Affective computing involves human–computer interaction (HCI) where an interface can detect and respond in context to a user’s emotions. Emotions play an essential role in the daily activities of a human during work and, as a consequence, can critically affect their decision-making. Recent research on affective computing opens the door to exploring the emotional aspects of computer aided tasks and HCI; however, there is little research in this area related to computer aided design (CAD). There is an established connection between emotional and cognitive processes and without these emotional markers decision-making in humans is almost non-existent. The holy grail of engineering design is for a concept to be ‘right-first-time’ and only by delving into the affect-cognitive domain will insights in terms of the associability of critical decision-making and the underlying intent of engineering decisions and tasks be better understood. Work in this field aims to recognize, interpret and process human emotions in order to support and adapt computer-based systems and their associated interfaces to user behaviors and to elicit appropriate system responses. This is a very contemporary area in engineering design research. One key aspect of provisioning CAD systems with the capability to detect the emotional state of the engineer is to lessen the chance of distorted reasoning as a result of irrational judgements. Work in this area can potentially identify emotional markers that influence the coherence/incoherence of reasoning against knowledge as well as the potential quality and suitability of a specific engineering solution or approach. This paper proposes and investigates a methodology to determine the emotional aspects attributed to a set of CAD design tasks by analyzing the CAD operators’ psycho-physiological signals. Two sets of experiments were conducted. A pilot case study focused on modeling tasks carried out in Solid Edge™ was run to prove the validity of the methodology, the tools applied and subsequent analysis. The main case study extended this to more wide-ranging user logging, incorporating a series of configuration and optimization tasks in Siemens NX™ for a real design task. Psycho-physiological signals of electroencephalography (EEG) and galvanic skin resistance (GSR)/electrocardiography (ECG) were recorded along with a log of CAD system user interactions. A fuzzy logic model was established to map the psycho-physiological signals to a set of key emotions, namely frustration, satisfaction, engagement and challenge and the results analyzed. The methodology and subsequent analysis applied was found to be repeatable. Over the various task action-chains the elicitation and interpretation of each participant’s emotions were successfully carried out with significant correlations demonstrated between the associated engineers’ CAD activities and their reported emotional states.

We present in this paper the first reported FPGA implementation of the Position Specific Iterated BLAST (PSI-BLAST) algorithm. The latter is a heuristic biological sequence alignment algorithm that is widely used in the bioinformatics and computational biology world in order to detect weak homologs. The architecture of our FPGA implementation is parameterized in terms of sequence lengths, scoring matrix, gap penalties and cut-off and threshold values. It is composed of various blmocks each of which performs one step of the algorithm in parallel. This results in high performance implementations, which easily outperform equivalent software implementations by one order of magnitude or more. Furthermore, the core was captured in an FPGA-platform-independent language, namely the Handel-C language, to which no specific resource inference or placement constraints were applied. This makes our core portable across different FPGA families and architectures.

This paper presents the design and implementation of the most parameterisable field-programmable gate array (FPGA)-based skeleton for pairwise biological sequence alignment reported in the literature. The skeleton is parameterised in terms of the sequence symbol type, i.e., DNA, RNA, or protein sequences, the sequence lengths, the match score, i.e., the score attributed to a symbol match, mismatch or gap, and the matching task, i.e., the algorithm used to match sequences, which includes global alignment, local alignment, and overlapped matching. Instances of the skeleton implement the Smith-Waterman and the Needleman-Wunsch algorithms. The skeleton has the advantage of being captured in the Handel-C language, which makes it FPGA platform-independent. Hence, the same code could be ported across a variety of FPGA families. It implements the sequence alignment algorithm in hand using a pipeline of basic processing elements, which are tailored to the algorithm parameters. This paper presents a number of optimizations built into the skeleton and applied at compile-time depending on the user-supplied parameters. These result in high performance FPGA implementations tailored to the algorithm in hand. For instance, actual hardware implementations of the Smith-Waterman algorithm for Protein sequence alignment achieve speedups of two orders of magnitude compared to equivalent standard desktop software implementations.

This paper presents the design and implementation of a high performance FPGA-based core for BLAST sequence alignment with the two-hit method. BLAST with two-hit is a very widely used heuristic biological sequence alignment algorithm, and this paper is the first reported FPGA implementation of it, to our knowledge. The architecture of our core is parameterized in terms of the sequence lengths, match scores, gap penalties, and cut-off and threshold values. It is composed of various blocks each of which performs one step of the algorithm in parallel with the others. This results in a high performance and efficient FPGA implementation, which outperforms equivalent software implementations by one order of magnitude or more. Real hardware implementations show that our core is 52 times faster than equivalent software implementations, on average. Furthermore, the core was captured in an FPGA-platform-independent language, namely the Handel-C language, to which no specific resource inference or placement constraints were applied. Hence, the same code can be easily ported to different FPGA families and architectures.

This paper presents the design and implementation of a generic and highly parameterised FPGA-based skeleton for pairwise biological sequence alignment. The skeleton is parameterised in terms of the sequence symbol type i.e. DNA, RNA, or protein sequences, the sequence lengths, the match score i.e. the score attributed to a symbol match or the penalty attributed to a mismatch or gap, and the matching task. Instances of the skeleton implement the Smith-Waterman and the Needleman-Wunsch algorithms. The skeleton has been captured in the Handel-C language which makes it FPGA-platform-independent. It implements the sequence alignment algorithm in hand using a pipeline of basic processing elements, which are tailored to the supplied parameters. Actual hardware implementations of the Smith-Waterman algorithm for protein sequence alignment achieve speed-ups in excess of 100:1 compared to equivalent standard desktop software implementations.

This paper presents the design and implementation of a generic and highly parameterised FPGA-based core for pairwise biological sequence alignment. The core is captured in the Handel-C language, which allows for high level software-like descriptions of hardware architectures. It implements the sequence alignment algorithm in hand using a pipeline of basic processing elements. This results in high performance FPGA implementations tailored to the algorithm in hand. For instance, actual hardware implementations of the Smith-Waterman algorithm for protein sequence alignment achieve speed-ups in excess of 100:1 compared to equivalent standard PC-based software implementations.