Technologies for imaging neural activity in large volumes

Neural circuitry has evolved to form distributed networks that act dynamically across large volumes. Collecting data from individual planes, conventional microscopy cannot sample circuitry across large volumes at the temporal resolution relevant to neural circuit function and behaviors. Here, we review emerging technologies for rapid volume imaging of neural circuitry. We focus on two critical challenges: the inertia of optical systems, which limits image speed, and aberrations, which restrict the image volume. Optical sampling time must be long enough to ensure high-fidelity measurements, but optimized sampling strategies and point spread function engineering can facilitate rapid volume imaging of neural activity within this constraint. We also discuss new computational strategies for the processing and analysis of volume imaging data of increasing size and complexity. Together, optical and computational advances are providing a broader view of neural circuit dynamics, and help elucidate how brain regions work in concert to support behavior

Machine Learning in Digital Signal Processing for Optical Transmission Systems

The future demand for digital information will exceed the capabilities of current optical communication systems, which are approaching their limits due to component and fiber intrinsic non-linear effects. Machine learning methods are promising to find new ways of leverage the available resources and to explore new solutions. Although, some of the machine learning methods such as adaptive non-linear filtering and probabilistic modeling are not novel in the field of telecommunication, enhanced powerful architecture designs together with increasing computing power make it possible to tackle more complex problems today. The methods presented in this work apply machine learning on optical communication systems with two main contributions. First, an unsupervised learning algorithm with embedded additive white Gaussian noise (AWGN) channel and appropriate power constraint is trained end-to-end, learning a geometric constellation shape for lowest bit-error rates over amplified and unamplified links. Second, supervised machine learning methods, especially deep neural networks with and without internal cyclical connections, are investigated to combat linear and non-linear inter-symbol interference (ISI) as well as colored noise effects introduced by the components and the fiber. On high-bandwidth coherent optical transmission setups their performances and complexities are experimentally evaluated and benchmarked against conventional digital signal processing (DSP) approaches. This thesis shows how machine learning can be applied to optical communication systems. In particular, it is demonstrated that machine learning is a viable designing and DSP tool to increase the capabilities of optical communication systems

Next-generation High-Capacity Communications with High Flexibility, Efficiency, and Reliability

The objective of this dissertation is to address the flexibility, efficiency and reliability in high-capacity heterogeneous communication systems. We will experimentally investigate the shaping techniques, and further extend them to more diverse and complicated scenarios, which result in more flexible systems. The scenarios include 1) entropy allocation scheme under uneven frequency response for multi-carrier system, 2) fiber-free space optics link using unipolar pairwise distribution, and 3) flexible rate passive optical network with a wide range of received optical powers. Next, we perform efficiency analysis in inter-data center and long-haul communications. We will characterize the impact of the laser linewidth, jitter tones, and the flicker noise on coherent systems with different baud rates and fiber lengths through theoretical analysis, simulation, and experimental validation. The trade-off analysis indicates the importance of setting up frequency noise power spectral density masks to qualify the transceiver laser design. Besides efficiency analysis, we will also work on efficient system architecture and algorithm design. We investigate the combined impact of various hardware impairments using proposed simplified DSP schemes in beyond 800G self-homodyne coherent system. The proposed scheme is very promising for next-generation intra-data center applications. On the other hand, to improve the data efficiency of the nonlinearity correction algorithm in broadband communication systems, we leverage the semi-supervised method and Lasso. Experimental results validate that Lasso can reduce the required pilot symbol number by exploiting the sparsity of the tap coefficients. Semi-supervised method can further enhance the system performance without introducing additional overhead. Last but not least, regarding reliability, we propose and experimentally demonstrate an ultra-reliable integrated millimeter wave and free space optics analog radio over fiber system with algorithm design. The multiple-spectra operation shows superior performance in reliability and sensitivity compared to the conventional systems, even in extreme weather conditions and strong burst interference.Ph.D

Disturbance Suppression in PMSM Drives Physical Investigation, Algorithm Design and Implementation

The work of this Ph.D. focuses on the investigation of advanced control algorithms for the control of constant and periodic disturbances in Permanent Magnet Synchronous Machines (PMSMs), with the discussion of different methods for improving their negative influence on the machine current and the torque produced at the shaft. The discussion of the disturbances from a control perspective starts with the study of the parameter uncertainties effect on the dynamical performances of the current control and after the detailed analysis in the frequency domain, simple methods for improving the state-of-art decoupling network are given and validated on the testbench. Thanks to the feature of the introduced estimator, the transient behavior of the proposed strategy results in a consistent fast and precise performance. The control scheme allows to avoid the implementation of anti-windup mechanisms in the current control, making the overall controller less sensitive to parameter mismatch. Further, due to the low computational burden, the algorithm is suitable for low cost hardware. Subsequently, the more complex issue of periodic disturbances has been deeply investigated. The theoretical model proposed is validated by comparing the real measured torque with an estimation based on the recovered disturbance affecting the observed voltages and currents. The results are clearly acceptable and further, the experimental validation stresses out the fact that few terms have a predominant role in producing the harmonic disturbances, compared to the others. This consideration lets develop two strategies for suppressing the different harmonic orders present in the machine torque at low-speed operation. One strategy relies on on-line adaptive policies, where the estimated information is passed through a sequence of optimization algorithms with different objectives. In this context, hints on the guaranteed stability are also provided in order to confirm the practical feasibility of the algorithm. The other strategy is based on the off-line generation of some pre-determined functions, limiting the on-line burden to the computation of look-up tables. Both methods brought satisfactory results during the experimental validation, confirming the validity of our approximations made on the original complex model. Although the hardware testbed setup limited the opportunity to validate the methodologies at low speed, this represents a realistic scenario, in fact at higher speed the artificial injection of harmonics within the machine current becomes challenging due to the high electrical rotational speed and it brings more negative effects, in terms of losses and audible noise than benefits on the shaft stress, in fact, the machine inertia acts as a natural filter for the high frequencies harmonics. In summary, it can be said that the research work on advanced control algorithms for the disturbance suppression in PMSM drives has produced affordable and reliable methodologies, which can be of practical implementation for various industrial drives