Advanced digital modulation: Communication techniques and monolithic GaAs technology

Communications theory and practice are merged with state-of-the-art technology in IC fabrication, especially monolithic GaAs technology, to examine the general feasibility of a number of advanced technology digital transmission systems. Satellite-channel models with (1) superior throughput, perhaps 2 Gbps; (2) attractive weight and cost; and (3) high RF power and spectrum efficiency are discussed. Transmission techniques possessing reasonably simple architectures capable of monolithic fabrication at high speeds were surveyed. This included a review of amplitude/phase shift keying (APSK) techniques and the continuous-phase-modulation (CPM) methods, of which MSK represents the simplest case

Document Classification Systems in Heterogeneous Computing Environments

Datacenter workloads demand high throughput, low cost and power efficient solutions. In most data centers the operating costs dominates the infrastructure cost. The ever growing amounts of data and the critical need for higher throughput, more energy efficient document classification solutions motivated us to investigate alternatives to the traditional homogeneous CPU based implementations of document classification systems. Several heterogeneous systems were investigated in the past where CPUs were combined with GPUs and FPGAs as system accelerators. The increasing complexity of FPGAs made them an interesting device in the heterogeneous computing environments and on the other hand difficult to program using Hardware Description languages. We explore the trade-offs when using high level synthesis and low level synthesis when programming FPGAs. Using low level synthesis results in less hardware resource usage on FPGAs and also offers the higher throughput compared to using HLS tool. While using HLS tool different heterogeneous computing devices such as multicore CPU and GPU targeted. Through our implementation experience and empirical results for data centric applications, we conclude that we can achieve power efficient results for these set of applications by either using low level synthesis or high level synthesis for programming FPGAs

The Importance of Monitoring Renewable Energy Plants: Three Case Histories

Many renewable energy plants are put into operation without providing a monitoring system to evaluate their performance over time. Then if is often difficult to realise the bad working of the system and the loss of efficiency results in an economic loss. In the Author\u2019s experience as designer or supervisor of such plants, he came across various examples that pointed out the advantages of having installed a monitoring system, of course with a careful data analysis. Problems sometimes arose from poorer performance than anticipated in the design, but more often from inefficient plant operations after some months or years from the starting. Three quite different examples, derived from the Author\u2019s direct experience, are reported to illustrate how real performance can be lower than designed due respectively: 1. To bad settings of the parameters; 2. To a hurried commissioning that did not reveal the mistakes in the design of the plant; 3. To a failure of a single component over time

A CMOS Spiking Neuron for Dense Memristor-Synapse Connectivity for Brain-Inspired Computing

Neuromorphic systems that densely integrate CMOS spiking neurons and nano-scale memristor synapses open a new avenue of brain-inspired computing. Existing silicon neurons have molded neural biophysical dynamics but are incompatible with memristor synapses, or used extra training circuitry thus eliminating much of the density advantages gained by using memristors, or were energy inefficient. Here we describe a novel CMOS spiking leaky integrate-and-fire neuron circuit. Building on a reconfigurable architecture with a single opamp, the described neuron accommodates a large number of memristor synapses, and enables online spike timing dependent plasticity (STDP) learning with optimized power consumption. Simulation results of an 180nm CMOS design showed 97% power efficiency metric when realizing STDP learning in 10,000 memristor synapses with a nominal 1M{\Omega} memristance, and only 13{\mu}A current consumption when integrating input spikes. Therefore, the described CMOS neuron contributes a generalized building block for large-scale brain-inspired neuromorphic systems.Comment: This is a preprint of an article accepted for publication in International Joint Conference on Neural Networks (IJCNN) 201