Bio-inspired radar: recognition of human echolocator tongue clicks signals

Echolocation is a process where sound waves are transmitted and the echoes are analyzed to determine information about the surrounding environment. Principle of echolocation method inspire by bat have been widely used in Radar and Sonar application. What is less known, this technique also used by a small group of blind humans in their daily life mainly for navigation and object recognition with high accuracy. To date, only a few technical studies look at how these echolocators are able to detect their own echoes. The conventional detection using match filter like in Radar application for this signal is not suitable due to existence of multiple frequency components. Thus, this paper discusses an alternative approach to recognize human echolocator tongue click signals by using the Linde-Buzo-Gray Vector Quantization Method. The significant click features which is the multiple frequencies itself were extracted from the raw transmits and echo signal and were used for the recognition process. Although there are gaps still need to be filled, the biologically-inspired technique presented here may provide useful information particular in signal processing for radar and sonar systems in the future

Texture classification using spectral entropy of acoustic signal generated by a human echolocator

Human echolocation is a biological process wherein the human emits a punctuated acoustic signal, and the ear analyzes the echo in order to perceive the surroundings. The peculiar acoustic signal is normally produced by clicking inside the mouth. This paper utilized this unique acoustic signal from a human echolocator as a source of transmitted signal in a synthetic human echolocation technique. Thus, the aim of the paper was to extract information from the echo signal and develop a classification scheme to identify signals reflected from different textures at various distance. The scheme was based on spectral entropy extracted from Mel-scale filtering output in the Mel-frequency cepstrum coefficient of a reflected echo signal. The classification process involved data mining, features extraction, clustering, and classifier validation. The reflected echo signals were obtained via an experimental setup resembling a human echolocation scenario, configured for synthetic data collection. Unlike in typical speech signals, extracted entropy from the formant characteristics was likely not visible for the human mouth-click signals. Instead, multiple peak spectral features derived from the synthesis signal of the mouth-click were assumed as the entropy obtained from the Mel-scale filtering output. To realize the classification process, K-means clustering and K-nearest neighbor processes were employed. Moreover, the impacts of sound propagation toward the extracted spectral entropy used in the classification outcome were also investigated. The outcomes of the classifier performance herein indicated that spectral entropy is essential for human echolocation

Bio-inspired signal detection mechanism for tongue click waveform used in human echolocation

Human echolocation is the ability of an individual (which is often a blind person) to use his/her signal such as sound from tongue clicks to perceive the surrounding. Basically this requires the person to listen and analyse to the return echo of the tongue clicks. The main characteristics of the tongue click signal waveform have been reported, however the fundamental principle on a person's ability to identify his/her own signal is still vague. The possible detection mechanism of the tongue click signal waveform used in human echolocation technique is discussed and imitated it as artificial detection system. The proposed mechanism which is based on human hearing process in synthesising the signal illustrates that the detection performance is improved as compared to the detection performance by the traditional matched filtering technique. The findings of this Letter create new potential for the development of any artificial human echolocator system, sensor systems like radar and sonar as well as applications inspired by human echolocation miracles

Detection of human echo locator waveform using gammatone filter processing

Human echolocation is a technique that is commonly used by blind persons to perceive their surroundings by analysing echo signals using an active signal (often tongue clicks). Over the years, studies into human echo location have explored vibrant disciplines, including the engineering perspective. The studies have been continuous and report on the human echo locator waveforms, which are individually unique, with the existence of multiple frequency components. However, possible explanations as to how blind people detect their own pair of transmitted and echo signals still remain vague. The detection process using the conventional matched filter has led to poor performance probably because the waveform consists of multiple frequency components. It was reported in a recent analysis that an ideal scheme for the detection of a human echo locator waveform click is possibly through the adoption of bio-inspired processing. Therefore, a similar detection mechanism based on a bio-inspired method incorporated with a gammatone filter was proposed in this paper for the transmitted-echo signal pair. The optimal detection output led to an ideal method for the detection of human echolocator signals. Furthermore, the need for alternative signal processing approaches for future man-made sensor systems has placed a demand on researchers to explore the perspectives in new fields of study. As such, the positive results explored in this paper can be beneficial for emerging concepts in new developments in the application of radar and sonar systems in the near future

Estimation of ground water level (GWL) for tropical peatland forest using machine learning

The tropical area has a large area of peatland, which is an important ecosystem that is regarded as home by millions of people, plants and animals. However, the dried-up and degraded peatland becomes extremely easy to burn, and in case of fire, it will further release transboundary haze. In order to protect the peatland, an improved tropical peatland fire weather index (FWI) system is proposed by combining the ground water level (GWL) with the drought code (DC). In this paper, LoRa based IoT system for peatland management and detection was deployed in Raja Musa Forest Reserve (RMFR) in Kuala Selangor, Malaysia. Then, feasibility of data collection by the IoT system was verified by comparing the correlation between the data obtained by the IoT system and the data from Malaysian Meteorological Department (METMalaysia). An improved model was proposed to apply the ground water level (GWL) for Fire Weather Index (FWI) formulation in Fire Danger Rating System (FDRS). Specifically, Drought Code (DC) is formulated using GWL, instead of temperature and rain in the existing model. From the GWL aggregated from the IoT system, the parameter is predicted using machine learning based on a neural network. The results show that the data monitored by the IoT system has a high correlation of 0.8 with the data released by METMalaysia, and the Mean Squared Error (MSE) between the predicted and real values of the ground water level of the two sensor nodes deployed through neural network machine learning are 0.43 and 12.7 respectively. This finding reveals the importance and feasibility of the ground water level used in the prediction of the tropical peatland fire weather index system, which can be used to the maximum extent to help predict and reduce the fire risk of tropical peatland

Detection of human echolocator mouth-click signal using bio-inspired processing

Echolocation is a process to perceive the surrounding by analysing reflected echo from space via an active emission signal often utilize by animals such as bat, dolphin, and whale as their main sensing modality mainly for navigation purposes. For almost a century, the fundamental and understanding of development radar and sonar application have been inspired by animal echolocation until today. What is lesser known is that there is a group of humans (often blind people) who have developed amazing skills and proficiency in using echolocation in their daily life. They have converted their hearing sense into visual perception mainly for navigation. In recent years, revisit studies have revealed that the human echolocator mouth-click is preferable for echolocation process. However, technical knowledge on the detection scheme for human echolocator mouth-click has yet to be discovered. What is more, the detection of human mouth-click using matched filter leads to poor outcome due to having higher multiple local maxima and lower side-lobe level values of less than 3 dB. Thus, the aim of this thesis is to propose an improved detection process of human echolocator mouth-click by implementing a Bio-Inspired processing strategy. The process is inspired by the human auditory system (using Gammatone-Filter) in synthesizing sound signal into tonotopic (sub-band channel) prior relaying it to the human brain. Then, the individual channel is correlated and is absolute to obtain correlation output. Next, correlated outputs are summed and normalized to get a single correlation output, used for decision making upon the detection phase. Improved detection output is achieved by, i) significantly reducing the presence of multiple local maxima, and ii) higher side-lobe level (SLL) exceeding 10 dB for static and moving target scenarios in different frequency range experiments surpassing output from matched filter. Finally, the Bio-Inspired signal processing proposed in this thesis can be considered as reliable detection scheme for of human echolocator mouth-click signal

Utilizing gammatone filter coefficient to improve human mouth-click signal detection using a multi-phase correlation process

This study introduces an enhanced structured signal processing scheme for detecting the “transmit-echo” of mouth-click signals used by blind individuals for echolocation. The processing scheme is based on coexisting work on the detection of the mouth-click signal, with an additional band-pass filter process introduced before synthesis, multi-phase correlation, and summation. The level of side lobes at the output was improved by more than −19 dB, and the number of local maxima was minimized by using a band-pass filter. The detection of “transmit-echo” results using artificial mouth-click signal data was validated and achieved a 100 success rate in detecting obstacles at 60 cm, 80 cm, and 100 cm. Nonetheless, the detection scheme discussed in this investigation is thought to be intuitive, having been learned from the human hearing process. The emerging concepts in this research are expected to benefit radar and sonar system applications in the near future

Sustainable Peatland Management with IoT and Data Analytics

Part 15: Interoperability of IoT and CPS for Industrial CNsInternational audiencePeatland is important to rural communities’ livelihood due to its potential for aquaculture and agriculture. Nonetheless, human activities such as slash-and-burn can greatly increase forest fire risk, which can release a great amount of greenhouse gases and carbon dioxide into the atmosphere. To sustainably manage and restore peatlands, the Internet of Things (IoT) system can incorporate with Cyber-Physical System (CPS) for peatland management. In this study, an IoT system is deployed in the peatland to monitor the ground water level (GWL) and upload it to the server for the machine learning (ML) process. The trend of GWL will be modelled, and the CPS using the developed ML model will control the peatland rewatering process. As a result, the peatland condition can be monitored in real-time, and the risk of forest fire can be mitigated through rewatering automation before the GWL drops to a critical level