Performance evaluation and implementations of MFCC, SVM and MLP algorithms in the FPGA board

One of the most difficult speech recognition tasks is accurate recognition of human-to-human communication. Advances in deep learning over the last few years have produced major speech improvements in recognition on the representative Switch-board conversational corpus. Word error rates that just a few years ago were 14% have dropped to 8.0%, then 6.6% and most recently 5.8%, and are now believed to be within striking range of human performance. This raises two issues - what is human performance, and how far down can we still drive speech recognition error rates? The main objective of this article is the development of a comparative study of the performance of Automatic Speech Recognition (ASR) algorithms using a database made up of a set of signals created by female and male speakers of different ages. We will also develop techniques for the Software and Hardware implementation of these algorithms and test them in an embedded electronic card based on a reconfigurable circuit (Field Programmable Gate Array FPGA). We will present an analysis of the results of classifications for the best Support Vector Machine architectures (SVM) and Artificial Neural Networks of Multi-Layer Perceptron (MLP). Following our analysis, we created NIOSII processors and we tested their operations as well as their characteristics. The characteristics of each processor are specified in this article (cost, size, speed, power consumption and complexity). At the end of this work, we physically implemented the architecture of the Mel Frequency Cepstral Coefficients (MFCC) extraction algorithm as well as the classification algorithm that provided the best results

Transport characteristics of guanidino compounds at the blood-brain barrier and blood-cerebrospinal fluid barrier: relevance to neural disorders

Guanidino compounds (GCs), such as creatine, phosphocreatine, guanidinoacetic acid, creatinine, methylguanidine, guanidinosuccinic acid, γ-guanidinobutyric acid, β-guanidinopropionic acid, guanidinoethane sulfonic acid and α-guanidinoglutaric acid, are present in the mammalian brain. Although creatine and phosphocreatine play important roles in energy homeostasis in the brain, accumulation of GCs may induce epileptic discharges and convulsions. This review focuses on how physiologically important and/or neurotoxic GCs are distributed in the brain under physiological and pathological conditions. Transporters for GCs at the blood-brain barrier (BBB) and the blood-cerebrospinal fluid (CSF) barrier (BCSFB) have emerged as substantial contributors to GCs distribution in the brain. Creatine transporter (CRT/solute carrier (SLC) 6A8) expressed at the BBB regulates creatine concentration in the brain, and represents a major pathway for supply of creatine from the circulating blood to the brain. CRT may be a key factor facilitating blood-to-brain guanidinoacetate transport in patients deficient in S-adenosylmethionine:guanidinoacetate N-methyltransferase, the creatine biosynthetic enzyme, resulting in cerebral accumulation of guanidinoacetate. CRT, taurine transporter (TauT/SLC6A6) and organic cation transporter (OCT3/SLC22A3) expressed at the BCSFB are involved in guanidinoacetic acid or creatinine efflux transport from CSF. Interestingly, BBB efflux transport of GCs, including guanidinoacetate and creatinine, is negligible, though the BBB has a variety of efflux transport systems for synthetic precursors of GCs, such as amino acids and neurotransmitters. Instead, the BCSFB functions as a major cerebral clearance system for GCs. In conclusion, transport of GCs at the BBB and BCSFB appears to be the key determinant of the cerebral levels of GCs, and changes in the transport characteristics may cause the abnormal distribution of GCs in the brain seen in patients with certain neurological disorders

A Vehicular Queue Length Measurement System in Real-Time Based on SSD Network

Vehicular queue length measurement is an important parameter to detect the traffic congestion, which is resulted from several issues such as traffic lights, accidents, and poor roads infrastructures. In this paper, a system in real-time is proposed to detect and measure the vehicular queue length at intersections. The proposed system consists of two main steps: the first step is the detection of queue by using frames differencing method to detect the motion in the target areas. If there is no a motion, then the second step is implemented to detect the vehicles in these areas by using Single Shot Multibox Detector (SSD) algorithm. If there are vehicles, that means the queue exists and the measurement process begins. Some modifications are applied on SSD algorithm to fit with in our system and to improve the accuracy of the vehicle detection process. The system is applied on videos obtained by stationary cameras. The experiments demonstrate that this system is able to accurately detect and measure the vehicular queue length