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By:

Commodore S.L. Deshmukh

31 October 2024 at 8:30:19 am

The Beam That Blinds the Drone

As cheap drones reshape the battlefield, India’s T-SHUL BEAM points to a future in which electronic warfare may matter as much as firepower The drone has definitively become the weapon of choice for the modern battlefield. Cheap, expendable and increasingly autonomous, unmanned aerial vehicles can now threaten troops, armour, airfields and critical infrastructure without requiring the attacker to risk a pilot. Their proliferation has created a corresponding demand for counter-drone systems...

The Beam That Blinds the Drone

As cheap drones reshape the battlefield, India’s T-SHUL BEAM points to a future in which electronic warfare may matter as much as firepower The drone has definitively become the weapon of choice for the modern battlefield. Cheap, expendable and increasingly autonomous, unmanned aerial vehicles can now threaten troops, armour, airfields and critical infrastructure without requiring the attacker to risk a pilot. Their proliferation has created a corresponding demand for counter-drone systems that can respond quickly and at a cost proportionate to the threat. This is where beam-based anti-drone technology enters the picture. Broadly, such systems fall into two categories. Hard-kill systems use concentrated laser energy to physically damage or destroy a drone. Soft-kill systems, by contrast, use radio-frequency or electromagnetic energy to disrupt the electronic links that allow a drone to communicate, navigate and transmit information. Sophisticated Weaponry Hard-kill laser systems focus an intense beam of light on a vulnerable part of an incoming UAV - its carbon-fibre structure, control surfaces, battery or other critical components. The concentrated energy rapidly heats the target, potentially burning through its structure or disabling optical sensors and bringing the aircraft down. India’s DRDO has demonstrated a 30kW laser system, while Israel’s Rafael has developed the Lite Beam system. Soft-kill systems take a different route. Rather than physically destroying the aircraft, they interfere with the electronic architecture that keeps it airborne. Directional radio-frequency energy can disrupt command-and-control links, video feeds, telemetry and satellite-navigation signals such as GPS or other GNSS services. Depending on the drone and the nature of the disruption, the aircraft may be forced to land, return to its launch point or lose control. It is in this category that the T-SHUL BEAM system developed by Indian defence company IG Defence deserves attention. T-SHUL BEAM is a man-portable, directional counter-drone system designed for tactical deployment. Its multi-band radio-frequency architecture is intended to target several of the links on which unmanned aerial systems depend, including command-and-control, telemetry, video transmission and GNSS navigation. Its directional configuration allows an operator to concentrate electronic countermeasures on a particular aerial target rather than indiscriminately radiating energy across a large area. That matters on a battlefield where the warning time against a small UAV or first-person-view drone may be measured in seconds. The appeal of such a system is therefore not simply that it can counter a drone. It is that it can potentially bring counter-drone capability closer to the soldier and to the tactical edge. Large counter-UAS installations have their place, particularly around fixed and high-value assets. But forward units require systems that can move with them, be deployed rapidly and operate against small, low-cost unmanned platforms without imposing the logistical burden associated with conventional weapons. The T-SHUL BEAM’s significance also lies in its proposed integration with an artificial-intelligence-enabled battle-management architecture. IG Drones, the original equipment manufacturer, has stated that T-SHUL BEAM has been integrated with GRID, its indigenous AI-powered platform. The GRID architecture is intended to bring sensors, intelligence systems, unmanned platforms and command elements into a common operational framework. The attraction of such integration is obvious. Countering drones is increasingly less about a single weapon and more about the speed of the kill chain: detecting a threat, identifying it, deciding what response is appropriate and directing that response before the target disappears. An AI-enabled command architecture can potentially improve situational awareness, facilitate real-time threat detection and coordinate different systems operating simultaneously. This is particularly relevant as drone warfare evolves from isolated attacks towards increasingly complex and potentially swarming operations. A battlefield crowded with drones cannot be managed effectively if every sensor and weapon operates as a separate island. The advantage will increasingly belong to forces capable of turning disparate streams of information into a coherent picture and responding at machine speed. The T-SHUL BEAM’s reported demonstration at Pokhran represents another step in India’s attempt to build a domestic ecosystem spanning drones, counter-drones, artificial intelligence and electronic warfare. Counter-drone warfare is likely to be a recurring requirement rather than a niche capability. Importing every component of such an ecosystem would leave India vulnerable to supply-chain disruptions, technology restrictions and foreign-exchange pressures. Indigenous development, even when undertaken by relatively small private-sector companies, can broaden the country’s technological base and give the armed forces greater freedom to adapt systems to their operational requirements. Economics of the Contest Yet the real measure of any counter-drone system will ultimately be operational rather than promotional. Electronic warfare is inherently a contest between countermeasure and counter-countermeasure. Drone designers can alter frequencies, communications protocols, navigation methods and levels of autonomy. A system that is effective against one generation of drones may require modification against the next. The battlefield, in other words, will remain a technological arms race. The economics of the contest nevertheless favour directed-energy and electronic-warfare solutions. A conventional interceptor expends a missile or projectile against each target. A beam-based system can, subject to its power supply, engagement envelope and other operational constraints, engage targets without expending conventional ammunition. The marginal cost of an interception can consequently be dramatically lower. The beam also travels at effectively the speed of light, removing the flight time associated with kinetic interceptors. In densely populated areas or around sensitive infrastructure, bringing down a drone with a projectile can create its own hazards. Electronic disruption, when properly controlled, offers the possibility of neutralising the threat without sending an interceptor crashing back to earth. These advantages should not obscure the limitations. Soft-kill systems depend on the vulnerabilities of the target’s electronic architecture and may be less effective against increasingly autonomous drones that require fewer external communications. Weather, range, power availability, frequency management and the sophistication of an adversary's electronic countermeasures can all affect performance. No single technology is likely to provide a complete answer. The future of counter-drone warfare will therefore belong not to the beam alone but to layered defence combining. The emergence of systems such as T-SHUL BEAM suggests that India is beginning to build the technological pieces of that architecture at home. (The writer is a retired naval aviation officer and a defence and geopolitical analyst. Views personal.)

AI in Sperm Sorting: An Unbiased Decision for A Better Outcome

Writer: Dr. Ramnath Babu T J
Dr. Ramnath Babu T J
Nov 7, 2024
3 min read

Artificial Intelligence or AI is revolutionising fertility treatments of the future. The inclusion of AI enhances the accuracy, efficiency, and objectivity of sperm selection, hence potentially improving fertility outcomes by leaps and bounds. Traditionally, sperm sorting through manual methods is subjective to judgments. Processes like centrifugation and swim-up methods are used to separate sperm based on motility and morphology. Although they are effective, they have their limitations, leading to human errors that affect the success rates of fertility treatment. For instance, studies have shown that traditional sperm sorting techniques can have variability in success rates, with reported live birth rates ranging between 15 per cent to 25 per cent per cycle depending on the method and quality of sperm. Hence the introduction of AI helps in maintaining consistency in evaluations of sperm, using the same data set for every sample which leads to better judgments.


Automation and Standardisation- Automation of sperm selection and also introduction of AI in the process have improved the results in ART. AI-assisted sperm selection improves the accuracy in choosing high-quality sperm for fertilisation purposes, and also, pregnancy and live birth rates might be improved. Technologies like Intracytoplasmic Morphologically Selected Sperm Injection along with AI ensure the chances of pregnancies increase by about 10-20 per cent compared to the standard procedures. AI and Automation will decrease time taken to analyze sperm and increase opportunities to select better sperm with DNA integrity for better development and higher success rates in embryo selection. These processes ensure that the sperm selection process follows consistent criteria, reducing variability in outcomes caused by human error.


Analysing Complex Data for Better Outcomes- AI plays a crucial in improving IVF outcomes by analysing complex data and providing tailored recommendations. AI-driven tools and models such as those on SpOvum.ai point towards an opportunity to optimise ovarian stimulation decisions by assessing patient characteristics and follicle growth patterns. A study revealed that the use of AI in IVF improved egg yield and reduced medication costs. AI enables fertility specialists to make data-driven choices, improving overall IVF success rates and streamlining treatment processes.


Reducing Human Error- AI models can continuously learn and refine their performance by being trained on newer data. This adaptability ensures the technology remains unbiased and up-to-date with the latest scientific insights into sperm quality and fertility success rates. Studies have shown that AI-driven sperm sorting can decrease human-related errors by up to 25 per cent, improving sperm selection quality in terms of morphology and motility.


Reduction of Sperm Damage- The new AI-driven sperm sorting techniques also include microfluidic systems that are known to exhibit several advantages over the most commonly used conventional method, which is centrifugation. Traditional centrifugation methods, such as density gradient centrifugation, also cause severe oxidative stress and DNA fragmentation of the sperm because of the very high mechanical forces involved. The AI-infused microfluidic sorting minimises this damage significantly by involving gentler processes that mimic the natural pathway of sperm selection. The studies show that the process of microfluidic sorting decreases DNA fragmentation in sperm, which gives improved opportunities for success for IVF. For example, DNA fragmentation is 20 percent lower in sperm sorted using microfluidic processes than in traditional processing methods.


AI is bound to play an increasingly definitive role in fertility treatments, which will improve the outcomes for couples experiencing infertility.


(The author is a Co-Founder & CEO at SpOvum® Technologies. Views personal.)

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