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Reconfigurable intelligent surface (RIS) is a promising technology for future 6G communications and has been used to enhance secrecy performance. However, the performance improvement is restricted by the “double fading” effect of the reflection channel link. To address this issue, we introduce an active RIS design, where the reflecting elements of RIS not only adjust the phase shift but also amplify the reflected signal through the amplifier integrated into its elements. To obtain a satisfactory solution to the non-convex problem resulting from this design, the penalty dual decomposition based alternating gradient projection (PDDAPG) method is proposed. We compare the proposed algorithm with decoupling-fraction-based alternating optimization (DFAO). Specifically, the complexity of the proposed algorithm grows linearly with the number of reflective units in the RIS, while the complexity of the benchmark algorithm increases with the power of 4.5 times the number of reflective units. To further address the quality of service (QoS) constraints regarding the information rate requirements of users, we apply the fractional programming (FP) method and the successive convex approximation (SCA) method to optimize the precoder of the base station (BS) and the active beamformers of the phase shifts. The simulation results have demonstrated the effectiveness of the proposed PDDAPG method. Moreover, the active RIS can effectively mitigate the influence of “double fading” effects and achieve higher energy efficiency (EE) compared to passive RIS.

Brachial plexus block is a common regional anesthesia method widely used in upper limb surgery. Nowadays, ultrasound-guided brachial plexus block has been extensively used in clinical anesthesia. However, accurate brachial plexus block is highly dependent on the physician’s experience, and a physician without extensive clinical experience may cause nerve injury when performing a nerve block. With the development of artificial intelligence technology, the deep learning method can automatically identify the brachial plexus in ultrasound images and assist doctors in completing the brachial plexus block accurately and quickly. In this paper, we aim to evaluate the performance of different deep learning models in identifying brachial plexus (i.e., segmentation of brachial plexus) from ultrasonic images to explore the best models and training strategies for this task. To this end, we use a new dataset containing 340 brachial plexus ultrasound images annotated by three experienced clinicians. Among the 12 deep learning models we evaluated, U-Net achieves the best segmentation accuracy, with an intersection over union (IoU) of 68.50%. However, the number of U-Net parameters is very large, and it can only process 15 images per second. Compared to U-Net, LinkNet can process 142 images per second and achieve the second-best segmentation accuracy with an IoU of 66.27%. It achieves the balance between segmentation accuracy and processing efficiency, which has a good potential for the brachial plexus’s real-time segmentation task.

Physically unclonable function (PUF) and true random number generator (TRNG) are critical primitives to provide lightweight hardware protection. As area is tightly restricted in IoT applications, merging PUF and TRNG is a novel trend to achieve higher area efficiency. In this work, a novel tri-functional module is proposed and experimental demonstrated using RRAM for the first time. A two-phase forming process is designed to generate and store PUF ID utilizing the impact of different forming conditions on the switching window. As PUF ID is stored by the switching window, instead of a fixed resistance, PUF cells can be written to low resistance state and high resistance state as usual, and can be used as memory and TRNG. The inter-Hamming distance of the generated PUF IDs approaches 0.5, and with the majority voting readout strategy, low BER can be achieved in a wide range of temperatures from -40°C to 125°C. TRNG produces the true random numbers based on the parity of the number of pulses consumed in a write-zero process. The generated random numbers are uniform and uncorrelated after XORed, and the statistical randomness is verified by NIST SP800-22 tests.

With the progressive increase of stress, anxiety and depression in working and living environment, mental health assessment becomes an important social interaction research topic. Generally, clinicians evaluate the psychology of participants through an effective psychological evaluation and questionnaires. However, these methods suffer from subjectivity and memory effects. In this paper, a new multi- sensing wearable device has been developed and applied in self-designed psychological tests. Speech under different emotions as well as behavior signals are captured and analyzed. The mental state of the participants is objectively assessed through a group of psychological questionnaires. In particular, we propose an attention-based block deep learning architecture within the device for multi-feature classification and fusion analysis. This enables the deep learning architecture to autonomously train to obtain the optimum fusion weights of different domain features. The proposed attention-based architecture has led to improving performance compared with direct connecting fusion method. Experimental studies have been carried out in order to verify the effectiveness and robustness of the proposed architecture. The obtained results have shown that the wearable multi-sensing devices equipped with the attention-based block deep learning architecture can effectively classify mental state with better performance.

Many production activities generate undesirable outputs apart from desirable outputs. In addition, the data related to undesirable outputs may be fuzzy (imprecise) in some cases. Based on the fuzzy data theory, we propose a group of additive integer-valued data envelopment analysis (DEA) models with fuzzy undesirable outputs. The advantages of the proposed models are as follows: (1) they enable decision-makers to evaluate the efficiency of decision making units (DMUs) with integer-valued variables and fuzzy undesirable outputs; (2) they can be easily solved without any transformation because they are linear programming models; (3) they are better than radial DEA models as they can identify inefficiencies in all the selected inputs and outputs. Particularly, minimum distance-based additive integer-valued DEA models with fuzzy undesirable outputs are developed in this paper to find the closest benchmarking targets for inefficient DMUs, and additive super-efficiency integer-valued DEA models with fuzzy undesirable outputs are presented to differentiate and rank efficient DMUs. The validity of the proposed models is examined by an empirical application in the pallet rental industry.

The development of the pallet rental industry is moving at a rapid pace. However, there is no literature on the performance evaluation of pallet rental companies except our previous study. This paper contributes to extending our previous super-efficiency integer-valued DEA (data envelope analysis) model to a group of integer-valued DEA models with generalized reference sets (G-IDEA). According to the proposed approach, the reference sets for decision-making units (DMUs) are composed of proper efficient sample units instead of efficient DMUs. These sample units, which may or may not be the same as DMUs, can be selected by decision-makers according to their demand. The advantages of the G-IDEA models are as follows: (1) they can be applied to find the projections of pallet rental companies with real-valued and integer-valued variables on the efficient frontier; (2) they are able to help the decision-makers in inefficient pallet rental companies make a step by step efficiency improvement scheme. Particularly, we develop a non-oriented integer-valued DEA model with generalized reference sets (NG-IDEA). The NG-IDEA model can be used to figure out how to improve the performance of inefficient pallet rental companies by simultaneously decreasing inputs and increasing outputs. The proposed models are applied to the performance evaluation of ten pallet rental companies. The results of the case study prove the effectiveness of our models. Furthermore, some valuable suggestions on how to improve the efficiency of inefficient pallet rental companies are presented based on the results.

The understanding of the retention and endurance degradation behavior of different levels of filamentary analog RRAM is critical for the development of the neuromorphic computing. This paper investigates the conductance distribution of different levels during retention and endurance tests. The low conductance states and high conductance states change from the normal distribution at the beginning to asymmetric skewed distribution with baking time increasing. But intermediate states remain the normal distribution. A model is proposed to predict the conductance evolution of different levels. It is also found that endurance lifetime depends on the ratio and position of endurance window. The longer endurance lifetime is attributed to switching in a smaller high-C window. Physical mechanism of endurance degradation is discussed.

Pallets play an important role in logistics. It is well known that pallet rental is beneficial to both pallet users and society, and the pallet rental industry is very large nowadays. A non-oriented super-efficiency integer-valued DEA (data envelopment analysis) model for the performance evaluation of pallet rental companies is presented. The method differs from traditional models in that (1) it can be used to accurately estimate the performance of efficient pallet rental companies with integer-valued and real-valued variables; (2) it is an alternative approach to providing numerical super-efficiency scores for efficient pallet rental companies when input-oriented or output-oriented VRS (variable returns to scale) super-efficiency models are infeasible for them. A case study of nine pallet rental companies proves the advantages of the proposed model, and it shows that the efficiency gap among pallet rental companies is very large. Based on the results, some suggestions on the development of the pallet rental industry are proposed. The methodology and results would provide valuable contributions to the decision-making processes of both pallet rental companies and their customers.

In this paper, we propose an efficient 2-D direction-of-arrival (DOA) estimation method for co-prime parallel arrays. By using the second-order statistics of the received signals with different time lags, a conjugate augmented spatial-temporal virtual array is constructed to extend the array aperture and enhance the degrees-of-freedom (DOFs). Vectorizing the cross covariance matrix of the virtual array, the 2-D DOA estimation can be cast as a 1-D sparsity-based recovery problem. A dictionary learning technique is then proposed to compensate the off-grid bias caused by discretization. Simulation results show the effectiveness of the proposed method as compared with the state-of-the-art methods.

With an increase of stress in work and study environments, mental health issue has become a major subject in current social interaction research. Generally, researchers analyze psychological health states by using the social perception behavior. Speech signal processing is an important research direction, as it can objectively assess the mental health of a person from social sensing through the extraction and analysis of speech features. In this paper, a series of four-week long-term social monitoring experiment study using the proposed wearable device has been conducted. A set of well-being questionnaires among of a group of students is employed to objectively generate a relationship between physical and mental health with segmented speech-social features in completely natural daily situation. In particular, we have developed transfer learning for acoustic classification. By training the model on the TUT Acoustic Scenes 2017 data set, the model learns the basic scene features. Through transfer learning, the model is transferred to the audio segmentation process using only four wearable speech-social features (energy, entropy, brightness, and formant). The obtained results have shown promising results in classifying various acoustic scenes in unconstrained and natural situations using the wearable long-term speech-social data set.

This paper proposes an underlay power control scheme for cognitive satellite terrestrial uplink networks, where the primary terrestrial mobile network coexists with the secondary satellite communication network. In particular, the schemes are developed to guarantee that both the interference power constraints and interference outage probability constraints (IOPCs) of primary terrestrial users (PTUs) are satisfied. Since the IOPCs of PTUs belong to the chance constraint and the system throughput optimization model is non-convex, we first transform the IOPCs of PTUs into a series of equivalent transmit power constraints by using the Marcum-Q functions. Then, the alternating direction method of multipliers (ADMM) is introduced to derive the power allocation by alternatively optimizing the subproblems, each of which has the exact closed-form solution. The global convergence of the ADMM is guaranteed. Simulation results demonstrate the effectiveness of the proposed method.

Resistive switching behaviors of oxide-based resistive random access memory (RRAM) and the applications for the data storage and computing systems have been widely studied. In this paper, the critical issues correlated with the applications of oxide-based RRAM are addressed. The physical mechanism and models of oxide-based RRAM are first introduced. Then the optimization design methodology of devices and operation schemes is discussed. Finally, a novel architecture for unifying memory/computing system is presented.