| Smart Devices are Different: Assessing and Mitigating Mobile Sensing Heterogeneities for Activity Recognition A Stisen, H Blunck, S Bhattacharya, TS Prentow, MB Kjærgaard, A Dey, ... The 13th ACM Conference on Embedded Networked Sensor Systems, 2015 | 565 | 2015 |
| DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices ND Lane, S Bhattacharya, P Georgiev, C Forlivesi, L Jiao, L Qendro, ... International Conference on Information Processing in Sensor Networks (IPSN '16), 2016 | 527 | 2016 |
| An Early Resource Characterization of Deep Learning on Wearables, Smartphones and Internet-of-Things Devices ND Lane, S Bhattacharya, P Georgiev, C Forlivesi, F Kawsar Proceedings of the 2015 International Workshop on Internet of Things towards …, 2015 | 246 | 2015 |
| Sparsification and Separation of Deep Learning Layers for Constrained Resource Inference on Wearables S Bhattacharya, ND Lane ACM Conference on Embedded Networked Sensor Systems (SenSys) 2016, 2016 | 230 | 2016 |
| Multimodal Deep Learning for Activity and Context Recognition V Radu, C Tong, S Bhattacharya, ND Lane, C Mascolo, MK Marina, ... Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous …, 2018 | 213 | 2018 |
| Squeezing deep learning into mobile and embedded devices ND Lane, S Bhattacharya, A Mathur, P Georgiev, C Forlivesi, F Kawsar IEEE Pervasive Computing 16 (3), 82-88, 2017 | 198 | 2017 |
| From Smart to Deep: Robust Activity Recognition on Smartwatches using Deep Learning S Bhattacharya, ND Lane | 175 | 2016 |
| Towards multimodal deep learning for activity recognition on mobile devices V Radu, ND Lane, S Bhattacharya, C Mascolo, MK Marina, F Kawsar Proceedings of the 2016 ACM International Joint Conference on Pervasive and …, 2016 | 142 | 2016 |
| DeepEye: Resource Efficient Local Execution of Multiple Deep Vision Models using Wearable Commodity Hardware A Mathur, ND Lane, S Bhattacharya, A Boran, C Forlivesi, F Kawsar The 15th International Conference on Mobile Systems, Applications and …, 2017 | 134 | 2017 |
| Using unlabeled data in a sparse-coding framework for human activity recognition S Bhattacharya, P Nurmi, N Hammerla, T Plötz Pervasive and Mobile Computing 15, 242-262, 2014 | 130 | 2014 |
| Energy-efficient trajectory tracking for mobile devices MB Kjærgaard, S Bhattacharya, H Blunck, P Nurmi Proceedings of the 9th international conference on Mobile systems …, 2011 | 124 | 2011 |
| A survey on different video watermarking techniques and comparative analysis with reference to H. 264/AVC S Bhattacharya, T Chattopadhyay, A Pal 2006 IEEE International Symposium on Consumer Electronics, 1-6, 2006 | 112 | 2006 |
| Identifying meaningful places: The non-parametric way P Nurmi, S Bhattacharya Pervasive Computing: 6th International Conference, Pervasive 2008 Sydney …, 2008 | 91 | 2008 |
| The company you keep: mobile malware infection rates and inexpensive risk indicators HTT Truong, E Lagerspetz, P Nurmi, AJ Oliner, S Tarkoma, N Asokan, ... Proceedings of the 23rd international conference on World wide web, 39-50, 2014 | 84 | 2014 |
| MobiSR: Efficient On-Device Super-Resolution through Heterogeneous Mobile Processors R Lee, SI Venieris, L Dudziak, S Bhattacharya, ND Lane The 25th Annual International Conference on Mobile Computing and Networking …, 2019 | 59 | 2019 |
| Using deep data augmentation training to address software and hardware heterogeneities in wearable and smartphone sensing devices A Mathur, T Zhang, S Bhattacharya, P Veličković, L Joffe, ND Lane, ... Proceedings of the 17th ACM/IEEE International Conference on Information …, 2018 | 56 | 2018 |
| Low-resource Multi-task Audio Sensing for Mobile and Embedded Devices via Shared Deep Neural Network Representations P Georgiev, S Bhattacharya, N Lane, C Mascolo Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), 2017 | 56 | 2017 |
| A grid-based algorithm for on-device GSM positioning P Nurmi, S Bhattacharya, J Kukkonen Proceedings of the 12th ACM international conference on Ubiquitous computing …, 2010 | 49 | 2010 |
| Robust and Energy-Efficient Trajectory Tracking for Mobile Devices S Bhattacharya, H Blunck, MB Kjærgaard, P Nurmi IEEE Transactions on Mobile Computing, 2014 | 48 | 2014 |
| DXTK: Enabling Resource-efficient Deep Learning on Mobile and Embedded Devices with the DeepX Toolkit. ND Lane, S Bhattacharya, A Mathur, C Forlivesi, F Kawsar MobiCASE, 98-107, 2016 | 41 | 2016 |
Nicholas LaneUniversity of Cambridge and Samsung AIBekræftet mail på cam.ac.uk
