Object Detection Algorithms for Ripeness Classification of Oil Palm Fresh Fruit Bunch

Ripe oil palm fresh fruit bunch allows extraction of high-quality crude palm oil and kernel palm oil. As the fruit ripens, its surface color changes from black (unripe) or dark purple (unripe) to dark red (ripe). Thus, the surface color of the oil palm fresh fruit bunches may generally be used to indicate the maturity stage. Harvesting is commonly done by relying on human graders to harvest the bunches according to color and number of loose fruits on the ground. Non-destructive methods such as image processing and computer vision, including object detection algorithms have been proposed for the ripeness classification process. In this paper, several object detection algorithms were investigated to classify the ripeness of oil palm fresh fruit bunch. MobileNetV2 SSD, EfficientDet (Lite0, Lite1 and Lite2) and YOLOv5 (YOLOv5n, YOLOv5s and YOLOv5m) were simulated and compared in terms of their mean average precision, recall, precision and training time. The models were trained on a dataset with four main ripeness classes: ripe, unripe, half-ripe, and over-ripe. In conclusion, object detection algorithms can be used to classify different ripeness levels of oil palm fresh fruit bunch, and among the different models, YOLOv5m showed promising results with a mean average precision of 0.842 (0.5:0.95)

Review on Digital Signal Processing (DSP) Algorithm for Distributed Acoustic Sensing (DAS) for Ground Disturbance Detection

Fiber break because of third-party intrusion has become one of the challenges in maintaining the fiber-based communication link, especially those buried underground. Hence, we investigate the feasibility of using Distributed Acoustic Sensing (DAS) system to sense possible surrounding activities that might cause fiber break. This paper reviews the current digital signal processing (DSP) algorithm used in the DAS system designed to detect ground disturbance, highlighting the specific design parameters for each technique. These parameters include identification rate, classification accuracy, detection accuracy, training time, and signal-to-noise ratio (SNR). The algorithms used are near-field beamforming, phased-array beamforming, image edge detection, gaussian mixture model (GMM), gaussian mixture model - hidden Markov model (GMM-HMM), faster region-based convolutional neural networks (R-CNN), transfer learning, dual-stage recognition network, group convolutional neural network (100G-CNN), and support vector machine (SVM). By reviewing the existing techniques used in the DAS system for ground disturbance detection, we can determine the best DSP algorithm that should be implemented for fiber break prevention, enabling us to design a DAS system specifically for it in the near future

Modelling of anomalous charge carriers transport in disordered organic semiconductors / Choo Kan Yeep

Performance of organic devices is affected by material disorders, which yields low mobility, dispersive current and scaling noise behaviour. Anomalous transport and scaling noise behaviour are inadequately described by Fick’s law and characterised by low-frequency noise method. This work reports the study of (i) scaling behaviour of current noise in organic field-effect transistors (OFETs) using methods of fractal noise analysis and, (ii) the modelling of anomalous charge transports in disordered organic semiconductors based on fractional calculus. Current noises of Poly(3-hexylthiophene) (P3HT) OFETs were measured at various source-drain voltages (Vds) and characterised using the power spectral density method and detrended fluctuation analysis. Current noises were found to follow white noise, 1/f and Brownian noise characteristic at low, intermediate and high Vds, respectively. For Vds above 40 V, Brownian noise will be masked out by 1/f noise. Multiple-trapping mechanism is integrated with the drift-diffusion equation and then generalised to the time-fractional drift-diffusion equation (TFDDE) to model the anomalous transports and reproduce the transient photocurrents in regiorandom P3HT (RRa-P3HT) and regioregular P3HT (RR-P3HT). The TFDDE is solved by using finite difference scheme and Poisson solver is implemented to calculate the electric field. It is found that by acquiring extra energy from high electric field, charge carriers escape easily from trap centres and propagate with higher velocity resulting in higher current. Larger amount of charge carriers will be generated at higher illumination and they will be hopping near the mobility edges, hence encountering lesser capturing events. This explains why movement of charge carriers at higher illumination is less dispersive than the movement of charge carriers at lower illumination. It is also noted that transport dynamic of charge carriers in RR-P3HT is relatively less dispersive and has higher mobility than that of the RRa-P3HT since RR-P3HT has lower capturing rate and is less energetically disordered

Monte Carlo Modelling Of High Field Electron Transport And Impact Ionisation In Semiconductors

Monte Carlo Modelling Of High Field Electron Transport And Impact Ionisation In Semiconductor

A Deep Learning-Based Social Distancing Surveillance System for The Edge Devices with A FPGA-Based Accelerator

The social distancing policy has been introduced in many countries to stop the spread of COVID-19 disease. This research proposed a deep learning-powered social distancing surveillance system that can monitor the physical distance between individuals in public areas in real-time. The system developed in this research was deployed on the edge device powered by an ARM processor. An FPGA accelerator was added alongside the ARM processor to accelerate the execution of the deep learning inference

Enhanced InP-based Gunn Diodes with Notch-d-doped Structure for Low-THz Applications

In this work, Monte Carlo simulation is performed for InP Gunn diode with a notch-d-doped structure. It is found that the presence of the d-doped layer has improved the Gunn diode performance significantly as compared to the conventional notch structure. The d-doped effect caused an increment in the fundamental operating frequency and current harmonic amplitude in InP Gunn diodes by modifying the electric field profile within the device. An InP notch-d-doped Gunn diode with device length of 800 nm under 3V DC bias is capable of producing AC current signal of 287 GHz, reaching the THz region, with its harmonic amplitude being 5.68×108 A/m2. It is observed that InP-based notch-d-doped Gunn diode is able to generate signals at a higher operating frequency with a larger output power as compared to that of GaAs due to the higher electron drift velocity and threshold field in InP material

Design a Document Verification System based on Blockchain Technology

Document forgery is a common method that has been used by many people for their own benefits. Document often has its own unique identity which contain a very sensitive content. The current problem related to document is about the document forgery where advanced technology capable in duplicating and modifying a document. The current document verification system often checks for its availability only and does not check for its integrity which is its content. Moreover, current practices provide a low efficiency in detecting a forged document which resulting in false results. Therefore, blockchain will be introduced where it needs to be used to create a new document verification system while integrating with Interplanetary File System (IPFS) to increase the efficiency in detecting a forged document

Notch-δ-doped InP Gunn diodes for low-THz band applications

The viability of the indium phosphide (InP) Gunn diode as a source for low-THz band applications is analyzed based on a notch-δ-doped structure using the Monte Carlo modeling. The presence of the δ-doped layer could enhance the current harmonic amplitude (A0) and the fundamental operating frequency (f0) of the InP Gunn diode beyond 300 ​GHz as compared with the conventional notch-doped structure for a 600-nm length device. With its superior electron transport properties, the notch-δ-doped InP Gunn diodes outperform the corresponding gallium arsenide (GaAs) diodes with up to 1.35 times higher in f0 and 2.4 times larger in A0 under DC biases. An optimized InP notch-δ-doped structure is estimated to be capable of generating 0.32-W radio-frequency (RF) power at 361 ​GHz. The Monte Carlo simulations predict a reduction of 44% in RF power, when the device temperature is increased from 300 ​K to 500 ​K; however, its operating frequency lies at 280 ​GHz which is within the low-THz band. This shows that the notch-δ-doped InP Gunn diode is a highly promising signal source for low-THz sensors, which are in a high demand in the autonomous vehicle industry

A REVIEW OF NON-DESTRUCTIVE RIPENESS CLASSIFICATION TECHNIQUES FOR OIL PALM FRESH FRUIT BUNCHES

Grading of oil palm fresh fruit bunches (FFB) is commonly conducted using visual inspection by trained workers who inspect the oil palm FFB according to the colour and the number of the loose fruits on the ground. However, this method is labour intensive and time consuming. In addition, the workers may misclassify the fruit’s ripeness due to the height of the tree, miscounting the loose fruits, unclear vision of the bunches on the tree and lighting conditions. Unripe or overripe bunches result in a less efficient palm oil refining process, low palm oil quality and profit losses. Non-destructive techniques can offer better solutions for ripeness classifications with higher accuracy. The techniques are field and lab spectroscopy, computer vision, hyperspectral imaging, laser-light backscattering imaging and fruit battery sensor. Spectroscopy, hyperspectral imaging and laser-light backscattering imaging techniques need to be deployed with a special set up which may not be suitable for real-time ripeness classification. Computer vision, using image processing techniques and machine learning algorithms allow real-time in situ ripeness classification via mobile devices. This article aims to review the feasibility of each method to allow real-time in situ ripeness classification of the oil palm fruit bunches with high accuracy

Effects of Anode Design and Configuration on the Growth Dynamics and Surface Morphologies of Electrodeposited Copper Films

The influence of different anode configurations on the growth mechanisms, transient currents and surface morphologies of copper film using localized electrodeposition technique have been studied. Measured transient currents during electrodeposition were used to investigate the underlying growth dynamics. SEM images were obtained and the surface morphologies of the deposited copper films were analyzed. It was found that the transient current increased when copper ions were able to grow directly on the empty surface of the copper film that was located away from the mini electrodes. This caused the copper ions to be deposited sporadically via the instantaneous growth mechanism and formed cluster of atoms on the empty surface of the copper film which led to rougher surfaces. In contrast, progressive growth was observed to occur at a faster rate for the deposition performed using insulated mini electrodes, especially in the case of collinear double insulated mini electrodes as indicated by the reduction of the transient current with time. Besides, copper films with uniform and smoother surfaces were obtained when the depositions were performed using multiple or a large number of closely spaced mini electrodes. This was due to the fact that large number of closely spaced mini electrodes produced parallel and uniform electric field patterns