As rescue and recovery operations continue following the June 12, 2025, plane crash in Ahmedabad, aviation safety experts are now focusing on the technical investigation phase. With 241 lives lost, the search for the cause isn’t just about accountability—it’s about prevention.

The Black Box: Aviation’s Memory Keeper

1. What Is the Black Box?

Despite the name, the black box is actually orange — for visibility. It consists of two components:

• Cockpit Voice Recorder (CVR): Captures conversations and audio from the flight deck.
• Flight Data Recorder (FDR): Logs dozens to hundreds of parameters — speed, altitude, engine status, control inputs.

These devices are housed in titanium or steel and can withstand:

• Temperatures above 1,000°C
• Underwater pressures up to 20,000 feet
• Crashes with up to 3,600 G-force

They also emit underwater locator beacons for up to 30 days.

2. Forensic Engineering & Flight Reconstruction

Beyond black boxes, investigators use:

• Radar data and air traffic control logs
• Wreckage analysis for structural failure clues
• Satellite-based tracking systems like ADS-B
• Weather data for turbulence or wind shear insights

Forensic teams often reconstruct the flight path virtually or even physically using recovered debris to determine failure points.

3. Human Factors & AI in Modern Investigation

New tools like machine learning and human factors analysis are used to identify procedural errors or lapses in judgement.

In many modern investigations, AI helps:

• Filter large datasets (e.g., over 1,000 flight parameters per second)
• Detect patterns missed by the human eye
• Predict similar risk scenarios in future flights

What Happens Next in the Ahmedabad Crash?

Authorities, in coordination with the Directorate General of Civil Aviation (DGCA), are likely:

• Retrieving and analyzing the black box
• Interviewing air traffic controllers
• Reconstructing the aircraft’s final seconds using both data and simulation

The final report may take months, but it will be critical in issuing safety directives or revising standard procedures.

In a breakthrough that could transform the future of wireless communication and edge computing, engineers at MIT have developed a novel AI hardware accelerator capable of processing wireless signals at the speed of light. The new optical chip, built for signal classification, achieves nanosecond-level performance—up to 100 times faster than conventional digital processors—while consuming dramatically less energy.

With wireless spectrum under growing strain from billions of connected devices, from teleworking laptops to smart sensors, managing bandwidth has become a critical challenge. Artificial intelligence offers a path forward, but most existing AI models are too slow and power-hungry to operate in real time on wireless devices.

The MIT solution, known as MAFT-ONN (Multiplicative Analog Frequency Transform Optical Neural Network), could be a game-changer.

“There are many applications that would be enabled by edge devices that are capable of analyzing wireless signals,” said Prof. Dirk Englund, senior author of the study, in a media statement. “What we’ve presented in our paper could open up many possibilities for real-time and reliable AI inference. This work is the beginning of something that could be quite impactful.”

Published in Science Advances, the research describes how MAFT-ONN classifies signals in just 120 nanoseconds, using a compact optical chip that performs deep-learning tasks using light rather than electricity. Unlike traditional systems that convert signals to images before processing, the MIT design processes raw wireless data directly in the frequency domain—eliminating delays and reducing energy usage.

“We can fit 10,000 neurons onto a single device and compute the necessary multiplications in a single shot,” said Ronald Davis III, lead author and recent MIT PhD graduate.

The device achieved over 85% accuracy in a single shot, and with multiple measurements, it converges to above 99% accuracy, making it both fast and reliable.

Beyond wireless communications, the technology holds promise for edge AI in autonomous vehicles, smart medical devices, and future 6G networks, where real-time response is critical. By embedding ultra-fast AI directly into devices, this innovation could help cars react to hazards instantly or allow pacemakers to adapt to a patient’s heart rhythm in real-time.

Future work will focus on scaling the chip with multiplexing schemes and expanding its ability to handle more complex AI tasks, including transformer models and large language models (LLMs).

On June 12, 2025, a tragic aviation accident struck Ahmedabad, India when a regional passenger aircraft, Air India flight A1-171, crashed during take-off at Sardar Vallabhbhai Patel International Airport. According to preliminary reports, the incident resulted in over 200 confirmed casualties, including both passengers and crew members, and several others are critically injured. The aviation community and scientific world now turn their eyes not just toward the cause but also toward understanding the complex science behind what should have been a routine take-off.

How Do Aircraft Take Off?

Take-off is one of the most critical phases of flight, relying on the precise orchestration of aerodynamics, propulsion, and control systems. Here’s how it works:

1. Lift and Thrust

To leave the ground, an aircraft must generate lift, a force that counters gravity. This is achieved through the unique shape of the wing, called an airfoil, which creates a pressure difference — higher pressure under the wing and lower pressure above — according to Bernoulli’s Principle and Newton’s Third Law.

Simultaneously, engines provide thrust, propelling the aircraft forward. Most commercial jets use turbofan engines, which accelerate air through turbines to generate power.

2. Critical Speeds

Before takeoff, pilots calculate critical speeds:

• V1 (Decision Speed): The last moment a takeoff can be safely aborted.
• Vr (Rotation Speed): The speed at which the pilot begins to lift the nose.
• V2 (Takeoff Safety Speed): The speed needed to climb safely even if one engine fails.

If anything disrupts this process — like bird strikes, engine failure, or runway obstructions — the results can be catastrophic.

Environmental and Mechanical Challenges

Factors like wind shear, runway surface condition, mechanical integrity, or pilot error can interfere with safe take-off. Investigators will be analyzing these very aspects in the Ahmedabad case.

The Bigger Picture

Take-off accounts for a small fraction of total flight time but is disproportionately associated with accidents — approximately 14% of all aviation accidents occur during take-off or initial climb.