This paper investigates the optimization of physical layer security (PLS) in intelligent reflecting surface (IRS)-assisted wireless networks under imperfect channel state information (CSI). Traditional PLS techniques often assume perfect CSI, which is difficult to obtain in IRS-assisted systems due to their passive nature and limited signal processing capabilities. To address this challenge, we derive, under the imperfect CSI condition, a novel expression for the secrecy outage probability (SOP) and propose an optimization framework to minimize the SOP by jointly designing the beamforming vector and the IRS phase-shift matrix. The proposed approach employs alternating optimization, utilizing the generalized Rayleigh quotient for beamforming and the generalized Dinkelbach algorithm for phase-shift adjustments. Furthermore, we derive a power scaling law that reveals significant energy efficiency gains with an increasing number of IRS elements. Simulation results validate the effectiveness of the proposed framework, demonstrating substantial improvements in secrecy performance over baseline schemes. This work underscores the potential of IRS-assisted systems in enhancing secure communications for future wireless networks.

This work describes the development, demonstration, and performance evaluation study of a wearable human machine interface for robotic teleoperation. We present a novel tetherless human machine interface in the form of a backpack, wearable 3D arm motion capture sensors, finger flexion sensors, and pneumatic haptic feedback muscles. The system is integrated in a complete teleoperation framework, enabling users to be immersed in a remote environment through virtual reality headgear, facilitating intuitive manipulation of an industrial articulated arm. The human machine interface samples the kinematic configuration of the user’s arm, hand, and fingers using multiple inertial measurement units and capacitive sensors respectively, and streams it to the teleoperation software stack. The gripping forces experienced at the robot’s end-effector are acquired using a custom three-dimensional Hall-effect magnetic sensor. The system simultaneously renders the kinesthetic and tactile feedback on the user’s fingers through custom designed pneumatically actuated soft robotic haptic muscles. The efficacy of the human machine interface and the teleoperation system was tested and evaluated by conducting user studies, which showed 31.4% faster teleoperation compared to a keypad controller, and 60% less gripping force utilized with haptics enabled. The findings of the study guided the design and prototype development of a printed electronics based stretchable sleeve and glove motion capture unit to improve the portability, ergonomics, and user experience of the human machine interface.

The authors present a method of utilisation diversity combining to improve the secrecy performance of an underlay cognitive radio network (CRN) with single or multiple primary users (PUs) over Rayleigh fading environment. They consider a practical scenario where a secondary transmitter , a secondary receiver , and an eavesdropper (Eve) receiver are equipped with multiple antennas while all PUs are employed with a single antenna. The confidential information is transmitted from to under the malicious attempt of an Eve. Generalised selection combining is applied at the , and maximal ratio combining is adopted at the Eve. Depending upon the availability of the global channel state information of the main and eavesdropper's channels at , the secrecy outage performances of optimal antenna selection and sub‐optimal antenna selection schemes have been examined. They derive a closed‐form expression for the exact secrecy outage probability, asymptotic secrecy outage probability and intercept probability in a Rayleigh fading environment. Numerical results show that secrecy performance of the network improves with increasing the transmitting antennas while it degrades with increasing the number of PUs.

This paper postulates the secrecy performance of an underlay cognitive radio network where legitimate receiver combines signal replicas per threshold-based maximal ratio combining (MRC), and secondary transmitter selects a single antenna based on combined signal to noise ratio at a legitimate receiver in the presence of multiple eavesdroppers. Output threshold-based MRC scheme is the variant of the MRC scheme which saves processing power at the legitimate receiver and also provides considerable improvement in secrecy performance. For this scenario, we derive new closed-form expressions for exact secrecy outage probability. Numerical results show that output- threshold MRC technique leads to less power consumption while providing the same performance as conventional MRC.