Odor descriptors are words used to express human olfactory perception. At a certain level, predicting the odor descriptor rating using an electronic nose(E-nose) equips the machine with the ability to perceive odors. In this paper, we propose a novel deep learning framework for predicting odor descriptor rating via E-nose signals. The proposed framework has multiple sibling neural networks, including a Convolutional LSTM(ConvLSTM) layer and a regression layer. The ConvLSTM layers are used to learn the spatiotemporal features from sensor signal. On the one hand, it effectively extracts the temporal features of the signal, and on the other hand, it makes good use of the sensor dependencies. The output of the regression layer corresponds to a combination of descriptors or single descriptors, depending on their similarity. The experiment results show that: i) the framework utilizing the spatiotemporal correlation of the E-nose signal is more effective than using the temporal or spatial model alone, and that ii) the combination of descriptors based on descriptors similarity can effectively improve the prediction accuracy. On the DREAM dataset, our proposed framework outperforms the state-of-the- art methods in odor descriptor rating prediction.

Decoding visual stimuli from brain activities is an interdisciplinary study of neuroscience and computer vision. With the emerging of Human-AI Collaboration, Human-Computer Interaction, and the development of advanced machine learning models, brain decoding based on deep learning attracts more attention. Electroencephalogram (EEG) is a widely used neurophysiology tool. Inspired by the success of deep learning on image representation and neural decoding, we proposed a visual-guided EEG decoding method that contains a decoding stage and a generation stage. In the classification stage, we designed a visual-guided convolutional neural network (CNN) to obtain more discriminative representations from EEG, which are applied to achieve the classification results. In the generation stage, the visual-guided EEG features are input to our improved deep generative model with a visual consistence module to generate corresponding visual stimuli. With the help of our visual-guided strategies, the proposed method outperforms traditional machine learning methods and deep learning models in the EEG decoding task.

Semantic odor perception descriptors, such as “sweet”, are widely used for product quality assessment in food, beverage, and fragrance industries to profile the odor perceptions. The current literature focuses on developing as many as possible odor perception descriptors. A large number of odor descriptors poses challenges for odor sensory assessment. In this paper, we propose the task of narrowing down the number of odor perception descriptors. To this end, we contrive a novel selection mechanism based on machine learning to identify the primary odor perceptual descriptors (POPDs). The perceptual ratings of non-primary odor perception descriptors (NPOPDs) could be predicted precisely from those of the POPDs. Therefore, the NPOPDs are redundant and could be disregarded from the odor vocabulary. The experimental results indicate that dozens of odor perceptual descriptors are redundant. It is also observed that the sparsity of the data has a negative correlation coefficient with the model performance, while the Pearson correlation between odor perceptions plays an active role. Reducing the odor vocabulary size could simplify the odor sensory assessment and is auxiliary to understand human odor perceptual space.

In this paper, a modified code index modulation scheme based on the multi-carrier M-ary DCSK system, referred to as a MC-MCIM-MDCSK system, is proposed. In the proposed MC-MCIM-MDCSK system, a modified code index modulation scheme is carried out, which selects two orthogonal Walsh codes for realizing the M-ary DCSK encoder. The theoretical BER expression of the proposed system is obtained over additive white Gaussian noise (AWGN) and the multipath Rayleigh fading channels. Abundant simulation results showed that the simulation results matched the theoretical results. The energy efficiency (EE), spectrum efficiency (SE), the data rate, and the complexity of the proposed system were carefully analyzed. The BER performance of the proposed system was compared with those of other systems. It is shown that the proposed system has better BER performance than its competitors.

In this paper, a new short reference differential chaos shift keying (SR-DCSK) cooperative communication system based on modified code index modulation, referred to as the MCIM-SR-DCSK-CC system, is proposed. In the proposed MCIM-SR-DCSK-CC system, the information bits are transmitted to both the relay and the destination in the first time slot. On the other hand, in the second time slot, the relay not only forwards the information bits but also sends new information bits to the destination. Specifically, the relay adopts the modified code index modulation to transmit more information bits to the destination. The theoretical bit error rate (BER) expressions of the proposed MCIM-SR-DCSK-CC system are obtained over additive white Gaussian noise (AWGN) and the multipath Rayleigh fading channels. It is demonstrated that the simulation results of BER performance match the theoretical results. The energy efficiency (EE), the data rate, and transmission throughput are carefully analyzed. The performance of the proposed system is better than its competitors’.