There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

LLNet: A deep autoencoder approach to natural low-light image enhancement

In surveillance, monitoring and tactical reconnaissance, gathering the right visual information from a dynamic environment and accurately processing such data are essential ingredients to making informed decisions which determines the success of an operation. Camera sensors are often cost-limited in ability to clearly capture objects without defects from images or videos taken in a poorly-lit environment. The goal in many applications is to enhance the brightness, contrast and reduce noise content of such images in an on-board real-time manner. We propose a deep autoencoder-based approach to identify signal features from low-light images handcrafting and adaptively brighten images without over-amplifying the lighter parts in images (i.e., without saturation of image pixels) in high dynamic range. We show that a variant of the recently proposed stacked-sparse denoising autoencoder can learn to adaptively enhance and denoise from synthetically darkened and noisy training examples. The network can then be successfully applied to naturally low-light environment and/or hardware degraded images. Results show significant credibility of deep learning based approaches both visually and by quantitative comparison with various popular enhancing, state-of-the-art denoising and hybrid enhancing-denoising techniques.

LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement

Ultra Wideband Signals and Systems in Communication Engineering

Nowadays, personal positioning systems are more necessary to build many location-based services. Pedestrian Dead Reckoning (PDR), which is a pedestrian positioning technique using the accelerometer sensor to recognize pattern of steps, is an alternative method that has advantages in terms of infrastructure-independent. However, the variation of walking pattern on each individual will make some difficulties for the system to detect displacement. This is really interested authors to develop a sensor-based positioning system that applied generally to all individuals. In the test, 15 test subjects was taken with the distance of each 10m, 20m and 30m. Experiment begins with the feasibility test of accelerometer sensor. In this work, a smartphone with average sampling rate 63.79 Hz and standard deviation of 1.293 is used to records the acceleration. Then, the acceleration data are analyzed to detect step and to estimate the travelled distance using several methods. Detection of steps are able to make an average error of 2.925%, while the most nearly correct displacement estimation is using Scarlet experimental method which is make a distance average error of 1.39metres at all the traveled distance.

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Smartphone-based Pedestrian Dead Reckoning as an indoor positioning system

In the pattern recognition system, there are many methods used. For speech recognition system, Mel Frequency Cepstral Coefficients (MFCC) becomes a popular feature extraction method but it has various weaknesses especially about the accuracy level and the high of result feature dimension of the extraction method. This paper presents the combination of MFCC feature extraction method with Principal Component Analysis (PCA) to improve the accuracy in Indonesian speech recognition system. By combining MFCC and PCA, it was expected to increase the accuracy system and reduce the feature data dimension. The result of MFCC data features extraction added with delta coefficients formed matrix data that later would be reduced using PCA. PCA method in the process of data reduction was designed to be two versions. Then the result of PCA reduction data was processed to the classification process using K-Nearest Neighbour (KNN) method. Composing the data was formed from 140 speech data that were recorded from 28 speakers. The research findings showed that adding PCA method version 1 could reduce the feature dimension from 26 to 12 by the same accuracy of speech recognition with the conventional MFCC method without PCA, that is 86.43%. Whereas PCA method version 2 could increase the accuracy of speech recognition from the conventional MFCC method without PCA in increasing from 86.43% to 89.29% and decreasing of the data dimension from 26 to 10 feature dimensions.

Improvement of MFCC feature extraction accuracy using PCA in Indonesian speech recognition

Mel frequency cepstral coefficient (MFCC) is a popular feature extraction method for a speech recognition system. However, this method is susceptible to noise even though it generates a high accuracy. The conventional MFCC method has a degraded performance when the input signal has noises. This paper presents the implementation of denoising wavelet on speech input of MFCC feature extraction method. The addition of denoising process using wavelet transformation was expected to improve the MFCC performance on noisy signals. The study used 120 speech data, with 30 data were used as the reference, and the other 90 were used as the testing data. The testing data were mixed with white Gaussian noise and then tested to the speech recognition system that already had the reference data. Parameters used in the wavelet denoising process were soft thresholding with the Minimaxi thresholding rule. Eleven wavelet methods on decomposition level 10 were tested on the denoising process. The classification process used K-nearest neighbor (KNN) method. The Fejer-Korovkin 6 wavelet was the best denoising speech signal method that achieved the highest accuracy on input signals with SNR of 5-15dB. Meanwhile, the Daubechies 5 method had a high accuracy on input signal with SNR of 3 dB. All of the tested denoising methods using wavelet transformation were able to improve the accuracy of the speech recognition system on input signals with SNR of 0-10 dB compared to the system without denoising method.

Denoising Speech for MFCC Feature Extraction Using Wavelet Transformation in Speech Recognition System

In the internet age, the security of data exchange becomes risky from some tapping attacks. One of the ways to maintain the data's security is by using steganography techniques. In this paper, steganography is combined with cryptography for security enhancement. The cover media is mp3 audio file. Since mp3 is a file that has been widely used in the Internet as a medium of entertainment, the secret message has been embedded to the homogenous frame of mp3 file. Before being embedded into the mp3 file, the secret message was encrypted with Advance Encryption Standard (AES) algorithm using a key that had been processed by Message Digest 5 (MD5) hash function. The secret message file type inserted could be in the form of text, image, audio, video, or compressed file. The result shows that the steganographic capacity does not depend on the size of mp3 but on the number of homogeneous frames owned by mp3 audio file. The result also shows that the method which combined steganography and cryptography, was successfully done without reducing the quality of used mp3 audio file.

Increasing the security of mp3 steganography using AES Encryption and MD5 hash function

A gigahertz analog multiplier based on OTA and squaring is proposed. The multiplier has gigahertz frequency response is suitable to use in communication system. The circuit is based on 0.18 μm CMOS technology simulated using PSPICE level 7. This technique provides; wide dynamic range, GHz-bandwidth response and low power consumption. The proposed circuit has been simulated with PSPICE and achieved −3dB bandwidth of 3.96GHz. The total power dissipation is 0.588mW with ±1V power supply voltages..

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Risanuri Hidayat

Papers

Smartphone-based Pedestrian Dead Reckoning as an indoor positioning system

Improvement of MFCC feature extraction accuracy using PCA in Indonesian speech recognition

Denoising Speech for MFCC Feature Extraction Using Wavelet Transformation in Speech Recognition System

Increasing the security of mp3 steganography using AES Encryption and MD5 hash function

OTA-based high frequency CMOS multiplier and squaring circuit