Flexible sensors are required to be lightweight, compatible with the skin, sufficiently sensitive, and easily integrated to extract various kinds of body vital signs during continuous healthcare monitoring in daily life. For this, a simple and low-cost flexible temperature and force sensor that uses only two carbon fiber beams as the sensing layer is reported in this work. This simple, flexible sensor can not only monitor skin temperature changes in real time but can also extract most pulse waves, including venous waves, from most parts of the human body. A pulse diagnostic glove containing three such flexible sensors was designed to simulate pulse diagnostic methods used in traditional Chinese medicine. Wearable equipment was also designed in which four flexible sensors were fixed onto different body parts (neck, chest, armpit, and fingertip) to simultaneously monitor body temperature, carotid pulse, fingertip artery pulse, and respiratory rate. Four important physiological indicators—body temperature (BT), blood pressure (BP), heart rate (HR), and respiratory rate (RR)—were extracted by the wearable equipment and analyzed to identify exercise, excited, tired, angry, and frightened body states.

used to learn the signals of the human grasping motion in a recent report. [15] However, most kinds of these sensors are aimed at simulating one type of stimulation. The ultimate goal of an electronic skin system requires to integrate these different kinds of sensors into a flexible, light, and thin planar array; however, this is a great challenge so far. If a sensing material has multisensing capability and the signals from different stimuli could be distinguished effectively by constructing different sensing structures, similar to different electronic devices being integrated into one silicon chip, the number of the sensors would decrease, and the difficulty in the integration of the electronic skin would be reduced. Fortunately, carbonbased sensing materials (such as carbon nanotubes, [16][17][18][19][20] graphene, [21][22][23][24] and carbon fibers. [25,26] ) have been recently reported to have not only strain sensing but also temperature sensing properties. This inspired us to construct different sensing structures based on the same carbon-based material to simultaneously detect pressure and temperature changes from the outside environment, and this product can be referred to as a touch sensor.Here, we report the feasibility to use only one single carbon fiber beam (CFB) to simultaneously detect pressure and temperature changes from the outside environment, and the signals from pressure and temperature stimuli can be distinguished through its transverse piezoresistance and longitudinal thermal resistance respectively. The flexible pressure sensors, temperature sensors, and their integrated sensors can be easily constructed like blocks to use the same material CFBs. Results and DiscussionIn our design model (as shown in Figure 1), a CFB is used that is usually composed of thousands of carbon fibers (CFs), where thousands of gaps exist between the carbon fibers. Once the carbon fiber beam receives a transverse pressure, these gaps between the CFs decrease, and the transverse resistance of the CFB (R ⊥ ) exponentially decreases, which contributes to an increasing probability of electron quantum tunneling between Electronic skins require to integrate multiple-sensing functions to sense stimuli from the outside environment (such as pressure, temperature, and humidity), and to distinguish the signals from the different stimuli. Here, reported is the feasibility to use only one single carbon fiber beam (CFB) to simultaneously detect pressure and temperature changes from the outside environment, and the signals from pressure and temperature stimuli can be effectively distinguished through its transverse piezoresistance and longitudinal thermal resistance. This work also reveals that the transverse piezoresistance follows the electron quantum tunneling between the carbon fibers, while the longitudinal thermal resistance follows the impurity scattering mechanism. The flexible pressure sensors, temperature sensors, and their integrated sensors can be easily constructed like blocks to use the same material CFBs, and the...

In a reconfigurable intelligent surface (RIS) assisted millimeter Wave (mmWave) communication system, the channel coefficient increases exponentially with the number of RIS elements which results in expensive pilot overhead. Most previous works have proposed some channel estimation algorithms for the estimation accuracy of cascaded channels, which have improved the estimation accuracy, but the pilot overhead is discouraging in the estimation process. To improve the channel estimation accuracy with reduced pilot overhead, we propose a two-stage channel estimation protocol by exploiting semi-passive elements and the coherent time difference of the channel, where the quasi-static channel between the base stations (BS) and RIS is estimated at the RIS, and the user (UE)-RIS time-varying channel is estimated at the BS. In the first stage, we formulate the BS-RIS channel estimation as a mathematical optimization problem by an iterative weighting method and then propose a gradient descent (GD)-based algorithm to solve it. In the second stage, we first transform the received the UE-RIS signal model into an equivalent parallel factor (PARAFAC) tensor model and estimate the UE-RIS channel by the least-squares (LS) algorithm. The simulation results show that the proposed method has better estimation accuracy than the LS, compression sensing (CS) and minimum mean square error (MMSE) methods with less pilot overhead, and the spectral efficiency is improved by at least 10.5% compared to the other three methods.

In order to investigate the effect of cooperative Intelligent Reflecting Surface (IRS) in improving spectral efficiency, this paper explores the joint design of active and passive beamforming based on a double IRS-assisted model. First, considering the maximum power constraint of the active vector and the unit modulus constraint of the cooperative passive vector, we establish the non-linear and non-convex optimization problem of multi-user maximization weighted sum rate (WSR). Then, we propose an alternating optimization (AO) algorithm to design the active vector and the cooperative passive vector based on fractional programming (FP) and successive convex approximations (SCA). In addition, we conduct a study on the optimization of the passive reflection vector under discrete phase shift. The simulation results show that the proposed beamforming scheme of double IRS-assisted model performs better than the conventional single IRS-assisted model.

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