Comparison of Neural Networks and Least Mean Squared Algorithms for Active Noise Canceling

Active Noise Canceling (ANC) is the idea of using superposition to achieve cancellation of unwanted noise and is implemented for many applications such as attempting to reduce noise in a commercial airplane cabin. One of the main traditional techniques for noise cancellation is the adaptive least mean squares (LMS) algorithm that produces the anti-noise signal, or the 180 degree out-of-phase signal to cancel the noise via superposition. This work attempts to compare several neural network approaches against the traditional LMS algorithms. The noise signals that are used for the training of the network are from the Signal Processing Information Base (SPIB) database. The neural network architectures utilized in this paper include the Multilayer Feedforward Neural Network, the Recurrent Neural Network, the Long Short Term Neural Network, and the Convolutional Neural Network. These neural networks are trained to predict the anti-noise signal based on an incoming noise signal. The results of the simulation demonstrate successful ANC using neural networks, and they show that neural networks can yield better noise attenuation than LMS algorithms. Results show that the Convolutional Neural Network architecture outperforms the other architectures implemented and tested in this work

SaferCross: Enhancing Pedestrian Safety Using Embedded Sensors of Smartphone

The number of pedestrian accidents continues to keep climbing. Distraction from smartphone is one of the biggest causes for pedestrian fatalities. In this paper, we develop SaferCross, a mobile system based on the embedded sensors of smartphone to improve pedestrian safety by preventing distraction from smartphone. SaferCross adopts a holistic approach by identifying and developing essential system components that are missing in existing systems and integrating the system components into a "fully-functioning" mobile system for pedestrian safety. Specifically, we create algorithms for improving the accuracy and energy efficiency of pedestrian positioning, effectiveness of phone activity detection, and real-time risk assessment. We demonstrate that SaferCross, through systematic integration of the developed algorithms, performs situation awareness effectively and provides a timely warning to the pedestrian based on the information obtained from smartphone sensors and Direct Wi-Fi-based peer-to-peer communication with approaching cars. Extensive experiments are conducted in a department parking lot for both component-level and integrated testing. The results demonstrate that the energy efficiency and positioning accuracy of SaferCross are improved by 52% and 72% on average compared with existing solutions with missing support for positioning accuracy and energy efficiency, and the phone-viewing event detection accuracy is over 90%. The integrated test results show that SaferCross alerts the pedestrian timely with an average error of 1.6sec in comparison with the ground truth data, which can be easily compensated by configuring the system to fire an alert message a couple of seconds early.Comment: Published in IEEE Access, 202

DeepWiTraffic: Low Cost WiFi-Based Traffic Monitoring System Using Deep Learning

A traffic monitoring system (TMS) is an integral part of Intelligent Transportation Systems (ITS). It is an essential tool for traffic analysis and planning. One of the biggest challenges is, however, the high cost especially in covering the huge rural road network. In this paper, we propose to address the problem by developing a novel TMS called DeepWiTraffic. DeepWiTraffic is a low-cost, portable, and non-intrusive solution that is built only with two WiFi transceivers. It exploits the unique WiFi Channel State Information (CSI) of passing vehicles to perform detection and classification of vehicles. Spatial and temporal correlations of CSI amplitude and phase data are identified and analyzed using a machine learning technique to classify vehicles into five different types: motorcycles, passenger vehicles, SUVs, pickup trucks, and large trucks. A large amount of CSI data and ground-truth video data are collected over a month period from a real-world two-lane rural roadway to validate the effectiveness of DeepWiTraffic. The results validate that DeepWiTraffic is an effective TMS with the average detection accuracy of 99.4% and the average classification accuracy of 91.1% in comparison with state-of-the-art non-intrusive TMSs.Comment: Accepted for publication in the 16th IEEE International Conference on Mobile Ad-Hoc and Smart Systems (MASS), 201

Recent Updates on Acquired Hepatocerebral Degeneration

Background: Acquired hepatocerebral degeneration (AHD) refers to a chronic neurological syndrome in patients with advanced hepatobiliary diseases. This comprehensive review focuses on the pathomechanism and neuroimaging findings in AHD. Methods: A PubMed search was performed using the terms “acquired hepatocerebral degeneration,” “chronic hepatocerebral degeneration,” “Non-Wilsonian hepatocerebral degeneration,” “cirrhosis-related parkinsonism,” and “manganese and liver disease.” Results: Multiple mechanisms involving the accumulation of toxic substances such as ammonia or manganese and neuroinflammation may lead to widespread neurodegeneration in AHD. Clinical characteristics include movement disorders, mainly parkinsonism and ataxia-plus syndrome, as well as cognitive impairment with psychiatric features. Neuroimaging studies of AHD with parkinsonism show hyperintensity in the bilateral globus pallidus on T1-weighted magnetic resonance images, whereas molecular imaging of the presynaptic dopaminergic system shows variable findings. Ataxia-plus syndrome in AHD may demonstrate high-signal lesions in the middle cerebellar peduncles on T2-weighted images. Discussion: Future studies are needed to elucidate the exact pathomechanism and neuroimaging findings of this heterogeneous syndrome

Soybeans Ameliolate Diabetic Nephropathy in Rats

Diabetic nephropathy is one of the most frequent and serious complications of diabetes mellitus. Soybeans have been shown to reduce urinary albumin excretion and total cholesterol in non-diabetic patients with nephrotic syndrome. However, reports focusing specifically on diabetic nephropathy are scarce and the available results are inconsistent. It was reported that soybean consumption reduced urinary protein excretion in type 1 diabetic patients with diabetic nephropathy, whereas it was found to elicit an increase in urinary protein excretion when soybeans were consumed by type 2 diabetic patients. This study aims to investigate the effects of soybean in diabetic nephropathy, particularly the effects of consuming soybeans on the histopathology of diabetic nephropathy, using aquaporin (AQP) and osteopontin (OPN) expression as diagnostic markers. Male Sprague-Dawley rats were assigned to one of three groups: control, diabetic with red chow diet and diabetic with soybean diet. For histological examination, the expression of OPN and AQP, renal function and hemoglobin A1c were evaluated at the end of the study. Improvements in glomerular and tubulointerstitial lesions were demonstrated in the diabetic rat group given a soybean diet. OPN and AQP expression were suppressed in the kidney specimens of diabetic rats with the soybean diet. In conclusion, soybeans may prevent the weight loss and morphological disruption of the kidney associated with diabetes mellitus. Soybeans also may improve glycemic control. It seems likely that long-term control of blood glucose levels using a soybean diet could prevent the progression of diabetes mellitus, and therefore, nephropathy could be prevented