Facial Emotion Recognition Based on Empirical Mode Decomposition and Discrete Wavelet Transform Analysis

This paper presents a new framework of using empirical mode decomposition (EMD) and discrete wavelet transform (DWT) with an application for facial emotion recognition. EMD is a multi-resolution technique used to decompose any complicated signal into a small set of intrinsic mode functions (IMFs) based on sifting process. In this framework, the EMD was applied on facial images to extract the informative features by decomposing the image into a set of IMFs and residue. The selected IMFs was then subjected to DWT in which it decomposes the instantaneous frequency of the IMFs into four sub band. The approximate coefficients (cA1) at first level decomposition are extracted and used as significant features to recognize the facial emotion. Since there are a large number of coefficients, hence the principal component analysis (PCA) is applied to the extracted features. The k-nearest neighbor classifier is adopted as a classifier to classify seven facial emotions (anger, disgust, fear, happiness, neutral, sadness and surprise). To evaluate the effectiveness of the proposed method, the JAFFE database has been employed. Based on the results obtained, the proposed method demonstrates the recognition rate of 80.28%, thus it is converging

802.11p Profile Adaptive MAC Protocol for Non-Safety Messages on Vehicular Ad Hoc Networks

Vehicular Ad hoc Networks (VANET) play a vital Vehicle to Infrastructure (V2I) correspondence frameworks where vehicle are convey by communicating and conveying data transmitted among each other. Because of both high versatility and high unique network topology, congestion control should be executed distributedly. Optimizing the congestion control in term of delay rate, packet delivery ratio (PDR) and throughput could limit the activity of data packet transmissions. These have not been examined altogether so far – but rather this characteristic will be fundamental for VANET system execution and network system performance. This paper exhibits a novel strategy for congestion control and data transmission through Service Control Channel (SCH) in VANET. The Taguchi strategy has been connected in getting the optimize value of parameter for congstion control in highway environment. This idea lessens the pointless activity of data transmission and decreases the likelihood of congested in traffic in view of execution for measuring the delay rate, packet delivery ratio (PDR) and throughput. The proposed execution performance is estimated with the typical VANET environment in V2I topology in highway driving conditions and the simulation results demonstrate and enhance network execution performance with effective data transmission capacity

802.11p Optimization for Delay Sensitive in Non-Safety Messages in VANETs

Vehicle density and high vehicle mobility are variables that measured the performance of Vehicular Ad Hoc Network (VANET) in unpredictable traffic data transmission environment. This paper is focused on non-safety messages transmission mainly for delay in time in test-bed simulation environment. Network optimization is an approach to evaluate the existing congestion control protocols and other network parameters for outlining a newly enhanced congestion control protocols. This paper presents a city and highway traffic data transmission scenarios for optimizing delay sensitivity utilizing the Taguchi method.  The avareage data transmission on delay is performance indicator applying OMNeT++ simulation tools. The optimization process could be achieved once the best fit performance parameters are being identified. The best fit performance values could conclude the optimal and efficient congestion control networks. The packet sizes are the main control factors for this test-bed experiment focusing on non-safety messages which are delay sensitive

Efficient P2P data dissemination in integrated optical and wireless networks with Taguchi method

The Quality of Service (QoS) resource consumption is always the tricky problem and also the on-going issue in the access network of mobile wireless part because of its dynamic nature of network wireless transmissions. It is very critical for the infrastructure-less wireless mobile ad hoc network that is distributed while interconnects in a peer-to-peer manner. Toward resolve the problem, Taguchi method optimization of mobile ad hoc routing (AODVUU) is applied in integrated optical and wireless networks called the adLMMHOWAN. Practically, this technique was carry out using OMNeT++ software by building a simulation based optimization through design of experiment. Its QoS network performance is examined based on packet delivery ratio (PDR) metric and packet loss probabilities (PLP) metric that consider the scenario of variation number of nodes. During the performing stage with random mobile connectivity based on improvement in optimized front-end wireless domain of AODVUU routing, the result is performing better when compared with previous study called the oRia scheme with the improvement of 14.1% PDR and 43.3% PLP in this convergence of heterogeneous optical wireless network

The Prognostic Impact of Time Interval Between Hysterectomy and Initiation of Adjuvant Radiation Treatment in Women With Early-Stage Endometrial Carcinoma

Purpose/Objective(s): Adjuvant radiation therapy (ART) is indicated for women with endometrial carcinoma (EC) who are at high risk for recurrence. However, due to various reasons, some patients do not receive ART in a timely manner. In this study, we evaluated the prognostic impact of the time interval (TI) between hysterectomy and starting date of ART. Materials/Methods: After institutional review board approval, we queried our prospectively-maintained institutional database for women with uterine endometrioid EC of 2009 FIGO stages I-II who received ART without chemotherapy after surgical staging. The patients were classified into two groups, based on whether they received ART ≤8 weeks (group A) or \u3e8 weeks (group B) after hysterectomy. We then compared the two groups with regards to the following survival endpoints: recurrence-free survival (RFS), disease-specific survival (DSS) and overall survival (OS). Univariate and multivariate analyses were also performed. Results: A total of 460 patients were identified. Median follow-up duration was 70.5 months. The median age for the entire cohort was 66.0 years. The cohort consisted of 176 patients with FIGO stage IA (38%), 207 (45%) with stage IB and 77 (17%) with stage II. Group A consisted of 354 (77%) patients, and group B had 106 (23%). The median TIs from hysterectomy to ART were 6 weeks and 10 weeks for groups A and B, respectively. There was no statistically significant difference between the groups in terms of baseline demographic and disease characteristics including age, race, grade, FIGO stage, extent of myometrial invasion, presence of lymphovascular space invasion and radiation treatment modality. A total of 52 patients experienced recurrences. Patients in group A (vs. group B) experienced significantly less recurrences overall (9% vs. 18%; p = 0.01). Rate of vaginal recurrence was significantly lower in group A (9% vs. 42%, p = 0.01). Univariate analysis showed that having RT ≤8 weeks was associated with significantly improved 5-year RFS rate, which was 89% and 80% for groups A and B (p = 0.04), respectively. The rates of 5-year OS (86% vs. 85% for groups A and B, respectively) and 5-year DSS (93% vs. 93% for groups A and B, respectively) were similar. In addition, multivariate analysis showed a statistical trend for improved 5-year RFS when receiving RT ≤8 weeks (p = 0.07). Conclusion: Our study suggests that delaying adjuvant radiation treatment beyond 8 weeks post-hysterectomy is associated with significantly more cancer recurrences for women with early-stage endometrial cancer

Drunken drive detection with smart ignition lock

Drink and drive issue have become solemnly that needs immediate attention. This is due to drivers’ ignorance towards road rules and regulations and their selfish attitude that caused loss of innocent lives. Although previously there is a drunk detecting mechanism using breathalyzer but it isn’t suitable for current fast-paced lifestyle. Therefore, to overcome these issues, this system is proposed. This system is fixed on vehicle’s steering to measure alcohol concentration reading using MQ-3 sensor from the driver’s exhaled breath. If the driver found to be drunk beyond the threshold level of 400 ppm, then ignition lock is activated and the car engine does not start till alcohol concentration falls to a safe level. Or, if the driver consumes an alcoholic drink while driving, upon exceeding permissible limit, the car slows down till it stops. Then, the location of the vehicle is tracked and sent as Google Map integrated link via text message to authorized unit. Simultaneously, the car buzzer goes off while the car slows down so that surrounding road users are aware of the driver’s condition and drives at a distance. The proposed detection system is highly potential to be implemented for reducing the drunk and drive accidents

Facial Expression Recognition Based on Radon and Discrete Wavelet Transform using Support Vector Machines

Extracting facial features remains a difficult task because of unpredictable of facial features largely due to variations in pixel intensities and subtle changes of facial features. The Radon transform inherits rotational and translational properties that are capable of preserving pixel intensities variations and also is used to derive the directional features. Thus, this paper presents a new pattern framework for facial expression recognition based on Radon and wavelet transform using Support Vector Machines classifier to recognize the seven facial emotions. Firstly, the pre-processed facial images are projected into Radon space via Radon transform at a specified angle. Then, the obtained Radon space or sinogram that represent the facial emotions is subjected to wavelet transform. In this framework, the Radon space is decomposed into four sub-band at a different level of decomposition. The approximate coefficients sub-band are independently extracted and used as intrinsic features to recognize the facial emotion. To reduce the data dimensionality, principal component analysis (PCA) is applied to the extracted features. Then, the Support Vector Machines (SVM) classifier is adopted as a classifier to classify seven (anger, disgust, fear, happiness, neutral, sadness and surprise) facial emotions. To evaluate the effectiveness of the proposed method, the JAFFE database has been employed. Experimental results show that the proposed method has achieved 93.89% accuracy

Prediction of signal attenuation due to duststorms using mie scattering

The present trend in radio design calls for the use of frequencies above 40 GHz for short links carrying wide-band digital communication signals. In order to utilize the new frequency band efficiently, signal attenuation studies due to duststorms is needed urgently for desert areas. This paper presents a mathematical model which has been developed to predict the signal attenuation due to duststorm. The proposed model enables the convenient calculation of the signal path attenuation based on Mie solution of Maxwell's equations for the scattering of electromagnetic wave by dust particles. The predicted values from the proposed mathematical model are compared with the measured values observed in Saudi Arabia and Sudan and show relatively close agreement

Iterative Prompt Refinement for Radiation Oncology Symptom Extraction Using Teacher-Student Large Language Models

This study introduces a novel teacher-student architecture utilizing Large Language Models (LLMs) to improve prostate cancer radiotherapy symptom extraction from clinical notes. Mixtral, the student model, initially extracts symptoms, followed by GPT-4, the teacher model, which refines prompts based on Mixtral's performance. This iterative process involved 294 single symptom clinical notes across 12 symptoms, with up to 16 rounds of refinement per epoch. Results showed significant improvements in extracting symptoms from both single and multi-symptom notes. For 59 single symptom notes, accuracy increased from 0.51 to 0.71, precision from 0.52 to 0.82, recall from 0.52 to 0.72, and F1 score from 0.49 to 0.73. In 375 multi-symptom notes, accuracy rose from 0.24 to 0.43, precision from 0.6 to 0.76, recall from 0.24 to 0.43, and F1 score from 0.20 to 0.44. These results demonstrate the effectiveness of advanced prompt engineering in LLMs for radiation oncology use