This paper presents a novel algorithm for image compression aiming to reduce data packet size, resulting in effective bandwidth utilization during data transmissions. The algorithm referred to as Differential Subtraction Chain (DSC) consists of three steps. First, it separates an image file to three matrices of RGB. Second, it computes element-wise different values in each pixel between R and G matrices, and between G and B matrices. Third, the different values are binary encoded and transformed to consecutive vectors in order for data transmissions. In our MATLAB simulations, the performance measure is compression ratio which is calculated by [1-(compressed data size/original data size)] 100%. The compression ratios yielded by our DSC tested with three benchmarking images of city, Lenna and Mandrill are 44.02%, 42.02% and 39.86%, respectively. The result shows that DSC is much better than other existing algorithms in the experiment.

A Lossless Image Compression Algorithm using Differential Subtraction Chain

An embedded system to measure ground-based solar irradiance signal

Abstract: Speech signal is widely and effectively used globally for communication purposes in all sectors of human eandeavour. Incidentally speech signals suffer from interference by noise components when transmission through a communication channel is involved. Such noise components include Additive White Gaussian Noise (AWGN), Random Noise, power line noise, high and low frequency noise components. Without removing these noise components the speech signal quality will be compromised, implying delivery of unreliable message. In this paper a FIR filter is designed with single cosine term generalised adjustable window with to filter out high frequency noise components from speech signals. The optimal parameters of the filter are sampling frequency of 44100Hz and filter order of 34. A real voice statement, “Education is the Key to the Development of any Nation” from the lead author of this paper is captured with the in-built microphone of a laptop system, recorded in windows media audio (.wma) format and stored in one of the files of the system as speech. The optimal sampling frequency of 44100Hz is applicable of the type of format of the speech signal. With “audioread” instruction the stored speech is loaded into a matlab edit window. A noise component of 4500Hz and above is generated with matlab and added to the speech to imitate real corrupt speech signal. When the corrupt signal is passed through the designed filter the noise component was drastically filtered out.

/pdf/filtering-out-noise-in-speech-signal-with-fir-filter-based-4rido6f89v.pdf

Filtering out noise in speech signal with fir filter based onsingle cosine termgeneralised adjustable window

Energy is a main concern in the design and deployment of Wireless Sensor Networks because sensor nodes are constrained by limitations of battery, memory, and a processing unit. A number of techniques have been presented to solve this power problem. Among the proposed solutions, the data compression scheme is one that can be used to reduce the volume of data for transmission. This article presents a data compression algorithm called Differential Run Length Encryption (D-RLE) consisting of three steps. First, reading values are divided into groups by using a threshold of Chauvenet’s criterion. Second, each group is subdivided into subgroups whose consecutive member values are determined by a subtraction scheme under a K-RLE based threshold. Third, the member values are then encoded to binary based on our ad hoc scheme to compress the data. The experimental results show that the data rate savings by D-RLE can be up to 90% and energy usage can be saved more than 90% compared to data transmission without compression.

https://www.mdpi.com/1424-8220/19/14/3190/pdf

Differential Run-Length Encryption in Sensor Networks.

Online teaching has taken up its importance post-pandemic period. Today, online teaching is considered to be one of the teaching pedagogy. This means every teacher and professor is generating online lecture videos and sharing them for students’ later use. Mostly, the environment for the video creation is in real time either in the live classroom or at home, various environmental noises interfere with the actual speech of the presenter. Therefore, there is a need for identifying the various noises that may be part of the lecture video to assess the quality of the video. Towards this, very few research works are observed. Researchers have worked on additive noises, but identifying convolutional noises is a challenge. We propose to work on the audio signal of the video lectures to identify the positions and durations of various convolutional noises and measure the amount of noise present in the audio part of the video lectures. We used various filters for identifying simultaneous talks, long silences, baby crying, kitchen sounds, and vehicle noises. The average accuracy of the proposed solution in identifying the noises and the noise positions is 97.37%. The MSE of the noise in the audio of each clip varies depending on the various noises present. This defines the quality of the audio in the lecture video.

Audio de-noising and quality assessment for various noises in lecture videos

In this paper a new compression scheme has been formulated based on jointly delta modulation with biasing and Run Length Encoding technique for Direct Normalized Solar Irradiance data. The key features of this Compression Algorithm (Delta Modulation with Biasing and Run Length Encoding techniques viz. DMBRLE) are: it requires less memory as it belongs to direct compression method and its lossless nature. The main Goal of this algorithm has been achieved by compressing successive same sample elements as well as low frequency sample changes region. This is substantiated on jointly First Sample Difference (FSD) and S-Run Length Encoding (S-RLE) techniques. FSD is modified to concentrate at low frequency spectra. Simultaneously S-Run length Encoding is structured to fix the region having successive same sample value. The Compression Algorithm is modified stepwise and its effect on that is a large non-linear in nature. However, the quality of the recovered signal is demonstrated by computing Normalized Root Mean Square Error (NRMES). The result establishes that this developed compression scheme increases the Compression Ratio up to a reasonable value without threw off the Lossless nature.

DMBRLE: A lossless compression algorithm for solar irradiance data acquisition

During transmission via any media signals get affected by unwanted components; which is adverse but inevitable. Elimination of such unwanted components termed as noise from transmitted signals persisted important as well as puzzling task for the researchers from the initial days of Digital Signal Processing. Among a significant number of techniques proposed for removal of noise from signals, use of digital filters has become most effectual in multiple ways. Slighter overheads in designing and lower hardware cost have made the Finite Impulse Response (FIR) filters popular. FIR filter is expansively used in video convolution functions, signal preconditioning, and various communication applications. Till date, most of the FIR filter designing techniques is based on Window method, Optimal Sampling Method, Frequency Sampling Method. In this paper a new subterfuge based on Genetic Operators and Kaiser Window function has been proposed to obtain the least noisy signal from a set of filtered signals of a corrupted audio signal.

/pdf/an-approach-for-obtaining-least-noisy-signal-using-kaiser-4x5sekp8lu.pdf

An Approach for obtaining Least Noisy Signal using Kaiser Window and Genetic Algorithm

In this paper we have analyzed the total solar irradiance signal obtained from Earth Radiation Budget Satellite After denoising this signal using simple exponential smoothing we experimented with this filtered signal to study its nature Fractal dimension analysis has been applied on it and the obtained value of fractal dimension demonstrates anti-persistent behaviour (short memory process) which may imply multi-periodic phenomenon To sort out the possible periods with higher confidences we have applied Ferraz-Mello's method of Date Compensated Discrete Fourier Transform as well as method of Periodogram which are very much useful for the present time series with uneven gaps We have found certain periodicities from these two methods with confidence levels all higher than 95%

Subhas Chandra Panja

Papers

DMBRLE: A lossless compression algorithm for solar irradiance data acquisition

An Approach for obtaining Least Noisy Signal using Kaiser Window and Genetic Algorithm

A search for latent periodicities of irregular time series of total solar irradiance