Development of a remote automatic weather station with a PC - based data logger

This paper presents the development of a prototype weather station to measure the following weather data: air temperature, relative humidity, dew point, wind speed, and rainfall. The weather station, which has been designed for remote operation, performs automatic or unmanned measurements of weather data and transmits it wirelessly to a PC for logging and display by means of a graphical user interface. The remote station is powered using solar energy through a battery which also stores charge for a 24-hour operation of the system. Experimental results show that the measured data is quite consistent with those obtained by similar weather measurement devices

Productive and Non-Productive Cough Classification Using Biologically Inspired Techniques

Cough is a common symptom of respiratory diseases and the type of cough, in particular, productive (wet) or non-productive (dry) cough, is an important indicator of the condition of the respiratory system. It is useful in differential diagnosis and in understanding disease progression. However, determining the cough type in clinical practice can be subjective and sometimes unfeasible. This work, therefore, aims to develop an objective assessment method of the cough type. The proposed approach emulates the sound recognition process of humans. In particular, it uses the human auditory model to reveal the frequency characteristics of the cough sound signals and convolutional neural networks for decision-making. It is validated on a dataset of smartphone recordings of 396 cough samples from 88 subjects annotated as wet or dry by up to four expert pulmonologists. The cough signals are automatically segmented and time-frequency image data augmentation is performed during training using the synthetic minority oversampling technique to prevent model overfitting. A sensitivity of 93.13% and specificity of 91.42% (AUC= 0.9700) is achieved in segmentation of cough and non-cough sounds and a sensitivity of 100% and specificity of 82.50% (AUC= 0.9234) is achieved in detecting subjects with wet and dry cough. The proposed fully automated system in detecting subjects with wet and dry cough demonstrates strong classification performance. It has the potential to provide objective assessment of cough type using smartphone technology, such as in virtual healthcare which has seen an increased uptake during the ongoing pandemic

Audio surveillance in unstructured environments

This research examines an audio surveillance application, one of the many applications of sound event recognition (SER), and aims to improve the sound recognition rate in the presence of environmental noise using time-frequency image analysis of the sound signal and deep learning methods. The sound database contains ten sound classes, each sound class having multiple subclasses with interclass similarity and intraclass diversity. Three different noise environments are added to the sound signals and the proposed and baseline methods are tested under clean conditions and at four different signal-to-noise ratios (SNRs) in the range of 0–20dB. A number of baseline features are considered in this work which are mel-frequency cepstral coefficients (MFCCs), gammatone cepstral coefficients (GTCCs), and the spectrogram image feature (SIF), where the sound signal spectrogram images are divided in blocks, central moments are computed in each block and concatenated to form the final feature vector. Next, several methods are proposed to improve the classification performance in the presence of noise. Firstly, a variation of the SIF with reduced feature dimensions is proposed, referred as the reduced spectrogram image feature (RSIF). The RSIF utilizes the mean and standard deviation of the central moment values along the rows and columns of the blocks resulting in a 2.25 times lower feature dimension than the SIF. Despite the reduction in feature dimension, the RSIF was seen to outperform the SIF in classification performance due to its higher immunity to inconsistencies in sound signal segmentation. Secondly, a feature based on the image texture analysis technique of gray-level cooccurrence matrix (GLCM) is proposed, which captures the spatial relationship of pixels in an image. The GLCM texture analysis technique is applied in subbands to the spectrogram image and the matrix values from each subband are concatenated to form the final feature vector which is referred as the spectrogram image texture feature (SITF). The SITF was seen to be significantly more noise robust than all the baseline features and the RSIF, but with a higher feature dimension. Thirdly, the time-frequency image representation called cochleagram is proposed over the conventional spectrogram images. The cochleagram image is a variation of the spectrogram image utilizing a gammatone filter, as used for GTCCs. The gammatone filter offers more frequency components in the lower frequency range with narrow bandwidth and less frequency components in the higher frequency range with wider bandwidth which better reveals the spectral information for the sound signals considered in this work. With cochleagram feature extraction, the spectrogram features SIF, RSIF, and SITF are referred as CIF, RCIF, and CITF, respectively. The use of cochleagram feature extraction was seen to improve the classification performance under all noise conditions with the most improved results at low SNRs. Fourthly, feature vector combination has been seen to improve the classification performance in a number of literature and this work proposes a combination of linear GTCCs and cochleagram image features. This feature combination was seen to improve the classification performance of CIF, RCIF, and CITF and, once again, the most improved results were at low SNRs. Finally, while support vector machines (SVMs) seem to be the preferred classifier in most SER applications, deep neural networks (DNNs) are proposed in this work. SVMs are used as a baseline classifier but in each case the results are compared with DNNs. SVM being a binary classifier, four common multiclass classification methods, one-against-all (OAA), one-against-one (OAO), decision directed acyclic graph (DDAG), and adaptive directed acyclic graph (ADAG), are considered. The classification performance of all the classification methods is compared with individual and combined features and the training and evaluation times are also compared. For the multiclass SVM classification methods, the OAA method was generally seen to be the most noise robust and gave a better overall classification performance. However, the noise robustness of the DNN classifier was determined to be the best together with the best overall classification performance with both individual and combined features. DNNs also offered the fastest evaluation time but the training time was determined to be the slowest

Automating the process of work - piece recognition and location for a pick - and - place robot in a SFMS

This paper reports the development of a vision system to automatically classify work-pieces with respect to their shape and color together with determining their location for manipulation by an in-house developed pick-and-place robot from its work-plane. The vision-based pick-and-place robot has been developed as part of a smart flexible manufacturing system for unloading work-pieces for drilling operations at a drilling workstation from an automatic guided vehicle designed to transport the work-pieces in the manufacturing work-cell. Work-pieces with three different shapes and five different colors are scattered on the work-plane of the robot and manipulated based on the shape and color specification by the user through a graphical user interface. The number of corners and the hue, saturation, and value of the colors are used for shape and color recognition respectively in this work. Due to the distinct nature of the feature vectors for the fifteen work-piece classes, all work-pieces were successfully classified using minimum distance classification during repeated experimentations with work-pieces scattered randomly on the work-plane

Simulating the arm movements of a stepper motor controlled pick - and - place robot using the stepper motor model

This paper describes the simulation of arm movements of a stepper motor controlled pick-and-place robot using the mathematical model of a stepper motor. The model includes: a) a model of the stepper model driver board, b) a model of the hybrid stepper motor and load combination, and c) the interconnection of the two models which is used to simulate the motions of the base, shoulder, elbow, and wrist (pitch) motions of the pick-and-place robot. The model is simulated using Simulink and the results of angular displacement from the simulation and actual measurements show good uniformity

Measurement of end-milling burr using image processing techniques

This paper presents a method that has been developed to measure the height of end-milling burr on the edges of a work-piece using techniques of image processing. This simple and economical system consists of a microscope with a digital camera mounted on the viewing lens and a personal computer for image processing. An image is captured and then processed whereby the whole burr profile is analysed and compared to traditional methods, where normally a few readings are taken at random locations. An added feature of this system is that disorientation of the reference horizontal axis, along which burr height measurements are taken, on the work-piece during image capture is catered for with relevant image processing functions. In addition, the system determines and plots the height of the burr against its location, together with the average burr height, which can be used later for deburring or further analysis

Comparison of manual and image processing methods of end -milling burr measurement

This paper compares the results for manual method of burr height measurement with the image-processing technique for end-milled work-pieces under various conditions. The manual method refers to the traditional way where a few readings are taken at random locations using a microscope and the burr height is approximated with an average value. In contrast, the image processing technique analyzes the whole burr profile as seen through the lens of the microscope and captured using a digital camera. With the results obtained using the image processing method as reference, the results show a significant difference between the two average readings in most cases and generally the percentage error is greater for work-pieces with irregular burrs

Client-server control architecture for a vision-based pick-and-place robot

This article presents the software development for a vision-based pick-and-place robot to provide the computational intelligence required for its operation. It follows a client-server control architecture and aims to expand the applications of simply PC-based mechatronics systems to achieve distributed and flexible control through the introduction of a multi-featured and application-suited control unit in the form of a microcontroller as a client to the PC-based server. The system includes a five degree-of-freedom pick-and-place robot whereby a vision system is incorporated in its workspace to identify workpieces with respect to their shape and color. On user specification of the class of workpiece to be manipulated through a graphical user interface, the robot performs the manipulation. A personal computer, operating under the Windows platform, carries out all vision related processing and motion planning for the robot. On request, relevant motion information is communicated through the parallel port of the computer to peripheral interface controller (PIC) microcontroller, which interfaces with the sensing and actuation devices for robot control. The development of the system sees the integration of a number of technologies to achieve a customized control unit including: a vision system, actuation and sensing devices for precise motions, personal computer, microcontroller, and enhanced parallel port. In addition, a layered approach towards software development enables reusability, maintainability, and testability of the system through data abstraction

A low - cost force measurement solution applicable for robotic grippers

Industrial robots find profound usage in today’s industries and an important characteristic required of such robots in pick-and-place operations is determining the gripping force when picking objects. This paper presents the use of a FlexiForce force sensor for measuring the gripping force when picking work-pieces using the two-finger gripper of a pick-and-place robot. This thin and flexible analogue force sensor is capable of measuring both static and dynamic forces. It has an output resistance that is inversely proportional to the applied force and easily calibrated and interfaced to a microcontroller with an in-built analogue-to-digital convertor. Through experimentation, a relationship between the weight of work-piece to be gripped and the force to be applied for gripping was determined. This was used to successfully manipulate work-pieces of various shapes and sizes up to the robot payload of 0.5 kg. Analysis of various forces acting on the work-piece was also carried out