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In many physical channels where multiuser detection techniques are to be applied, the ambient channel noise is known through experimental measurements to be decidedly non-Gaussian, due largely to impulsive phenomena. This is due to the impulsive nature of man-made electromagnetic interference and a great deal of natural noise. This paper presents a robust multiuser detector for combating multiple access interference and impulsive noise in code division multiple access (CDMA) communication systems. A new M-estimator is proposed for "robustifying" the detector. The approach is corroborated with simulation results to evaluate the performance of the proposed robust multiuser detector compared with that of the linear decorrelating detector, and the Huber and the Hampel M-estimator based detectors. Simulation results show that the proposed detector with significant performance gain outperforms the linear decorrelating detector, and the Huber and the Hampel M-estimator based detectors. This paper also presents an improved robust blind multiuser detection technique based on a subspace approach, which requires only the signature waveform and the timing of the desired user to demodulate that user's signal. Finally, we show that the robust multiuser detection technique and its blind adaptive version can be applied to both synchronous and asynchronous CDMA channels.

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Recent studies state that, for a person with autism spectrum disorder, learning and improvement is often seen in environments where technological tools are involved. A robot is an excellent tool to be used in therapy and teaching. It can transform teaching methods, not just in the classrooms but also in the in-house clinical practices. With the rapid advancement in deep learning techniques, robots became more capable of handling human behaviour. In this paper, we present a cost-efficient, socially designed robot called `Tinku’, developed to assist in teaching special needs children. `Tinku’ is low cost but is full of features and has the ability to produce human-like expressions. Its design is inspired by the widely accepted animated character `WALL-E’. Its capabilities include offline speech processing and computer vision—we used light object detection models, such as Yolo v3-tiny and single shot detector (SSD)—for obstacle avoidance, non-verbal communication, expressing emotions in an anthropomorphic way, etc. It uses an onboard deep learning technique to localize the objects in the scene and uses the information for semantic perception. We have developed several lessons for training using these features. A sample lesson about brushing is discussed to show the robot’s capabilities. Tinku is cute, and loaded with lots of features, and the management of all the processes is mind-blowing. It is developed in the supervision of clinical experts and its condition for application is taken care of. A small survey on the appearance is also discussed. More importantly, it is tested on small children for the acceptance of the technology and compatibility in terms of voice interaction. It helps autistic kids using state-of-the-art deep learning models. Autism Spectral disorders are being increasingly identified today’s world. The studies show that children are prone to interact with technology more comfortably than a with human instructor. To fulfil this demand, we presented a cost-effective solution in the form of a robot with some common lessons for the training of an autism-affected child.