01358nam0 2200277 i 450 VAN0009625120240806100703.59820131128d2013 |0itac50 baitaIT|||| |||||Accorciamento dei telomeri: invecchiamento e cancrotesi di laureaRosaria Ostunirelatore Giovanni Di Bernardo[Caserta]201358 p.ill.30 cmSeconda Università degli studi di Napoli. Dipartimento di Scienze e Tecnologie Ambientali, Biologiche e Farmaceutiche, corso di laurea triennale in Biotecnologie, anno accademico 2012-2013TesiBiologia molecolareVANC029646SATesiBiotecnologieVANC029651SACasertaVANL000252OstuniRosariaVANV076625722327Di BernardoGiovanniVANV071207727ITSOL20250321RICABIBLIOTECA DEL DIPARTIMENTO DI SCIENZE E TECNOLOGIE AMBIENTALI BIOLOGICHE E FARMACEUTICHEIT-CE0101VAN17VAN00096251BIBLIOTECA DEL DIPARTIMENTO DI SCIENZE E TECNOLOGIE AMBIENTALI BIOLOGICHE E FARMACEUTICHE17CONS Tesi V 5 17OM 2442 1 20131128 Accorciamento dei telomeri: invecchiamento e cancro1408497UNICAMPANIA04682nam 22006735 450 991104600790332120251101120406.0981-9683-83-110.1007/978-981-96-8383-3(CKB)41997254700041(MiAaPQ)EBC32387930(Au-PeEL)EBL32387930(OCoLC)1549520282(DE-He213)978-981-96-8383-3(EXLCZ)994199725470004120251101d2026 u| 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierSlow Electronics with Reservoir Computing Energy-Efficient Neuromorphic Edge Computing for Low-Frequency Signals /edited by Isao H. Inoue1st ed. 2026.Singapore :Springer Nature Singapore :Imprint: Springer,2026.1 online resource (231 pages)Computer Science Series981-9683-82-3 Introduction: what is ‘slow electronics’ -- Reservoir Computing Models for Slow Electronics -- Fabricating Elements of Slow Electronics with Functional Materials -- Analog CMOS Implementations of Hardware Neurons for Slow Electronics -- Learning and inference in slow electronics: numerical simulation -- Learning and Inference in slow electronics: FPGA emulation and implementation -- Slow Electronics and Attractor -- Decoding the Unseen, Shaping the Future. .This open access book discusses “slow electronics”, the study of devices processing signals with low frequencies. Computers have the remarkable ability to process data at high speeds, but they encounter difficulties when handling signals with low frequencies of less than ~100Hz. They unexpectedly require a substantial amount of energy. This poses a challenge for such as biomedical wearables and environmental monitors that need real-time processing of slow signals, especially in energy-limited 'edge’ environments with small batteries. One possible solution to this issue is event-driven processing, which entails the use of non-volatile memory to read/write data and parameters every time a slow (sporadic) signal is detected. However, this approach is highly energy-consuming and unsuitable for the edge environments. To address this challenge, the authors propose “slow electronics” by developing electronic devices and systems that can process low-frequency signals more efficiently. The biological brain is an excellent example of the slow electronics, as it processes low-frequency signals in real time with exceptional energy efficiency. The authors have employed reservoir computing with a spiking neural network (SNN) to simulate the learning and inference of the brain. The integration of slow electronics with SNN reservoir computing allows for real-time data processing in edge environments without an internet connection. This will reveal the determinism or periodicity behind unconscious behaviours and habits that have been difficult to explore due to privacy barriers thus far. Moreover, it may provide a more profound understanding of a craftsman's skills, which they may not even be aware of. This book emphasises the most recent concepts and technological developments in slow electronics. Discussion on the captivating subject of slow electronics are given by delving into the complexities of reservoir calculation, analogue CMOS circuits, artificial neuromorphic devices, and numerical simulation with extended time constants, paving the way for more people-friendly devices in the future.Computer Science SeriesComputersMachine learningNeural networks (Computer science)Neural circuitryBionicsComputer HardwareMachine LearningMathematical Models of Cognitive Processes and Neural NetworksNeural CircuitsBioinspired TechnologiesComputers.Machine learning.Neural networks (Computer science)Neural circuitry.Bionics.Computer Hardware.Machine Learning.Mathematical Models of Cognitive Processes and Neural Networks.Neural Circuits.Bioinspired Technologies.005.758Inoue Isao H1863236MiAaPQMiAaPQMiAaPQBOOK9911046007903321Slow Electronics with Reservoir Computing4469765UNINA