Sleep pattern and posture recognition have become of great interest for a diverse range of clinical applications. Autonomous and constant monitoring of sleep postures provides useful information for reducing the health risk. Prevailing systems are designed based on electrocardiograms, cameras, and pressure sensors, which are not only expensive but also intrusive in nature, and uncomfortable to use. We propose an unobtrusive and affordable smart system based on an electronic mat called Sleep Mat-e for monitoring the sleep activity and sleep posture of individuals living in residential care facilities. The system uses a pressure sensing mat constructed using piezo-resistive material to be placed on a mattress. The sensors detect the distribution of the body pressure on the mat during sleep and we use convolution neural network (CNN) to analyze collected data and recognize different sleeping postures. The system is capable of recognizing the four major postures—face-up, face-down, right lateral, and left lateral. A real-time feedback mechanism is also provided through an accompanying smartphone application for keeping a diary of the posture and send alert to the user in case there is a danger of falling from bed. It also produces synopses of postures and activities over a given duration of time. Finally, we conducted experiments to evaluate the accuracy of the prototype, and the proposed system achieved a classification accuracy of around 90%.

A novel, programmable, mode-locked fiber laser design is presented and numerically demonstrated. The laser programmability is enabled by an intracavity optical phase-only pulse shaper, which utilizes the same linearly chirped fiber Bragg grating (LC-FBG) from its two opposite ends to perform real-time optical Fourier transformation. A binary bit-pattern generator (BPG) operating at 20-Gb/s and producing a periodic sequence of 32 bits every 1.6 ns, is subsequently used to drive an optical phase modulator inside the laser cavity. Simulation results indicate stable programmable intensity profiles for each optimized user defined 32 code words. The laser operated in the self-similar mode-locking regime, enabling wave-breaking free operation. The programmable 32 bit code word targeting a specific intensity profile was determined using 100 generations of the genetic algorithm. The control of ultrashort pulse intensity profiles on the picosecond and femtosecond time scales is difficult. The process of stretching and compressing the pulse in the time domain allows for a slower BPG to impose a predefined phase modulation prior to pulse compression. This results in control over the fine features of the intensity profile of the compressed pulse on a picosecond or femtosecond time scale inside the laser cavity. The stability of the proposed scheme depends on the consistency and accuracy of the BPG rise and fall times in practice.