-processing correction, but as a first-class optimisation objective, explored alongside accuracy rather than subordinated to it. I would like to congratulate my co-authors about this who are: 1. the doctoral candidate Zahid Irfan, Dr. Roisin Loughran, and Professor Fergal Mc Caffery. Basically this is the work was done by Zahid, who is a colleague as well as a very close friend of mine.

Why Bias Persists in Machine Learning

Bias in AI systems often reflects deeper structural issues: biased data collection, historical inequalities, and spurious correlations that models eagerly exploit. When these correlations involve protected attributes—such as sex, age, or race—the resulting systems may achieve impressive accuracy while still producing unfair outcomes.

Traditional bias-mitigation approaches typically fall into three categories:

• Pre-processing, where the data is modified before training
• In-processing, where fairness is incorporated into the learning algorithm
• Post-processing, where predictions are adjusted after training

While all three have their place, many approaches operate largely as black-box fixes. They may improve a fairness metric, but often at the cost of interpretability and deeper understanding.

This is where causal modelling becomes essential.

Bringing Causality into the Picture

Correlation alone cannot tell us why a model behaves unfairly. Causal models, on the other hand, explicitly represent cause–effect relationships between variables.

We use Causal Bayesian Networks (CBNs)—directed acyclic graphs where nodes represent variables and edges encode causal influence. These structures allow us to reason about dependencies, confounders, and interventions, rather than relying solely on statistical association.

However, learning causal structures from data is a notoriously difficult problem. The search space of possible graphs grows exponentially, making exhaustive search infeasible.

To address this, we turned to Evolutionary Computation.

Evolving Causal Graphs with Grammatical Evolution

Our approach uses Grammatical Evolution (GE) to automatically generate and evolve causal graph structures. A context-free grammar constrains the search space to valid causal graphs, while still allowing a rich variety of structures to emerge.

Each individual in the evolutionary population represents a candidate causal graph. From this graph, we build a CBN, train it on data, and evaluate its performance.

Crucially, we do not evaluate performance using a single objective.

Fairness and Accuracy as Joint Objectives

Instead of collapsing everything into one score, we adopt a multi-objective optimisation framework using NSGA-II, a well-established evolutionary algorithm.

We optimise two objectives simultaneously:

1. Accuracy, measuring predictive performance
2. Fairness, measured using Equal Opportunity Difference (EOD), which captures disparities in true positive rates between protected groups

This produces not a single “best” model, but a Pareto front—a set of non-dominated solutions representing different fairness–accuracy trade-offs.

This is a powerful shift in mindset. Rather than asking “What is the best model?”, we ask:
“Which trade-off best fits the ethical and operational requirements of this domain?”

What We Observed

Using the German Credit dataset as a case study, our experiments showed that:

• It is possible to achieve very low fairness disparity while maintaining competitive accuracy
• Multiple causal graphs can yield similar performance, offering flexibility and interpretability
• The evolved graphs are non-trivial, capturing meaningful dependencies among features
• Practitioners can choose models that slightly sacrifice accuracy for substantial gains in fairness—or vice versa

Importantly, the causal graphs themselves provide insight. They allow us to inspect how features influence outcomes, opening the door to causal reasoning, domain validation, and future intervention analysis.

Why This Matters

Fair AI is not just about metrics—it’s about understanding.

By combining causality with multi-objective evolutionary optimisation, this work demonstrates that:

• Fairness does not have to be an afterthought
• Accuracy does not have to be blindly maximised
• Interpretability and performance can coexist

Most importantly, it reframes fairness as an optimisation problem, not a moral constraint imposed from outside the model.

Looking Ahead

Future directions include:

• Exploring additional fairness metrics to capture different notions of equity
• Extending experiments to larger and more diverse datasets
• Incorporating causal interventions and counterfactual analysis
• Further strengthening the link between ethical requirements and model design

As AI systems continue to shape society, approaches that integrate ethics, causality, and optimisation will be essential—not optional.

Fairness is not something we bolt onto AI.
It is something we design for

In mobile development,

(Android) and

(iOS) are the “go-to” tools for saving simple settings like a user’s theme preference or a “Remember Me” toggle. However, using these for session tokens or authentication secrets in a banking application is a critical security flaw.

Even on a non-rooted device, this practice exposes users to significant risks and fails to meet modern financial security standards.

The Risk: The “Backup” Backdoor

The primary risk of storing a session token in

on a non-rooted device is Unauthorized Data Extraction via Backups.

How the Attack Works

By default, Android’s backup system (via

or cloud sync) includes the application’s private data folder, where

files are stored as plain-text XML.

1. Physical Access: If an attacker gets physical access to an unlocked device for just a few minutes, they can trigger a backup to an external machine.
2. The Extraction: Because
   SharedPreferences

   is not encrypted by the OS, the attacker can extract the

   shared_prefs/*.xml

   file.

3. Session Hijacking: The attacker now has a valid, active session token. They can “replay” this token from their own device or a browser to bypass the login screen and gain full access to the victim’s bank account without ever needing the password or MFA.

The “Malware” Pivot

While Android isolates apps through “sandboxing,” vulnerabilities in the OS or other high-privilege apps can sometimes allow a malicious app to bypass these boundaries. If a token is stored in plain text, any process that manages to escalate privileges can scrape the token instantly.

The Modern Standard: Hardware-Backed Security

To meet modern security standards (like OWASP MASVS), sensitive data must be stored in a location that provides encryption at rest and hardware-level isolation.

The Secure Alternative: Android Keystore & EncryptedSharedPreferences

The correct approach for a banking application is to use the Android Keystore System in conjunction with EncryptedSharedPreferences.

1. Android Keystore (The Vault)

The Keystore does not store the token itself; it stores the cryptographic keys used to encrypt the token. These keys are stored in a Trusted Execution Environment (TEE) or a Secure Element (SE)—dedicated hardware on the phone that is physically isolated from the main Android OS. Even if the OS is compromised, the keys cannot be extracted.

2. EncryptedSharedPreferences (The Wrapper)

This is a part of the Jetpack Security library. It acts as a wrapper around the standard SharedPreferences but adds two critical layers:

• Value Encryption: Every value (like your session token) is encrypted using keys managed by the Keystore.
• Key Encryption: The keys themselves are protected, ensuring that even if an attacker steals the XML file, they see only encrypted gibberish.

Why This Works

If an attacker triggers a backup of an app using

, they will successfully steal the XML file, but they cannot steal the keys from the hardware-backed Keystore. Without the keys, the stolen session token is mathematically impossible to decrypt, rendering the backup attack useless.

Conclusion

For a banking application, “it works” is not enough. You must assume the device might be physically accessible to an attacker or that the software environment might be compromised.

By moving session tokens from standard

to hardware-backed EncryptedSharedPreferences, you ensure that the “keys to the kingdom” never leave the device’s physical security module.

In financial software, if the hardware isn’t protecting the secret, the secret isn’t protected.

In the world of medical IoT, the stakes aren’t just about data—they are about human life. Imagine a smart Continuous Glucose Monitor (CGM) that reports blood sugar levels to an insulin pump. If that device is compromised, the risk isn’t just a privacy leak; it’s a lethal dose of medication based on tampered data.

For manufacturers operating under extreme cost and hardware constraints (minimal RAM and processing power), every CPU cycle counts. In these scenarios, security architects must make a difficult choice: where do we spend our limited “security budget”?

The answer is clear: Secure Boot is mandatory, and it is significantly more critical for these devices than Full-Disk Encryption (FDE).

The Core Solution: Secure Boot

Secure Boot is a security standard that ensures a device boots using only software that is trusted by the Original Equipment Manufacturer (OEM).

How it Works

1. The Root of Trust: The device hardware contains a “Read-Only” public key burned into the chip during manufacturing.
2. Signature Verification: Every time the device powers on, the bootloader checks the digital signature of the firmware (the operating code).
3. The “Kill Switch”: If the signature doesn’t match—meaning the code has been altered by an attacker—the device refuses to boot. It effectively “bricks” itself rather than running untrusted code.

Why Secure Boot Trumps Encryption in Resource-Constrained IoT

While Full-Disk Encryption (FDE) is standard on laptops, it is often the wrong priority for a low-power medical sensor. Here is why Secure Boot is the superior control for a CGM:

1. Integrity is More Critical than Confidentiality

For a glucose monitor, the primary threat is unauthorized modification. An attacker who modifies the firmware can force the device to report “normal” sugar levels while the user is actually in a state of hypoglycemic shock.

• FDE protects data at rest (if the device is stolen).
• Secure Boot protects the logic of the system. In medical devices, ensuring the device does exactly what it was designed to do (Integrity) is a higher safety priority than ensuring the data on the device can’t be read (Confidentiality).

2. The Computational “Tax”

Encryption is “expensive.” FDE requires the CPU to constantly encrypt and decrypt data every time the system reads or writes to storage. On a device with minimal RAM and a tiny processor, this overhead can drain the battery in days rather than months and cause latency in life-critical readings.

Secure Boot, by contrast, is a one-time check. The “tax” is paid only at startup. Once the firmware is verified as authentic, the device runs at full speed without the ongoing burden of real-time decryption.

3. Preventing Persistence

Without Secure Boot, an attacker who finds a way to remotely access the device can install a “rootkit”—malicious code that stays on the device even after a reboot. Because the CGM is connected to the internet, it could become part of a botnet or a persistent gateway into the user’s home network. Secure Boot ensures that any such malicious changes are detected and blocked the moment the device restarts.

The Verdict

For a smart glucose monitor, the greatest risk is a “silent failure”—a device that looks like it’s working but is actually lying to the user. Secure Boot is the only way to guarantee that the firmware running on the device is the same code that was tested, validated, and signed by the manufacturer.

In resource-constrained medical design, we must prioritize Trust over Privacy. Secure Boot provides that trust without sacrificing the battery life and performance required to keep a patient safe.