The increasing bandwidth demand of artificial-intelligence computing and data-center interconnects has driven silicon photonic optical engines toward higher capacity, compact packaging, and improved scalability. This work presents the packaging design and characterization of a completed 16-channel silicon photonic tran...
Edge computing&s;s rise enables low-latency, decentralized processing near IoT devices and sensors but exposes networks to tampering, unauthorized access, and impersonation due to resource constraints. Centralized security falls short. This paper proposes a blockchain-empowered dynamic cybersecurity platform using smar...
Antony Helena Antony Alwyn, Antony Alwyn Michael Adrian, R.L. Yadava· Computing, Communication, an...· 0 citations
Efficient and sustainable scheduling techniques with the potential to balance energy efficiency, carbon emissions, performance, and scalability are necessary for the swift development of cloud computing. In an effort to establish sustainable cloud resource management, we introduce SpikeSynth, a novel hybrid neuromorphi...
R. R. Sahoo, N. R. Sabat, S. Radhakrishnan· Ceylon Journal of Science· 0 citations
Abstract. The Steiner tree problem is one of the most prominent problems in network design. Given an edge-weighted undirected graph and a subset of the vertices, called terminals, the task is to compute a minimum-weight tree containing all terminals (and possibly further vertices). The best-known approximation algorith...
Jarosław Byrka, Fabrizio Grandoni, Vera Traub· SIAM Journal on Computing· 0 citations
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Cloud computing&s;s data explosion demands robust security for distributed storage, where traditional systems falter in transparency and fault tolerance. This paper proposes a blockchain-AI hybrid architecture for resilient, trustless systems. Blockchain enables tamper-proof logs, smart contract access control, and dat...
For 6G telecommunications, there needs to be a paradigm shift in public safety. This chapter introduces a methodological approach to proactive, intelligence-based policing, as a strategy that is able to address new threats such as quantum computing and advanced malware that pose problems to traditional reactive policin...
Gul Shaira Banu Jahangeer, Bhavesh Santoshkumar Tupe, Sarah Altowairqi et al.· Advances in wireless technol...· 0 citations
Rising natural calamities demand intelligent disaster management beyond traditional reactive methods. IoT-ML integration revolutionizes this by enabling real-time environmental monitoring via sensors, drones, and satellites tracking temperature, humidity, air quality, seismic activity, and water levels. ML algorithms—d...
Graph Neural Networks (GNNs) have become a fundamental tool for learning over graph-structured data. Under the message-passing framework, mainstream GNN models alternate between feature transformation and neighborhood aggregation. Fusing these two phases into a node-level pipelined push dataflow, in which each node’s t...
Shi Chen, Jun-Sheng Chang, Yang Guo et al.· ACM Transactions on Architec...· 0 citations
Urban areas face severe traffic congestion, pollution, and economic losses from rising populations and vehicle density. Traditional fixed-timing controls fail under dynamic conditions. IoT-ML integration enables smart city dynamic routing. IoT sensors, GPS, and connected vehicles provide real-time traffic data. ML tech...
Shamshad Ahmed Khan, Faiz Ahmed Khan, Ram Om Sai Chethana· 0 citations
Music recommendation systems typically model long-term taste more effectively than moment-to-moment affective context. This paper presents a prototype reference architecture for emotion-adaptive music playback that integrates three artificial-intelligence components: MobileNetV3-based facial emotion recognition (FER) f...
O. Johnson, G. U. Nwamuruamu· Asian Journal of Research in...· 0 citations
Industrial time-series data analysis poses significant challenges due to multichannel sensor inputs, complex temporal dependencies, and strong dynamic variability. While large generative models (LGMs) can process temporal information effectively, their reliance on large training datasets and substantial computational p...
Zi-Chen Wang, Yi-Qun Zhu· IEEE Transactions on Systems...· 0 citations
Early and automated identification of valvular heart diseases (VHDs) using phonocardiogram (PCG) signals provides a cost-effective solution for developing intelligent healthcare applications. In this letter, a lightweight deep convolutional neural network, LWHSNet, implemented on a field programmable gate array (FPGA)-...
R. Tripathy, S. Sahoo, R. B. Pachori· IEEE Sensors Letters· 0 citations
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 2, 2026
