Efficient FPGA implementation of high-throughput mixed radix multipath delay commutator FFT processor for MIMO-OFDM

This article presents and evaluates pipelined architecture designs for an improved high-frequency Fast Fourier Transform (FFT) processor implemented on Field Programmable Gate Arrays (FPGA) for Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM). The architecture presented is a Mixed-Radix Multipath Delay Commutator. The presented parallel architecture utilizes fewer hardware resources compared to Radix-2 architecture, while maintaining simple control and butterfly structures inherent to Radix-2 implementations. The high-frequency design presented allows enhancing system throughput without requiring additional parallel data paths common in other current approaches, the presented design can process two and four independent data streams in parallel and is suitable for scaling to any power of two FFT size N. FPGA implementation of the architecture demonstrated significant resource efficiency and high-throughput in comparison to relevant current approaches within literature. The proposed architecture designs were realized with Xilinx System Generator (XSG) and evaluated on both Virtex-5 and Virtex-7 FPGA devices. Post place and route results demonstrated maximum frequency values over 400 MHz and 470 MHz for Virtex-5 and Virtex-7 FPGA devices respectively

Gastric Electrical Stimulation for Gastroparesis

Gastric electrical stimulation (GES) for gastroparesis has been in use for more than a decade. Multiple publications, consisting almost entirely of open label single center studies, reported a beneficial effect on symptoms, quality of life and nutritional status. Some predictors of better response to GES have been lately identified, primarily diabetic etiology and nausea and vomiting as the predominant symptoms. However, individual response to GES remains difficult to predict. The mechanism of action of GES remains poorly understood. Stimulation parameters approved in clinical practice do not regulate gastric slow wave activity and have inconsistent effect on gastric emptying. Despite such limitations, gastric electrical stimulation remains a helpful intervention in some patients with severe gastroparesis who fail to respond to medical therapy

Gastroparesis and functional dyspepsia: excerpts from the AGA/ANMS meeting

Despite the relatively high prevelance of gastroparesis and functional dyspepsia, the aetiology and pathophysiology of these disorders remain incompletely understood. Similarly, the diagnostic and treatment options for these two disorders are relatively limited despite recent advances in our understanding of both disorders.This manuscript reviews the advances in the understanding of the epidemiology, pathophysiology, diagnosis, and treatment of gastroparesis and functional dyspepsia as discussed at a recent conference sponsored by the American Gastroenterological Association (AGA) and the American Neurogastroenterology and Motility Society (ANMS). Particular focus is placed on discussing unmet needs and areas for future research.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/78690/1/j.1365-2982.2009.01434.x.pd

Code Inspection Support for Recurring Changes with Deep Learning in Evolving Software

Developers often make recurring changes, similar but different changes across multiple locations. They inspect such code changes per source file (i.e., a diff patch) during code reviews; however, diff patches represent low-level code modification without summarizing recurring changes, leading to tedious and error-prone code inspection. To address this problem, we propose a novel code review approach, Recurring Code Changes Inspection with Deep Learning (RIDL) that leverages change patterns of an edit script by learning code clones, identical or nearly similar code fragments. To train a classifier, RIDL learns four different clone types (e.g., Type-1, Type-2, Type-3, and Type-4 clones) from a clone database mined from 25,000 subject programs. Our approach then leverages the classifier to (1) interactively summarize recurring changes and (2) detect change mistakes, potential anomalies in a given codebase. In the evaluation, we assessed the predicting capability of RIDL by analyzing code changes in several open source projects. We evaluated how accurately RIDL summarized recurring changes and detected change anomalies. Our results showed that RIDL can help developers effectively inspect recurring changes during code reviews