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In this paper, we present the first Hardware (HW) / Software (SW) co-design based generic discrete Gaussian sampler architecture on the Xilinx Zynq platform. The area optimized and secure sampler can produce a distribution based on an arbitrary standard deviation and center given as input. We use multi-level logic optimization on Knuth-Yao algorithm's Discrete Distribution Generating (DDG) tree travel-based Boolean mapping of random bits and samples instead of the previous two-level logic optimization to reduce the resource utilization. This method results in nearly 60% lesser LUT utilization compared to the previous designs on Xilinx FPGAs. Further, we introduce improvements in the shuffling algorithm leveraging the HW/SW co-design methodology compared to the existing shuffling architectures for randomizing Gaussian samples to protect against timing-based side-channel attacks.

Emotion recognition or affect detection from speech is an old and challenging problem in the field of artificial intelligence. Many significant research works have been done on emotion recognition. In this paper, the recent works on affect detection using speech and different issues related to affect detection has been presented. The primary challenges of emotion recognition are choosing the emotion recognition corpora (speech database), identification of different features related to speech and an appropriate choice of a classification model. Different types of methods to collect emotional speech data and issues related to them are covered by this presentation along with the previous works review. Literature survey on different features used for recognizing emotion from human speech has been discussed. The significance of various classification models has been presented along with some recent research works review. A detailed description of a prime feature extraction technique named Mel Frequency Cepstral Coefficient (MFCC) and brief description of the working principle of some classification models are also discussed here. In this paper terms like affect detection and emotion recognition are used interchangeably.

Emotions and affect are universal means of expressing the physiological state of an individual. Most of our daily interactions with other individuals involve emotions as an integral part. It has become highly prominent in technological research, as new technologies related to human-machine interaction or medical applications are developed. Detecting and analyzing emotions have become quite an important area of research. In this paper, we took the effort to find the effect of different emotions on physiological signals. We used International Affective Picture System (IAPS) database images as stimuli. We have considered five different types of emotions — High Valence High Arousal (HVHA), High Valence Low Arousal (HVLA), Low Valence High Arousal (LVHA), Low Valence Low Arousal (LVLA), and Neutral (Blank), and we have used six different physiological signals — Skin Conductance (SC), Electro CardioGram (ECG), Electro MyoGram (EMG), Skin Temperature (ST), Respiration Rate (RR), and Arterial Pulse Rate (PR). Our experiment shows that there are enough variations in different parameters of physiological signals for different emotional stimuli.

This paper considers two dimensional valence-arousal model. Pictorial stimuli of International Affective Picture Systems were chosen for emotion elicitation. Physiological signals like, Galvanic Skin Response, Heart Rate, Respiration Rate and Skin Temperature were measured for accessing emotional responses. The experimental procedure uses non-invasive sensors for signal collection. A group of healthy volunteers was shown four types of emotional stimuli categorized as High Valence High Arousal, High Valence Low Arousal, Low Valence High Arousal and Low Valence Low Arousal for around thirty minutes for emotion elicitation. Linear and Quadratic Discriminant Analysis are used and compared to the emotional class classification. Classification of stimuli into one of the four classes has been attempted on the basis of measurements on responses of experimental subjects. If classification is restricted within the responses of a specific individual, the classification results show high accuracy. However, if the problem is extended to entire population, the accuracy drops significantly.

Emotion Recognition always has been one of the key areas in human machine interaction, machine learning or affective computing. Two dimensional valence arousal model has been used here. In this paper, we present how simple emotion recognition can be done by measuring nine basic non-invasive Biological markers or physiological signals including BR [BReathing], ECG [ElectroCardioGram], EMG [ElectroMyoGram], GSR [Galvanic Skin Response], HR [Heart Rate], PR [Pulse Rate], RR [Respiration Rate], and ST [Skin Temperature] on thirty healthy subjects. Pictorial emotional stimuli categorized as High Valence High Arousal [HVHA], High Valence Low Arousal [HVLA], Low Valence High Arousal [LVHA] and Low Valence Low Arousal [LVLA] were shown using International Affective Picture System (IAPS) for approximately thirty minutes. Six features from each signal were extracted for analysis. Different types of classification algorithms like QDC [Quadratic Discriminant Classifier], kNN [k Nearest Neighbour], Naive Bayes and LDA [Linear Discriminant Analysis] were used in classification of data. Maximum accuracy around 75% for each classifier was obtained. Further improvements are required to make this more robust and to deploy commercially.