Miracle-dz/UniFAR-FTUs huggingface dataset:
Expert system for diagnosis mango diseases using leaf symptoms analysis

This research presents the development of an expert system for diagnosis plant diseases in Barracuda mango (Nam-Dok Mai) which is one of a major export agricultural yield of Thailand. However, Thailand is in a tropical country and the climate causes the variation of plant diseases that affect to the growth of mango trees. Many type of agriculture yield are decreased due to an agriculturist are lacking of knowledge on how to classify type of plant disease correctly. Moreover, there is no suggestion system for a decision-making in choosing a suitable way to prevent or treat the disease that occur in their farm. This causes a lot of error in their infected plant treatments. Therefore, this system has been developed for help an agriculturist to diagnose the infected plant and to solve the problem immediately. The agriculturist should have the application which work in process of specific plant disease diagnosis as an expert human work. The plant diagnosis application applies a knowledge base system in form of rule-based model obtained by data mining technique. This paper present rule-based model with leaf image dataset. Experimental results performed that the rule-based model with 129 leaf images which collected from mango field area under supervision of product quality and standardization, Maejo University and 3 answer of class label (Anthracnose, Algal Spot, normal) has 89.92% of accuracy. From the experimental result indicated that the rules-based model can be applied to the plant diagnosis application.
Event-Based, Timescale Invariant Unsupervised Online Deep Learning With STDP

Learning of hierarchical features with spiking neurons has mostly been investigated in the database framework of standard deep learning systems. However, the properties of neuromorphic systems could be particularly interesting for learning from continuous sensor data in real-world settings. In this work, we introduce a deep spiking convolutional neural network of integrate-and-fire (IF) neurons which performs unsupervised online deep learning with spike-timing dependent plasticity (STDP) from a stream of asynchronous and continuous event-based data. In contrast to previous approaches to unsupervised deep learning with spikes, where layers were trained successively, we introduce a mechanism to train all layers of the network simultaneously. This allows approximate online inference already during the learning process and makes our architecture suitable for online learning and inference. We show that it is possible to train the network without providing implicit information about the database, such as the number of classes and the duration of stimuli presentation. By designing an STDP learning rule which depends only on relative spike timings, we make our network fully event-driven and able to operate without defining an absolute timescale of its dynamics. Our architecture requires only a small number of generic mechanisms and therefore enforces few constraints on a possible neuromorphic hardware implementation. These characteristics make our network one of the few neuromorphic architecture which could directly learn features and perform inference from an event-based vision sensor.
A Novel Serpentine Gait Generation Method for Snakelike Robots Based on Geometry Mechanics

In this paper, a novel serpentine gait generation method is proposed based on geometry mechanics. From the view of differential geometry, the configuration of snake-like robots can be described by a fiber bundle, in which the net locomotion corresponds to the fiber space and the gait corresponds to the bases space. The serpentine gait can be generated by calculating the connection of the fiber bundle. Compared with the traditional gait generation method, such as the curve-based method and the central pattern generator-based method, the proposed method does not assume that the serpentine gait is a standard sinusoidal signal. Models have been developed based on geometric mechanics, which build the relation between the net locomotion and the serpentine gaits analytically. Based on these models, the serpentine gaits for snakelike robots with passive wheels can be generated by planning the quasi-velocity and the serpentine gaits for underwater snakelike robots can be generated by analyzing the integral of the height function. The serpentine gaits generated by the proposed method directly couple with the locomotion of the snakelike robot, which enables the desired locomotion to be realized efficiently. To validate the proposed method, both the snakelike robot with passive wheels and the underwater snakelike robot are taken to perform the experiments. Additionally the recursive Newton–Euler method with the model of hydrodynamic forces is also taken to test the underwater serpentine gaits. Experiments and simulation have validated our proposed method.
A Low-Noise, Wireless, Frequency-Shaping Neural Recorder

This paper presents a low-noise, wireless neural recorder that has a frequency dependent amplification to remove electrode offset and to attenuate motion artifacts. The recorder has 2.5 <inline-formula> <tex-math notation="LaTeX">$\text{G}\Omega $ </tex-math></inline-formula> and 50 <inline-formula> <tex-math notation="LaTeX">$\text{M}\Omega $ </tex-math></inline-formula> input impedance at 20 Hz and 1 kHz for recording local field potentials and extracellular spikes, respectively. To reduce the input-referred noise, we propose a low-noise frontend design with multiple novel noise suppression techniques. To reduce the power consumption, we have integrated an exponential component and polynomial component spike processor that automatically adjusts the recording bandwidth based on the signal contents. In bench-top measurement, the proposed neural recorder has 2.2-<inline-formula> <tex-math notation="LaTeX">$\mu \text{V}$ </tex-math></inline-formula> input-referred noise integrated from 300 Hz to 8 kHz and consumes 98-<inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> maximum power. In animal experiments, the output data of the neural signal processor are serialized and connected to a customized WiFi data link with up to 10 Mbps data rate. Through <italic>in vivo</italic> experiments, we find that the noise generated by the WiFi does not prevent brain recordings with microelectrodes and a clear interpretation of the neural signals; however, the noise can mask the weaker neural signals in nerve recordings with epineural electrodes.
Gradient descent in sample-based single-query path planning algorithm

Rapidly-exploring random tree (RRT) is a sampling based single query algorithm that is used to generate a path. This papers describes RRT algorithm, modifies RRT algorithm by applying gradient descent method for generating the path. Simulations are run in iterations and the results are compared with and without gradient descent. Finally this paper concludes with advantages and limitations of the gradient approach.
SOX9 has distinct regulatory roles in alternative splicing and transcription

Abstract SOX9 is known as a crucial transcription factor for various developmental processes and for tissue homeostasis. We examined here its potential role in alternative splicing by analyzing global splicing changes, using RNA-seq of colon tumor cells. We show that SOX9 knockdown alters the splicing of hundreds of genes without affecting their expression levels, revealing that SOX9 controls distinct splicing and transcriptional programs. SOX9 does not affect splicing patterns through the control of splicing factors expression. We identify mutants that uncouple SOX9 splicing function from its transcriptional activity. We demonstrate that SOX9 binds to RNA and associates with several RNA-binding proteins, including the core exon junction complex component Y14. Half of SOX9 splicing targets are also modulated by Y14 and are no longer regulated by SOX9 upon Y14 depletion. Altogether, our work reveals that SOX9 is a moonlighting protein which modulates either transcription or splicing of distinct sets of targets.
SAR image retrieval based-on fly algorithm

SAR (Synthetic Aperture Radar) image retrieval, of which the crucial problem is how to search and organize SAR images effectively, has long been a hotspot in SAR-related researches with the increasing amount of SAR images. Besides, SAR image retrieval is also more challenging compared with normal optical image retrieval due to its complicated imaging mechanism. Many SAR image retrieval methods use the high-dimensional feature to improve accuracy, which brings a high computational cost and storage cost for large scale search. In this paper, a system based on fly algorithm for SAR image retrieval is presented. The fly algorithm can generate hash codes for images effectively by mimicing the fruit fly olfactory circuit, which can greatly reduce the dimension of feature space. Three computational strategies extracted from the logic of an important sensory function are used in the fly algorithm to improve the performance of computational similarity searches. Moreover, another effective binary quantization method, different from the quantization method in fly algorithm, is adopted to generate binary hash code in SAR image retrieval system. Experiment results on two representative SAR image datasets, MSTAR and OpenSAR show that the new method based-on fly algorithm outperform other state-of-the-art SAR image retrieval methods.
Trouble Ticket Routing Models and Their Applications

A trouble ticket is an important information carrier in system maintenance, which records problem symptoms, the resolving process, and resolutions. A critical challenge for the ticket management system is how to quickly deal with trouble tickets and fix problems. Thousands of tickets, bouncing among multiple expert groups before being fixed, will consume limited system maintenance resources and may also violate the service level agreement. Thus, trouble tickets should be routed to the right expert group as quickly as possible in order to reduce the processing delay. In this paper, to address the challenge in ticket routing, we exploit three different routing models by mining the combination of problem descriptions and resolution sequences from the historical resolved tickets, and develop the corresponding routing recommendation algorithms to determine the next expert group to solve the problem. To evaluate the performance of routing recommendation algorithms, we conduct extensive experiments on a real ticket data set. The experimental results show that the proposed models and algorithm can effectively shorten the mean number of steps to resolve with a high ratio of the number of successfully resolved tickets to the total number of tickets, especially for the long routing sequences generated from manual assignments. These models and algorithms have the potential of being used in a ticket routing recommendation engine to greatly reduce human intervention in the routing process.
Enhancing coverage in narrow band-IoT using machine learning

Narrow Band-Internet of Thing (NB-IoT) is a recently proposed technology by 3GPP in Release-13. It provides low energy consumption and wide coverage in order to meet the requirements of its diverse applications that span social, industrial and environmental aspects. Increasing the number of repetitions of the transmission has been selected as a promising approach to enhance the coverage in NB-IoT up to 164 dB in terms of maximum coupling loss for uplink transmissions, which is a significant improvement compared with legacy LTE technologies, especially to serve users in deep coverage. However, a large number of repetitions reduces the system throughput and increases the energy consumption of the IoT devices, which reduces their battery lifetime and increases their maintenance cost. In this work, we propose a new method for enhancing the NB-IoT coverage based on machine learning algorithms. Instead of employing a random spectrum access procedure, dynamic spectrum access can reduce the number of required repetitions, increase the coverage, and reduce the energy consumption.
A 60 GHz Low Power Self-mixing Receiver in 65-nm CMOS for a Radio-Triggered Battery-Less Monolithic Wireless Sensor

With the growing attention at 60 GHz ISM band on millimeter-wave therapy for diseases treatment of sedative, anti-inflammatory actions or immune system stimulation, growing demand for new biomedical monitoring and treating systems with more bandwidth, high data rates and low power creates new challenges for 60 GHz body area network. Most smart wireless sensors wireless sensing operates on battery, which limits their life-time. To overcome this limitation, a system of radio-triggered batteryless monolithic wireless sensor based on mm-wave wireless power transfer technique is introduced in this work. From the system power budget analysis, it could support 10 bits communication within 10 cm, which would be sufficient for mm-wave wireless sensing application. Furthermore, it is identified that the receiver is power limited. Based on the system evaluation, an injection locked oscillator based self-mixing receiver is presented. In this architecture, the IJLO based LO scheme is used to save receiver power consumption. The sensitivity of the receiver is improved by applying the IJLO into the self-mixing architecture. This single chip receiver is implemented in a 65 nm CMOS technology. The DC power consumption of this receiver is only 16.4 mA from 1 V power supply. This power consumption is lower than other mm-wave receivers, which is an important step towards fully integrated monolithic sensor nodes.
Learning factor influence on the perceptual-auditory analysis.

PURPOSE To investigate the learning factor during a perceptual-auditory analysis of an unusual task in three different groups. METHODS 269 listeners, divided into three groups: 73 voice specialists Speech Language Pathologists (EG), 84 voice specialists Speech Language Pathologists (NEG); and 112 non-speech pathologists in the Naive Group (NG). They all completed a listening session that included 18 synthesized and 18 human voices with different types and degrees of deviation (50% of repetition for intra-rater consistency analysis). The task was to classify the voices as human or synthesized. We analyzed the learning factor by comparing the initial error percentage, first 18 voices, with the final, last 18 voices. RESULTS EG presented less error towards the end of the task (25.5%) than at the beginning (28.6%) with statistical difference (p = 0.024). The error percentage of the beginning and the end of the task did not differ for the NEG and the NG (NEG beginning = 36.5%, end = 35.3%; NG beginning = 38.3%, end = 37.7%). CONCLUSION The EG was the only group to present evidence of learning factor. Therefore, it seems that professional experience positively influences the perceptual-auditory analysis, which reinforces the impact of its training to become a voice specialist. Moreover, the voice specialists seem to be more prepared and more susceptible to use learning strategies to improve their performance during a perceptual-auditory analysis task, even if unusual.
Distributed Packet Forwarding and Caching Based on Stochastic Network Utility Maximization

Cache-enabled network architecture has great potential for enhancing the efficiency of content distribution as well as reducing the network congestion. This, in turn, has called for joint optimization of traffic engineering and caching strategies while considering both network congestion and content demands. In this paper, we present a distributed framework for joint request/data forwarding and dynamic cache placement in cache-enabled networks. Specifically, to retrieve the information about content demands and network congestion over the network, we establish a dual queue system for both requests and data, and define a dynamic mapping between the two queues with the help of dummy data such that the nodes can determine packet forwarding and caching strategies based only on local information. As the local objective function associated with Lyapunov optimization is time-varying due to the stochastic evolution of request/data queues, we develop a low-complexity distributed forwarding and caching algorithm via stochastic network utility maximization. We also prove the proposed algorithm achieves queue stability, and derive its region stability property for time-varying local optimization to demonstrate the convergence behavior. The simulation results verify queue stability and shows the proposed algorithm outperforms the existing ones.
Theoretical Design of an Integrated Optical Sensor for a Standard Immunoassay

Immunoassay is a biochemical test that measures the presence or concentration of biomolecule in a solution by the means of the antibody (or antigen). Most immunoassays involve chemically linking antibodies or antigens with some kind of detectable labels which produce measurable signals in response to the binding. The use of labels creates problems, including complexity in the screening process and probable influence on the nature of target molecular. To overcome the drawbacks, we theoretically demonstrate an integrated Mach–Zehnder interferometer (MZI) with $n$ -shaped slot waveguide based on finite-element method. Dual polarization can be strongly confined in the slot where the analytes are accommodated. The interaction between the optical field and tested solution is greatly enhanced, resulting in high sensitivity of the device. The detection of surface adsorbed molecule densities is down to 0.176pg/mm2 for transverse electric mode and 1.27pg/mm2 for transverse magnetic mode. The integrated MZI with identical configuration of both arms make the parallel-control trial available thereby effectively preventing the unspecific binding and background from biological materials. Wavelength interrogation was taken to monitor the antibody-antigen recognition.
VSC control of grid connected PV for maintaining power supply during open phase condition in distribution network

The grid-connected PV system with BESS has been in use for many demand-related issues. This paper discusses the use of grid-connected PV in case of open phase condition in the distribution system. An Open phase condition at medium voltage (11 kV) will result in the reduced voltage at low tension (LT) voltage level (400 V) which may be detrimental to constant power loads connected to it. This paper presents an approach to mitigate such scenario by using VSC terminal of grid-connected PV with the distribution transformer (DT). The usefulness of the proposed configuration is validated by restoring the voltage in all phases at the LT level during an open phase at MV level. A MATLAB/SIMULINK model is developed and results obtained for a different mode of operation has been presented.
RGB-D SLAM in Indoor Environments With STING-Based Plane Feature Extraction

In this paper, the RGB-D camera-based simultaneous localization and mapping (SLAM) of indoor environments is addressed using plane features. The plane parameter space (PPS) is defined for a compact representation of planes in the Cartesian space. The statistical information grid (STING) structure is constructed in the PPS to extract plane features. The plane association graph is developed to determine the correspondences between the plane features from two successive scans. The RGB-D camera pose is directly calculated using the matched plane features if they can provide sufficient constraints for the pose estimation. Otherwise, a novel STING-based scan matching method is developed in the PPS to achieve a full six degrees of freedom camera pose estimation. The proposed method uses only the plane features independent of any other features to estimate the RGB-D camera poses and can thus be suitable for some challenging scenes. The experimental results demonstrate that the proposed plane feature-based RGB-D SLAM can achieve high accuracy and robustness in both on-board and hand-held applications.
The Precoding Scheme Based on Domain Selective Interference Cancellation in 3-D Massive MIMO

3-D MIMO is an efficient technology to improve system capacity by exploiting the spatial characteristics in both the azimuth and elevation domains. To simplify the precoding process in 3-D massive MIMO systems, the precoding problem is divided into elevation-domain precoding and azimuth-domain precoding. In this letter, we show that to cancel the interference, the elevation and azimuth domains can be complemented to each other, and a 2-D precoding algorithm based on the domain-selective interference cancellation which aims to maximize the received signal-to-noise ratio is proposed. The simulation results validate that our precoding scheme outperforms the previous 2-D precodings in terms of the spectral efficiency.
Simulation modeling as a service for intelligent systems

Many modern economic processes depend on the availability of data. And qualitative data about some process often is difficult to collect. Sometimes it is unreal. This article is about decision support system BPsim. Web, the main focus is on a simulation service of this system. The main idea is using a simulation to generate a data set. At the moment the simulation service are being developed. We described a task's set that are solved by service, also in this paper a description of its internal processes and methods of integration are given below. In the end of the article we showed an example of a task decided with the help of the developed simulation simulation service. This example consists of a description of the subject area and a description of the experiments being conducted.
Does Mid-Treatment Insomnia Severity Mediate between Cognitive Behavioural Therapy for Insomnia and Post-Treatment Depression? An Investigation in a Sample with Comorbid Insomnia and Depressive Symptomatology

Background: Recent treatment studies with cognitive behavioural therapy for insomnia (CBT-I) have demonstrated effects on both sleep problems and depression. Two previous studies have indicated that the beneficial effect from CBT-I on depression may come through improved sleep, although insomnia severity during treatment had not previously been investigated as a mediator. Aims: Our aim was to investigate if insomnia severity during treatment mediated between CBT-I and depression severity after treatment, in a sample with co-morbid insomnia and depressive symptomology. We also examined whether depressive severity during treatment mediated between CBT-I and insomnia after treatment. Method: The participants were recruited from advertisements and fulfilled criteria for insomnia diagnosis, and had depressive symptomatology (Beck Depression Inventory-second edition: BDI-II > 13). Two-thirds of the participants were diagnosed with major depressive disorder. The participants received four biweekly group sessions of CBT-I or relaxation training (active control). Insomnia severity (Insomnia Severity Index) and depressive severity (BDI-II) were measured at baseline, mid-treatment, post-treatment and 6-month follow-up. The mid-treatment measures were used as mediators. Results: Mediational analyses demonstrated a significant reciprocal relationship between insomnia severity and depressive severity throughout CBT-I, although mid-treatment insomnia had a stronger effect on depression than mid-treatment depression had on insomnia. The results were similar for both post-treatment and follow-up. Discussion: Some improvement in depressive severity after CBT-I is explained by improved sleep. The findings emphasize the importance of making comorbid insomnia a treatment focus in its own right.