Journal of Progress in Engineering and Physical Science

Creating AI Assistants with Spectral Consciousness Based on Electromagnetic Waves

2026-07-07T03:08:27+00:00

The creation of AI assistants with spectral consciousness based on electromagnetic waves relies on periodic functions that can be expanded in a Fourier series and represented by a Fourier integral. Harmonic analysis of the periodic functions is performed to represent them as the sum of a mean value and a set of sinusoidal terms with a fundamental frequency of 1/T and harmonic frequencies of 2/T...n/T electromagnetic waves. Morphogenic cryptocoding of the sinusoidal harmonics is performed based on mental mathematics. Then, cognitive morphogenic modeling of reasoning using harmonic electromagnetic waves is performed according to a standard linguistic template of the judgment process. Based on this modeling, an AI learning model with a Fourier support function is created to create intelligent assistants with spectral consciousness for the development of new knowledge through cognitive verbal and written communication with a specialist and for training in various fields of knowledge and life.

An Improved ShuffleNet with SE Attention and Focal Loss for Lightweight Facial Expression Recognition

2026-07-07T03:11:02+00:00

Facial expression recognition (FER) plays a critical role in human-computer interaction, mental health monitoring, intelligent security inspection and classroom emotional computing. Traditional deep convolutional neural networks for FER suffer from excessive computational overhead and large parameter volume, which limits their deployment on mobile terminals and embedded devices with limited computing resources. As a classic lightweight network, ShuffleNet adopts group convolution and channel shuffle operations to reduce model complexity, yet it lacks the ability to capture subtle facial emotional features and cannot well handle the class imbalance problem existing in mainstream FER datasets. To address these defects, this paper proposes an improved lightweight ShuffleNet model for facial expression detection. First, Squeeze-and-Excitation (SE) channel attention modules are embedded after each shuffle block to dynamically recalibrate feature channel weights and strengthen the extraction of discriminative facial expression features. Second, a multi-dimensional data augmentation strategy combining geometric transformation, color jitter and Gaussian noise injection is designed to expand sample diversity and enhance the model’s generalization ability under complex lighting, shooting angles and background interference. Third, Focal Loss is introduced to replace the standard cross-entropy loss, which suppresses the loss contribution of easy-classified majority samples and forces the model to focus on hard-to-distinguish minority facial expressions such as anger and disgust. Comprehensive experiments are conducted on three public datasets FER2013, CK+ and AffectNet. The results demonstrate that the proposed model achieves higher recognition accuracy compared with original ShuffleNet V2 while maintaining low computational cost and small parameter size, and presents superior robustness against unbalanced data and complex real-world scenes.

The Four Necessary and Sufficient Canonical Tests of the Special Theory of Relativity

2026-07-07T03:13:27+00:00

The present paper is a continuation of our previous published work where we dispelled the misconception that the three canonical experiments mentioned by H.P. Robertson in his 1949 seminal paper, are all that is necessary and sufficient in deciding if and when an alternative theory of relativity is equivalent to Einstein’s special relativity.

Anthropometric Studies of the Diurnal Human Height Shrinkage Across Selected Professionals in Nigeria

2026-07-14T08:39:17+00:00

Diurnal variation in stature is one potentially significant source of error in the evaluation of short-term growth. Hence, there is the need to observe whether there are diurnal variations when assessing humans’ (subjects) body composition, using physical anthropometric methods. In this paper, the diurnal human height shrinkage have been investigated by measurements, such that twice-daily measurements and body weight of subjects were examined and recorded. A total of 70 subjects were examined altogether. This comprises of 35 males and 35 females of which 2 nursery school children (a boy and a girl) were involved, aging 4 years and 3½ years respectively. For most of the subjects, Morning measurements were made immediately after rising. The time of the measurements was also recorded and diurnal changes during daytime were estimated. Results showed that stature decreased during the day in the entire 70 subjects surveyed and sampled. From the results, it could be seen that an average female subject will shrink by 0.88% of the usual (morning) height. Also, the mean average shrinkage in height amounts to 1.43 ± 2cm for the females. Likewise, an average male subject will shrink by 0.91% of his usual morning height. The mean average shrinkage in height in male subjects equals 1.56 ± 2cm. The findings of this study confirm the existence of diurnal variation and that the greater proportion of the height loss occurs during the earlier part of the day, especially when subjects are engaged with some form of activity or the other.