India utilizes its indigenous satellite navigation system, NavIC, developed by ISRO. This system covers the entire country and the surrounding region, ensuring India's positioning independence from systems controlled by other parties. However, the issue with using this system has always related to ground equipment, not aviation.
A satellite navigation signal is useless without a corresponding chip to receive it. These chips are produced by only a small number of companies, and there are almost none in India. Consequently, devices sold in the country could receive GPS signals but not NavIC, as commercial receivers supporting NavIC did not exist.
MumbaiSemi was founded in 2024 to address this commercial gap. Its founders spent years working on this problem at IIT Bombay.
Vijay Kanchetla received a Ph.D. in RF integrated circuit design at this institute and previously worked at Intel. Professor Rajesh Zele teaches in the Electrical Engineering department and holds a Ph.D. from Carnegie Mellon University. The company is located in the SINE incubator, the institute's technology business incubator.
The technology initially existed as a research project called Dhruva, funded by the Ministry of Electronics and Information Technology with SAMEER participating as the authorized agency. This project was created by students and researchers in Zele's laboratory. The first silicon sample was obtained from a fab in December 2019. Zele noted that the chip was designed in 18 months and became the first receiving chip from his lab fully developed by students.
The essence of the development lies in its reconfigurability. A standard receiver is built for specific frequency bands, so supporting multiple navigation systems usually requires multiple chips or hardware changes. The team's work describes a receiver that is fully programmable for the bands used by NavIC, GPS, Galileo, and BeiDou, without the need for external components or hardware changes on the chip between them.
The main engineering challenge relates to signal weakness. The signal coming from the satellite is significantly weaker than the surrounding noise. According to Zele's description of the chip, it is capable of separating the navigation signal and amplifying it by approximately 400,000 times before digital conversion on the same chip.
The commercial version is called DhruvaPro. MumbaiSemi claims that it has passed silicon validation, meaning it has been fabricated and tested, not just simulated, and the company holds two patents on this design. The smaller number of external components around the chip also leads to a more compact and less expensive receiver module, which is what the device manufacturer actually acquires.
The work received recognition within the institute: IIT Bombay awarded Zele and Kanchetla the Dr. P. K. Patwardhan Technology Development Award in 2024 for DhruvaPro during National Engineers Day. The research was also peer-reviewed. The paper describing the receiver, with Kanchetla as lead author and Zele among co-authors, was published in IEEE Transactions on Microwave Theory and Techniques in March 2022, which is a stronger form of external validation than most companies in this field can demonstrate.
The company participated in the Bharat Innovates exhibition in Nice in June 2026, where it sought partners in communications and navigation, rather than announcing clients. Nevertheless, the commercial stance remains closed: no data has been disclosed regarding clients, orders, revenue, or reliable funding amounts, and the company still needs to transition from silicon validation to mass production.
Oral cancer, or oral malignancy, represents one of the most serious public health challenges in India. Most patients are diagnosed with the disease only at late or critical stages. The primary reason for this is the necessity of invasive tests, such as biopsy, which requires an incision.
However, researchers from the Indian Institute of Technology (IIT) Indore have created an innovative, low-cost, and needle-free methodology. This technology allows for the detection of early signs of oral cancer using only saliva and swabs from the oral cavity.
In the study, scientists employed liquid chromatography-mass spectrometry technique, comparing samples from healthy individuals, patients with fibrosis, and cancer patients. This method detects chemical changes occurring inside the mouth by collecting proteins and cells directly from the mucous membranes.
It was found that specific molecular changes related to fat, energy, oxidative stress, and the body's immune system manifest in saliva and swabs even before visible symptoms of the disease appear.
This groundbreaking research was conducted under the guidance of Professor Hema Chandra Jha from the 'Faculty of Life Sciences and Biomedical Engineering' at IIT Indore. Dr. Tarun Prakash Verma and Dr. Dipak Agrawal from the Government Dental College Indore participated in the work.
The study focused primarily on two conditions, as oral cancer is the most common and deadly type. This condition often develops in people who consume tobacco, supari, or chuna, which can subsequently lead to cancer development.
The results of this work were published in the prestigious journal 'British Journal of Cancer' and a patent application has been filed. The Director of IIT Indore, Professor Suhas Joshi, noted that the ultimate goal of research should serve society. He emphasized that this study transforms complex laboratory science into a very simple saliva analysis method, enabling painless cancer detection for ordinary patients across the country.
Professor Hem Chandra Jha, the lead researcher, stated that his intention is to convert these non-invasive markers into practical devices for regular monitoring of high-risk patients.
Researchers at the University of São Paulo (USP) have created an artificial intelligence (AI) tool designed to identify teeth and flag signs of cavities in dental radiographic exams. This technology, developed in Ribeirão Preto, aims to support dentists in interpreting these images, making clinical assessments faster and more accurate.
The development arose from a partnership between computer science specialists from the Faculty of Philosophy, Sciences, and Letters of Ribeirão Preto and professionals from the USP Faculty of Dentistry. The InReDD group, composed of approximately 30 researchers, is responsible for creating digital solutions aimed at the dental field.
Using convolutional neural networks, the tool was trained with thousands of radiographs that had already been analyzed by specialists. The main objective is not to replace the dentist but rather to provide a second opinion that can support decisions regarding diagnosis and treatment planning.
The functioning process begins with the preparation of a vast set of dental images, accompanied by their respective expert evaluations. This material serves as a reference base for training the system, which learns to correlate the visual patterns of the radiographs with the changes it needs to recognize.
The architecture employed belongs to the category of convolutional neural networks, specifically designed to process visual information. Alessandra Alaniz Macedo, a professor of Computer Science at FFCLRP and project coordinator, explained that this type of structure is ideal for tasks involving images, such as dental radiographs.
During the training phase, the model compares its own predictions with the data established by specialists. When discrepancies are identified, it adjusts its parameters to reduce errors, allowing the tool to continuously improve its ability to locate modifications in the images.
However, creating a system of this nature goes beyond just coding the algorithm; it also encompasses data selection and preparation, as well as training and verifying its performance, constituting a scientific process conducted by computer scientists.
In a clinical setting, one of the applications suggested by the group is to use AI as an additional support to the analysis performed by the professional. The tool can help both in discovering problems and in structuring the treatment plan for a clinical case.
The scope of the project extends beyond radiographs. The team indicated that AI solutions could also be applied to administrative functions in clinics, such as appointment management, answering simple queries, and producing communication materials for patients.
Despite the results being considered promising, researchers emphasize that the technology should not yet take over the role of the dentist. A notable challenge lies in the complexity of fully explaining how certain models arrive at their conclusions, which often leads to these systems being associated with the concept of a 'black box'. Camila Tirapelli, a professor at the Ribeirão Preto Faculty of Dentistry and co-coordinator with Alessandra Macedo, stresses that no AI system is perfect, keeping the interpretation of the result and the clinical decision under the responsibility of the professional.
The quality of the data used in training directly impacts the system's performance. According to Tirapelli, insufficient information can cause the tool to replicate flaws present in the material used for its learning. Thus, the union between health and computer science professionals is seen as crucial for building reliable models.
It is expected that, for patients, these tools can reduce the time required for certain evaluations and contribute to more accurate diagnoses. In the realm of scientific research, the ability to process large volumes of radiographs can facilitate the identification of population patterns and support epidemiological investigations in oral health.
The solutions generated by InReDD are intended for various audiences, including dentists, radiologists, and researchers. Although current advances are positive, the systems still require refinements before broader adoption in clinical practice.
This work is the result of a collaboration between two USP units in Ribeirão Preto, bringing together faculty, students, and researchers of various levels. The group includes undergraduates, master's students, doctoral candidates, a master's holder, a postdoctoral researcher, and professors from relevant fields.
The initiative also receives financial support from the São Paulo State Research Foundation. The original project that gave rise to the tool seeks to integrate AI and dentistry resources through the so-called multimodal fusion, expanding the possibilities of computing in oral health.
For Alessandra Macedo, the aggregation of multiple functionalities into a single solution represents a differential aspect of the team's work. The project has already generated academic publications, dissertations, and theses, in addition to a technological product whose code has been registered by USP. The researchers aim for future studies to improve the tools and expand their applications, aiming for a dentistry where AI is a complementary resource, without diverting clinical responsibility from the professional.
